Across many of these engagements, we’ve also heard the same story from founders, content marketing teams, and SaaS marketers that their previous SEO agency ran a monthly technical audit and showed them a running list of issues they were fixing or “actively working on.” Most of those issues were real, but the ones that actually impacted the site’s ability to rank for keywords that drive organic traffic, qualified leads, trial signups, demo requests, and pipeline usually required engineering or web development work to fix, which rarely got prioritized.

In our experience, most technical SEO audits mix together two very different types of problems. That’s why when we audit SaaS websites, we separate these issues into two categories: 

• Structural technical SEO: These are decisions made during a site redesign, replatforming, or major launch that can compound for years and are often expensive to reverse. For SaaS companies, it includes decisions such as whether to host the blog on a subdomain or subfolder, whether the marketing site renders server-side or client-side, and how the overall site structure is organized.
• Maintenance technical SEO: These are recurring, smaller fixes that audit tools tend to flag, such as broken links, redirect chains, missing meta descriptions, schema, and other crawl hygiene issues. These issues are real, but they usually don’t compound the same way and can be handled through quarterly audits or basic monitoring.

In this article, we walk you through the technical SEO issues we most often see on SaaS websites, in roughly the order they impact rankings and pipeline.

Issue 1: The Blog Is Hosted on a Subdomain Instead of a Subfolder

Google has publicly said that subdomains and subfolders are treated equally, but across many SaaS SEO engagements, we’ve seen that hosting content in a subfolder consistently outperforms a blog on a subdomain in both organic traffic and rankings.

That’s why we treat subfolder hosting as the default recommendation for any SaaS company starting a blog or planning a migration, and we often recommend migrating existing subdomain blogs back to subfolders when the SEO upside justifies the engineering effort.

We usually see this problem on SaaS websites where the marketing site is built on a custom framework or static site generator, and the engineering team doesn’t want to maintain a CMS as part of the main site. So they just stick a blog running on WordPress, Webflow, or another CMS on a subdomain. But this comes with a serious SEO tradeoff that engineering teams usually don’t consider. 

The problem is that subdomains behave like separate domains, and the backlinks you’ve earned through link building, the brand authority, and the topical relevance built on your main domain don’t carry over cleanly to the subdomain blog, even though both appear to be the same brand to users. As a result, the blog ranks with less authority than it would if it were a subfolder on the root domain.

Our Circuit case study is one of the clearest examples. Circuit had previously moved its blog to a subdomain and saw rankings drop. When they moved the blog back to a subfolder on the root domain, traffic recovered and, within six months, grew from around 920 sessions per month to over 14,577. 

How to Fix?

Host your blog at /blog/ on your root domain.

If your blog is already on a subdomain, plan a migration that includes:

• A reverse proxy or CMS setup that serves the blog from /blog/ on the main domain
• 301 redirects from every existing subdomain URL to its new subfolder URL
• Updated internal links across the marketing site, footer, and product to point to the new URLs
• An updated XML sitemap submitted to Google Search Console

If the content is intended to rank in organic search and contribute to your overall SaaS SEO strategy, content marketing, and link building efforts, it should usually reside on the main domain. 

We also recommend the same principle to other content hubs that often end up on subdomains for engineering convenience, such as help centers, community forums, resource libraries, and changelogs.

Issue 2: The Marketing Site Renders Client-Side Instead of Server-Side

Most SaaS marketing sites are built by the same engineering team that builds the product, on the same JavaScript framework (typically React, Next.js, Vue, or Nuxt). That choice makes sense from a stack-consolidation and engineering-ownership perspective. 

The problem is that when the marketing site renders entirely on the client side, search engines and their crawling algorithms have to work much harder to see your content, which usually results in delayed indexing, partial content rendering, and product pages that struggle to rank for keywords they should easily own.

The technical issue is that search engine crawlers don’t render JavaScript the way browsers do. Google does have a second-pass rendering stage for JavaScript, but it runs on a delay (sometimes days or weeks after the initial crawl), and pages with heavier JavaScript or rendering errors often don’t make it through cleanly. Other search engines, including Bing and the crawlers behind AI search products like ChatGPT and Perplexity, render JavaScript less reliably or not at all. 

We see this most often on SaaS sites built on Nuxt, Angular, or custom React frameworks, where the team has defaulted to client-side rendering for the marketing pages. In these engagements, we typically find that a meaningful portion of web pages are missing from Google’s index, even though every page is internally linked and submitted in the XML sitemap. 

Once the marketing site is switched to a server-rendered or statically generated configuration, indexation usually recovers within weeks.

You can diagnose this on your own site in under a minute. Disable JavaScript in your browser’s developer tools (in Chrome, open DevTools, then use the command menu to search for “Disable JavaScript”) and reload any marketing page. If the main content, headings, or internal links disappear, search engines that don’t fully execute JavaScript are seeing the same empty page.

How to Fix?

For the marketing site, use server-side rendering (SSR) or static site generation (SSG). Both deliver fully rendered HTML to crawlers on the first request, eliminating rendering delays and reliability issues.

If you’re on Next.js, this means using getServerSideProps or getStaticProps (Pages Router) or server components and static export (App Router). On Nuxt, it means running in ssr: true mode rather than SPA mode. For custom React or Vue setups, it means adding a server-rendering layer or migrating to a framework that handles it.

The deeper structural fix is to separate the marketing site stack from the product app stack. The product app can remain a client-rendered SPA since users authenticate before they see it, and SEO doesn’t apply. The marketing site should be its own deployment, ideally on a static site generator or an SSR framework, with content management handled through a CMS that the marketing team can edit without filing engineering tickets.

If switching frameworks isn’t realistic in the near term, the interim fix is dynamic rendering or pre-rendering specifically for crawlers. Services like Prerender.io serve a pre-rendered HTML version to crawlers while serving the SPA to users, which solves the indexation problem without changing the underlying stack. This works as a bridge but isn’t a long-term substitute for proper SSR or SSG optimization.

Issue 3: Thin and Duplicate Pages Don’t Have a Clear Canonical or Noindex Policy

Most SaaS websites accumulate thin pages or duplicate content over time, but many don’t have a consistent canonical or indexation policy for handling them.

We’ve seen login-gated marketing pages appearing in search results, internal search pages getting indexed, and UTM-tagged URLs attracting external links that should have pointed to the canonical version.

Individually, these issues may look minor. Collectively, they create a search profile where the marketing site has far more indexable web pages than it should. This can dilute link authority across near-duplicate pages, waste crawl budget, and make the site appear lower-quality because of its thin and duplicate content footprint.

Over time, this can also lead to indexing and ranking issues, especially on larger SaaS sites with hundreds or thousands of blog posts, feature pages, integration pages, comparison pages, resource pages, and template-based URLs.

How to Fix?

Apply canonical and noindex rules consistently at the template level so that every URL of the same type receives the same treatment automatically.

• Internal search URLs: Add noindex tags at the template level and exclude these URLs from XML sitemaps. Also, verify your robots.txt file isn’t accidentally blocking important sections of the site while handling internal search URLs.
• Filter, sort, and parameter URLs: Add a <link rel=”canonical”> tag pointing back to the clean, unparameterized version of the page. For very heavy filter combinations, consider adding noindex to specific parameter groups.
• UTM-tagged URLs: Make sure UTM parameters canonicalize back to the clean URL. If these URLs are being linked externally, update the source links when possible so authority flows directly to the canonical page.
• Paginated pages: Use a self-referential canonical tag on each paginated URL rather than canonicalizing page 2, page 3, and beyond back to page 1. Keep paginated pages crawlable and indexable so search engines can reach the deeper content linked from them. Avoid blanket noindexing paginated URLs, since that can also prevent crawlers from following links to deeper content. rel=next and rel=prev tags are no longer required for Google, though leaving them in place generally doesn’t hurt.
• Login-gated marketing pages: Add a noindex directive or serve a 401 or 403 status code, depending on whether you want the URL discoverable in any form.

Once these rules are in place, you can audit the indexation report in Google Search Console quarterly. Look for sudden growth in indexed URLs, especially when that growth is not tied to new content production. That usually means a new template, parameter group, or URL pattern is being indexed without the right canonical or noindex rule.

If your indexed URL count is growing faster than your content volume, there’s almost certainly a canonical or noindex policy issue worth investigating.

Issue 4: Pages Don’t Follow a Scalable URL Taxonomy

Most SaaS product sites already have CMS collections for features, integrations, comparison pages, use cases, industries, FAQ pages, and help docs. The issue we often see is that these page types are created in isolation.

For example, different teams produce feature pages, integration pages, comparison pages, or industry pages, and each team organizes its pages around its own campaign or internal workflow.

The site has organized page types, but no consistent structure for how commercial pages relate to one another.

For example, a SaaS company might have:

• A feature page targeting “[feature] software”
• A use case page targeting “[job to be done] software”
• An industry page targeting “[feature] software for [industry]”
• An integration page targeting “[partner] integration”
• A comparison page targeting “[competitor] alternative”

Without a clear taxonomy, these pages often overlap, compete, or remain disconnected from one another. The site ends up with multiple commercial pages targeting adjacent intent, but no clear parent-child structure, internal linking model, or rule for which page should rank for which query type.

This is where URL architecture becomes more than a formatting issue. A strong SaaS URL taxonomy is part of your overall site structure and defines how commercial intent is organized across the site.

When this isn’t defined, several issues show up over time:

• Feature, solution, and industry pages start targeting the same keywords.
• Integration pages sit isolated from relevant feature and use case pages.
• Comparison pages are launched as one-off landing pages rather than as part of a scalable competitor hub.
• Internal linking becomes manual instead of template-driven.
• Breadcrumbs don’t reinforce the commercial hierarchy.
• XML sitemaps group pages by CMS origin rather than by search intent.
• Reporting can’t easily show which commercial page type is driving rankings, qualified traffic, trials, demos, or pipeline in Google Analytics or your internal dashboards.

How to Fix?

Build a commercial URL taxonomy and a clear site structure before scaling new SaaS landing pages. 

This means defining the role of each page type, the query class it should target, the URL pattern it should use, and how it should link to related pages.

A simple taxonomy might look like this:

Once the taxonomy is defined, apply it across the site:

• Move existing pages into the correct URL structure when the SEO benefit outweighs the migration risk.
• Add 301 redirects from old URLs to new URLs.
• Update internal links so related features, use cases, industry, integration, and comparison pages reinforce each other.
• Add breadcrumb logic that matches the taxonomy.
• Segment XML sitemaps by commercial page type.
• Track rankings, traffic, conversion rates, and pipeline by page type, not just by individual URL.

For SaaS companies, this matters most once the site has more than a handful of product-led SEO pages. At that point, every new feature, integration, use case, industry, and comparison page either strengthens the site’s commercial search architecture or adds another disconnected URL that has to be manually managed later.

A strong taxonomy lets the site compound and supports a scalable, high-quality content strategy, consistent on-page SEO, and ongoing optimization across the site. Each new page fits into a known structure, links to related pages by default, appears in the right sitemap, inherits the right breadcrumbs, and has a clear role in the broader keyword strategy.

Issue 5: 404s, Redirect Chains, and Broken Internal Links

For SaaS sites that undergo frequent product changes, redesigns, and migrations, this is one of the clearest examples of a maintenance-related technical SEO issue. It may not require daily attention, but it does need regular cleanup.

The most common causes of these issues are when features are renamed, and the old feature pages start returning 404s, when blog posts are deleted but remain in the XML sitemap, or when site migrations leave behind redirect chains where one URL passes through two or three intermediate URLs before reaching the final destination. 

None of these issues is critical or usually breaks the site on its own. But over time, they create technical drag, leading to crawlers wasting time on dead URLs, internal links passing through unnecessary redirects, and users occasionally landing on pages that no longer exist, which hurts user experience and crawl efficiency.

The most common patterns we see are:

• 404s with internal or external links pointing to them: Pages get deleted, but the links pointing to them don’t get updated. If the old URL had backlinks from your link building efforts, rankings, or meaningful internal links, redirect it to the closest relevant live page instead of letting those signals die on a 404.
• Redirect chains: URL A redirects to URL B, which redirects to URL C. The cleaner setup is for URL A to redirect directly to URL C.
• Redirect loops: URL A redirects to URL B, and URL B redirects back to URL A. This makes the page inaccessible to users and crawlers.
• Internal links pointing to redirected URLs: The link technically works, but every click and crawl has to pass through a redirect. 
• 302 redirects used for permanent moves: If a page has permanently moved, use a 301 redirect instead of a 302 so search engines understand that the new URL should replace the old one.
• Soft 404s: These are pages that return a 200 status code but display “not found,” “page no longer exists,” or similarly thin content. The response code says the page exists and functions normally, but the content says otherwise.

How to Fix?

Use Screaming Frog, Ahrefs Site Audit, Semrush, or a similar crawler to identify 404s, redirect chains, redirect loops, soft 404s, and internal links pointing to redirected URLs.

Then clean up each issue based on the URL type:

• For 404s, decide whether to restore the page, redirect it to the closest relevant live equivalent, or leave it as a 404 if the page was intentionally removed and has no meaningful links or traffic.
• For pages that are permanently removed and should not come back, consider using a 410 Gone status code instead of a 404. This gives search engines a clearer signal that the URL has been intentionally removed.
• For redirect chains, update the first redirect to point directly to the final destination URL.
• For redirect loops, fix the redirect rule causing the loop.
• For internal links pointing to redirected URLs, update the source links so they point directly to the final URL.
• For 302 redirects, replace them with 301 redirects when the move is permanent.
• For soft 404s, either improve the page so it serves a real purpose, redirect it to a relevant live page, or return a proper 404 or 410 status code.

After the cleanup, remove dead URLs from XML sitemaps.

For SaaS businesses, especially fast-moving startups that undergo multiple redesigns, migrations, feature launches, and pricing changes, this cleanup should happen right after each change goes live, so issues are caught immediately rather than accumulating into broader crawl and internal linking problems.

Issue 6: Schema Markup Is Missing or Doesn’t Match the Page Type

Schema markup is structured data that tells search engines what type of content is on a page, beyond what they can infer from the HTML alone. For a SaaS company, a well-implemented schema can unlock rich results and rich snippets in the SERPs and feed into Google’s knowledge panel for the brand.

Most SaaS websites have either no schema markup at all or generic schema generated by the theme or website template they use (usually the case on WordPress websites). 

The implementation is inconsistent across page types, and the schema often doesn’t match the page’s actual content.

The most common patterns we see are:

• No schema markup at all. The site has no structured data on any page, which doesn’t break anything but means it misses out on rich results opportunities and provides less context to AI search products.
• Wrong schema type used. A blog post tagged with Product schema, or a feature page tagged with Article schema. The schema technically validates but doesn’t accurately represent the content.
• Required fields missing. Schema types have required and recommended fields. When required fields are missing, the schema may not be eligible for rich results, even when it validates as syntactically correct.
• Product or SoftwareApplication schema added to the homepage to surface star ratings. A common misuse of schema on SaaS sites is adding the Product or SoftwareApplication schema with aggregateRating fields to the homepage, often pulling those ratings from third-party review sites such as G2, Capterra, or TrustRadius. Google’s structured data guidelines explicitly prohibit this. The reviewed item must be the main subject of the page, the ratings must be backed by reviews visible to users on the same page, and the reviews can’t be self-serving. Sites that misuse review markup this way can lose rich result eligibility across the entire site, often without warning.

How to Fix?

Audit the existing schema, then implement appropriate schema types for each page type at the template level so that every page of the same type receives the same treatment automatically.

• Audit: Run the Rich Results Test and Schema Markup Validator on a representative page of each template (homepage, blog post, feature page, integration page, comparison page, pricing page, help doc). Note what schema exists, what’s missing, and what’s flagged with errors.
• Implement at the template level: Add schema to each page template so new pages inherit the correct markup automatically. Don’t add schema page by page.

The most common schema types for SaaS sites are:

• Organization site-wide: company name, logo, social profiles, contact information. Add to every page that describes the organization (homepage, about page, etc.) through a global template.
• Article or BlogPosting on blog posts: author, publish date, modified date, headline, image.
• SoftwareApplication on product or feature pages: name, application category, operating system, offers. Only include aggregateRating if the page itself displays legitimate first-party reviews. Don’t pull ratings from third-party sites like G2 or Capterra or use self-reported numbers.
• HowTo on procedural content like setup guides or tutorials.

For SaaS sites, schema is a low-effort, low-risk investment that complements high-quality content with meaningful upside in search visibility and click-through rates from rich snippets. The goal shouldn’t be to add schema everywhere for its own sake, but to ensure every page type has schema that matches its purpose, and that the markup actually reflects the content visible to users.

Issue 7: Chasing Perfect PageSpeed Scores

Core Web Vitals are rarely the main ranking bottleneck for SaaS websites.

Most modern SaaS sites already use decent hosting, CDN delivery, caching, compressed assets, and frontend frameworks that make pages fast enough to meet basic performance thresholds for site speed and load times. 

Because of that, we rarely see cases where moving a page from a “good” PageSpeed score to a perfect score materially changes rankings. The issue is that teams often focus on the overall PageSpeed score rather than the specific performance problem affecting user experience on the page.

The most common patterns we see are:

• Oversized HTML documents: Googlebot can read large HTTP responses, but that does not mean large HTML documents are good for performance. HTML documents over 2MB often point to bloated templates, inline base64 images, large JSON blobs in the page source, excessive inline CSS or JavaScript, or uncompressed HTML.
• Hero image and web font issues hurting LCP: Largest Contentful Paint is often the hero image or above-the-fold heading on a SaaS marketing page. If the hero image is too large, served in the wrong format, not preloaded, or paired with render-blocking web fonts, the page can feel slow even if the overall score looks acceptable.
• Cumulative Layout Shift from cookie banners, popups, and lazy-loaded content: CLS often comes from elements that load late and push existing content down. Cookie consent banners, popups, embedded widgets, and lazy-loaded images without dimensions are common causes.
• Render-blocking JavaScript and CSS: Scripts and stylesheets that block the initial render delay everything else. This often comes from third-party tags loaded synchronously, large CSS files without critical CSS extraction, and JavaScript that is not deferred or loaded asynchronously.
• INP issues from heavy JavaScript: Heavy JavaScript bundles, third-party scripts, chat widgets, and analytics libraries can make a page feel unresponsive even when LCP and CLS are fine.

How to Fix?

Treat Core Web Vitals as a threshold to pass, not a score to perfect. Start by auditing different page types, not just the homepage.

Then fix the issue based on what the audit shows:

• For oversized HTML documents: Compress HTML with gzip or Brotli, move large JSON blobs out of the initial HTML, replace inline base64 images with proper image URLs, and reduce unnecessary inline CSS and JavaScript.
• For LCP issues: Compress hero images, serve them in WebP or AVIF, set explicit width and height attributes, preload the LCP image, use font-display: swap, and preload critical fonts where needed.
• For CLS issues: Add explicit dimensions or aspect-ratio CSS to images and embeds, reserve space for cookie banners and embedded widgets, and avoid inserting content above existing content after the page loads.
• For INP issues: Break up long JavaScript tasks, reduce unused JavaScript, load third-party scripts with async or defer, audit tracking libraries, and move non-critical JavaScript out of the initial bundle.
• For render-blocking resources: Defer non-critical JavaScript, extract and inline critical CSS, reduce large stylesheet dependencies, and delay non-essential third-party scripts like chat widgets, heatmaps, and marketing tags until after the page is interactive.

For SaaS sites, page speed optimization should be targeted rather than exhaustive. Fix the templates and scripts that create real performance problems on important web pages. Don’t spend weeks chasing a perfect score when the site already passes Core Web Vitals, and the same engineering time could be spent on higher-impact structural SEO efforts.

Issue 8: XML Sitemaps and Robots.txt Don’t Match What Should Be Crawled and Indexed

XML sitemaps and robots.txt files are supposed to help search engines understand which URLs should be discovered, crawled, and indexed.

We often see XML sitemaps that include redirected URLs, 404 pages, noindexed pages, parameter URLs, staging URLs, thin CMS-generated pages, or old blog URLs that should no longer be submitted.

At the same time, robots.txt files sometimes block important sections of the site from being crawled, including blog directories, integration pages, JavaScript assets, CSS files, or documentation pages that should be accessible to search engines.

The most common issues we see are:

• Noindexed URLs in the XML sitemap: These pages are being submitted to Google even though the page itself says not to index them.
• Redirected URLs in the XML sitemap: The sitemap includes old URLs that now redirect elsewhere instead of the final canonical URLs.
• 404 or 410 URLs in the XML sitemap: Deleted pages remain in the sitemap long after they have been removed.
• Parameter or filtered URLs in the XML sitemap: URLs with filters, sorting parameters, tracking parameters, or internal search parameters get submitted even though they are not meant to rank.
• Important pages missing from the XML sitemap: Feature pages, integration pages, comparison pages, or new blog posts are live and internally linked, but not included in the sitemap.
• Robots.txt blocking important content: Blog directories, docs, integration pages, or page assets are blocked from crawling, even though they need to be accessible for proper rendering and indexation.
• Robots.txt used where noindex would be more appropriate: If a page is blocked in robots.txt, Google may not be able to crawl the page and see the noindex directive. For pages that need to be removed from the index, a page-level noindex is usually the cleaner solution.

