How to Build an AI SEO Strategy That Actually Works

If you’ve been following conversations around optimizing your site and content for LLMs like ChatGPT, Perplexity, or Google AI Overviews (referred to as AI SEO or GEO, AIO, or AEO), you’ve probably noticed that most of the advice treats it as if it requires an entirely different approach than traditional SEO.

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.