10 “Not-So-Obvious” Sources of SEO Content Ideas & Audience Insights (Beyond Keyword Research)

By Ethan Lazuk

To quote Google’s latest SEO starter guide, are you “feeling adventurous?”

Want to get off the beaten path and find more ideas for SEO content?

In this article, we’ll explore 10 less obvious alternatives to keyword research, going beyond Google Search data, competitor content, and SERP analysis to explore other sources of topic ideas your audience will care about.

Now, “alternatives,” doesn’t mean replacements, per se. Just additional sources to add richer context for creating more helpful content.

What are these “not-so-obvious” sources?

The list is theoretically endless.

Once we recognize how to pull audience insights from different sources, it opens up infinite opportunities for SEO research.

That said, here’s a list of 10 we’ll cover:

1. Google Search (the less obvious parts)
2. Google Search Console (queries)
3. Bing (web search and Copilot)
4. Bing Webmaster Tools
5. Perplexity AI (Answer Engine)
6. Reddit (comments)
7. TikTok (trends)
8. YouTube (summaries)
9. Amazon (reviews)
10. ChatGPT (analysis)

We’ll start with the more obvious ones, then work our way toward the more obscure or less familiar ones.

You can use those jump links above to visit any section or bookmark it for later, or buckle up to read through them all, plus some additional context before and after.

Reading this full post should take around:

To that end, let’s get started by asking a basic question:

Why do we need alternative sources?

You don’t have to look far in most entry-level SEO guides to find “keyword research” mentioned as a central part of content creation.

As an example, Yoast recently published The SEO checklist for beginners, which “starts with doing proper keyword research.”

The reason why is:

“Because if you’re not optimizing your content for the words your target audience uses, you won’t be visible to the right people.”

Makes total sense, but there’s also nuance to this idea.

While it’s important to know “the words your target audience uses,” it’s not necessarily just for “optimizing your content.”

To elaborate on this, we can reference another recent post, this time from Google Cloud, called Your RAGs powered by Google Search technology, part 1, which includes information about how Google Search works.

One of its sections is on semantic search:

“Semantic search using deep learning has become a crucial feature for most search engines, letting developers build systems that can understand the meaning of query texts rather than simply using keyword matching.

Despite these advancements, most RAG systems still use simple similarity search in vector databases to retrieve information. This approach can often lead to the return of low-quality, irrelevant results.

The primary reason for the lower search quality lies in the principle that “the question is not the answer.” A question like “Why is the sky blue?” and its answer, “The scattering of sunlight causes the blue color,” have distinctly different meanings.” [Highlights added.]

Given there are instances where “the question is not the answer,” we can see how SEO research isn’t always about choosing keywords for “optimizing your content.”

Sometimes, our goal is creating content that addresses the “meaning” of those words — the underlying search intent — or what our audience wants to achieve.

This goal not only can apply to individual pages but also clusters of topically related content that inform or assist audiences as they make a myriad of decisions during their respective buyers’ journeys.

This is also where alternative sources beyond keyword research can play a role.

Additional sources can give us more context or inspiration for creating helpful and people-first content that can surface organically all throughout a user’s journey, contributing to brand awareness and authority and related business goals.

Here’s a thoughtful tip from Pedro Dias on LinkedIn that speaks to a similar idea:

Of course, lexical matches between content and queries sometimes matter to users, and search engines will oblige in rankings.

As Google Cloud also discusses in their blog post:

“semantic search is not a cure-all. In some cases, the embedding model may not grasp the meaning of an item, rendering the search useless. … Additionally, the majority of users continue to expect a conventional keyword search experience, which typically includes using exact or partial keyword matches and keyword-based filtering mechanisms. This functionality is especially crucial in sectors like healthcare and finance, where keyword searches are fundamental for accessing precise and critical patient information, financial data, or compliance-related documents.

… a hybrid search engine … simultaneously performs both keyword and semantic searches for each query. The results are then merged and re-ranked based on their respective scores, combining the best aspects of both search approaches to fill in the gaps left by each.” [Highlights added.]