How to Fix?

Audit your XML sitemap and robots.txt file at least quarterly, and after any redesign, migration, CMS change, or major template launch.

Your XML sitemap should only include URLs that are:

• Canonical
• Indexable
• 200-status
• Important enough to be discovered and crawled
• Not blocked by robots.txt
• Not parameterized or duplicated versions of another page

Remove any URLs that are noindexed, redirected, broken, canonicalized to another URL, blocked by robots.txt, or not meant to rank.

Then review the robots.txt file to ensure it is not blocking important pages or assets. In most cases, SaaS sites should avoid blocking major content sections like /blog/, /features/, /integrations/, /compare/, /solutions/, /docs/, or /help/ unless there is a clear reason. 

While reviewing robots.txt, also confirm the sitemap URL is declared at the bottom of the file (e.g., Sitemap: https://www.yoursite.com/sitemap.xml) so crawlers can find it even when they don’t check Google Search Console.

Also, avoid using robots.txt as your primary indexation control. Robots.txt controls crawling, not indexing. If a page should not appear in search results, use a noindex directive instead, and make sure the page is crawlable long enough for search engines to see that directive.

For SaaS websites, the sitemap should list the URLs you want search engines to crawl and index, and robots.txt should only block areas that genuinely should not be crawled. 

Issue 9: Staging, Preview, or App URLs Are Indexable

For SaaS companies, this issue usually becomes more important as the engineering and marketing stack gets more complex. The more environments, preview systems, CMS collections, and app surfaces the company has, the easier it is for non-production URLs to leak into search.

There may be a production marketing site, a staging site, CMS preview URLs, Vercel or Netlify deployment URLs, a documentation subdomain, a help center, a public product app, partner landing pages, and old campaign pages that were never fully removed. Over time, some of these URLs get discovered and indexed by search engines.

The issue is not just that these pages look messy in Google but that they can create duplicate versions of important marketing pages, expose thin or unfinished content, and make indexation harder to manage.

The most common patterns we see are:

• Staging subdomains indexed in Google: URLs like staging.company.com, dev.company.com, or test.company.com appear in search results.
• Preview deployment URLs indexed: Vercel, Netlify, Webflow, or CMS preview URLs get indexed alongside the production page.
• Duplicate marketing pages on alternate hosts: The same homepage, feature page, or blog post exists on both the production domain and a staging or preview URL.
• Public app URLs showing in search results: Product app URLs, login-gated pages, onboarding screens, or public user-generated pages get indexed even though they are not part of the marketing site’s SEO strategy.
• Old campaign or landing page environments still live: Temporary pages created for launches, ads, partner campaigns, or sales enablement stay accessible long after the campaign ends.
• Sitemaps include non-production URLs: XML sitemaps accidentally include staging, preview, or alternate-domain URLs instead of only canonical production URLs.

How to Fix?

Start by auditing what Google has indexed across your main domain and common subdomains.

Search for indexed URLs across different environments, like:

• site:staging.company.com
• site:dev.company.com
• site:company.vercel.app
• site:company.netlify.app
• site:app.company.com
• site:preview.company.com

Then decide how each environment should be handled.

For staging and preview environments, the cleanest fix is to require authentication so these URLs aren’t publicly crawlable in the first place. If authentication is not possible, add a noindex directive at the template level and make sure the pages are not included in XML sitemaps.

For duplicate production content on preview or deployment URLs, add canonical tags pointing to the production URL, but don’t rely on them alone in staging environments. If the environment should not be public, block access or noindex it.

For app URLs, decide whether the content has search value. Most authenticated app pages should not be indexable. Public app pages, user profiles, dashboards, reports, or shared assets should have a clear indexation policy based on whether they are useful landing pages or thin or duplicate content.

For old campaign pages, either keep them live and indexable if they still serve a purpose, redirect them to the closest relevant page, or noindex or remove them if they are no longer useful.

After cleanup, remove non-production URLs from XML sitemaps, and use the Removals tool only when you need to temporarily hide sensitive or urgently problematic URLs until the permanent fix takes effect.

Improve Your Technical SEO With Grow and Convert

Across hundreds of B2B SaaS engagements, we’ve found that technical SEO lays the foundation for the rest of the work to compound. When the structural decisions are right, every piece of content you publish, every internal link you add, and every keyword you target builds on a stable base. When they’re wrong, the rest of the work fights against a site that isn’t built to rank.

That’s why we treat technical SEO as a finite set of decisions to make well, not an ongoing retainer-level discipline. We make recommendations and fix the structural issues once, and audit the maintenance items quarterly. Then,  we put our time and budget into the work that actually moves the pipeline, like keyword research, content production, internal linking, and external link building tied to keywords that drive trials, demos, and revenue.

For more on the broader SaaS SEO strategy that technical work supports, see our articles on B2B SaaS SEO and Pain Point SEO.

• Our Agency: If you want to hire us to execute a conversion-focused SEO strategy built around driving demo requests, trial signups, and pipeline, not just traffic, you can learn more about working with us here.
• Join Our Team: If you’re a content marketer, writer, or SEO who’d love to do SEO and content marketing in this way, we’d love to have you apply to join our team.
• Our Course and Community: Individuals looking to learn our agency’s SEO and content strategy and become better marketers, consultants, or business owners can join our private course and community, taught via case studies and presented in both written and video formats.

One pattern we’ve seen consistently is that enterprise companies come to us after hiring an agency that technically did content marketing, published regularly, grew traffic, and hit their deliverable counts, but never generated pipeline. The content looks fine on the surface, ranks for keywords, and drives traffic, but rarely converts enterprise buyers into demos or sales opportunities.

In our experience, that failure comes down to a few recurring problems.

• Most agencies treat enterprise content marketing as a volume problem rather than a strategy problem rooted in how enterprise buyers actually make purchasing decisions.
• Most enterprise content marketing services target high-volume, top of the funnel keywords that attract early-stage researchers, not the active buyers who are already evaluating vendors. This has always been a flawed approach for pipeline generation, and it’s getting worse as AI tools absorb informational queries that used to drive organic search traffic.
• Most agencies don’t have a process for identifying genuine product expertise, so the content they produce is too generic and doesn’t really tell enterprise buyers how it could solve their pain points.
• Most enterprise content teams have no visibility into whether their brand appears in AI-generated responses when buyers are researching vendors on tools like ChatGPT or Perplexity, and most agencies they’ve hired don’t have a strategy for that either.

What’s behind all of this is a distinction we think matters a lot when evaluating agencies at the enterprise level. There are two fundamentally different things an enterprise business might need from a content marketing agency:

• Managing a content program at enterprise scale, with complex approval workflows, brand governance, compliance requirements, and global operations.
• Creating content that actually converts enterprise buyers who are making six or seven-figure purchasing decisions with long sales cycles and multiple decision-makers involved.

Most enterprise content marketing agencies are built for the first, but most enterprise marketing teams actually need the second, or some combination of both.

In this article, we’ll walk through the key factors enterprise marketing teams should use to evaluate content agencies, explain how we address each at Grow and Convert, and share profiles of the agencies whose names come up most often in this space.

Grow and Convert is an SEO and GEO-focused content marketing agency with experience working with several enterprise companies. If you want to hire an agency to handle your enterprise content marketing, you can learn more about working with us here. If you’d like to learn how to do this yourself, check out our content marketing course and community here.

Factors to consider when evaluating enterprise content marketing agencies

We’ve worked with enough enterprise companies to have a clear sense of what separates agencies that generate real pipeline from those that produce content without much to show for it. 

Below are the four factors we’d focus on when evaluating enterprise content marketing agencies.

Factor 1: Is their strategy built to generate pipeline, not just traffic?

The number one factor to evaluate in any content marketing agency is whether their strategy is designed to drive their company’s bottom line, i.e., generate leads and pipeline, or to just grow traffic. Most agencies, including those that market themselves to enterprise companies, usually focus on traffic growth. They target keywords with high search volume, report on sessions and rankings, and treat pipeline contribution as something that’s hard to measure as a vendor in an enterprise setting, and therefore not their responsibility.

Enterprise content marketing has a specific version of this problem. Because enterprise deals are large and sales cycles are long, there’s a temptation to justify content investment through brand awareness and thought leadership metrics rather than direct pipeline influence. Most agencies are happy to operate that way because it removes measurement pressure from them.

The agencies that actually drive enterprise pipeline start with the keywords and topics that enterprise buyers search (in both Google and with LLMs) when they’re already evaluating solutions, not when they’re trying to understand a problem category for the first time. 

In our research across dozens of B2B clients, bottom of the funnel content converts at 4.78% compared to 0.19% for top-of-funnel content. For enterprise companies where a single converted account can represent hundreds of thousands of dollars in revenue, that difference matters enormously.

When evaluating agencies on this factor, ask them to show you a client’s content strategy and explain how each piece connects to the pipeline. Ask what metrics they report on each month. If the answer centers on traffic, rankings, or domain authority rather than demos or sales opportunities sourced from content, that’s a meaningful signal about how they measure success.

Factor 2: Do they have a process for capturing genuine product expertise?

Enterprise buyers are sophisticated practitioners. A VP of Finance evaluating spend management software, or a CISO evaluating a security platform, can immediately tell whether a piece of content was written by someone who understands their problem at a practitioner level or by a writer who spent a few hours researching the topic online or used AI to produce generic copy.

Most agencies produce what we call “Google research paper” content, either on their own or with AI help. A writer is handed a keyword and assembles an article by just Googling what already ranks for that term. The output is generic, tells the reader nothing they couldn’t find on other websites, and gives enterprise buyers no reason to trust the vendor behind it. AI writing has only exacerbated this problem, with writers asking ChatGPT to just produce a piece on this topic, which, by definition, is AI just repeating what others have already said on that topic. 

In contrast, at Grow and Convert, we have an interview-based writing process, where writers conduct detailed conversations with product managers, engineers, sales leaders, and customer success teams before writing anything. Those interviews help the writers surface the specific insights, differentiators, and customer pain points that competitors cannot replicate because they require insider knowledge. For enterprise companies with complex products and sophisticated buyers, this is the content writing process that has consistently produced results for our clients.

When evaluating agencies on this factor, ask them to walk you through exactly how their writers develop domain expertise on a client’s product. If the answer involves content briefs and keyword research but no direct interviews with subject matter experts, the content they produce will reflect that gap.

Factor 3: Do they have an AI search strategy for enterprise visibility?

Enterprise buyers increasingly use AI tools like ChatGPT, Perplexity, and Claude to research vendors and build shortlists before ever talking to a sales team. If your brand isn’t appearing in AI-generated responses when buyers search for solutions in your category, you’re being filtered out of consideration before the conversation starts.

Most content marketing agencies have not built a structured methodology for AI search visibility. They may list GEO or LLM optimization as a service, but what they’re actually doing is applying tactics, like optimizing a few pages, adding FAQ schema, or pursuing mentions on third-party sites like Reddit, without a coherent model for how AI systems retrieve and cite sources or how to systematically improve visibility across an entire content program.

Our research found a 72% correlation between ranking on the first page of Google and being cited by AI tools like ChatGPT and Perplexity, which increases to 77% for the top three positions. Owned content ranking in traditional search is still the foundation of AI visibility, but there are additional layers around off-site brand mentions, third-party citations, and on-site content structure that influence whether AI tools recommend your brand when enterprise buyers ask relevant questions.

What makes AI search meaningfully different from traditional SEO is that enterprise buyers aren’t typing short, repeatable keywords into Google. They’re having detailed, personalized conversations with AI tools that factor in their company size, existing tools, and specific pain points, and getting recommendations tailored to their exact situation. That means generic, high-volume informational content doesn’t give AI systems enough to work with when matching your product to a specific buyer’s situation.

When evaluating agencies on this factor, ask them to describe their specific framework for improving AI search visibility, not just whether they offer it as a service. Ask how they measure it, which platforms they track, and what their process looks like for improving a brand’s citation rate over time.

Factor 4: Do they understand enterprise buying dynamics, not just enterprise operations?

This is the distinction we raised in the introduction, and it’s worth going deeper on because it’s where many agency evaluations go wrong.

There’s a meaningful difference between an agency that knows how to operate inside a large organization and an agency that knows how to create content for the buyers that large organizations are trying to reach. The first type has experience managing complex approval workflows, navigating brand governance processes, coordinating content across multiple business units, and producing high volumes of content at scale. Those are real capabilities that some enterprise companies need.

The second type understands how enterprise deals actually happen. They know that a six-figure software purchase involves multiple stakeholders with different objections, that the buying timeline is measured in months, that the content a VP sees during the evaluation phase needs to speak to business outcomes rather than product features, and that category-level keywords with enterprise modifiers like “project management software for large teams” or “SOC 2 compliant data integration” convert at far higher rates than broad informational queries.

Most agencies that market to enterprise companies are strong at the first and weak at the second. They understand procurement and compliance. They don’t necessarily understand how to create content that generates inbound interest from the specific decision-makers their clients are trying to reach.

When evaluating agencies on this factor, ask them to show you examples of content they’ve produced specifically for enterprise buyers. Ask how their keyword strategy accounts for longer sales cycles and multi-stakeholder buying committees. The answers will quickly tell you which type of agency you’re looking at.

Best Enterprise Content Marketing Agencies

Below are content marketing agencies we’ve run into or names we’ve heard associated with enterprise content marketing. Before listing those, we start with a detailed overview of our own and how we built Grow and Convert to address each of the four factors above, and the results we’ve achieved for clients.  For each, we’ll explain their approach, what they specialize in, and what types of companies they work best with. 

1. Grow and Convert

Grow and Convert is our SEO and GEO-focused content marketing agency. We’ve spent over 10 years executing content marketing strategies that drive demo requests and pipeline for enterprise B2B companies, not just traffic. 

Unlike agencies that optimize for top of the funnel metrics like traffic or rankings, we focus on bottom of the funnel content that generates qualified leads and measurable ROI through our Pain Point SEO and Prioritized GEO frameworks.

We work with enterprise B2B companies that sell complex products to sophisticated buyers, including large organizations with long sales cycles, multiple decision-makers involved in each deal, and significant revenue attached to each closed account.

Here’s how we address each of the factors we mentioned above:

Factor 1: We build content strategies around pipeline, not traffic

The core differentiator of our agency is that we measure performance on leads generated, not just traffic or rankings.

For every client, we create an ROI graph that tracks leads generated from our content each month against the number of leads they need to break even on their monthly investment with us. This gives enterprise marketing teams something concrete to show leadership: not sessions and impressions, but actual pipeline contribution from content.

For enterprise clients specifically, this means our SEO keyword strategy is built around the queries enterprise buyers search during an active evaluation, not during early-stage research. There are a few distinct keyword types we target for enterprise companies:

• Category keywords that reflect an active buying decision, such as “best [software category] for enterprise” or “enterprise [software category] software”
• Comparison keywords where buyers are already evaluating specific vendors, such as “[your product] vs. [competitor]”
• Jobs-to-be-done keywords that map to the specific outcomes enterprise buyers are trying to achieve, such as “how to manage compliance across multiple business units” or “how to consolidate HR software for large teams”
• Keywords with enterprise-specific modifiers that signal buying intent, such as “SOC 2 compliant,” “for teams of 500 or more,” or “with Salesforce integration”

Note, as we explain below, these keywords map really well to a company’s bottom of funnel topic map for AI search: the collection of topics users may ask AI tools about and where you want LLMs to mention your brand as a possible solution. 

In our research across enterprise B2B clients, bottom of the funnel content converts at 4.78% compared to 0.19% for top of the funnel content. For enterprise companies where a single converted account can represent hundreds of thousands of dollars in revenue, that difference is the entire argument for doing content this way.

Factor 2: We have a structured process for capturing genuine product expertise

Enterprise buyers are practitioners and senior decision-makers. A CISO evaluating a security platform or a CFO evaluating financial software will immediately recognize whether a piece of content was written by someone with genuine domain expertise or assembled from generic research. 

Most content produced by agencies falls into the latter category because writers are handed a keyword and asked to research the topic online, producing what we call Google research paper content that tells enterprise buyers nothing they couldn’t find elsewhere.

Our process is different. Before writing anything, we conduct in-depth interviews with product managers, engineers, sales leaders, and customer success teams at each client company. For enterprise clients, this often means interviewing the people closest to large-deal sales cycles, the account executives who understand enterprise objections, the customer success managers who know how large organizations actually implement and use the product, and the subject matter experts whose perspectives carry credibility with senior buyers. 

The resulting content contains what we call originality nuggets, specific insights and perspectives that competitors cannot replicate because they require direct access to your team and customers.

Factor 3: We have a structured AI search strategy

Enterprise buyers increasingly use AI tools like ChatGPT, Perplexity, and Claude to build vendor shortlists before engaging sales teams. Procurement teams at large organizations use these tools to identify solutions, compare vendors, and understand the landscape before a formal RFP process begins. If an enterprise company’s brand isn’t appearing in those AI-generated responses, it’s being filtered out of consideration before a sales conversation ever starts.

Most content marketing agencies approach AI search visibility through isolated tactics rather than a coherent strategy. We built the Prioritized GEO framework specifically to address this problem.

The framework treats AI visibility as a layered strategy: owned content ranking in traditional search as the foundation, followed by off-site brand mentions and third-party citations that signal authority to AI systems, and finally, on-site content structure optimized for AI retrieval. 

For enterprise clients specifically, our Tier 1 work goes beyond ranking for high-intent buying keywords. We build what we call a GEO topic map: a collection of content covering every product angle, use case, buyer persona, and competitive scenario in which an LLM could recommend you.

Our goal is to produce content that teaches LLMs to associate your brand as a possible solution when enterprise buyers describe their specific pain points in a conversation — content written at enough depth and specificity that when an AI tool searches the web while formulating a response, it finds clear, detailed signals about when and why your product is the right fit.

We track AI visibility at the topic level for every enterprise client using Traqer.ai (our own AI visibility tracking tool), which monitors brand citation rates across ChatGPT, Perplexity, and Google AI Overviews over time. We’ve also written about how we build topic-based GEO for clients in detail here.

Factor 4: We build content for enterprise buying dynamics, not just enterprise scale

Most agencies that serve enterprise companies understand enterprise operations. They know how to manage approval workflows, navigate brand governance, and produce content at scale inside a large organization. What they often don’t understand is how enterprise deals actually happen and what content moves enterprise buyers through the purchasing decision.

Our content strategies for enterprise clients are built around that buying process specifically. We map keyword strategies to the stages of an enterprise evaluation, from the initial queries a VP searches when they first recognize a problem, through the comparison and vendor evaluation searches that happen mid-cycle, to the compliance and implementation questions that come up before a deal closes. 

We write for the business outcomes and risk considerations that senior decision-makers care about, not the feature-level details that would appeal to an individual contributor. And because enterprise deals involve multiple stakeholders, we think about the full buying committee when developing content, not just the primary user persona.

You can learn more about working with us here, or read our full guide to enterprise content marketing strategy here. 

2. Tendo Communications

Tendo Communications is a B2B content and UX agency that works exclusively with large enterprise companies. Founded in 1999, they provide services including content strategy, content creation, UX design, content operations, and content performance. They are one of the oldest agencies in this space and one of the few that focuses entirely on the operational complexity of large B2B organizations rather than growth-stage or mid-market clients.

Unlike most agencies in this space, they integrate content strategy with user experience design. They acquired a UX design agency to bring both disciplines under one roof, making them well-suited for enterprise organizations managing large content ecosystems across multiple products, regions, and audiences.

Their production model centers on a Content Studio approach that provides dedicated editorial teams for sustained content production at scale. They use a proprietary audience-centric framework built around persona development and journey mapping, and work with clients on a long-term strategic basis across multiple regions, languages, and business units rather than as a project-based content vendor.

They have worked with companies like Cisco, Salesforce, HPE, Autodesk, VMware, Informatica, and Okta.

Visit their site for more information.

3. Animalz

Animalz is a content marketing agency that works with B2B SaaS companies, enterprise software companies, and venture capital firms. They describe their programs as built to drive organic growth through content that reflects genuine domain expertise rather than research-assembled writing, and they have worked with enterprise technology companies alongside growth-stage SaaS clients.

Their production process starts with a deep onboarding phase where they study the client’s product, industry, and go-to-market strategy before developing a content strategy and moving into production through direct SME interviews. They emphasize building content that requires insider knowledge, which they believe is what separates effective B2B content from generic writing assembled from existing search results.

They also offer Answer Engine Optimization as a dedicated service, which includes brand visibility audits across Google AI Overviews, ChatGPT, Perplexity, and Gemini. They have published detailed frameworks on improving AI citation rates and employ a dedicated AI and marketing operations function internally.

They have worked with companies including Google, Amazon, Atlassian, GoDaddy, Airtable, Amplitude, Intercom, UiPath, and Ramp.

Visit their site for more information.

4. Siege Media

Siege Media is an organic growth agency specializing in SEO, GEO, content marketing, and digital PR. Their content strategy begins with a proprietary keyword prioritization framework, and they are explicit that their writers are senior specialists matched by vertical expertise rather than generalist freelancers.

Their production process emphasizes quality over volume, with approximately half of their content creation time dedicated to design alongside writing. They are also vocal about their selective use of AI in content production, stating that while they use it for certain tasks like brainstorming and outlines, writing is rarely one of them.

On the AI search side, they have built two proprietary tools. BlueprintIQ audits content against live AI search results across ChatGPT, Gemini, and Perplexity to identify gaps in topical coverage. DataFlywheel refreshes existing content assets on a rolling quarterly basis to maintain relevance for AI citation. These tools put them among the more technically developed agencies on this list when it comes to GEO execution.

They have worked with companies like Zapier, Figma, Instacart, Asana, and Intuit.

Visit their site for more information.

5. Brafton

Brafton is a full-service content marketing agency that has been operating since 2008. Their services cover blog writing, graphic design, video production, email marketing, social media management, PPC, web design, and SEO consulting. They work with a wide range of company sizes across industries including technology, healthcare, finance, and manufacturing, and have offices across the United States, United Kingdom, and Australia.

A distinguishing feature of their model is a proprietary content marketing platform they have built for workflow, project management, and content performance tracking. Each client is assigned a dedicated content marketing strategist who provides strategic direction alongside the production team. Their writers are described as full-time in-house staff rather than outsourced freelancers, matched with clients based on industry expertise.

They also offer generative engine optimization as a dedicated service, covering content optimization for AI retrieval, structured data, and citation tracking across AI platforms. Their engagement model is unit-based rather than per-piece pricing, which gives clients flexibility across different content types and volumes.

They have worked with companies like Webex, ViewSonic, Oxford University Press, and TruGreen.

Visit their site for more information.

6. Column Five

Column Five is a B2B content marketing agency that focuses on brand storytelling for enterprise SaaS and AI companies. Founded in 2009, they position themselves around helping companies develop a differentiated point of view and then expressing it across multiple content formats, including video, motion graphics, infographics, interactive content, and written content. Their tagline is “the best story wins,” which reflects their emphasis on narrative and brand identity alongside organic growth.

Their process begins with a proprietary Story Scan Program that audits a client’s brand messaging, competitive positioning, and content gaps before any production begins. They describe their approach as audit-first, strategy-second, and execution-third, and have published case studies showing multi-year engagements with enterprise clients where they work across multiple internal teams simultaneously.

They also offer Answer Engine Optimization consulting and AI-powered content services as part of their service line, and they explicitly position these services to help brands get found by both human audiences and LLMs. They also operate a proprietary AI content tool called Iris for clients that need content at scale.

They have worked with companies including Instacart, Databricks, Vercel, Salesforce, LinkedIn, GitHub, Microsoft, and SAP.

Visit their site for more information.

7. Omniscient Digital

Omniscient Digital is an organic growth agency that helps B2B software companies drive pipeline through SEO, GEO, and content. Founded in 2019 by alumni from HubSpot, Shopify, and People.ai, they position themselves around revenue as the end goal rather than traffic growth, and they publish detailed thinking about content strategy and organic growth through their own blog and podcast.

Their research methodology, which they call OmniscientX, includes competitive analysis, buyer research, content gap analysis, LLM visibility indexing, keyword exploration, product alignment, and SME interviews before strategy development begins. They emphasize hiring for business acumen alongside SEO skills and integrate closely with client product marketing, sales, and go-to-market teams rather than operating at arm’s length.

Their GEO work incorporates LLM visibility indexing and citation analysis from the research phase onward, and they apply RAG principles and semantic modeling to improve how AI systems retrieve and surface client content.

They have worked with companies like Jasper, SAP, Asana, Loom, Hotjar, Adobe, and Smartling.

Visit their site for more information.

8. Foundation Inc

Foundation Inc is a distribution-first content marketing agency for B2B companies, founded by Ross Simmonds. Their core premise is that most agencies invest heavily in content creation while underinvesting in distribution, and their entire process is built around that belief. They describe their methodology as research, create, distribute, and optimize, with distribution treated as equally important as production.

Before producing any content, their team studies client audience newsletters, online reviews, sales calls, Reddit communities, social platforms, and competitor content. They then build a 12-month content engine with specific distribution playbooks for each piece, covering newsletter placement, founder content, social media, YouTube, Reddit, and sales enablement channels.

Their content creation process includes formats built specifically for LLM visibility, and their optimization work targets ChatGPT, Claude, SGE, Gemini, and Perplexity. Their homepage explicitly positions them as an agency that increases LLM visibility alongside pipeline generation.

They have worked with companies including Webex, Mailchimp, Snowflake, Canva, Procore, and Paychex.

Visit their site for more information.

9. Power Digital

Power Digital is a full-service performance marketing agency that works with both B2B and consumer brands. Founded in 2012 and backed by private equity, they are one of the largest agencies on this list by headcount and operate across a wide range of services, including SEO, paid media, paid social, email, influencer marketing, PR, affiliate marketing, programmatic, and content. 

Their core technology is a proprietary platform called Nova, which connects first-party client data to inform strategy and performance measurement across channels.

Content marketing sits within their broader owned media offering rather than as a standalone specialty. Their model is built around integrating content with paid and earned media channels rather than treating organic content as an independent growth driver. This makes them a strong fit for companies seeking a single agency to manage multiple marketing channels simultaneously.

They offer generative engine optimization as a dedicated service, covering LLM visibility audits, content structuring for AI responses, technical schema implementation, and citation engineering across ChatGPT, Perplexity, Gemini, Claude, and Microsoft Copilot. Their Nova platform also connects AI search performance data to broader marketing attribution.

They have worked with companies like ASICS, Crocs, Taylor Guitars, and Data.World.

Visit their site for more information.

10. iPullRank

iPullRank is an enterprise content marketing and SEO agency based in New York. They describe their focus as Relevance Engineering, a proprietary framework that integrates content strategy, technical SEO, information retrieval, digital PR, and user experience to optimize content for both traditional search and AI search platforms. 

They work primarily with enterprise and mid-market companies and claim to have delivered over $4 billion in organic search results for their clients.

Their approach is built around the idea that enterprise brands need specialized partners rather than generalist agencies that treat content and SEO as interchangeable services. Their Relevance Engineering framework uses semantic content structures, passage scoring, and retrieval modeling to engineer brand visibility across AI search platforms alongside traditional Google rankings. They also offer AI search audits that surface a client’s current presence across both traditional and generative platforms.

On the content side, their team combines technical SEO expertise with content strategy and content engineering, and they are explicit that their writers and strategists are specialists rather than generalists. They publish detailed original research and thought leadership on natural language processing, machine learning applications in SEO, and how AI systems retrieve and rank content, which reflects how technically grounded their methodology is.

They have worked with enterprise companies like LG, American Express, Citi, and DocSend, as well as Fortune 500 companies across financial services, media, and retail.

Visit their site for more information.

Want to work with us or learn how to implement our enterprise content marketing strategy?

• Our Agency: You can learn more about working with us here.
• Our Content Marketing Course: Individuals looking to learn how to grow their enterprise business with content can join our private course, taught via case studies, here. We include a lot of information and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we and other members give feedback. We also get on live Zoom calls about once a month and dissect members’ actual content strategies and brainstorm ideas on how we’d form content strategies for their businesses.
• Join Our Content Marketing Team: Alternatively, if this style of enterprise content marketing appeals to you, consider joining our content marketing team as a writer or content strategist. We have awesome clients. We’re a remote company. We pay well. And you won’t have to stress about getting your own clients or spend a bunch of time doing outreach to get them.

Both are built on the principle that most content teams waste time on well-established but ineffective tactics that, at best, drive traffic with extremely low conversion rates to qualified leads. 

Over the years, we’ve noticed that this problem is especially acute in enterprise companies. Enterprise organizations have all the content operations infrastructure you’d expect: content teams, agency retainers, editorial calendars, brand guidelines, content governance policies, multi-step content workflows, and they publish content consistently and in large volume. But where those resources are pointed to is at a strategy that’s fundamentally flawed: it’s focused on chasing search volume in SEO or citations in AEO/GEO (or frankly, in AEO/GEO, many organizations right now simply don’t have a coherent strategy and aren’t sure what to focus on). 

So,  when leadership asks what all that investment actually produced and how it connects to business goals, the content team can only point to traffic numbers or publishing volume, and they can’t really trace content to pipeline or revenue.

Specifically, here are the flaws in the typical enterprise content strategy: 

• Traffic, not conversion-focused strategy: Their content strategy is built around high-volume, top of the funnel keywords that drive traffic but have extremely low conversion rates to sales-qualified leads (SQLs). Enterprise content marketing teams gravitate toward broad, educational terms mostly because they operate in a silo and are disconnected from sales data or product teams, and traffic is the metric that’s easiest to report on.
• Content just summarizes Google: Enterprise companies often have large content teams or multiple agency partners, but the actual writing process follows the same playbook: a writer or AI tool researches the keyword, reads what’s already ranking, and produces a synthesized version of the same information. At enterprise scale, this is a particular problem because the people reading this content are practitioners who already know the basics and can immediately tell when an article was written by someone who has never actually done the work.
• Too little product sales copy: They avoid selling their product in their content. Enterprise content teams often treat blog content as a brand awareness exercise, so articles deliberately avoid explaining how the product actually solves the problem being discussed. This exacerbates the low conversion problem. 

The approach we’ve built at Grow and Convert addresses all three of these problems, and in this article, we’ll walk you through our entire enterprise content marketing process:

1. Prioritize high-buying-intent keywords: Prioritize keywords based on buying intent rather than search volume, focusing on the terms enterprise buyers actually search when they’re evaluating solutions to specific problems. Traffic and brand awareness are fine goals, but if the brand isn’t consistently visible for bottom of funnel, high-intent searches, you’re leaving SQLs on the table for no reason. This is even more pressing in AI search because AI tools simply don’t link to or mention brands for top of funnel queries (more on this here). 
2. Produce content through internal employee interviews, not via genAI or humans regurgitating Google: Instead of having writers (or an AI tool) research topics on their own, which simply creates generic, me-too content, conduct interviews with internal subject matter experts to create content that contains genuine product and domain expertise. Enterprise buyers are not beginners, they’re advanced, and therefore your content also needs to be advanced. 
3. Sell your product in each piece of content: Because we emphasize producing high buying intent content, in this content, you need to talk about your product extensively, including specific features, differentiators, and use cases, rather than staying vague in the name of brand awareness.
4. Build links and AI search citations: Generate backlinks and optimize for visibility in AI search tools like ChatGPT and Perplexity.
5. Set up conversion tracking and attribution: Track actual pipeline metrics like demos, trials, and sales conversations sourced from content, not just traffic and rankings.

Grow and Convert is an enterprise content marketing and SEO agency. If you want to hire us to handle your enterprise content marketing, you can learn more about working with us here. 

Our 5-Step Enterprise Content Marketing Process

We’ve written at length about each of these steps individually across our site. Here, we’re going to walk through how each step applies specifically to enterprise content marketing and link to further resources for each step:

Step 1: Identify High-Intent Keywords

The most fundamental part of an enterprise content marketing strategy is keyword selection. Keywords (or equivalently, prompts and topic areas in AI search) indicate the level of buying intent of the user.

If you’re only going after low-intent keywords, ones where the searcher isn’t in the market for your product at all,  then nothing else in your enterprise content marketing strategy is going to matter. The volume of content you publish doesn’t matter. The quality of your editorial calendar doesn’t matter. Your brand guidelines and approval workflows don’t matter. You will simply generate low intent, low converting traffic over and over again. 

Instead,  to create an enterprise content marketing strategy that actually generates appreciable pipeline and revenue, you need to prioritize keywords based on buying intent, not search volume. We’ve written extensively about this concept in our article on Pain Point SEO, the strategy we coined that prioritizes content ideas around high-intent keywords over high-volume ones to drive conversions.

Most enterprise content teams do exactly the opposite. They build their content calendar around broad, high-search-volume keywords that attract the widest possible audience, because the logic is that more traffic means more potential customers, but that’s not really the case. 

In practice, ranking for a keyword like “what is supply chain management” at 8,000 monthly searches will generate far fewer leads than ranking for “manufacturing supply chain management software” at 350 monthly searches, because the second keyword captures someone who already knows they need a solution and is actively looking for one.

This problem is especially pronounced in enterprise companies, where content teams don’t have visibility into which keywords actually drive the pipeline. If they operate in a silo, disconnected from sales data, they usually default to optimizing for the metric they can see: traffic.

We approach keyword selection differently. Instead of starting with search volume, we start with the customer journey and work backward to find the keywords that indicate someone is ready to evaluate or purchase a solution.

This means understanding your target audience and buyer personas deeply enough to know the language they use when actively looking for a product like yours, not just when researching a general topic.

These keywords are also where enterprise content marketing becomes a genuine lead generation channel rather than a brand awareness exercise. When someone searches for a specific problem your product solves, they’re further along in the buyer’s journey than someone searching for a general industry term, and content that meets them at that moment with real expertise and a clear product connection will convert at a fundamentally different rate.

We break these keywords into a few main categories and prioritize them roughly in this order.

Category Keywords

Every enterprise content strategy should start here. These are the keywords that indicate someone is actively looking to buy or evaluate a product like yours right now. Software category keywords describe the type of product someone is searching for, and for enterprise companies, they tend to follow patterns like:

• “Best [use case] software for enterprise”
• “[Use case] platform for large teams”
• “Enterprise [use case] software”
• “[Use case] software for [industry]”

Enterprise keyword research also tends to surface modifiers that don’t appear in SMB or mid-market searches. 

Enterprise buyers search for solutions that meet compliance requirements (“SOC 2 compliant [use case] software”), specific integrations (“marketing analytics platform with Salesforce integration”), and scale indicators (“project management software for 500+ users”). The more use cases your product serves and the more verticals you operate in, the more keyword opportunities you’ll find in this category alone.

These are the keywords that most enterprise companies are bidding on in paid ads, but, in our experience, aren’t pursuing organically through blog content. The search results for many of these terms are dominated by software review sites like G2 and Capterra, which means a well-written, expert-driven article from the company itself can compete effectively by going deeper into how the product actually works and what makes it different.

Comparison Keywords

The second category of high-intent keywords we prioritize is comparison keywords, including “[competitor] alternatives” and “[brand] vs [competitor]” searches. These are valuable because the searcher is already in evaluation mode. They know which product category they need and are now deciding between specific options.

For enterprise companies, comparison keywords often look like:

• “[Competitor] alternatives for enterprise”
• “[Brand] vs [competitor] for [industry]”
• “[Competitor] vs [competitor]” (where you insert your brand as a third option)
• “Best [competitor] alternatives for large teams”

The third variation is particularly useful for enterprise companies that are newer or less well-known in their space. If two established competitors have significant search volume for their head-to-head comparison, you can create content targeting that keyword and position your product alongside both of them. This puts your brand in front of buyers who are already deep in the evaluation process, even if they haven’t heard of you yet.

Enterprise comparison content also tends to be more nuanced than what works at the SMB level. Enterprise buyers are evaluating products across multiple dimensions (security certifications, integration depth, implementation timelines, support SLAs), so the comparison content needs to address these factors specifically rather than just listing features side by side.

Use Case, Pain Point, and Jobs-to-be-Done Keywords

After covering the core bottom of funnel category and comparison keywords, we move to use case, pain point, and jobs-to-be-done keywords. These are terms where the searcher has a specific problem or operational need but isn’t yet searching for a product category by name. They’re either describing a challenge they’re trying to solve, or a specific task they need to accomplish.

For enterprise companies, pain point and use case keywords often follow patterns like:

• “How to [solve specific operational problem] at scale”
• “[Specific pain point] for [industry] companies”
• “How to manage [process] across multiple teams”
• “[Operational challenge] for [enterprise role]”

Jobs-to-be-done keywords are closely related but the distinction matters. Pain point keywords describe a problem the searcher is experiencing, while JTBD keywords describe a specific task or outcome the searcher needs to accomplish. The searcher isn’t necessarily in pain — they just have a job to do and are looking for the best way to do it. These tend to follow patterns like:

• “How to [complete specific task] for [enterprise context]”
• “Best way to [achieve specific outcome] across [multiple departments]”
• “[Specific task] template for [industry]”
• “How to automate [process] at scale”

Both types convert well because the searcher is describing something that the enterprise product directly solves, even if they aren’t aware a product category exists for it. Someone searching for “how to standardize vendor onboarding across 12 offices” might not know that vendor management platforms exist, but that search describes exactly the job your product was built to do.

This is also where selling your product in the content (which we’ll discuss in Step 3) becomes especially important — if you write a thorough article about the problem but never explain how your product solves it, you’ve done all the hard work without capturing any of the value.

Enterprise pain points and JTBD keywords are where internal knowledge from sales and customer success teams becomes critical. The best keywords in this category often come from listening to the specific language enterprise buyers use during sales calls, reading through support tickets, and understanding the operational problems that drove your largest customers to seek out a solution in the first place. Asking your CS team “what are the top five things enterprise customers use the product to accomplish in their first 90 days?” will often generate keyword ideas that no research tool would surface on its own.

We recommend enterprise companies only move to top of the funnel content after they’ve exhausted their bottom of the funnel and middle of the funnel keyword opportunities. Top-of-the-funnel content can help build topical authority and support rankings for higher-intent pages, but it should never be the starting point for an enterprise content strategy.

This is even more important now that AI search engines like ChatGPT, Perplexity, and Google AI Overviews are answering broad, educational queries directly without sending users to your site. A top of the funnel strategy that used to drive significant traffic now drives very little, and that traffic still doesn’t convert well, making it the worst of both worlds.

In contrast, bottom of funnel queries where users are asking for product recommendations are exactly where AI tools list and cite brands, which makes BOFU content valuable for both traditional search and AI visibility.

One exception to the bottom-up prioritization is worth mentioning. In the first few months of an enterprise content marketing initiative, SEO content takes time to rank, which creates a gap between when you start investing and when you start seeing organic results. 

To bridge that gap and show early traction to leadership, we produce what we call “Disruption Stories” alongside our SEO content. These are non-SEO narrative pieces that tell the story of why the company was founded, what specific problems the product was built to solve, and how it works differently from existing solutions.

We then promote these through paid channels, primarily LinkedIn, to drive traffic and conversions while the SEO content is ramping up. This gives leadership visibility into early results and buys the time needed for the organic content strategy to compound.

Step 2: Produce Content Through Expert Interviews That Deeply Satisfies Search Intent

Finding the right keywords is only half the problem. The other half is producing content that does two critical things simultaneously: 

1. Fulfill search intent enough to actually rank for those keywords
2. Is written well enough to actually  convert your ICP when they read it.

The “Google Research Paper” Problem

Most enterprise content programs get both of these parts wrong in a specific and consistent way. Whether the content is produced by in-house writers, freelancers, or agency partners, the content creation process is usually some variation of the same thing: the writer receives a keyword and a brief, researches the topic by reading what’s already ranking on the first page of Google, and writes a synthesized version of that information in the company’s brand voice.

We call this the “Google research paper” approach, and it produces content that is factually accurate but offers nothing the reader couldn’t find elsewhere. This is a problem for any company, but it’s a particular problem at enterprise scale for two reasons.

First, the people reading enterprise content are practitioners, decision-makers, and technical evaluators who are not beginners, they are advanced in their field. Their needs and questions are advanced. They don’t need a “beginners guide” to their own field or to know the “10 trends” in that field “to look out for” next year. They can immediately tell when an article was written by someone who doesn’t have hands-on experience with the subject matter.

Second, enterprise buying decisions involve multiple stakeholders, which means the content needs to be substantive enough that a champion inside the organization can share it with their team and it holds up to scrutiny.

AI-generated content has made this problem worse, not better. AI tools can produce content faster and cheaper, but the output is fundamentally the same as the Google research paper approach — it can only synthesize what already exists. It cannot produce original insights from someone who has actually built, sold, or implemented the product.

Our Solution: Internal Interview-Based Content Writing

At Grow and Convert, we solve this by building every article around a direct interview with a subject-matter expert within the client’s company. This is not a casual conversation or a quick fact-check. It’s a structured 30-45 minute recorded interview designed to extract the specific insights, examples, and perspectives that make the article genuinely different from everything else ranking for that keyword.

Before the interview, our content strategist researches the target keyword, analyzes search intent, reads top-ranking content, and identifies specific gaps and angles that existing content doesn’t cover. They then prepare a set of interview questions designed to pull out exactly those missing pieces from the expert.