In other words, Google Search can find and rank a webpage based on whether its content addresses the meaning of a query (semantic search) or matches the query more exactly (lexical or keyword search), but that outcome is determined largely by user expectations.

So when it comes to optimizing our content, we should consider whether a user would expect to find a keyword they searched on a page or just simply the answer to what that query implies.

Either way, the insights we can draw about the types of information users expect to find in content from search results aren’t limited to interpreting their queries.

Rather, they can be gleaned from sources all around us.

While keyword research is certainly an important part of the content equation, we should be looking at sources to not only tell us the “words” our target audiences use, but also the “goals“ they want to achieve — the meaning behind those words.

If we depend on keyword research alone for content ideas, that can inadvertently build a mental wall that conceals us from broader context or audience insights. (I’ll speak about this from a personal perspective at the end in the takeaways.)

Meanwhile, by embracing alternative sources, we can start to recognize the meaning behind users’ words — the context of a question or pain point — more holistically, including certain details that maybe haven’t been (or couldn’t be) revealed through keyword research.

Once again, the “not-so-obvious” sources we’ll review here include:

1. Google Search
2. Google Search Console
3. Bing
4. Bing Webmaster Tools
5. Perplexity AI
6. Reddit
7. TikTok
8. YouTube
9. Amazon
10. ChatGPT

But as we go over each source, keep in mind how it can contribute to your content ideas directly, but also how the bigger advantage is gaining the perspective to recognize how SEO research opportunities can exist wherever audience insights do.

It’s a big world “outside the wall.”

Speaking of which, if you enjoy music while you read, I’d recommend these tunes:

Now, on with the show! 😉

1. Google Search (people also have “perspectives”)

The best place to start our research journey is Google, the search engine where we’re most likely trying to achieve organic visibility, whether it’s in normal search results or other surfaces, like SGE, Discover, or People also view.

Google Search has a lot of research opportunities, including both popular and less apparent ones. We’ll take a look at both, starting with the better known ones and working down to the newer or more obscure.

1a. People also ask

People also ask questions are front-and-center after the first organic result in most SERPs.

The questions are implied to be related to the query searched, making PAA an apparent source for exploring content ideas.

Something I’ve also found is clients often like to see a screenshot from AlsoAsked.

Many people are familiar with pillar-cluster content, so seeing a tree of PAA questions helps them conceptualize how one topic can lead to another.

I know it’s tempting, but I wouldn’t just start pulling PAA questions verbatim for content.

We first need to ask two key questions:

• “Are these questions relevant; do our readers care?”
• “Do we have unique expertise to answer these?”

I also wouldn’t scrape PAA questions and use generative AI to answer them (which is arguably spam).

Instead, what I like to do is either gather PAA data or export it from AlsoAsked, and then I use ChatGPT (Code Interpreter) to analyze the questions for larger themes to better understand an audience’s user journey. Then I develop content topics from that data. (I’ll show an example in the ChatGPT section.)

A little later, we’ll also explore how augmenting Google’s PAA questions with Perplexity AI’s Related questions could help with creating more holistic content or original ideas.

1b. Related searches

Another apparent source, Related searches are queries users have (presumably) entered on Google related to a topic.

Personally, I view these searches more as seed topics that can be used to conduct further research for content ideas as opposed to actual topics.

That said, if you search long-tail queries on Google, you may find some Related searches that could work for content topics directly.

1c. Autocomplete suggestions

Similar to Related searches, Autocomplete suggestions are an apparent source. These are suggestions from Google for how to complete or rephrase a query based on historical searches.

The purpose of Google autocomplete is to save time, not really to suggest alternative or related searches.

Even still, you could find some content topics or ideas in there.

1d. A few more apparent sources

Google Search has plenty of go-to sources for content topic research.

Essentially, any SERP feature or query refinement that suggests a keyword, question, or angle a user would have for a topic falls in this category.

Things to know is one example:

It’s similar to PAA but more broadly thematic and can show multiple sources at once, thus offering ideas for content topics, formats, and more.

Another one is People also search for (PASF), which appears whenever you click back to the SERP from a result.

Meanwhile, although we’re technically supposed to be going beyond keyword research here, I will mention Google also has free tools for organic search data.

Google Ads Keyword Planner is a great resource, especially if you can’t afford a paid tool. (I mention other free tools in another post about SEO tips for artists.)

Additionally, Google Trends is more for topic research but can be useful in other cool ways.

For example, I used Trends with an investment client to identify new AI companies that we could include in our content strategy, helping us be the first to appear in search results for their service.

The problem was that our competitors quickly followed suit.

That’s why it’s not enough to just be first to cover a topic; you must also enrich your content with unique and relevant insights that differentiate your content from competitors.

To do that it helps to get off the beaten path.

Now let’s look at some “not-so-obvious” sources from Google Search.

When I say these are less obvious, I mainly mean they’re not the go-to sources, but they still have potential value.

1e. Topic bubbles (related topics)

I’m never sure what these are called. I’ve seen so many names, like filter pills and refinement chips.

I believe “related topics” is their official name, but I prefer “topic bubbles.” 😉

As Google says, these topic bubbles are “relevant topics … based on what we understand about how people search and from analyzing content across the web.”

I don’t think you can draw too many content topics directly from these bubbles.

However, they can guide you toward topic ideas that are relevant to an audience’s buyer’s journey. From there, you can explore specific questions for associated content.

Particularly for ecommerce or transactional queries, you may find a second row of topic bubbles.

But in such cases, I like to use filters, which we’ll discuss next.

1f. Filter by (product filters)

Google Search product filters (shown below on the lefthand side of a desktop SERP) can create a lot of ideas for product or category page or even review content.

These filters can reveal combinations of product attributes you can create additional pages for or mention in current product descriptions, depending on the terms’ search volume and what makes sense for a user’s journey.

For example, I once worked with a client who wanted to rank a category page for “XYZ uniforms,” but as it turned out, focusing on terms like “women’s sleeveless pink XYZ uniform” with individual product pages was a better investment.

The terms had lower search volume, sure, but the SERPs were less competitive, and the customers were much lower funnel and higher converting.

1g. Google SGE follow-up questions

Google’s Search Generative Experience (SGE) may or may not roll out to all Google users in 2024. (It’s been an experiment via Search Labs since early last year.)

But while we have SGE, we can use it for research.

At the bottom of each SGE snapshot (answer) is a search bar to ask a follow-up question but also suggested follow-up questions.

These suggested questions usually aren’t groundbreaking, but they can provide context for user journeys or give you ideas for topics or additional SERPs to explore.

1h. Perspectives

Google’s Perspectives filter (and carousel), this is one I really enjoy talking about.

I wrote a blog post about it, which led to a follow-up post about social media content in Google Search.

Personally, I think Perspectives and other social content in Google Search is a valuable topic of exploration for SEOs trying to understand search intents from, well, different perspectives. (You only need to listen to social media professionals on TikTok to hear why they often think Google Search is a dinosaur. Of course, that’s one perspective.) 😉

In terms of content ideas, the value of Google’s Perspectives filter (or carousel) is that its content can introduce us to different topical angles or audience pain points.

Perspectives results are frankly hit or miss on quality, but for SEO research purposes, you can take a lot of inspiration from them and apply that to your content. This includes not only topic ideas but also alternative formats, like mixed-media or multimodal content.

2. Google Search Console (mining queries)

We’ll be using Google Search Console for keyword research, but of a different sort.

The value of GSC is that it’s first-party data, so unlike third-party tools, the insights you pull are unique to your site.

The reason I included GSC as a “not-so-obvious” source is based on anecdotal experience. I’ve found a lot of SEOs are familiar with GSC for performance analysis, but don’t always think to use it for new topic research.