During the interview, the goal is to get the expert talking about their actual experience: how the product works in practice, what specific problems enterprise customers face, how implementation typically plays out, what objections come up during the sales process, and what makes their approach different from competitors. These are the details that a freelance writer or AI tool could never produce on their own because they simply don’t have access to this information.

After the interview, the writer builds the article around these insights, weaving them into a piece that satisfies the search intent for the target keyword while containing perspectives and details that are genuinely original. We call these details “Originality Nuggets,” the specific pieces of information that make the content impossible to replicate by reading other articles or prompting an AI tool.

Writing With Specificity, Not Generalities

We also apply what we call the “Specificity Strategy” throughout the content creation process. This means replacing generic statements with concrete, specific details from the interview.

Instead of writing “our platform helps large companies streamline their onboarding process,” the writer uses the specific details the expert provided: “our platform reduced onboarding time from 6 weeks to 9 days for a manufacturing company with 12 regional offices, primarily by automating the document collection and compliance verification steps that previously required manual coordination across three different departments.”

That level of specificity is what makes enterprise content credible to practitioners, and it’s only possible when the content is built from expert interviews rather than desk research.

Enterprise content needs to demonstrate real product and domain expertise because the buyers are more sophisticated and the stakes of the purchase are higher. An SMB buyer might read a comparison article and sign up for a free trial within the same session. An enterprise buyer is going to share that article with their procurement team, their IT security team, and their VP, and every person in that chain needs to come away feeling like the company genuinely understands the problem and has a credible solution.

Interview-based content also creates a competitive moat that scales. Every article you produce through this process contains insights that exist nowhere else on the internet, which means competitors cannot replicate it and AI tools cannot synthesize it from existing sources. Over time, this builds a library of genuinely differentiated content, which becomes increasingly valuable as AI search engines begin to favor original, expert-driven content over recycled information.

Matching the Right Expert to Each Piece of Content

For enterprise companies with large teams, matching the right expert to each piece of content is also equally important. Not every article should feature the same person. The best approach is to match the expert to the keyword category:

• Product managers and engineers are the best sources for software category content, because they can explain how the product actually works, what architecture decisions were made and why, and how specific features solve specific problems at enterprise scale.
• Sales leaders and account executives are the best sources for comparison and alternatives content, because they hear directly from prospects why they’re evaluating competitors, what gaps they’ve found in other products, and what factors ultimately drive the buying decision.
• Customer success managers are the best sources for use case, pain point, and jobs-to-be-done content, because they see how enterprise customers are actually using the product day to day, which workflows they’ve built around it, and which outcomes they’re measuring.

This process ensures that every article draws from the most relevant firsthand experience available inside the company, which produces content that feels authoritative and specific rather than generic and surface-level.

It also solves a problem that enterprise content creators consistently struggle with: producing content that is both on-brand and genuinely expert-driven. When content workflows rely on writers working from briefs and existing materials alone, the output tends to be either on-brand but shallow or detailed but off-message.

The interview process bridges that gap because the writer gets the substance directly from the expert and shapes it within the company’s messaging and content management guidelines.

Step 3: Sell Your Product in Each Piece of Content

This is one of the most counterintuitive parts of our process for enterprise content teams, and it’s also one of the most impactful. Most enterprise content programs operate under an unspoken rule that blog content should educate, not sell. The assumption is that if you provide enough value upfront, the reader will eventually find their way to your product on their own. In practice, that rarely happens.

We see this play out in a very specific way across enterprise blogs. The article will do a thorough job of explaining a problem, walking through different approaches, and providing useful information. But it will never explain how the company’s own product solves that problem. It might mention the product once in a CTA at the bottom of the page, or it might not mention it at all. The content team considers this a feature — they believe that “selling” in content would undermine the reader’s trust.

We think this is a mistake, and a costly one. If someone is reading your article about vendor onboarding challenges for enterprise companies, and your product directly solves that problem, not telling the reader how it works is doing them a disservice. They came to your site looking for a solution. They’re reading your content because the keyword they searched indicated they have a real problem. The most helpful thing you can do is show them exactly how your product addresses it.

Selling your product in content doesn’t mean turning every article into a product landing page. It means weaving specific, relevant product details into the article at the points where they naturally answer the reader’s questions.

For a software category article targeting “enterprise project management software,” this means going beyond a generic feature list and explaining how your product handles the specific concerns enterprise buyers have: how permissions and access controls work across teams of 500+ people, how the platform integrates with the tools their teams are already using, how implementation and onboarding work for a company with multiple departments, and what the security and compliance posture looks like.

For a pain-point article targeting “how to reduce vendor onboarding time across multiple offices,” this means explaining the problem thoroughly, acknowledging the different ways companies try to solve it, and then walking through how your product automates the parts of the process that create the most friction. Show screenshots. Describe the actual workflow. Explain what changes for the user on day one versus day thirty.

For a comparison article targeting “[competitor] alternatives for enterprise,” this means being direct about where your product is stronger and where the competitor might be a better fit for certain use cases. Enterprise buyers are sophisticated enough to know that no product is perfect for everyone, and being transparent about tradeoffs actually builds more trust than claiming superiority across the board.

The underlying principle here is what we call “Customer-Content Fit,” which means matching the content format, depth, and messaging to where the reader is in their buying journey and what they need at that specific moment. Not every piece of enterprise content should look the same. A software category article needs detailed product walkthroughs and feature explanations. A pain point article needs problem diagnosis and workflow-level product demonstrations. A comparison article needs honest, side-by-side analysis.

This also applies to content formats beyond blog posts. Many enterprise organizations spread their marketing campaigns across case studies, white papers, webinars, podcasts, and video content without a clear strategy for how each format connects to the pipeline. The same Customer-Content Fit principle applies: case studies work best at the bottom of the funnel when a prospect needs social proof to share with their procurement team. White papers can work at the middle of the funnel when a prospect needs to understand a problem more deeply before evaluating solutions. But every format should tie back to a specific stage in the customer journey and include clear product messaging rather than treating content as a brand awareness exercise with no conversion path.

Enterprise content teams resist selling content for several reasons, all understandable but ultimately counterproductive.

The first is organizational. In many enterprise companies, the content team sits under brand or communications, not under demand generation. Their mandate is awareness and thought leadership, not pipeline, so they naturally produce content that prioritizes brand perception over conversion. Shifting this requires either changing the team’s mandate or building a content function that reports into a revenue-focused leader.

The second is the belief that buyers don’t want to be sold to. This is partially true because enterprise buyers don’t want to read a thinly disguised product pitch that provides no real value. But that’s not what we’re advocating. We’re advocating for content that provides genuine value through expert insights and thorough problem analysis, and then earns the right to explain how the product fits into the solution. When the content is good enough, talking about the product feels natural, not salesy.

The third is that many content teams simply don’t know their own product well enough to write about it in detail. This is where the interview-based process from Step 2 becomes essential. When the writer has conducted a 45-minute interview with a product manager or solutions architect, they have the specific details, language, and examples they need to talk about the product credibly. Without that interview, the writer defaults to vague descriptions pulled from the marketing site, which is exactly the kind of content that enterprise buyers ignore.

The difference between content that mentions the product and content that doesn’t is significant. Across the articles we’ve produced for clients, bottom of funnel content that includes detailed product positioning converts at rates that are dramatically higher than top of funnel content that doesn’t. 

We published the data behind this in our article on what keywords convert the highest, where we analyzed conversion rates across 95 blog posts and found that bottom of the funnel posts converted at 4.78% on average, compared to 0.19% for top of funnel posts. That’s a 25x difference, and a large part of it comes down to whether the content actually sells the product or stays deliberately vague.

For enterprise companies, where each conversion can represent a six or seven-figure deal, that difference is even more consequential. A single well-positioned article that ranks for the right keyword and clearly demonstrates how the product solves the reader’s problem can generate more pipeline than an entire quarter’s worth of top of the funnel blog content.

Step 4: Build Backlinks and Brand Mentions

Even with the right keywords and expert-driven content, enterprise articles won’t rank if the site doesn’t have the backlink profile to compete. Link building is especially important for enterprise content marketing because the keywords worth targeting tend to be competitive, and the sites currently ranking for them, software review platforms like G2 and Capterra, established industry publications, and competitor blogs with years of domain authority, have strong link profiles.

Most enterprise companies approach link building in one of two ways, and neither is particularly effective.

The first is to do nothing and hope that good content attracts links on its own. This occasionally works for top of the funnel content that gets shared on social media, but it almost never works for bottom of the funnel content because articles about “best enterprise project management software” don’t tend to go viral.

The second is to run generic outreach campaigns that blast hundreds of sites with templated emails asking for links. This produces low-quality links from irrelevant sites that do little to move rankings.

We approach link building differently. We focus on building a smaller number of high-quality, relevant links from sites that actually have authority in the space. The specifics of how we do this are covered in depth in our articles on content distribution, but the core idea is that relevant links from sites in your industry or adjacent industries carry far more weight than a large volume of links from random directories and guest post farms.

For enterprise companies, there are a few link building advantages that large companies don’t typically leverage. Enterprise brands tend to have existing relationships with industry publications, analyst firms, partner companies, and professional organizations. These relationships can be leveraged for link building in ways that feel natural rather than forced, like co-authored research reports, contributed expert commentary, partner integration pages, and industry roundtable content all generate high-quality links while also serving a legitimate business purpose.

Repurposing existing content assets, such as webinar recordings, research data, or customer interviews, into linkable digital assets can also generate backlinks without requiring new content creation. LinkedIn, in particular, is a valuable distribution channel for enterprise content because it’s where enterprise buyers and decision-makers already spend time, and content that performs well there tends to attract organic links and mentions from industry peers.

Beyond traditional link building, brand mentions, and citations across authoritative third-party sites are becoming increasingly important for AI search visibility. AI tools like ChatGPT, Perplexity, and Google AI Overviews decide which brands to recommend based partly on what we call third-party consensus — how frequently and consistently your brand is mentioned, reviewed, and cited across independent sources on the web. When multiple authoritative sites mention your product in the context of solving a specific problem, AI tools interpret that as a signal that your brand is a credible option worth recommending.

This is why we actively build third-party citations as part of our link building process. Getting your brand mentioned in relevant industry publications, software review sites, partner directories, expert roundups, and contributed articles creates the kind of consensus that both Google and AI search tools reward.

For enterprise companies, this matters because when a buyer asks ChatGPT or Perplexity “what are the best vendor management platforms for enterprise companies,” the tools are pulling from that web-wide consensus to decide which brands to include in the response. Companies that only have their own website talking about their product will lose out to competitors who have built that third-party presence.

We’ve written extensively about how to approach this systematically in our article on Prioritized GEO, which lays out our three-tier framework for improving visibility across both traditional and AI search.

Step 5: Set Up Conversion Tracking and Attribution

This is the step that most enterprise content programs skip entirely, and it’s the reason content gets treated as a cost center. If you can’t show leadership that content generated 40 qualified leads last month, or that three enterprise deals currently in the pipeline originated from blog content, then content will always be the first budget line to get cut when the company needs to tighten spending.

The mistake most enterprise content teams make is treating measurement as something they’ll figure out later, after they’ve published enough content to have something worth measuring.

We take the opposite approach. We set up conversion tracking before we publish the first article because every piece of content we produce targets a high-intent keyword where the reader has a real problem and a real reason to take the next step. If we’re not tracking whether they take that step, we’re flying blind from day one.

Enterprise content teams tend to report on the metrics that are easiest to measure: pageviews, organic traffic, keyword rankings, time on page, and social shares. These metrics are fine for understanding content reach, but they tell you nothing about whether content is actually contributing to the business. A blog post that gets 10,000 monthly visits and zero demo requests is not a successful piece of content, no matter how impressive the traffic number looks in a slide deck.

The KPIs that matter for enterprise content marketing are conversion metrics: demo requests, trial signups, contact form submissions, and sales conversations that originated from or were influenced by content. These are the numbers leadership cares about because they directly connect to the pipeline and revenue.

This doesn’t mean you should ignore traffic and rankings entirely. They’re useful leading indicators, especially in the first few months of a new content marketing initiative when content is still building rankings and it’s too early to expect significant conversion volume. But they should never be the primary metric for evaluating content performance.

For our clients, the specific attribution setup depends on the enterprise company’s tech stack, including their content management system and CRM, but the core approach is consistent. Every article needs a clear call to action that matches the intent of the keyword it’s targeting.

For bottom of the funnel content, that’s typically a demo request or free trial signup. For middle of the funnel content, it might be a consultation request or a product walkthrough. The CTA needs to be tracked as a conversion event in your analytics platform so you can see exactly which articles are generating leads.

We use GA4 conversion tracking and BigQuery as the foundation. For each article, we track the number of users who arrive from organic search, the number who click through to a conversion page (demo request, trial signup, contact form), and the number who actually complete the conversion. This gives us a clear conversion rate for every piece of content, which we can then report on by keyword category, by funnel position, and by individual article.

For enterprise companies with longer sales cycles, we also recommend connecting content attribution to the CRM. When a lead comes in through a blog post and eventually closes as a six-figure deal, that information needs to flow back to the content team so they can demonstrate the revenue impact of their work. Most enterprise CRMs (Salesforce, HubSpot) support this kind of first-touch or multi-touch attribution, but it requires intentional setup and coordination between marketing and sales operations.

When you have conversion data, the conversation with leadership shifts fundamentally. Instead of presenting a content report that says “we published 12 articles and grew organic traffic by 15%,” you can present a report that says “our content generated 47 demo requests this month, 12 of which are now in active sales conversations, and three articles account for 70% of that volume.”

That second report is the one that gets content marketing treated as a revenue driver rather than a cost center. It’s also the report that justifies continued investment, because you can show exactly what the company is getting for its content spend and which types of content are producing the best returns.

We build these reports for every client engagement using what we call ROI graphs, which plot the cumulative number of conversions generated by content against the monthly investment over time. These graphs make it easy to see when content marketing becomes profitable and how the return compounds as more articles rank and generate leads.

For enterprise companies with larger budgets and higher deal values, the break-even point is often surprisingly fast, because a single enterprise deal closed through content can pay for months of content investment.

Conversion tracking is also not just a reporting function but a strategic advantage. When you know which keywords and articles are driving the most conversions, you can double down on what’s working, adjust what isn’t, and make every subsequent content decision with real data instead of gut instinct.

Want to Work With Us or Learn More About How We Approach Enterprise Content Marketing?

We hope this article gives enterprise content teams a clear framework for building a content program that drives pipeline, not just traffic. If you’d like to take the next step, there are a few ways to work with us:

• Hire our agency. Grow and Convert is an enterprise content marketing and SEO agency that handles strategy, keyword research, content production, link building, and conversion tracking. If you want us to execute enterprise content marketing for your company, you can learn more about working with us here.
• Join our content marketing course. If you’d like to learn how to do this yourself, we teach our full content marketing process in our course and community. Members get access to detailed lessons on every step covered in this article, plus live calls where we review real content strategies and give feedback. You can learn more and join here.
• Write for us. If you’re a content writer or strategist who wants to work with us, you can learn about open positions here.

But the point of determining search intent isn’t just to fill in a column in your keyword spreadsheet or the content brief document, it’s to understand what you actually need to create to rank for a term and drive business results from that content.

The problem with relying on those labels is that a keyword labeled “informational” might just require a definition or a glossary page, a detailed guide or product tutorial, or a comparison of different products and approaches. If you treat all “informational” keywords the same way, you’ll end up creating generic content for broad intent categories and mostly beginner-level “what is” posts when your users actually need expert-level guidance that can help them solve a specific pain point.

If you’re working with a SaaS or B2B company, the goal of determining search intent should be to understand what type of content Google wants to rank for a given keyword, and whether that keyword is even worth targeting based on its potential to drive conversions, not just traffic. This also matters for AI search visibility, as understanding search intent helps you identify queries that LLMs might reference when generating responses about your product category.

In this article, we’ll cover:

• What the four types of search intent actually mean and where most marketers go wrong when using them
• How to determine search intent so you know what kind of content to create for your target keywords
• How to optimize for search intent to rank your target keywords and drive conversions from the content

What is Search Intent (and Where Most Marketers Go Wrong)

Search intent (also known as user intent or keyword intent) is the reason behind why someone searches a keyword or a phrase on a search engine like Google. It can be anything from trying to find an answer to a question, comparing different products and services, making a purchase or signing up for a service. 

The concept of search intent traces back to information retrieval research, most notably Andrei Broder’s 2002 taxonomy of informational, navigational, and transactional queries. Google and the SEO community later adopted and expanded this framework, and today most SEOs recognize four main types of search intent:

• Informational intent: searchers want to find an answer or learn something. These are mostly the “how to,” “what is,” and “why does” queries, where the searcher might not necessarily be looking to buy anything.
• Navigational intent: searchers are looking for a specific website or page. These are keywords like “Facebook login” or “Ahrefs pricing page” where a user already knows where they want to go and is searching for the URL or a link to get to the website or a page.
• Commercial intent: searchers are comparing products or services to make a purchase. These are keywords like “best CRM software” or “Mailchimp vs Convertkit” where a user wants to evaluate different options but hasn’t made a decision yet.
• Transactional intent: searchers are ready to take an action, like signing up for a service or making a purchase. These can be keywords like “buy iPhone 15 case” or “sign up for Notion” because they’ve already decided what they want and are looking for a way to complete the action.

This simple four-category framework is useful as a starting point if you are learning how to do keyword research, write content or create a content strategy, but they miss critical differences in intent within one of the four categories. Most marketers simply pass these labels to their writers without much additional context, which is required to determine whether to produce an article, a landing page or a product page and what content format or depth the page needs to be. This leads to several problems.

We already mentioned earlier that there can be two keywords with the same intent that might require completely different types of pages or content. For example, “What is a CRM” and “How to choose a CRM for a sales team” are both “informational” intent keywords on the surface, but the first one simply requires a definition to fulfill user intent, while the second keyword needs a detailed guide that walks through criteria, CRM options, and tradeoffs.

If there’s no additional context provided about these keywords, writers could treat them the same, and you might end up wasting resources in producing content that doesn’t rank or convert.

We have also noticed that a lot of marketers assume that the same keyword modifiers always indicate the same intent.  Or, similarly, SEO tools often misclassify intent based on keyword patterns. A keyword with “how to” might get labeled as informational even when the top results are product pages, or a keyword might be labeled commercial when Google is actually ranking educational content. The patterns these tools use to assign labels don’t always reflect what’s actually ranking in SERPs. 

Even though the keyword modifier is the same, two keywords can have completely different intent and conversion potential depending on the context, which is why you can not really shortcut this by matching keyword modifier patterns.

It’s also important not to rely just on SEO tools when classifying search intent and to actually do SERP analysis yourself.  Keyword research and SEO tools like Ahrefs and Semrush use automated systems to assign intent labels, which they might often get wrong, especially when the keywords have mixed, ambiguous, or dual intent or when the keywords have very low search volume (in such cases, these tools might not have any data). 

We have also noticed that most content that does not rank well on traditional search only focuses on the primary intent when there might be mixed motivations for the searchers. 

For example, when a user is searching for a keyword like “how to choose CRM software,” they are mostly looking for different factors and ways to evaluate other tools, but product recommendations are also extremely helpful for them. If you only focus on the educational part without mentioning any specific tools, you might not be able to fully satisfy what the searcher needs. The pages that rank well on Google and other search engines for these kinds of queries usually address both.

How to determine search intent for your target keywords

The process of determining search intent should go beyond just checking what an SEO tool says or matching keyword modifiers into different intent categories. 

Here’s how we approach it:

1. Do SERP Analysis

The most reliable way to determine search intent is to look at what is already ranking, so that you can see all the naunces of what search engines have decided are the best results for a query, based on their data on how millions of users have interacted with those search results.

We have written a complete guide on how we do SERP analysis, but in short, we recommend you start by reading the titles of the search results that are ranking on page 1. This alone will give you a lot of ideas about what Google might be thinking is best for the users. 

For example, if you’re an accounting software company, you might think “accounting best practices” would be a good keyword to target. But if you look at the titles, you’ll see a mix of tips posts, GAAP compliance discussions, and podcasts — there’s mixed intent and not much buying intent. Compare that to “best accounting software” where almost every result is a list of software options. The intent is clear, and you know exactly what you need to create to rank.

Titles also help you catch nuances that matter for your content. For a keyword like “tree care techniques,” you might notice that half the titles mention pruning specifically, which tells you that your content should probably address pruning as a significant component even though the keyword doesn’t explicitly mention it.

After you’ve scanned the titles, read through the top results and take notes on what they’re covering. Look at what topics they all have in common, what they’re doing well, what’s missing, and how they’ve organized their content. Most keywords have multiple layers of intent — someone searching “best accounting software” wants a list of options, but they also want to understand what makes one option better than another. Your job is to identify all the layers and address each one.