I created another post about GSC SEO tips that goes more in-depth on topics like finding striking distance keywords and new content topic ideas.

I won’t rehash those instructions here, but I will summarize the quickest way to use GSC for topic research.

In terms of content ideas, I like to filter GSC Performance reports by queries that have either low impressions or high average positions.

In either case, a site likely doesn’t have relevant enough pages to drive traffic for these terms, so they hint at content opportunities, either for new content topics or pages to refresh and/or improve.

For new topic ideas specifically, you’ll still want to confirm the terms are on your audience’s buyer’s journey.

But you can also often find queries in GSC that haven’t been revealed in third-party keyword research, particularly for new topics, niche topics, or specific search intents.

You can also use regex to filter GSC queries by word count or that contain specific words. In that screenshot above, for example, I used regex filters for terms containing “who|how|why.”

GSC does have a 1,000-row limit for data, but you can bypass that in a few ways, including using the API or creating a Looker Studio report.

I also learned that setting a comparative timeframe pulls 1,000 rows from the first timeframe plus any different queries from the second.

Maybe I never noticed this before, but when doing a comparison in Google Search Console, you can get over 1k rows of data, and it exports.

Looks like it takes 1k rows for the 1st item plus any missing data from the 2nd item. Can be any comparison: date range, page, country, etc. pic.twitter.com/AkOk1mni7f

— Ethan Lazuk 🪬 (@EthanLazuk) December 28, 2023

In terms of prioritizing content topics in your calendar, one trick you can use is to publish a broader guide page for a topic first. Let that rank in Google Search for a while to accumulate a bunch of impressions and then use that query data to find new related topics (or additional sections to add).

3. Bing (obviously not so obvious)

Many of the same research opportunities from the Google Search section are likewise available from Bing.

The only difference is Bing isn’t an obvious search engine choice (although its 2023 U.S. market share did bump up to 7.9% vs. Google Search’s 87.5%).

Aside from differences in their audiences, Bing and Google Search also have differences in how they rank search results.

This means the insights you pull from Bing for related searches or other topic ideas could build more context, especially when combined with Google Search data. (The same can be said later for Perplexity AI and most all of the sources we’ll discuss hereafter.)

Let’s quickly run through some of the opportunities for content topic research on Bing, using the example query, “what is SEO.”

Bing has a People also ask feature, like Google:

Bing also shows related searches, sometimes on the left side (like below) or the bottom of a SERP, and you can expand them by clicking “More”:

In Copilot (formerly Bing Chat), which can be powered by GPT-4, we can also find follow-up questions to queries or prompts, just like for SGE earlier:

It’s probably also worthwhile to check some other less-popular search engines.

Most will have related searches, at a minimum, like the example below from DuckDuckGo:

While search engines’ user profiles may differ somewhat, most topics have fundamental questions that will be relevant to all users. By checking multiple search engines, we can build that additional context.

After all, if it’s on the buyer’s journey, why not embrace it?

4. Bing Webmaster Tools (secret weapon?)

Bing has its own Webmaster Tools that offers similar capabilities as mentioned in the Google Search Console section.

One downside of Bing Webmaster Tools is that it doesn’t do comparative timeframes or custom filtering, which means you’ll have to filter keywords by position (high to low) or impressions (low to high) and look that way.

That said, BWMT does offer some capabilities GSC doesn’t, including a Keyword Research tool. (But technically, we’re supposed to be looking beyond keyword research here.) 😉

There’s also Yandex Webmaster tools, which can provide “Query statistics” from that search engine, but I’ve only just started using it personally, so TBD on that.

5. Perplexity AI (exploring Related questions)

Perplexity is an AI answer engine (perplexity.ai) that operates similarly to a search engine using RAG (retrieval augmented generation) by combining web results with generative AI answers from LLMs.

The part of Perplexity AI that I find most valuable for SEO research is the Related questions that appear on the bottom of each answer, or chat engine results page (CHERP).

These Related questions can provide content topic ideas themselves, or they can be used for buyer’s journey research and other exploration.