You should also pay attention to the SERP features Google includes as well. People Also Ask and Related Searches show you what other questions searchers have around your keyword, which can inform both what to cover in your content and what other keywords might be worth targeting. 

Featured snippets tell you that Google wants a clear, direct answer to a specific question, often a definition or a list of steps. If you see videos ranking prominently, that suggests searchers may prefer video content for that topic; however, if they’re lower on the page, you can likely rank with a detailed post instead.

SERP analysis helps you understand the baseline requirements for rankings (format, depth, and structure) that search engines expect. You can use this analysis to precisely determine what searches are needed and combine it with original insights, specific expertise, or perspective that comes from your company, and not copy what everyone else has written so your content can stand out. 

2. Use SEO or keyword research tools (with caution)

As we mentioned above, if you need a quick and dirty way of assessing search intent, SEO tools like Ahrefs and Semrush can show you an intent label for most keywords, which can be a helpful starting point if you’re working through a long list of keywords and need to sort them quickly. 

If you are using Ahrefs, you can find the intent of any keyword by searching it on the Keyword Explorer tool. Once you enter a keyword, look for the “Intents” column in the “Keywords” table.  

If you are using Semrush, you can go to the “Keyword Magic Tool” and search for your keyword. You’ll also see an “Intent” column where you’ll be able to find what intent the tool says the keyword might be. 

Note that these labels are generated by automated systems that look at keyword patterns more than search results, and as we showed with examples at the top of this article, they can often get it wrong, not really show you any intent, or show different intent depending on what tool you are using. This is especially true for bottom of the funnel keywords or keywords that might have low search volume or those that might have mixed intent. 

We suggest you only use the intent labels as a starting guide to get a rough sense of a keyword if you are working through a list of a lot of keywords, but make sure to always verify it with SERP analysis before you decide what kind of page or content to create for your target keyword. 

How to optimize for search intent?

Once you’ve determined the search intent for a keyword, the next step is to create content that can satisfy that intent so that it can rank and generate results you created the page for. This is where most content fails — not because the writers didn’t understand what searchers wanted, but because they didn’t execute on that understanding in a way that search engines want.

When you are optimizing for search intent, you should be able to decide on the format, depth, angle, and structure based on what you learned from analyzing the SERP. It also means being deliberate about what pages you create and how you position them to rank for specific queries rather than trying to make one page do too many things at once.

Here’s how we approach it:

1. Match content type and format to what’s ranking

If the top results for a keyword are all landing pages or product pages, the safest approach is to create a similar page type. But that doesn’t mean a blog post can’t rank. We’ve helped clients rank with articles and blog posts on keywords where most of page one consisted of landing pages. What matters is whether your content addresses the searcher’s actual intent.

The more common mistake isn’t choosing the wrong format but creating content that doesn’t address what the searcher actually wants. This may seem obvious, but it’s a mistake we often see. A company wants to rank for a keyword like “project management software,” so they write a blog post about project management best practices. But when you look at the SERP, the top results are all product category pages and comparison lists. The blog post never had a chance because it was the wrong content type for that query.

When it comes to content format (listicle vs guide, comparison post vs tutorial), focus on what the user needs and present it in a way that makes sense. If they want to compare different product options, a list with pros and cons about each option works. If they need to understand a process, a step-by-step guide is probably better. The format should follow from what the searcher is trying to accomplish, not from copying whatever structure the top results happen to use.

2. Address all layers of intent

Most keywords have more than one layer of intent, and the content that ranks the highest usually addresses all of them.

Someone searching “best CRM software” wants a list of options, but they also want to understand what makes one CRM better than another. They might also want to know how much these tools cost, what features matter most for their use case, and how the options compare to each other. If your content only provides a list of names without addressing these secondary questions, you’re leaving gaps that competitors will fill.

The People Also Ask boxes and Related Searches at the bottom of the SERP are useful signals for what these secondary layers might be. If you see questions like “What is the easiest CRM to use?” or “How much does CRM software cost?” showing up, that tells you searchers care about those things and your content should probably address them.

The goal is to fully satisfy what the searcher came for so they don’t need to go back to Google and click on another result. If you can do that better than the pages currently ranking, you have a good chance of outranking them.

3. Use the right content angle

Content angle is about the specific perspective or hook that makes your content relevant to the searcher. It’s not just about covering the right topics — it’s about framing them in a way that matches what searchers actually care about.

For example, if you look at a keyword like “best project management tools” and notice that most of the top results emphasize “for small teams” or “for remote work” in their titles, that tells you something about who’s searching and what they’re looking for. If you write a generic comparison that doesn’t speak to a specific audience, you might struggle to compete with content that does.

Freshness is another angle that matters for certain queries. If the top results all include the current year in their titles, that’s a signal that searchers want up-to-date information and Google is prioritizing recent content. For keywords like “best laptops” or “top marketing tools,” outdated content often drops in rankings as newer posts get published.

When you’re deciding on your angle, think about what would make someone click on your result over the others. If you’re writing for a SaaS or B2B audience, consider whether there’s a specific segment or use case you can speak to more directly than the generic posts that already exist.

4. Create dedicated pages for specific intents

If a keyword has a specific intent, it usually needs its own dedicated page. Trying to rank one page for multiple different intents rarely works because you end up with content that doesn’t fully satisfy any of them.

For example, “what is CRM software” and “best CRM software” have different intents. The first one needs an explainer that defines what CRM is and how it works. The second one needs a comparison of options with recommendations. If you try to combine both into a single page, you’ll end up with an awkward hybrid that doesn’t do either job well — and Google will likely rank dedicated pages from competitors above yours for both queries.

This also applies to keywords that seem similar but have subtle differences in intent. “CRM for small business” and “enterprise CRM software” might both be commercial intent keywords, but the searchers have different needs and are evaluating different criteria. Creating separate pages for each allows you to match the specific intent more closely and rank for both.

5. Align Title Tags, Meta Descriptions and Schema with Intent

Your title tag should align with what searchers expect to see based on the intent. If the top results for a keyword all include specific phrases like “2024” or “for beginners” or a number of items in a list, consider whether including something similar would help your result stand out and get clicked. The title is often the first thing a searcher sees, and if it doesn’t match what they’re looking for, they’ll skip it regardless of how good your content is.

Meta descriptions don’t directly affect rankings, but they do affect click-through rate. Use the description to reinforce that your page delivers on the intent — if someone is searching for a comparison, mention that you compare the top options; if they want a step-by-step guide, say that’s what they’ll get.

Schema markup can also help Google understand your content and display it with rich features in the search results. If you’re publishing a how-to guide, recipe, FAQ, or product review, adding the appropriate schema can make your result more visible and informative in the SERP.

6. Include relevant visuals and media

If videos or images are ranking prominently for a keyword, that’s a signal that searchers want visual content, and including it can help your page perform better.

For some queries, Google shows a video carousel near the top of the results. If you’re targeting one of these keywords, creating a video or embedding a relevant one can help you capture that space. For other queries, image packs or featured images are prominent, which suggests that visual assets are important to searchers.

Even when the SERP doesn’t show dedicated media features, visuals can improve engagement with your content. Screenshots, diagrams, comparison tables, and infographics help break up text and make information easier to digest. If you’re writing a tutorial, step-by-step screenshots can be the difference between content that’s actually useful and content that leaves readers confused.

The key is to look at what the top results are doing and consider whether adding similar visual elements would help your content better satisfy the intent.

Using Search Intent Analysis to Drive Conversions, Not Just Traffic

Finally, most guides on search intent focus on how to create content that ranks. But ranking is only useful if the content generates business results — and that depends on which keywords you’re targeting in the first place.

At Grow and Convert, we use search intent analysis not just to figure out what content to create, but to identify which keywords are worth creating content for. This is the foundation of our Pain Point SEO framework, which prioritizes keywords based on conversion potential rather than search volume.

The traditional approach to keyword research is to (1) find high-volume terms, (2) determine their intent, and (3) create content that matches. The problem is that most high-volume keywords are top-of-funnel queries where the searcher isn’t anywhere close to making a purchase decision. You can rank for these terms and still see almost no leads from the content.

Our approach flips the order of these steps. We start by identifying keywords where the searcher has a problem our client’s product solves — what we call Category, Comparison, and Jobs-to-be-Done keywords. Then, we do a detailed SERP analysis to understand exactly what type of content, format, and angle is required to rank. The result is content that satisfies search intent and drives conversions, not just traffic.

This is also why we use interview-based content creation. When you’re targeting high-intent keywords, generic content doesn’t cut it. Searchers evaluating products or solutions need specific, expert-level information that helps them make a decision. If someone is searching for “Best IT security software for SMBs” they want nuanced and techncial discussion of product differences. Just regurgitating what everyone else is saying on a topic (i.e. a “Google research paper”) isn’t going to cut it. By interviewing our clients’ internal experts, we create content that satisfies search intent in a way that competitors copying surface-level information can’t match.

The difference shows up in the numbers. Across 64 articles we produced for Geekbot, bottom of the funnel content converted at 4.78% compared to 0.19% for top-of-funnel content, a 25x difference in conversion rate. Despite receiving 7x less traffic, the bottom of the funnel posts generated more than 3x the total conversions. This is why we prioritize keywords where the intent aligns with buying, not just browsing.

If you want to learn more about how we approach content marketing and SEO, you can read about our Pain Point SEO framework or see case studies from clients we’ve worked with.

In fact, we’ve seen AI visibility companies, agencies, and consultants recommend tactics like adding llms.txt files to your site, rewriting headings as questions, creating FAQ sections, including “key takeaways” at the top of articles, running marketing campaigns on Reddit, and implementing schema markup specifically for AI engines. 

The problem is that most of these ideas aren’t actually strategies but simply a collection of random tactics packaged as the new frontier of search optimization, which gives agencies something new to sell and add to their retainers. Meanwhile, marketing teams are scrambling to implement these disparate tactics without any real evidence that they work.

We’ve tested many of these tactics over the past year, and the results have been underwhelming. Most are unproven, and some, like adding an llms.txt file to your site, have actually been shown (through our testing and others’) to make no measurable difference in AI visibility. Yet they keep getting promoted because they’re easy to sell and technically sound like they should work.

Instead, what we’ve found to work is focusing on getting your brand exposed to LLMs rather than tweaking your content to supposedly help LLMs better digest your content. This finding led us to develop a framework called Prioritized GEO that organizes all of the random AI SEO tactics floating around online into a clear hierarchy of what to prioritize.

In this article, we’ll walk through the AI SEO framework we use to rank and drive visibility for our own clients in AI search, the types of content we create, and the process you can follow to improve your visibility in ChatGPT, Perplexity, Google AI Overviews, and other LLMs.

Our AI SEO Strategy to Increase Brand Visibility in LLMs

Based on our research and client work, we’ve developed a framework called Prioritized GEO to help brands understand where to focus their resources and in what order so they can get their brand mentioned and cited in AI search.

We call it the GEO Priorities Pyramid, and it looks like this:

Every AI SEO tactic we’ve seen fits into one of these three tiers:

• Owned content
• Off-site mentions
• On-site tactics

Owned content is content you write and publish on your own site.

Off-site brand mentions are getting online publications like magazines, industry sites, or communities like Reddit or Wikipedia to mention you (“digital PR”).

On-site tactics is what we’re calling everything that involves changing your site or content to help LLMs better understand it, like adding llms.txt, starting articles with “key takeaways”, wording headings as questions, adding schema and more.

Most of the AI SEO advice online focuses on the top of the pyramid (on-site tactics like llms.txt files and FAQ schema), probably because they’re the newest and most “AI-specific.” But in our experience, those tactics have the least impact on AI visibility.

The foundation of your AI SEO strategy should be owned content that ranks in traditional search. That’s why we have that at the bottom of the pyramid. You can then amplify it with off-site brand mentions, the next tier in the pyramid. Finally, on-site tactics are worth experimenting with, but don’t expect them to move the needle if you’re not already visible in the web searches LLMs are performing.

Below we explain the details behind each of these tiers and why brands should prioritize Tier 1 and Tier 2 over on-site tactics.

Why Being Visible in Web Searches that LLMs Perform Is the Foundation of AI SEO

We spent the past year performing several studies to identify what actually drives AI search visibility. We’ve tested various tactics, analyzed 400+ keywords and prompts across 16 of our own clients, studied which sites LLMs actually cite, and even built our own AI visibility tool (Traqer.ai) to help track clients’ AI visibility because existing tools had too many issues.

We’re not claiming to have everything figured out, but we do have some concrete examples, including our agency and our clients who are consistently appearing in ChatGPT, Perplexity, and Google AI Overviews. 

And the most consistent finding across all of our research is a strong correlation between traditional Google rankings and AI mentions. When we analyzed 400+ bottom of the funnel keywords across 16 clients, we found that when our clients rank on Google’s first page for a given keyword, they show up in ChatGPT and Perplexity responses for that same keyword 77% of the time. When they rank in the top 3 positions, that correlation jumps to 82%.

When they rank in the top 3 positions, that correlation jumps to 82%.

This wasn’t what most of the AI SEO conversation online would have you expect. If ranking on Google correlates this strongly with showing up in AI responses, then the foundation of any AI SEO strategy isn’t some new set of LLM-specific tactics, it’s traditional SEO.

The reason for this correlation comes down to how these AI tools actually work. LLMs like ChatGPT and Perplexity (and definitely Google AI mode or AI overviews) rely on web search to generate their responses, particularly when users ask product-related questions. 

ChatGPT’s web search feature uses third-party search engines, including Bing and Google, and Perplexity’s CEO has publicly acknowledged that they rely on third-party web crawlers in addition to their own. When someone asks these tools for product recommendations or solutions to a problem, they search the web, look at what’s ranking, and synthesize that information into their response.

This means that if your content ranks well in traditional search for the keywords that matter to your business, you’re being exposed to the LLMs that power AI search. In most cases, you don’t need to do anything special to get “crawled” by AI other than continue to work on your traditional SEO and rank for the bottom of the funnel keywords.

We also found a correlation between domain authority and AI visibility. Clients with higher domain ratings were more likely to be mentioned by ChatGPT and Perplexity, which makes sense given that domain authority is itself a function of backlinks and the authority signals that help you rank in Google.

The same factors that make you rank well in traditional search appear to influence whether LLMs mention you. 

But there’s more to it than just ranking. What you rank for and how you communicate your product’s value matter more in AI search than they ever did in traditional search. 

In traditional Google search, you rank for a keyword, the user clicks through to your page, and your page does the selling. The user evaluates your content, navigates your site, and decides whether your product is a fit for them.

In AI search, LLMs don’t just list blue links for users to click through and evaluate on their own. They read your content, synthesize it, and use it to actively recommend (or not recommend) your product to users who are asking for solutions. Your content has become your sales pitch, delivered by AI on your behalf.

This has significant implications for what kind of content you should be creating and how you should be writing it, which we’ll cover in the sections below.

Now, let’s go through each tier in detail: 

Tier 1: Build a Foundation of Bottom of the Funnel Content That Ranks

The foundation of any AI SEO strategy is having content on your own website that ranks in Google for the keywords where you want AI visibility. This is Tier 1 because, as we covered above, LLMs search the web when users ask product-related questions, and if the content on your own website ranks for those queries, you get cited or mentioned by LLMs.

But not all content is equally valuable for AI SEO. The type of content that matters most is bottom of the funnel content: content targeting keywords that indicate the searcher is actively looking to buy or evaluate products like yours.

Why? 

Because in AI search, top-of-funnel content is essentially not going to drive any traffic. This is something we’ve been saying for a while now, but it’s worth repeating because so many content strategies are still built around high-volume, top of the funnel keywords with a focus specifically around generating more traffic.

Consider a typical top of the funnel query in our space: “what is content marketing.” In the traditional SEO world, this was a classic keyword that content marketing companies would target. Ahrefs shows 8,000+ searches a month and a keyword difficulty of 85, meaning it’s competitive with tons of big-name sites ranking for it.

But look what happens when you ask ChatGPT that question. It answers directly and doesn’t link to anything. There’s also no reason for the AI to mention any specific company because the user isn’t looking for a product or service. They’re looking for information, and the AI can provide that information itself.

Google is doing the same thing now. Search “what is content marketing” and you’ll see either a dictionary definition or an AI Overview at the top of the page. The first organic result doesn’t even appear above the fold. Both ChatGPT and Google now default to answering top of the funnel questions themselves instead of sending traffic to a blog post.

For top of the funnel queries, Google might still send you some traffic because it still links to other websites in the AI overviews, but ChatGPT, Perplexity, or Claude will likely not send any. And even the Google traffic is declining as AI Overviews become more prominent. We’ve seen this pattern across our clients where impressions are holding steady or growing, but clicks are declining because AI Overviews are answering queries directly.

This is why you should focus on bottom of the funnel queries because the response for these queries are different. 

When someone asks ChatGPT “what’s the best project management software for remote teams” or “Asana alternatives for small businesses,” the AI can’t just answer with a definition. The user is asking for product recommendations, which means the AI needs to recommend products. This is where your brand can get mentioned.

It also means LLMs will nearly always search the web for these queries because they know their training data alone doesn’t have the most up-to-date product information. They need web search to supplement their knowledge. It’s via these web searches that you can influence the LLMs generated response and get your brand mentioned. 

Here is a screenshot we quickly took of ChatGPT saying it’s searching the web for the prompt above, right before it generated its response. This was without clicking the “search” button when typing in this prompt.

(Also note how the answer to this query above shows citations from “FastrackPR” and “Nova Media Group”. This is in contrast to the “What is content marketing” example above where ChatGPT neither searched the web nor had any citations in its response.)

This is why your AI SEO strategy should focus exclusively on showing up for bottom of funnel, product related prompts and topics, not top of funnel informational topics. 

At Grow & Convert, we’ve actually  been focusing on bottom of the funnel content since 2018, long before ChatGPT and AI search were popular, for a separate reason: traditional SEO traffic from bottom of funnel keywords converts far, far higher than top of funnel traffic. We published our original article called Pain Point SEO explaining why. 

We showed through multiple case studies that bottom of the funnel keywords convert 10x to 25x better than top of the funnel keywords. Now, in AI search, this gap is even wider because as we explained above, top of the funnel content often gets zero brand visibility for you (the user gets their question completely answered by the LLM tool so unlike traditional SEO you don’t even get the user to your site to get even the possibility of them converting).

So, we suggest you do the same and focus on creating content to rank for bottom of funnel topics so that your brand and content gets mentioned and cited for related prompts in AI search. 

Types of bottom of the funnel keywords you should be targeting

We cover these extensively in our article on SaaS content strategy, but here’s a summary with examples:

Software category keywords are searches like “best [use case] software,” “[industry] software,” or “[use case] tools.” These are people actively looking for a product in your category.

For example, if you sell time tracking software, keywords like “best employee time tracking software,” “time clock app with GPS,” or “construction time tracking software” would fall into this category. The variations are extensive and you can combine use cases with industries, business sizes, integrations, and more to find dozens of keyword opportunities.

Comparison keywords include “[competitor] alternatives” and “[brand] vs. [competitor]” searches. These are people who are evaluating options and are very close to making a decision.

For example, “Asana alternatives,” “Monday vs. Asana,” or “Hubspot alternatives for small businesses.” If you’re a smaller brand competing against well-known players, comparison content is particularly valuable because you’re inserting yourself into a conversation that’s already happening.

Jobs-to-be-done (JTBD) keywords are searches where someone is trying to accomplish a specific task that your product can help with. These often start with “how to” but the searcher isn’t necessarily looking for software yet.

For example, “how to plan delivery routes for multiple stops” or “how to track employee hours.” The content that ranks for these keywords typically explains how to do the thing manually and then introduces your product as a better solution.

Use case and template keywords are related to jobs-to-be-done but more specific. Searches like “daily standup meeting template” or “delivery driver training checklist” indicate someone is trying to solve a specific problem.

You can rank for these by providing a free template and then explaining how your product makes the template unnecessary or more effective.

Write Content That LLMs Will Actually Use to Recommend You

How you write content matters as much as what keywords you target. This is where AI search differs most from traditional SEO, and it’s something most marketers haven’t fully internalized yet.

In traditional SEO, your content’s job is to rank and then get clicked. Once someone lands on your page, your website does the selling. The user reads your content, navigates your site, checks out your pricing page, maybe looks at a few case studies, and eventually decides whether to sign up or request a demo.

In AI search, that whole process gets compressed because when someone asks ChatGPT for product recommendations, the AI reads your content (and your competitors’ content), synthesizes it, and delivers a recommendation in its own wording directly to the user. Look at the screenshots above about project management software. ChatGPT is writing its own bullets selling ClickUp and Asana. So in AI search, your content is not just helping you rank but also teaching the LLMs how to position or message your brand or products.