For example, by searching a seed topic like “what is SEO,” we can find Related questions for SEO techniques, how long to see results, and business benefits.

By clicking any of those questions, Perplexity then generates a new answer with a set of three more Related questions.

Similar to Google’s PAA questions, the value of the Related questions depends on whether they’re relevant to an audience’s buyer’s journey and if we have the expertise to answer them with original insights of value to users.

Also, compared to PAA (which continuously expand in a Google SERP), research of Related questions on Perplexity is narrower in scope, since you’re viewing a select few questions in a new context every time.

For example, here’s a comparison of Google PAA and Perplexity Related questions for the query “what is the business value of SEO”:

Google PAA results:

Perplexity Related questions:

The PAA answers are more specific to the original query (at least lexically) but are more general in scope. It’d be hard to give any one of them a specific answer as opposed to a value judgment.

The Perplexity Related questions go further into new topical territory from the query, yet they’re more specific, lending themselves to more straightforward answers or being used directly for content topics.

Let’s try another example for the query “buying a new car on eBay.”

Google PAA:

Perplexity Related questions:

Here some of the PAA questions have a specific yes-or-no or otherwise straightforward answer.

The Perplexity questions are also specific, like before, except they also go in different topical directions from PAA.

It appears that following Related questions on Perplexity for a seed topic could lead to relevant questions for an audience that are perhaps more detailed than Google PAA, and thus more directly applicable to content topics or can take your research in new directions.

As Perplexity’s About page states, its purpose is to save users time by delivering straightforward “user-focused answers.”

That seems to apply to the specificity of its Related questions.

In my opinion, combining Perplexity’s Related questions with Google PAA could be a powerful research combination for content ideas.

*I plan to do a future post to draw further conclusions about Related questions vs. People also ask.

Since Perplexity is a relatively new company and search experience, I think it’s helpful to understand more about its history and how it works.

If you know this background already, feel free to skip ahead to the Reddit section.

5a. More information about Perplexity

Perplexity started in August 2022 and launched its first answer engine (Ask) later that year.

While other AI answer engines have come and gone (Neeva, for example), what’s interesting about Perplexity is that it has investment backing from people associated with notable tech brands, including members of OpenAI, Meta, HuggingFace, Databricks, Dropbox, and NVIDIA, as well as former members of Microsoft and YouTube. Even Jeff Bezos is attached.

The company raised $3.1 million in its seed round, $25.6 million in series A funding, and then $73.6 million in series B funding. For perspective, Google’s search ad revenue in Q4 (2023) was $48 billion, with a “B.”

However, Perplexity also recently partnered with the Rabbit R1 (a handheld AI device that got a lot of buzz upon its launch at CES) to power its AI answers.

I do think Perplexity has a place in the search ecosystem. It’s not really a Google competitor but more of an alternative, and I believe its “answer engine” experience may align with how Gen Z (the TikTok generation) may be accustomed to searching, with quick snapshots of information.

The company also says its ability for users to take a journey helps set it apart as “an alternative to traditional search engines.” As it will “process your questions and tasks while taking into account the entire conversation history for context.”

We already know Microsoft Copilot and Google’s SGE have follow-up questions. (Even traditional Google web search can use “past Search history” to rank results.)

But as mentioned above, the one aspect that makes Perplexity’s user journeys a valuable research source for SEOs is the Related questions.

5b. How does Perplexity work?

Perplexity has its default search experience, then there are Perplexity Copilot and Perplexity Pro.

In my opinion, the closest comparison to using Perplexity (default or Copilot; I haven’t tried Pro yet) is Microsoft Copilot with Bing.

As I understand it, default Perplexity searches are free and unlimited.

Then there’s Perplexity Copilot, a “digital assistant” version that offers more “in-depth answers” using GPT-4 and Claude 2. Copilot is free, but it only allows users five queries every 4 hours, or 30 searches per day.

Then there’s a paid version called Perplexity Pro, which uses the previous LLMS plus “Perplexity’s Experimental 70b.” The Pro version allows users “over 300 daily Copilot queries” or 600 uses per day. (I’m not totally clear on that math.)

While I haven’t tried Pro yet, there is a noticeable difference in the quality (or depth) of answers between the base model and Copilot, at least in terms of sources.

Here’s a search for “what is SEO” on Perplexity’s default search experience:

That answer has five sources and is more of a long unstructured paragraph with the citations listed at the end.

Meanwhile, here’s the same search query using Perplexity Copilot:

You can see the answer has 8 sources (including the previous 5) with a more organized formatting and contextual citations in the answer.

5c. Library of queries

What’s nice about Perplexity is your searches get saved in a Library to reference again. (I tried the same search for “what is SEO” on back-to-back days, and the answers were nearly identical, so referencing older answers likely wouldn’t be a disadvantage for evergreen topics.)

The library also keeps track of your search journeys when you ask follow-ups or click Related questions.

You can see the third item in the Library screenshot below has an extra icon indicating multiple queries, while the star represents the use of Copilot or not.

Related questions generate new answers, so I’m assuming they’re included in your search allotment for Copilot.

For our purposes here, Perplexity’s Related questions appear to be the same whether you’re using Copilot or not. So if you’re just using the questions themselves for SEO research purposes, I’d turn off Copilot.

6. Reddit (good karma)

For years, appending “Reddit” to searches on Google has been a practice for some users.

I found this interesting perspective from notJim on Hacker News from 2019:

With the advent of Google’s “hidden gems“ (announced May 2023 and fully rolled out in November of that year) and probably other ranking system changes, the search engine has been showing a higher amount of Reddit content in search results, particularly in Discussions and forums features.

Now, Reddit can certainly be a difficult platform to mine for content ideas.

You can find some insightful comments, but also a lot of offhand remarks.

One solution is to copy text from Reddit threads and then ask ChatGPT to analyze the key themes (assuming it fits the context window).

Here’s a ChatGPT prompt I used for the text from the screenshot above:

“Here is a discussion from Reddit. I’d like to extract the main themes or questions to inform my research for SEO content topics. Please convert this text to bullet points of main themes I can use: [copied text]”

The key points ChatGPT produced read almost like an outline for an article on this topic:

And if we wanted to retain the authenticity of a Redditor’s voice, we could even embed replies into a webpage, thus having both UGC from the original source plus our expert context and analysis.

As for finding relevant Reddit threads to analyze, you can search on the platform itself or simply search on Google for a topic of interest and find comments from Discussions and forums features (which appear in most SERPs) or the Perspectives filter.

7. TikTok (it’s trending)

TikTok is growing in its own right as a search engine as well as a source of content that appears in Google’s search results. (I wrote a bit more about TikTok in another post.)

Technically, we’re supposed to be looking beyond keyword research in this article, but this is sort of a loophole when it comes to TikTok.

TikTok has a Keyword Insights tool where you can see what’s trending on the platform.

It also has an AI-based Creative Assistant to help with your research.

There’s also more manual research you can do using TikTok’s content.

For example, looking at video comments (similar to Reddit) can present insights into how people are discussing a topic.

In my experience, the best advantages of TikTok for SEO content come from watching videos to identify new ideas that add context to enrich content or discover new topics altogether, similar to Google Trends.

As for finding relevant TikTok content to analyze for topic ideas, you can use the platform’s own search functionality.

In fact, TikTok search even has “Others searched for” suggestions for additional ideas:

Alternatively, you can search for a topic of interest on Google and then look for TikTok content in the Perspectives filter.

8. YouTube (key themes)

YouTube can be a great source of inspiration for topics, whether you’re trying to find video content ideas or just alternative sources of context for other SEO content.

If you have a YouTube channel, you can use the Research tab under Channel Analytics in YouTube Studio to find content ideas:

However, since our goal is to go beyond keyword research, another use of YouTube videos for content ideas that I like to use is extracting key points from videos that can inform content.