Effectively, LLMs are acting as your SDRs (sales development reps). They’re reading your content and using it to pitch your product to potential customers. So, if your content doesn’t clearly communicate who your product is for, what problems it solves, why it’s better than alternatives, and what makes it different, AI engines won’t know when or how to recommend you.

This is why specificity is so important in AI search. Generic content that could apply to any product in your category won’t help you. 

You need content that clearly articulates:

• Who your product is for (specific industries, company sizes, use cases, personas)
• What specific problems it solves (not vague benefits, but concrete pain points)
• How it solves those problems differently than competitors
• Proof that it works (case studies, specific results, customer examples)

For example, when someone asked ChatGPT for “best pain point focused SEO agency”, it recommended Grow & Convert and described us as an agency that  “prioritizes content that solves specific buyer problems and targets bottom-of-funnel keywords with clear purchase intent.” 

That description obviously comes from our site. We’ve talked about prioritizing bottom of funnel content for a decade on Grow & Convert and our most popular framework is Pain Point SEO, which is where ChatGPT gets the line “solves specific buyer problems”.. The AI read our content, understood our positioning, and used it to match us with a user whose needs aligned with what we do.

The companies that get recommended most consistently by LLMs are the ones that have published content clearly explaining who they’re best for, what they’re best at, and why they’re different from everyone else.

This is also why AI-generated content fails for AI SEO. It sounds counterintuitive, but content written by AI is actually the worst kind of content for getting recommended by AI.

The reason comes back to how LLMs work. They’re trained on massive amounts of text from the internet, and they’re designed to produce responses that are similar to their training data.

When you use AI to write content, you get output that reflects the most average, most common way of saying things. It’s grammatically correct and sounds articulate, but it doesn’t say anything original or different. So if this is what you’re publishing, then when ChatGPT et al are trying to pitch your brand to users, it’s going to say generic, unoriginal, undifferentiated things about your products or services. 

We wrote about this years ago when we coined the term Mirage Content (content that looks good on the surface but is really just high-level fluff that regurgitates what everyone else has already said). We argued back then that most content written by freelance writers was Mirage Content.

AI-generated content is essentially Mirage Content at scale. It’s fast to produce, but it doesn’t contain any original thinking, unique perspectives, or specific details about your product that would help an LLM understand when to recommend you.

The content that performs best for AI visibility is content written by humans who actually understand the product, informed by interviews with people inside your organization (sales, founders, product leaders) who can speak to specific features, use cases, and differentiators that you won’t find anywhere else online. This is the content that helps LLMs understand what makes you different, and it’s the content that gets recommended.

Tier 2: Build Authority with Off-Site Brand Mentions

The next tier of your AI SEO strategy is getting your brand mentioned on other websites. This is what we call off-site brand mentions, and it includes tactics like digital PR, citation outreach, guest posting, and link building.

Off-site mentions matter for AI visibility for the same reason owned content matters: LLMs search the web to formulate their responses. When someone asks ChatGPT for product recommendations, it doesn’t just look at your website. It looks at what other sites are saying about products in your category and if your brand is mentioned on sites that rank for the keywords you care about, you’re more likely to show up in the AI’s response.

But not all off-site mentions are equally valuable. If you’ve followed the AI SEO conversation online, you’ve probably seen advice to get your brand mentioned on Reddit, Wikipedia, and Forbes because these are supposedly the sites that LLMs cite most often. Infographics showing the “top cited domains by AI” usually accompany this advice.

The problem with this thinking is that it assumes there’s a universal list of most-cited sites that applies to all businesses. There isn’t.

When someone asks ChatGPT “what are the best dispatch software options for trucking companies” or “what are some good alternatives to Asana for project management,” ChatGPT doesn’t pull just look for answers on a static list of the most popular sites on the internet. It searches the web for that specific query and cites whatever sources are most relevant.

Our research on which sites LLMs actually cite found that for product-related queries, LLMs cite industry-specific sites 86% of the time and generic sites like Reddit only 16% of the time.

This makes sense when you think about it. If you’re asking for trucking dispatch software recommendations, the AI is going to cite sites that review trucking software, not a random Reddit thread.

This means your off-site strategy should focus on getting mentioned on the sites that actually rank for the keywords you care about. 

Don’t waste time trying to get mentioned on Reddit or Forbes just because someone told you LLMs cite those domains a lot. Instead, look at the specific prompts and keywords where you want AI visibility, see which sites LLMs are actually citing for those queries, and focus your efforts on getting mentioned there.The process is straightforward.

Take the keywords you identified in Tier 1 and enter them into ChatGPT, Perplexity, Google AI Overview, or any other LLM you’re interested in. Look at which sources get cited in the responses. These are the sites you should be targeting for off-site mentions.

For example, here is such a list from our AI tracking tool, Traqer:

Citation outreach is how we execute this for clients as a part of our engagement. We get a list of the sites that LLMs cite for our clients’ target keywords via Traqer, and we reach out to those sites to try to get our clients mentioned in their content. 

Sometimes this means getting added to an existing listicle or comparison article, contributing a quote or expert comment, or writing a guest post. The specific tactic varies, but the goal is always the same: to get mentioned on sites that rank for keywords where we want AI visibility.

Why Are Off-Site Mentions Tier 2 and not Tier 1?

The key distinction between Tier 1 and Tier 2 is control. With owned content (Tier 1), you have complete control over what you publish, how you position your product, and how detailed you get. With off-site mentions, you’re dependent on other sites to mention you accurately and favorably.

This is why we put owned content on Tier 1 and, therefore, slightly higher priority than off-site mentions. Both your content and mentions in others’ content work via the same mechanism: helping expose your brand to LLMs when they search the web to answer users questions (a process called “grounding”). 

But your owned content is where you have the space and freedom to tell your full story: who you’re for, what problems you solve, how you’re different from competitors, and proof that you deliver results. Off-site mentions help to amplify that story by getting your brand name in front of LLMs through additional sources. 

Tier 3: On-Site AI SEO Tactics

Finally, we get to the top of the pyramid: Tier 3. This is what we think should be your last priority but ironically is where most of the AI SEO advice online is focused: on-site tactics designed to help LLMs better understand and process your content. These include things like adding llms.txt files to your site, structuring headings as questions, adding FAQ sections to pages, including “key takeaways” at the top of articles, and implementing schema markup specifically for AI engines.

We put these tactics at the top of the pyramid (meaning lowest priority) for a reason. They are purported to help AI search visibility in a fundamentally different way than Tiers 1 and 2: the aim of these on-site tactics is to help LLMs better understand or digest the content on your site. 

They don’t help expose your brand to LLMs.  

Also, in our testing over the past year, most of these tactics have made no measurable difference in AI visibility. That doesn’t mean they’re completely useless, but it does mean your time is better spent on getting your own rankings (Tier 1) for queries LLMs may search for when generating responses or getting brand mentions in other sites that are ranking (Tier 2).

For example, llms.txt is a proposed standard from Jeremy Howard, the developer and founder of fast.ai, that would help LLMs crawl and understand a site’s content. The idea is similar to robots.txt but designed specifically for AI crawlers. It sounds logical in theory, but there’s no evidence that OpenAI, Anthropic, or other major LLM providers actually use it. Howard simply proposed it as an idea that he thought would be good for the industry to adopt. Our tests suggest it doesn’t make a difference, and we don’t recommend spending time on it.

Structuring headings as questions is based on the idea that LLMs process question-and-answer formats better than declarative statements. Again, it sounds plausible, but we haven’t seen any data showing it improves AI visibility. If your content naturally lends itself to Q&A formatting, fine. But rewriting all your headings as questions just for AI optimization isn’t worth the effort, in our opinion. Plus the entire magic of LLMs is that they understand natural language as well as humans do, so it makes sense that they don’t need hand-holding in the form of reformatting your content in a specific way.

Adding FAQ sections and key takeaways falls into the same category. The logic is that these formats make it easier for LLMs to extract and summarize information. We’ve tested articles with and without these elements, and we’ve seen clients show up in AI responses consistently with content that has none of these tactics applied. So while they might help marginally, they clearly aren’t necessary.

Schema markup for AI is another tactic being pushed by some agencies. The idea is that structured data helps LLMs understand your content the same way it helps Google understand your content. This may have some validity, but we haven’t seen enough evidence to prioritize it over the tactics in Tier 1 and Tier 2.

Tier 3 tactics are fine to experiment with, but we strongly believe that your focus should be on Tiers 1 and 2. If you have a solid foundation of bottom of the funnel content that ranks and you’re actively building off-site mentions on relevant industry sites, then sure, test some FAQ sections or key takeaways and see if they move the needle. 

Think of it this way: Tier 3 tactics are meant to help LLMs better digest your content. If LLMs aren’t finding your content in the first place (because it doesn’t rank), making it easier to digest won’t help you. The priority is getting your content in front of LLMs, which happens through traditional SEO (Tier 1) and off-site mentions (Tier 2). Only then does it make sense to optimize how that content is structured.

How to Measure AI SEO Performance and Results

Measuring the results of your AI SEO strategy is one of the more challenging aspects of this work, and it’s only getting harder as AI becomes more integrated into how people search and discover products.

Traditional SEO attribution methods are breaking down. For years, the measurement model was straightforward: someone searches a keyword, clicks on your organic result, lands on your page, and eventually converts. You could track this journey through Google Analytics and attribute conversions to specific pages and keywords (via the Google/organic source/medium, for example).

AI search disrupts this model in several ways. When someone asks ChatGPT for product recommendations, they might see your brand mentioned, open a new tab, Google your company name, and land on your homepage. That conversion shows up as branded organic traffic or even direct traffic, not as a conversion from the AI mention that actually drove it. The AI did the work of surfacing your brand, but your analytics won’t reflect that.

Google AI Overviews create similar attribution challenges. When your brand gets mentioned in an AI Overview, users might click through to your site, but they might also just note your brand name and search for it separately later. Or they might click one of the links in the AI Overview, which could be your homepage, a product page, or a blog post, depending on how Google constructs the search results for that query. What used to be a clean path from keyword to landing page is now fragmented across multiple possible user journeys.

We’ve written about this in detail, but the short version is that you’re likely underestimating how much AI is contributing to your conversions if you’re only looking at traditional attribution models.

The most reliable way to measure AI visibility impact is to ask people directly. 

We recommend adding a “how did you hear about us” field to your lead forms and demo request forms. This gives prospects a chance to self-report whether they found you through ChatGPT, Perplexity, Google, a referral, or another channel. We’re also encouraging clients to have their sales teams ask this question during discovery calls.

This isn’t a perfect measurement method. People don’t always remember exactly how they found you, and they might say “Google” when they actually started with ChatGPT and then searched your brand name on Google. But it gives you directional data that traditional analytics can’t provide, and we’ve found that more and more prospects are explicitly mentioning ChatGPT and Perplexity in their responses.

You should also track referral traffic from AI platforms directly. 

ChatGPT, Perplexity, and other AI tools do send some clickable traffic, and you can see this in your analytics. It won’t capture all the AI-influenced conversions (for the attribution reasons we mentioned above), but it gives you a baseline. We’ve built reporting that specifically identifies referral sources from LLM platforms so we can see how much direct traffic is coming from AI tools.

Track AI visibility itself, not just traffic from AI. 

This is also why we built Traqer.ai. We needed a way to track whether our clients are showing up in AI responses for their target keywords, regardless of whether that visibility translates into trackable clicks.

There are other AI visibility tracking tools on the market now, but there’s a problem with how most of them work. A lot of these tools generate their own list of prompts and tell you which ones you’re “ranking” for. But these prompts are often random and made up by the tool. You have no idea if anyone is actually typing them into ChatGPT, or if they’re even relevant to your business. That’s letting the tool lead the strategy instead of the other way around.

The right approach is to decide which topics and keywords matter for your business first, then track visibility for prompts related to those topics. You decide the prompts based on your bottom of the funnel keyword strategy, not some tool generating random queries.

With Traqer, we start by identifying the bottom of the funnel topics that matter for each client, then track visibility across multiple prompts related to those topics in ChatGPT, Perplexity, and Google AI Overviews week over week. This lets us see what’s actually working and adjust accordingly.

Track Overall Site Conversions

You should also start to measure conversions holistically rather than just attributing to individual blog posts. 

In the past, we focused heavily on tracking which specific articles drove conversions. That’s still valuable, but it’s no longer the complete picture. We’re now also tracking organic traffic and conversions to homepages and product pages, because AI visibility often drives users to those pages rather than the blog content that actually influenced the AI’s recommendation.

The goal is to look at the overall trajectory of organic performance (rankings, traffic, and conversions across your whole site) rather than trying to attribute every conversion to a specific piece of content. If your rankings are growing, your AI visibility is growing, and your overall organic conversions are growing, your AI SEO strategy is working, even if the attribution is messy.

The brands that are succeeding with AI SEO right now are the ones that understand this measurement challenge and don’t expect perfect attribution. They track what they can, ask prospects directly, monitor AI visibility over time, and look at the overall trend rather than trying to tie every lead to a specific source.

Want to work with us or learn how to implement this AI SEO strategy?

• Our SEO + GEO Agency Service: You can learn more about working with us here.
• Our Content Marketing Course: Individuals looking to learn how to grow their SaaS business with content can join our private course, taught via case studies, here. We include lots of detail and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we and other members give feedback. We also get on live Zoom calls about once a month and dissect members’ actual content strategies and brainstorm ideas on how we’d form content strategies for their businesses.

At Grow and Convert, our primary goal for doing SEO for B2B SaaS companies is to generate more demo requests, trial signups, and pipeline—not just to grow traffic or website authority.

We’ve demonstrated how we drive pipeline growth in numerous case studies:

• All-in-one SaaS Product Case Study – We share how we grew demo signups to 150+/month for a large SaaS platform that has numerous à la carte solutions inside of it.
• Geekbot Case Study – We share how we drove 1,700+ signups for the self-service SaaS company Geekbot by starting with bottom of the funnel keywords and working our way up the funnel to mid/top of the funnel keywords. We also show how conversion rates differ. 
• Circuit Case Study – This article shares how we moved a blog from a subdomain to subfolder while following our SEO content strategy and grew trial signups to 250+/month.

So, in this post, we’re going to share the key factors B2B SaaS companies should consider when evaluating SEO agencies, explain how we’ve addressed each at our own agency (including case studies showing the results we’ve achieved), and share a list of other SaaS SEO agencies whose names we hear come up often.

If you’d like us to apply our SEO content marketing strategy to your SaaS business, you can learn more about our service here.

Key Factors to Consider When Evaluating B2B SaaS SEO Agencies

Below are the key factors we’ve found that separate effective B2B SaaS SEO agencies from those that don’t work out. These are based on directly seeing the work produced by agencies our clients have previously worked with, seeing what results those agencies achieved (or didn’t achieve), and hearing the frustrations our clients have voiced about those engagements.

• Factor 1: Do they prioritize ranking for high buying-intent keywords over technical SEO fixes?
• Factor 2: Do they actually create content, or just deliver keyword lists and briefs?
• Factor 3: Do they prioritize conversions over traffic?
• Factor 4: Do they have a process for writing product-focused content that converts?
• Factor 5: Do they have an actual AI search strategy?

Factor 1: Do they prioritize ranking for high buying-intent keywords over endless technical SEO audits or general-purpose linkbuilding? 

We’ve noticed that SaaS marketing agencies (as well as in-house SEO teams) often focus the majority of their SEO efforts on “on-site SEO” (e.g. technical SEO audits, sitemaps, site structure, internal linking, etc.) and general purpose link building (links built to the homepage or some key pages aimed at increasing overall domain authority), while giving a lot less focus, priority, and effort to what we feel is the most important part of SaaS SEO strategy: Identifying high buying-intent keywords and creating content or pages designed to rank for those keywords. 

We think this is backwards. 

In our view, technical SEO audits are something you do once at the start of an engagement and then revisit maybe once a year or when a major change happens to the site architecture, which is rare for most B2B SaaS companies. Internal linking should be part of your content strategy, not a standalone SEO activity, and link building should only be done as a means of ranking for your highest value keywords, not as an end in itself.

Because otherwise, what’s the point? If you (1) haven’t identified what your highest value keywords are and (2) don’t have pages on your site specifically created to fulfill the search intent of those keywords, then what are you possibly hoping to achieve with technical SEO or link building? 

What use is high domain authority (the goal of most efforts to acquire backlinks) if you don’t have pages on your site created specifically to rank for your highest value keywords? Do you know what your highest value keywords are?

These may sound like obvious mistakes that no one would make, but it’s shockingly common. We see this backwards approach to SEO all the time. A ton of effort is spent on technical SEO and “cleaning up the site,” while a lot less effort is spent on identifying highest value keywords and creating content aimed specifically at ranking for them. 

Inevitably, in these scenarios, SaaS companies become frustrated when all the budget and time spent on SEO fails to produce any measurable increase in qualified leads or product signups.

It’s also worth noting that most B2B SaaS companies that can afford to hire an SEO agency already have a decent website. Technical SEO fixes, if needed at all, shouldn’t take months to complete. At our agency, we handle any necessary technical cleanup in month one of an engagement so we can move on to what actually drives results—ranking for high-intent keywords.

Factor 2: Do they actually create content, or just deliver keyword lists and briefs?

When we have seen keyword lists from SEO consulting services or agencies, there are often several issues with them. 

First, they’re often just massive lists of hundreds of keywords that B2B SaaS companies could get for themselves from any SEO tool. Some agencies do the hard work of categorizing or prioritizing these keywords by buying intent, but from what we’ve seen, most do not. And these SEO tools (where most agencies are getting these lists) almost always suggest tons of low buying-intent, high search volume keywords. 

They don’t interview sales, product, or customer support to deeply understand pain points of the target audience and uncover non-obvious but high converting, high buying-intent keyword ideas. You need humans to do this hard work. 

Second, these lists aren’t actionable. The vast majority of companies don’t have the resources to produce over 200 articles that are high-quality and crafted well enough to actually have a shot at ranking. So while this kind of keyword research may make the SEO agency feel productive, it leaves the client overwhelmed and wondering how they can possibly rank for all of those keywords. 

Compounding this issue is that most of the time, the SEO agency that provided the list will only create “content briefs” for each keyword, but they won’t actually write and publish the pieces. (Or, if they do, they’ll charge an enormous amount to do so.) 

Unfortunately, content briefs don’t rank. Pages published on the site that match search intent rank. These take time and effort to produce. So, it’s no wonder many agencies don’t do this. Heck, many SaaS brands don’t even do this. If an agency isn’t producing the actual content, they’re leaving the hardest part of SEO to you.

Factor 3: Do they prioritize conversions over traffic?

Next, in addition to emailing their clients unreasonably large keyword lists produced by an SEO tool, most B2B SaaS SEO agencies prioritize keywords by chasing traffic over conversions.  

We’ve talked a lot about why this is a problem, but in short, these agencies tend to spend a lot of their efforts trying to rank for top of funnel keywords that have high search volume but low buying intent.

By our measures, these keywords have very low conversion rates — a point that is neatly summarized in this Google analytics screenshot from our article on Pain Point SEO (our agency’s foundational SEO content strategy): 

The right-most column shows new user signups for each of the URLs listed. The three boxed posts follow the Pain Point SEO approach and rank for keywords with high buying-intent. The rest rank for something the target audience could search for, but not a high buying-intent keyword. 

The new user signups from the three Pain Point SEO posts are hundreds of percent higher. 

We also showed this at a larger scale in an analysis of 60+ posts for our client Geekbot:

The posts targeting high buying-intent keywords (which we’ve traditionally referred to as “bottom of funnel”) didn’t just convert a bit better than the higher-volume-lower-intent posts, they converted 2400% better. 

Within that case study, you can read about how the higher conversion rates more than made up for any differences in search volume or traffic between the two buckets. 

This is why, in our experience, prioritizing keywords by intent — not search volume and traffic potential — is far more effective for driving conversions. But most B2B SaaS SEO agencies don’t use strategies that follow this logic.

Factor 4: Do they have a process for writing product-focused content that converts?

Next, in order to rank for high buying-intent keywords (e.g., “best accounting software,” “social media management software,”) and convert that traffic into leads, you need to be good at product copywriting. 

Pages ranking for high buying-intent keywords usually talk a lot about products. So, if you want to rank for these keywords, your pages need to talk about products in-depth, including, for example, an explanation of key features, the use cases for each feature, and what differentiates your product from others in your space. 

This requires a different writing skill set than traditional “blog writing.” First, most blog writing is self-researched, but product copywriting shouldn’t be. The traditional blog writing workflow involves handing a writer a keyword and asking them to come up with what to write on their own. Sometimes the writer is given a “content brief”, but even those are mostly dictating the subheaders based on what the existing ranked pages are already saying. 

That may be fine for introductory, top-of-funnel keywords like “accounting tips for startups,” where most of the articles ranking say the same thing, so a reasonably smart writer could digest the tips in a couple of hours and produce something similar but professional. But if you’re going after a buying-intent keyword like “accounting software for startups,” then you need to actually discuss the details of your software: 

• Outline your features and benefits
  • Why does your feature set look the way it does?
  • What are the most important benefits?
  • Are there any design details that are important? 
• Compare your software with others
  • What differentiates yours from others?
  • Where in the market do you sit? 
  • Are there certain use cases where a competitor might be better?

No freelance writer is going to be able to write this on their own. 

First, they’re going to need to interview product experts at your company to get this information. Most B2B SaaS SEO agencies don’t have these interviews as part of their content creation process. 

Second, they need to be able to write this kind of content well. How do you sell the features without being too salesy? How do you contrast with competitors without being too aggressive or trashing them? These are hard skills to learn. Some writers on our team have told me that our writing is more like landing page copywriting than blog content writing. They’re not wrong.

Factor 5: Do they have an actual AI search strategy?

With the rise of AI search tools like ChatGPT, Perplexity, and Google AI Overviews, many B2B SaaS companies are asking whether their SEO agency has a strategy for showing up in these platforms.

The problem is that most agencies don’t have a clear AI search strategy yet. Instead, most SEO agencies just test random tactics they see online, such as adding llms.txt files to the site, changing headings to questions, adding FAQs to every page, or commenting on Reddit threads. These tactics may or may not help, but they aren’t a real AI search strategy.

Our approach to AI search visibility at Grow and Convert is what we call Prioritized GEO, and the pyramid below shows how we recommend brands prioritize their efforts to improve their visibility in LLMs and AI engines:

The foundation of any AI search strategy is showing up in traditional SEO searches for topics where you want visibility. This is because LLMs search the web to formulate their responses, particularly when users ask for product recommendations. In our research, we have also found a 72% correlation between ranking on the first page of Google and being mentioned by AI tools like ChatGPT and Perplexity. When our clients ranked in the top three positions in traditional search, that correlation increased to 77%. We do this through the bottom two sections of our pyramid: Content on your site that tanks for relevant keywords (Tier 1) and getting mentions of your brand on other people’s content that also ranks for these keywords and topics (Tier 2). 

So when evaluating B2B SaaS SEO agencies, it’s worth asking whether they have a strategy for AI search visibility, and whether that strategy is grounded in creating content that ranks for bottom-of-funnel keywords.

Best B2B SaaS SEO Agencies

Below, we cover the best B2B SaaS SEO agencies to consider, starting with our own. For each, we’ll explain their approach to doing SEO, what they specialize in, and what types of companies they have worked with.

Since we outlined the key factors for evaluating B2B SaaS SEO agencies above, we’ll also share how we’ve addressed each of these factors at our agency, including case studies showing the results we’ve achieved.

1. Grow and Convert

Grow and Convert is our SEO and GEO-focused content marketing agency. We’ve spent over 10 years doing SEO and executing content marketing strategies that drive trial signups and demo requests for B2B SaaS companies, not just traffic.

Unlike agencies that optimize for top of the funnel metrics like traffic or rankings, we focus on bottom of the funnel content that generates qualified leads and measurable ROI through our Pain Point SEO and Prioritized GEO frameworks.

We have worked with B2B SaaS companies at various stages, from startups starting their first scalable content strategy to growth-stage companies looking to scale growth through content, SEO, and AI search.

Fundamentally, we feel our process is better aligned with B2B SaaS clients’ desired outcomes (rankings for high buying-intent search terms) and is more likely to actually achieve those outcomes because of what we prioritize. 

Below is each step of our process, listed in order of priority. 

Priority 1: Finding the Most Valuable Keywords to Target

Unlike traditional SEO firms that launch headfirst into technical SEO or link building without a keyword strategy in place, our focus starts with the keywords for which we want to rank. 

Ranking for target keywords that can drive meaningful results is literally the entire point of SEO. Nothing matters if you don’t know which keywords you’re targeting. Link building doesn’t matter. Technical SEO doesn’t matter. Yet, as we said above, we’ve had many clients who’ve spent months on all kinds of SEO activities without an agreed upon list of their most valuable keywords. Or, only slightly better, their target keyword list is only one or two keywords. 

Also, a crucial detail is that both the words “valuable” and “keywords” are equally important. Who cares about ranking for keywords loosely related to your product if they don’t bring you any business? Sure, your organic traffic will increase, and you can post that on Twitter and brag about it, but if the keywords aren’t ultimately leading to sales, then it doesn’t give clients what they want.

We’ve linked to these articles already, but to recap, you can read more about our high-value keyword strategy here and here. A case study quantifying the value of high buying-intent (bottom of funnel) keywords can be found here. 

To emphasize my point about priority, every other SEO activity we do (listed below) is in the service of ranking for these high-value keywords. This is our entire goal for clients. 

Priority 2: Creating Product-Focused Content Aimed to Rank for These Keywords

Second, and equally as important as the first priority, we produce individual pages to rank for each high-value keyword we are targeting. We don’t use the sprinkle method, meaning we don’t produce a single “small business accounting guide” that’s aimed at ranking for 10 different accounting keywords. 

We analyze the existing search engine results page (SERP) for each keyword, determine search intent, and produce a unique piece of B2B SaaS content that (a) has the necessary on-page optimization to rank and (b) better fulfills search intent than the existing results. 

And because most of our keywords are high buying-intent (“best accounting software,” for example), we sell the heck out of our client’s products and services in these articles. 

We get into the details of features, we explain the nuance of benefits, we weave in testimonials and case studies, and we differentiate our client’s products from those of their competitors (sometimes gently, sometimes aggressively). 

We base all of this on extensive interviews with product experts, product demos, and sometimes demo accounts (so we can use the product ourselves). We have been doing this for years and have an extensive writer training process that has helped us build a tight-knit team of product copywriters. 

This product-focused content is also the foundation of our AI search/Prioritized GEO strategy. LLMs search the web when users ask for product recommendations, and your content is how they understand what you offer, who you serve, and how you’re different from competitors. If your content doesn’t clearly communicate your product’s features, use cases, and differentiators, LLMs won’t know when or how to recommend you.

Priority 3: Site Cleanup and Technical SEO

You may be wondering, “How could this be third in the priority list? If the technical SEO isn’t in order, won’t you have trouble ranking for those high value keywords?!” Our answer is, “Yes, you would.” In fact, we start every client engagement with an SEO audit precisely to check for technical issues with the site that may hurt our ability to rank. Then, we either fix or suggest fixing the technical issues before we get started publishing.

So, like I said above, just because we list this as our third priority doesn’t mean it happens third chronologically. 

But, it’s third on our priority list by importance. Meaning, for us, site cleanup and technical SEO is only important insofar as it helps our content (Priority #2) rank for the most valuable keywords (Priority #1). Yes, if there are glaring technical issues, such as multiple H1s on a page, thousands of no-value, no traffic pages eating crawl budget, the entire site randomly no-indexed (you’d be surprised!), etc. — we’ll fix those problems immediately. 

But many times, there are no problems with the site! 

Contrary to what most SEO agencies will tell you, most B2B SaaS websites of decently sized companies that can afford to hire these agencies aren’t a hot mess. Take, for example, the marketing sites of most B2B SaaS companies: they’re not even that big. They usually have something like 10–20 largely static pages (product, solution, pricing, etc.). So why are these companies being sold months of SEO audits and technical SEO “cleanup” by SEO agencies? It makes no sense to us. 

We do technical SEO and site cleanup fixes; we’ve done a lot of it for our clients. We do a site audit, and we prioritize issues by importance and severity. But for us, it’s just something to get out of the way so that our content (#2) can rank for our high value target keywords (#1), not something to extend for as long as possible to keep charging the client.  

Priority #4: Link Building and Off-site Mentions

The final part of our SEO process is getting your brand mentioned on other sites. This includes both traditional link building and off-site mentions for AI search visibility.

For traditional SEO, link building works. But like technical SEO, it doesn’t pay the bills without high-quality content (#2) aimed at the right keywords (#1). It’s just a means to an end.

For AI search, off-site mentions matter because LLMs search the web to formulate their responses. Getting your brand mentioned on sites that rank for your target keywords can increase the likelihood that AI tools like ChatGPT and Perplexity recommend you. This is the second tier of our Prioritized GEO framework — it supports AI visibility, but only after you have the foundation of owned content that ranks.

In both cases, we’ve seen clients come to us after spending years paying untold sums on link building or PR without even having a clear set of high value keywords for which they’re trying to rank, much less the content they need to actually achieve those rankings. When we ask, “Why are you building links?”, they tell us, “Our SEO agency said it was important to get rankings.”

What rankings are these exactly? No one knows.

If you’d like us to apply our SEO content marketing strategy to your SaaS business, you can learn more about our service here. 

2. Siege Media

Siege Media is an SEO-focused content marketing agency founded in 2012 that works with both B2B and B2C SaaS companies. They specialize in content creation, link building, and digital PR, with a particular emphasis on design-heavy content and data-driven strategies.

Their services include content strategy, SEO, GEO (Generative Engine Optimization), link building, digital PR, technical SEO, and graphic design. They are known for creating visual and interactive content such as infographics, quizzes, maps, and calculators that attract backlinks from authoritative publications.

Their approach centers on SERP analysis to identify high traffic value keywords, after which they build content assets designed to rank for those opportunities. They also conduct and publish original data studies, which they use to generate backlinks and establish thought leadership.

They’ve worked with companies including Zapier, Figma, Zoom, Zendesk, and Shutterstock.

Visit their site for more information.

3. Omniscient Digital

Omniscient Digital is an organic growth agency founded in 2019 that works primarily with B2B software companies. Their leadership team includes former growth leaders from HubSpot, Shopify, and Workato who have built and scaled organic search programs at major tech companies.

They position themselves as focused on revenue-driving outcomes like qualified leads, pipeline, and conversions rather than traffic volume. Their services include SEO strategy, content strategy and production, link building, digital PR, technical SEO, GEO (Generative Engine Optimization), programmatic SEO, and analytics.

Their approach combines thought leadership content with SEO, and they have been early movers in optimizing content for AI search platforms. Just like our agency, they also interview subject matter experts and produce content that speaks directly to ICP pain points, though they are mostly focused on high-volume production than quality and differentiation.

They’ve worked with companies including Jasper, Loom, HotJar, Adobe, SAP, Asana, and BetterUp.

Visit their site for more information.

4. Skale

Skale is a B2B SaaS SEO agency founded in 2020 and based in London. They focus on driving revenue outcomes for SaaS companies rather than traditional SEO metrics like traffic or rankings.

Just like our agency, they also focus on KPIs like SQLs, qualified signups, and MRR rather than vanity metrics. They work with SaaS companies through long sales cycles and understand the need to target both decision-makers and influencers within organizations.

Their services include SEO strategy and execution, content marketing, technical SEO, link building, and GEO (Generative Engine Optimization) to help brands stay visible across AI search platforms like ChatGPT, Gemini, and Perplexity.

They’ve worked with companies including HubSpot, Lightspeed, and Freshworks.

Visit their site for more information.

5. GrowSERP

GrowSERP is an organic growth marketing and SEO agency based in Vancouver, Canada that works with B2B and SaaS companies. They focus on generating leads and revenue through editorial content, programmatic SEO, and product-led SEO strategies.

They approach SEO from a bottom of the funnel perspective where they help rank for keywords and topics that can generate more leads, signups and revenue rather than high-volume, low-intent traffic. Their content production process relies on expert writers who specialize in specific industries rather than generalist writers who research a topic before writing.

Their services include content strategy, content writing, technical SEO, link building, and AI search optimization. They track client visibility across AI platforms using proprietary internal tools and position themselves as a full-service organic growth and SEO partner for companies looking to show up in both traditional and AI-driven search.

Visit their site for more information.

6. Animalz

Animalz is a content marketing agency that works primarily with B2B SaaS companies. They focus on building authority through thought leadership content and emphasize heavily on editorial quality.

Their services include content strategy, blog articles, ebooks, whitepapers, case studies, thought leadership content, technical SEO, content audits and refreshes, LinkedIn campaigns, and AEO (Answer Engine Optimization). They also developed Revive, a free tool that helps marketers identify content decay and find opportunities to refresh outdated articles.

Their content production process involves deep interviews with subject matter experts rather than surface-level research. On the AI search side, they conduct AEO audits to map visibility across ChatGPT, Claude, and Google AI Overview, including competitive benchmarking and entity mapping to show who LLMs trust in your space. They then build content restructuring plans that include summaries, FAQs, comparison tables, and schema implementation.

They’ve worked with companies like Wistia, GoDaddy, Zendesk, Intercom, Retool, and Amplitude.

Visit their site for more information.

7. Powered by Search

Powered by Search is a demand generation agency based in Toronto, Canada that works with B2B technology and SaaS companies, particularly those with long sales cycles and high ACVs.

They integrate SEO with demand generation, content marketing, paid advertising, and ABM rather than treating it as a siloed channel. They map buyer journeys across multiple stakeholders and build SEO strategies tailored to each.

They have built a Predictable Growth System specifically for high-ACV SaaS companies. Their services include SEO, content marketing, demand generation, paid media, and Answer Engine Optimization (AEO).

For AI SEO, they have developed what they call the RAISE framework, which is focused on helping clients show up in ChatGPT, Perplexity, Gemini, and Google’s AI Overviews by structuring content to directly answer questions, ensuring AI crawlers can access your site, building contextually rich content, establishing third-party validation through digital PR, and leveraging user behavior that can influence AI results.

They’ve worked with companies including Varonis, Fortra, and Collibra.

Visit their site for more information.

8. Organic Growth Marketing (OGM)

Organic Growth Marketing (OGM) is an SEO and content agency that works with growth-stage SaaS companies. They embed a growth advisor directly into client teams, and execute with a back-end team of specialists.

Their approach focuses on understanding each client’s ICP and building organic growth programs tied to revenue outcomes rather than traffic volume. They position themselves as working best with post-product-market-fit companies that already have at least one channel working and have some brand momentum in the market.

They emphasize the importance of buyer intent over top-line traffic numbers. On the AI SEO side, they note that success in AI search depends on having a brand with signals across the web, and they execute their strategies around off-site signals.

They’ve worked with companies like Hotjar, Intercom, Ramp, ProfitWell, Matterport, Klaviyo, Twilio, Unsplash, and ActiveCampaign.

Visit their site for more information.

9. Omnius

Omnius is a B2B SEO agency based in Europe that works exclusively with SaaS and Fintech companies. They believe that narrow focus and deep expertise in specific industries yield better results than being a generalist agency.

Their services include SEO strategy, technical SEO, content production, link building, programmatic SEO, and GEO (Generative Engine Optimization). On the AI search side, they optimize websites for visibility in LLM-generated answers through AI crawler optimization, content scoring and rewriting, source citation monitoring, and schema implementation.

Their approach covers both traditional search engine optimization and AI search visibility, with a focus on positioning B2B companies everywhere people search, not just Google.

They’ve worked with companies including TextCortex, Airbnb, WorldFirst, Meniga, and Native Teams.

Visit their site for more information.

10. SimpleTiger

SimpleTiger is an SEO agency that works exclusively with SaaS companies. They focus on building search strategies that generate business outcomes like qualified leads and building predictable, recurring revenue over time.

They provides services including SEO strategy, technical SEO audits, content strategy and production, link building, digital PR, paid search, web design, and GEO (Generative Engine Optimization) for AI-powered search. They position themselves as a unified system across search, content, design, and paid channels rather than offering these as separate services.

Their approach emphasizes speed, execution, and scale. They work with both early-stage SaaS companies and enterprise brands. On the AI search side, they focus on content structure, clarity, and credibility to help SaaS brands stay visible in both traditional search and AI platforms like ChatGPT and Google AI Overviews.

They’ve worked with companies including Segment, Gelato, JotForm, Bitly, Unsplash, and Invoca.

Visit their site for more information.

Want to work with us or learn how to implement our B2B SaaS SEO strategy?

• Our Agency: You can learn more about working with us here.

• Our Content Marketing Course: Individuals looking to learn how to grow their SaaS business with SEO and content strategy can join our private course, taught via case studies, here. We include a lot of information and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we and other members give feedback. We also get on live Zoom calls about once a month and dissect members’ actual content strategies and brainstorm ideas on how we’d form content strategies for their businesses.

• Join Our SEO Team: Alternatively, if this style of B2B SEO and content marketing appeals to you, consider joining our team as a writer or content strategist. We have awesome clients. We’re a remote company. We pay well. And you won’t have to stress about getting your own clients or spend a bunch of time doing outreach to get them.

This rise in demand for AI search visibility has created a flood of agencies and consultants offering “AI SEO” or “GEO” services. What we’ve noticed is that many of these marketers are pushing tactics like adding llms.txt files, FAQ schema, rewriting headings as questions, and running marketing campaigns on Reddit.

In our experience, most of these tactics are unproven or have actually been proven (via our testing or others) to not make any measurable difference in AI search visibility. As a result, it’s our strong belief that these short-term tactics shouldn’t be your top priority if you really want to be mentioned by ChatGPT, Perplexity, Google AI Overviews, and other LLMs.

We’ve spent the last year studying what actually drives AI visibility for SaaS and B2B brands. We analyzed 400+ keywords across 16 of our own clients, built our own AI tracking tool (Traqer.ai), and developed a framework called Prioritized GEO to separate what works from what doesn’t.

So, to help you find the right agency for AI search, we’re going to share what we’ve learned and the key factors you should consider when evaluating AI SEO agencies.

Then, we’ll share how we’ve addressed each of these factors at our agency. And since most companies like to evaluate multiple options, we’ll cover other AI SEO agencies we’ve seen that are publishing actual strategies and studies to improve AI visibility, not just pushing tactics to justify higher retainers.

Factors to Consider When Choosing the Best AI SEO Agency for Your Business

To begin, we’ll cover the 4 biggest factors you need to consider when evaluating AI SEO agencies:

• Factor 1: Do they understand the connection between traditional SEO and AI visibility? 
• Factor 2: Do they prioritize bottom of funnel content over top of funnel?
• Factor 3: Is their content process human-led or AI-led?
• Factor 4: How do they measure AI visibility?

Let’s look at each of these in more detail.

Factor 1: Do they have a clear AI optimization strategy?

This is the most important factor because it determines whether an agency actually understands how AI search works or is just capitalizing on the hype.

As we said above, there’s a flood of GEO tactics being promoted across the internet right now, like adding llms.txt files to your site, rewriting your headings as questions, adding content summaries or key takeaways, creating FAQ sections on every page, and implementing structured data markup specifically for AI.

The problem is that these are random tactics, not a strategy.

If an agency’s approach is creating content without a clear framework, then sprinkling on some FAQs or changing headings to questions and calling that their GEO strategy, they don’t have a strategy. They’re just throwing tactics at the wall.

Similarly, if they’re doing their normal content process and then using some AI visibility tool to report a single visibility percentage to you, as if that number alone constitutes a GEO strategy, it means they fundamentally don’t understand what drives AI visibility.

Trying random tactics isn’t a strategy. Doing business as usual and then reporting on a single metric isn’t a strategy either.

Instead, look for an agency that can clearly separate tactics from strategy. That means they understand prioritization, and specifically, they know what doesn’t matter so they can focus on what actually does. 

Strategy is an overarching framework that determines which tactics deserve your time and budget, and which ones are distractions. A real AI search optimization strategy explains why certain approaches work and why others don’t, based on how LLMs actually gather and process information.

In fact, knowing what doesn’t matter is often the clearest indicator that an agency has developed a thoughtful strategy rather than just being willing to try anything that sounds promising.

You can see what our GEO strategy looks like through our Prioritized GEO framework below. This pyramid clearly deprioritizes on-page tactics like bullet points and FAQ creation because we understand that what’s far more important is getting exposure to LLMs by showing up in the web searches they perform when generating responses.

This kind of strategic thinking, where an agency can explain not just what they do but what they deliberately don’t do and why, is what separates real AI optimization expertise from agencies that are just repackaging their existing services with AI buzzwords.

Factor 2: Do they prioritize bottom of the funnel content over top of the funnel?

Most traditional SEO agencies prioritize top of the funnel topics because they have the highest search volume. They do keyword research using SEO tools, sort by volume, and build content calendars around the biggest numbers. The assumption is that with enough traffic, conversions will follow.

But generating more traffic doesn’t automatically result in more leads. We’ve been writing about this since 2018 when we published our Pain Point SEO framework. Top of the funnel visitors usually aren’t looking to buy. They want a definition or a quick answer, and then they leave. 

Conversion rates on this type of content are minimal compared to bottom of the funnel content where the reader is actively researching solutions.

In our experience, bottom of the funnel keywords convert 10x to 25x better than top of the funnel. We’ve documented this across multiple case studies, including our Geekbot case study, where we showed the difference in conversion rates between content targeting different parts of the funnel:

So even in traditional SEO, we’ve argued for years that companies should focus first on bottom of the funnel, pain-point-focused keywords, then work their way up. If an agency is still building content calendars filled with “What is X” and “Beginner’s Guide to Y” posts, they’re operating on an outdated playbook.