For example, a lot of Google SERPs have video results in them:

You can analyze the top videos for a query or topic of interest, and then use those insights to inform your blog or video content (even embedding videos in webpages for added context).

One way to analyze YouTube videos is to ask Google Gemini (formerly Bard) to extract possible content topics or key points from them:

Gemini summaries are especially helpful for longer YouTube videos that you don’t have time to watch in full.

Additional sources of content insights from YouTube include viewer comments, key moments in videos (the themes YouTube identifies), as well as the related topics of suggested videos.

9. Amazon (details delivered)

Amazon can be a wealth of information for content ideas, and much of it can be found in similar ways to what we’ve discussed for other sources.

True, Amazon as a source is limited to its available products, but pretty much everything is sold on Amazon these days.

The product filters in Amazon (sort of like Google’s “filter by” earlier) can inspire tons of product variations to speak about in content.

Here are just some of the attributes listed for “blenders”:

Then we have customer reviews on Amazon. Sometimes, a lot of them.

Just like we did with Reddit posts, we can copy Amazon reviews into ChatGPT and ask for key points to inform content ideas.

Positive sentiments can be used to embellish product pages or comparisons, while negative comments can be used as preemptive FAQs or in other creative ways that temper customer expectations.

Similarly, the themes of reviews that Amazon’s AI pulls can also open up avenues of topical discovery for content.

These same approaches from Amazon can also be taken with eBay, Walmart, and other large ecommerce or online retailers.

10. ChatGPT (analyze this)

ChatGPT (and other LLMs like Gemini or Claude 2) can be direct sources of content ideas with a simple prompt. But then we run into the issue of the information not being very original or possibly inaccurate.

I went into detail in another post about different ways to use ChatGPT for content ideas, but to summarize them here, I think the greatest value of ChatGPT for SEO content research is to feed it your existing content or research to analyze them and extract new data, themes, or opportunities.

For example, let’s say you have a buyer’s persona for your audience. You can enter those demographics and other data into ChatGPT and then ask it what questions that person might have about your product or service during their buyer’s journey.

As another example, you can find several competitor articles that rank well for a topic and ask ChatGPT (with Link Reader) to identify commonalities among them, as well as any topical gaps. That information can then inform how you approach the topic.

Similarly, you can also provide your existing content to ChatGPT and ask it to identify related questions or topics that someone might have before or after reading that page.

Earlier on in the Google section, I also mentioned how I’d input People also ask data into ChatGPT to analyze a buyer’s journey.

For example, we can take an export from AlsoAsked and upload it to ChatGPT with a prompt like:

“Based on this data, can you outline the order a user might ask these questions as they go through the sales funnel of awareness, consideration, and decision.”

I used an example seed topic that wasn’t the best for this (“what is SEO”), but you can still get the idea:

Additionally, we can combine PAA data with other sources, like Perplexity AI’s Related questions, for richer context, or even create visualizations or export our findings into whichever format we’re using to keep all of our content topic research together.

Takeaways (getting beyond keywords)

For content topics in an SEO strategy to have business value, they should be on a target audience’s buyer’s journey.

Having advanced knowledge of keyword research methods can certainly help us separate out more relevant terms or even find less obvious topics on those journeys.

However, one drawback of third-party keyword data is that it’s available to anyone who wants it.

If we’re all referencing the same keyword data to inform content topics, page outlines, or the SERPs we analyze, won’t we end up creating similar content?

Well, maybe.

That’s also where expertise, quality, and execution come into play.

A topic may have been covered several times:

But if we’re able to create original content that’s more satisfactory to an audience, and there’s a business case to do so, why not go for it?

Inspiration, not imitation (great content and authority)

Of course, the more we know about our audience’s search intent, the better informed our website content (and SEO strategies overall) will be for a topic.

Ultimately, what makes helpful content advantageous for SEO isn’t just that it can outrank competitors, but that it can also better satisfy an audience with insights they can’t get elsewhere.

As customers travel down from the top of a sales funnel, great website content can build trust, inspire returning visits, and create brand evangelism, all of which support conversions and business goals.

Of course, great content usually comes from inspiration, not imitation.

Competitive analysis is a cornerstone of SEO research because knowing what similar brands are doing lends insights into our strategies. Yet, there’s always a risk of losing our originality.

But even being the first to cover a topic isn’t worth all that much, because all it takes is one gap analysis for competitors to create the same.

Our goal isn’t just unique or high-ranking SEO content, it’s to answer our audience’s questions completely to become a trusted source.

By achieving topical coverage throughout the sales funnel, we can build authority, satisfy users best, and earn competitive advantages, all while increasing our brand awareness, qualified clicks, and conversions.

In order to achieve that topical coverage, though, we need audience insights, including from “not-so-obvious” sources. That means knowing where to look, and when.

Tearing down the wall (a mindset change)

While having a practical knowledge of how to reference different sources of content ideas is helpful for creating SEO strategies, what’s equally valuable is having the vision to recognize when a source can provide information of value.

Once that mental switch flips, you’ll be spotting audience insights for SEO content ideas practically everywhere.

Personally, it took me a while before I started to see the value in less mainstream sources of SEO research.

I’m sure I’m not alone.

As an SEO beginner, I defaulted to keyword research for content ideation.

In a way, it’s like I had a mental block, an inner background voice saying:

“If you don’t use keyword research, how can you find content topics?! You can’t find content topics if you don’t use keyword research! “

Then something happened that helped me get beyond that thinking.

To tear down the wall, if you will. 😉

Basically, it was a change to the core question of my work.

I went from asking, “Is this good for SEO?” to instead asking, “What’s good for our users, and how can we get Google to understand and reward us for it?”

Instead of seeing SEO work as a list of tactics aimed at gaining search engine rankings, I saw it as part of an overarching strategy, interwoven throughout a brand’s marketing, customer relations, and business initiatives.

That may sound obvious to some, particularly more experienced SEOs and those who came from other marketing disciplines.

But it took me years to think that way.

It’s what I call a “people-first approach” today.

Or to mix metaphors for a moment, it’s like seeing The Matrix for what it is.

When we get beyond the mental wall of keywords and tactics, we start appreciating how SEO can translate a business’s real-world qualities to online representations — what we could describe as E-E-A-T alignment, or satisfying Google’s ranking signals holistically.

It’s also at that point that our perspective widens, and the relevance of alternative data sources becomes obvious, even if the source itself always wasn’t. 😉

“Outside the wall” (conclusion and what’s next)

I hope this post has shown how less obvious sources of content ideas and audience insights beyond keyword research can help us find new avenues of inspiration.

While any one data source can be a starting point, cross-referencing multiple sources can give us a more holistic context and richer understanding of an audience’s buyer’s journey, which then leads to creating more helpful content about topics that align with that journey.

In addition to the digital sources mentioned in this article, there are other real-world sources of content ideas as well. Internal company data is one — interviewing salespeople, customer service teams, product experts, or even customers directly can also shed light on topics your target audience cares about.

As for how we address all of these content ideas, sometimes alternative formats are in order, as well.

Blog posts and website content aren’t always the right solutions for an SEO content strategy. We’ve seen referenced how social media content can appear in multiple ways in Google Search. We also have Google Business Profiles plus opportunities associated with Google’s knowledge and shopping graphs.

I’m personally a fan of mixing content formats and targeting different organic search surfaces along a buyer’s journey. This includes repurposing content for different platforms or use cases, and also creating multimodal experiences (text, video, etc.) to satisfy multiple audiences.

In terms of what’s next for this post, I plan to add more examples of how to use each of these sources. I’ll also add more sources as I find them.

I also have plans to do some related posts to explore a few topics more in-depth, particularly a comparison of Perplexity AI’s Related questions and Google’s People also ask.

Until then, enjoy the vibes:

Thanks for reading. Happy optimizing! 🙂