But now, in AI search, this becomes even more critical.

Top of the funnel content doesn’t just have low conversion rates in AI search. It drives almost no traffic. When you ask ChatGPT, “What is content marketing?”, it just gives you the answer. No links. No sources. No reason for the user to click through to your site. Google is heading in the same direction with Google AI overviews, answering the query and occupying most of the top fold in the search results page. 

In contrast, for bottom of the funnel queries or prompts, LLMs do 2 things differently. 

1. They actually list brands. For example, when someone asks ChatGPT “What’s the best project management software for a small marketing team?” or “What are the best alternatives to HubSpot?”, ChatGPT, for example, literally lists tools in its outputs. That’s because it knows that the user is actively looking for product recommendations. Your goal is to be one of these recommended brands or products.
2. They cite sources (and link to them) a lot more often than for TOF prompts. Note below how each tool has a link associated with it. We didn’t have to even click “web search” to do this, ChatGPT just does that automatically.

Factor 3: Is their content writing process expert-led or AI-led?

A lot of agencies have started using AI to write content for clients because it allows them to produce more content at a faster pace and lower cost. But 100% AI-generated content is too generic to stand out, and if done at scale, it can also be impacted by Google’s scaled content abuse policies.

LLMs are trained on what already exists online, and they’re programmed to produce responses that match the patterns in their training data. They find the most likely word that goes after the word they just wrote, over and over again. The result is content that sounds articulate on first read but says the same thing as everything else, with no original argument, no unique perspective, and no reason for an LLM to recommend it.

We wrote about this back in 2016 and called it Mirage Content. On the surface, it looks well-written. But when you actually dissect the arguments, it’s just stating generic points in different ways. AI content is essentially Mirage Content 2.0, making it easier to produce undifferentiated content at scale.

If an agency uses AI to generate your articles, they’re just regurgitating the average of what already exists. Generative engines have no incentive to recommend content that looks and sounds like everything else on the web.

The agencies doing this well use a human-led process. They interview subject matter experts inside your organization to understand your product, your differentiators, and your point of view. They create content that’s original, specific to your company, and worth citing. This is the kind of content that gets recommended by LLMs. 

To be clear, we’re not against using AI in the writing process. It can be useful for research, creating briefs, fleshing out sentences, and other parts of the workflow. But if you want to produce content that actually stands out, you need to spend a lot of time providing the prompts and details that are specific to your business. That specificity is what makes content worth recommending. The agencies that just feed a topic into ChatGPT and publish whatever comes out are producing content that looks and sounds like everything else on the web.

When we asked ChatGPT for the best SaaS marketing agencies, it knew we specialize in content strategies that drive demos and signups, not just traffic. It pulled that information directly from the content we’ve published on our site.

If the content on your site has the same copy, benefits, and use cases as every competitor in your space, your SEO strategy will also be lost in the shuffle.

Factor 4: How do they measure AI visibility?

Traditional SEO tools can tell you where you rank on Google. They can show you traffic, impressions, and click-through rates, but they don’t know you if you’re being mentioned in ChatGPT, cited by Perplexity, or appearing in Google AI Overviews.

This is a real problem because these are different outcomes. You can rank on page one of Google and still not show up when someone asks ChatGPT for recommendations. You can appear in Perplexity but not in Google AI Overviews. Without a way to track this, you’re blind to whether an agency’s work is actually producing AI visibility.

Some agencies have started using AI visibility tools to track this. But there’s a problem with how most of them use these tools.

A lot of AI visibility tools work by generating a list of prompts and telling you which ones you’re “ranking” for. But these prompts are often random and made up by the tool. You have no idea if anyone is actually typing them into ChatGPT, or if they’re even relevant to your business.

For example, we’ve seen many AI visibility tracking tools generate prompts that have nothing to do with what a company actually sells or the topics their customers care about. That’s letting the tool lead the strategy instead of the other way around.

In contrast, a good AI SEO agency will decide which topic areas matter for your business first, then measure visibility for prompts related to those topics. They decide the prompts and topic, not some tool. You and the agency need to agree on what’s important, and then track visibility against that. Otherwise, you’re just looking at data on random AI generated prompts that your real customers may or may not ever be searching.

We start by identifying the bottom of the funnel topics that matter for each client, then track visibility across multiple prompts related to those topics in ChatGPT, Perplexity, and Google AI Overviews week over week. This lets us see what’s actually working and adjust accordingly.

When evaluating agencies, ask them how they measure AI visibility. If they don’t have a clear answer, or if they’re just pointing to traditional SEO metrics, they’re not set up to deliver on what they’re promising.And if they’re using a tool but can’t explain how they decide which prompts to track, they’re letting the tool lead them instead of leading the tool.

Now, let’s walk through how our agency addresses each of these factors, including our AI visibility strategy, content and promotion processes, and the research we’ve published on what actually drives results in AI search.

Then, since most companies like to evaluate multiple options, we’ll profile other agencies that are publishing real strategies and data around AI visibility.

Best AI SEO Agencies

Here are the best AI SEO agencies to consider, starting with our own. For each, we’ll explain their approach, what they specialize in, and how they address the factors above.

1. Grow and Convert

Grow and Convert is our SEO and GEO-focused content marketing agency, and we’ve spent over 10 years specializing in content strategies that drive leads and revenue for B2B companies, not just traffic. Unlike agencies that optimize for top of the funnel metrics, we focus on bottom of the funnel content that generates qualified leads and produces measurable ROI.

We’ve done this for years with traditional SEO and over the past year, we’ve extended this approach to AI search. We developed our Prioritized GEO framework, built Traqer.ai to track AI visibility the right way, and published the research we referenced throughout this article, including our study of 400+ keywords that showed a 77% correlation between Google rankings and AI mentions.

We work with companies at various stages, from startups looking to build their first scalable content strategy to growth-stage companies that are looking to expand their visibility in both traditional search and AI engines.

Our approach is built around the four factors we outlined above, and we’ve documented our entire process publicly through detailed case studies and research.

Here’s how we address each factor:

1. Our Strategy Is Built on Exposing Your Brand to LLMs (By Showing up in Web Searches Those LLMs Conduct)

Our Prioritized GEO framework prioritizes getting exposure to LLMs first, (Tiers 1 and 2) and only then testing on site tactics (Tier 3) which are only meant to help LLMs better undrstand your content (less important). 

The foundation of our pyramid is Tier 1 – Owned Content that Ranks on Google. This is based on the fact that SEO is the #1 way to influence LLM outputs. LLMs search the web to supplement their responses, a process known as grounding. They do this very often, in particular, for bottom of funnel or product related prompts, which are the only prompts that really matter for GEO, as we established above. And the absolute most efficient way to get exposure to LLMs via traditional SEO is to have content on your site that is aimed at ranking for SEO keywrods that are related to the topics you want AI visibility for. 

Related to this is Tier 2 – Off-Site Mentions. This is getting exposed to LLMs via other sites’ content that is ranking on Google. We do this for clients through citation outreach, where we see what sites are ranking for the prompts you care about and reach out to them to try to get placement in their articles. 

Only after those have been exhausted do we test on-site tactics (Tier 3). 

This has resulted in great AI visibility for our clients like the following for a client in the innovation management software space: 

Or workflow management: 

2. We Prioritize Bottom-of-Funnel Content That Gets Recommended by AI

We’ve been focused on bottom-of-funnel content since 2018, when we published the Pain Point SEO framework. We argued that companies should focus first on keywords with high buying intent, then work their way up the funnel. 

In our experience over 10 years of working with 100+ clients and tracking conversion rates across thousands of keywords, we’ve seen bottom-of-funnel keywords convert 10x to 25x better than top of the funnel.

In AI search, this strategy is even more important. Top-of-funnel queries don’t drive any traffic because ChatGPT just answers them directly. Bottom-of-funnel queries are where LLMs actually recommend products and services.

When someone asks ChatGPT for the best software in your category or alternatives to a competitor, your goal is to be one of the brands it names. Our content strategy is built around making that happen.

3. We Use an Interview-Based, Human-Led Writing Process

We don’t produce articles by feeding topics into AI or having writers do Google research and regurgitate what’s already ranking. Instead, our writers start by interviewing people inside your organization who have the expertise to speak on the topic and explain how your product is differentiated.

We’re not talking about grabbing a few quotes to throw into an article. We’re talking about hour-long recorded interviews where we shape an entire article around the viewpoint and knowledge of an expert inside your company. This creates content that’s original, specific, and worth citing. Here’s a case study showing how this even lets us produce detailed technical content without prior domain expertise. 

This matters for AI visibility because LLMs recommend brands based on how clearly their content articulates who they serve, what pain points they solve, and how they’re different. Generic content that sounds like everything else on the web doesn’t get recommended.

4. We Track AI Visibility with Traqer.ai

Traditional SEO tools can’t tell you if you’re being mentioned in ChatGPT or appearing in Google AI Overviews. And most AI visibility tools focus on prompts they randomly generate and hide the details in single visibility % numbers. This is not a proper AI search strategy, which is why we built our own tool, Traqer.ai.

Traqer is based on topics, not prompts. Since none of us know what users are actually typing into ChatGPT, Traqer flips the script and gets you, the brand, to first decide what topics matter to you and then it comes up with multiple prompts that get at that topic in different angles.

You can then see how often your brand gets mentioned across a variety of prompts on that topic. If you’re mentioned every time, you’re likely to be mentioned when a real user asks about that topic. If you’re hardly mentioned at all, you have work to do for visibility on that topic. 

We also use Traqer to identify which domains are being cited most frequently for queries relevant to our clients’ industries. This informs our citation outreach, so we’re targeting the sites that AI tools are actually pulling from.

Our Content Promotion Process

To drive traffic to your articles as we wait for them to rank in organic search, we use a three-pronged promotion strategy:

• Paid Ads (short-term traffic): We use paid ads to promote content using cold audiences and lookalike audiences based on your existing customer list or website visitors. We test channels like Facebook, LinkedIn, and Google Ads based on where we’re most likely to reach your audience.
• Manual Link Building (long-term traffic): When certain pieces start ranking, we strategically build backlinks to boost them to page 1 or the top of page 1 in Google.
• Citation Outreach (AI visibility): We track which sources AI tools are citing when answering queries relevant to your industry, then work to get your content mentioned by those same sources. This ensures that when AI tools answer questions in your space, they’re more likely to cite content that mentions your brand.

We do all of the above from our own budget, with no extra spend for our clients.

Case Studies and Research

Here are some of the case studies and research we’ve published that demonstrate our approach:

• Does Google SEO Affect LLM Optimization? We Analyzed 400+ Keywords to Find Out
• Prioritized GEO: How Brands Should Focus Resources on AI Visibility
• AI Search: How Content Strategy Needs to Change
• How we scaled Leadfeeder’s signups to over 200/month
• Scaling SEO traffic from 920 to 14,577 sessions in 6 months (Circuit Case Study)

If you’re interested, you can learn more about our agency, pricing, and reach out about working with us here.

2. Siege Media 

Siege Media is an SEO-focused content marketing agency founded in 2012 that has expanded into GEO and AEO services. They specialize in content creation, link building, and digital PR, with an emphasis on creating design-heavy, interactive content assets.

They offer comprehensive services, including content strategy, SEO, GEO (Generative Engine Optimization), link building, digital PR, technical SEO, and graphic design. They are mostly known for creating unique data studies and interactive content like quizzes, maps, and calculators that earn backlinks and citations from authoritative sources.

Just like our agency, their GEO methodology centers on creating owned assets. Their research found an 83% lift in traffic value when content includes unique data, and they report that companies publishing proprietary data gain 45% more AI citations than those using traditional content formats. They also have a proprietary tool, DataFlywheel, to automatically refresh existing content with new data and updated information on a quarterly basis.

For AI visibility tracking, they use third-party tools like Profound and Knowatoa to monitor brand mentions across ChatGPT, Perplexity, and Google AI Overviews.

They’ve worked with companies including Zapier, Figma, Zendesk, Shutterstock, and Zillow.

Visit their site for more information.

3. iPullRank

iPullRank is a New York-based enterprise SEO agency known for its technical SEO services and a data-driven approach to search. They offer Generative Engine Optimization services through their proprietary framework called Relevance Engineering.

They provide comprehensive services, including technical SEO, content strategy, audience research, digital PR, and GEO. Their work focuses on enterprise and mid-market clients with complex sites, particularly in finance, media, and e-commerce.

Their GEO methodology is centered around information retrieval, content strategy, UX, and AI to optimize content for visibility across traditional and AI-powered search platforms. The framework focuses on understanding how LLMs retrieve and synthesize content, including concepts like query fan-out, passage retrieval, and vector embeddings. They’ve published a comprehensive resource called The AI Search Manual that covers their entire approach to GEO, from strategy to measurement.

iPullRank emphasizes structuring content so it can be retrieved and cited by AI systems, not just ranked in traditional search. They focus on semantic chunking, entity optimization, and creating content that LLMs can easily parse and include in generated responses.

They’ve worked with companies including LG, DocSend, and Citi and report delivering $2B+ in results across their client base.

Visit their site for more information.

4. GrowSERP

GrowSERP is an organic growth marketing agency based in Canada that helps B2B and SaaS companies generate leads and revenue through editorial content, programmatic SEO, and product-led SEO strategies. The agency prioritizes bottom of the funnel keywords and topics and focuses on scaling traffic, authority and qualified leads.

Their content production model relies on expert writers who specialize in specific industries rather than generalist writers who research a topic before writing. 

On the AI visibility side, GrowSERP offers AI search optimization services and tracks client visibility across AI platforms using their own proprietary internal tools. They combine their traditional SEO capabilities in content writing, technical SEO and link building, and position themselves as a full-service organic growth partner for companies looking to show up in both traditional and AI-driven search.

GrowSERP primarily works with B2B companies and SaaS platforms who want to scale their traffic and customer acquisition.

Visit their site for more information. 

5. Spicy Margarita

Spicy Margarita is a UK-based SEO and content agency that works with B2B services and SaaS companies. They’ve built specialist services in generative engine optimization alongside their core SEO offering.

They offer SEO strategy, content production, digital PR, and GEO services. The agency is intentionally small, working with no more than 10 clients at a time, and positions itself as an extension of in-house marketing teams rather than a traditional agency.

Like our agency, Spicy Margarita focuses on bottom-of-funnel strategies designed to drive revenue rather than traffic. Their GEO approach centers on making brands visible when buyers are actively researching solutions in LLMs like ChatGPT, Claude, and Gemini. They run AI SEO audits and build playbooks for getting brands cited in AI-generated answers.

Founder Ben Goodey is active in the GEO space, sharing experiments and strategy breakdowns on LinkedIn and running How the F*ck, an SEO publication with case studies and interviews. The agency emphasizes ongoing experimentation in AI SEO as the field evolves.

They’ve worked with Series A and Series B tech startups, financial services companies, and other fast-growing B2B businesses.

Visit their site for more information.

6. Omniscient Digital

Omniscient Digital is an organic growth agency founded in 2019 that helps B2B software companies turn SEO and content into growth channels. Their leadership team includes former growth leaders from HubSpot, Shopify, and Workato.

They offer comprehensive services, including SEO strategy, content production, content optimization, technical SEO, programmatic SEO, link building, digital PR, and Generative Engine Optimization (GEO). 

Their GEO approach focuses on making content that LLMs can retrieve, understand, and cite. They emphasize building authority signals through backlinks and PR placements and creating structured content that’s easy for AI systems to parse, and positioning brands as trusted sources across third-party sites that LLMs reference.

Omniscient publishes research on AI search trends, including studies on how SEO roles are changing with AI search and how retrieval-augmented generation (RAG) influences which content gets surfaced by LLMs.

They’ve worked with companies including Jasper, Loom, HotJar, Lokalise, and GatherContent.

Visit their site for more information.

7. Avenue Z

Avenue Z is a Miami-based marketing, PR, and communications agency with offices in New York City and Orlando. Founded in 2023 by Jeffrey Herzog, the agency blends PR, content strategy, and AI search optimization into an integrated offering. Their model works particularly well for founder-led brands focused on building thought leadership and brand authority.

Their AEO approach connects technical SEO, content, digital PR, and analytics to position brands to be cited and recommended by AI systems. Rather than relying on isolated tactics, Avenue Z starts with prioritization by building strong owned assets, reinforcing them with off-site authority through media placements in authoritative publications, and focusing on decision-stage content that clearly explains who the brand serves and why it’s credible. On the technical side, they focus on citation optimization and structured data deployment that enhances brand recognition on AI platforms.

Avenue Z measures AI search visibility at the topic level and ties it to business outcomes like qualified demand and brand trust. They track visibility across ChatGPT, Perplexity, Gemini, and Google AI Overviews. The agency serves industries focused on growth and innovation, including fintech, emerging technology, B2B SaaS, venture capital, health and healthtech, and DTC ecommerce.

They’ve worked with companies including Acorns, Better, Dave, e2open, and April.

Visit their site for more information.

8. Omnius

Omnius is a Europe-based B2B SEO and LLMO agency that works exclusively with SaaS, fintech, and AI companies. Their approach treats GEO as an added layer to SEO rather than a separate discipline. 

On the technical side, their services include AI crawler optimization, schema markup, llms.txt file creation, and reducing JavaScript to improve crawlability. On the content side, they focus on structuring content for citation, embedding what they call “citation triggers” that prompt AI systems to treat information as authoritative. They also build authority across platforms that AI engines tend to favor, including Reddit, Quora, G2, and LinkedIn.

They frequently publish research on GEO topics, including a GEO Industry Report and case studies on AI visibility experiments. 

They’ve worked with companies like AuthoredUp, TextCortex AI, Zencoder, and Anna Money.

Visit their site for more information. 

9. Skale

Skale is a SaaS-focused organic growth agency that has expanded into GEO services alongside their core SEO, content, and link building offerings. They position themselves around revenue metrics like MRR, pipeline, and CAC payback rather than traffic and rankings alone.

They focus on making content citable by AI platforms through structured formatting, entity-rich language, and what they describe as “passage-level optimization” since LLMs pull from specific sections of content rather than evaluating entire pages. 

The agency has published research analyzing citation drivers across AI platforms, including a study of six million LLM answer snapshots that identified factors like citation density and link breadth as predictors of AI visibility.

Skale works primarily with B2B SaaS and fintech companies and has worked with companies like Maze, Rezi, Holded, Pitch, TravelPerk, and HubSpot.

Visit their site for more information.

10. Digital Elevator

Digital Elevator is a B2B content marketing agency that specializes in technical verticals, particularly healthcare, biotech, and medtech. They provide SEO-driven content strategy, content marketing, product-led SEO, UX/UI, and PR services.

They offer Answer Engine Optimization (AEO) services built around owned content optimization through product-led SEO content. They focus most on bottom of the funnel content that appeals to both search engines and LLMs.

They also get their clients featured in high-authority articles and directories that LLMs cite and use strategic PR and media placements to build the authority signals that AI models use to determine credibility. They track visibility across ChatGPT, Perplexity, Google AI Overviews, and Microsoft Copilot.

They’ve worked with companies like McKesson BPS, Solvias, Champions Oncology, and Halo Labs.

Visit their site for more information.

11. First Page Sage

First Page Sage is a San Francisco-based SEO and thought leadership marketing agency that positions itself as an early mover in the GEO space. 

Their methodology centers on what they call Thought Leadership SEO, which involves ghostwriting approximately 80 pieces of content per client over 12 months. They focus on ranking the content they produce in traditional search while also meeting the technical requirements for AI citation. 

They position their clients as subject matter experts which they believe is what leads AI systems to recommend the brands they work with when users ask questions in their category. The agency combines content production with technical optimization, conversion funnel setup, and tracking. They also offer online reputation management and outreach to authoritative publications as part of their broader GEO service.

First Page Sage works primarily with B2B companies in technology, healthcare, medtech, manufacturing, and professional services. Some of their notable clients include Salesforce, Logitech, Microsoft, Verisign, LinkedIn, and US Bank.

Visit their site for more information. 

Want to Work with Us or Learn How to Implement Our AI SEO Strategy?

• Conversion-Focused Content Marketing for SEO & GEO: You can learn more about working with us here.
• Our Content Marketing Course: Individuals looking to learn how to grow their SaaS business with content can join our private course, taught via case studies, here. We include a lot of information and examples not found on this blog. Our course is also built into a community, so people ask questions, start discussions, and share their work in the lesson pages themselves, and we and other members give feedback. We also get on live Zoom calls about once a month and dissect members’ actual content strategies and brainstorm ideas on how we’d form content strategies for their businesses.
• Join Our Content Marketing Team: Alternatively, if this style of B2B content marketing appeals to you, consider joining our content marketing team as a writer or content strategist. We have awesome clients. We’re a remote company. We pay well. And you won’t have to stress about getting your own clients or spend a bunch of time doing outreach to get them.