Let’s Talk About “Category Entry Points” in the Context of SEO/GEO: A Hamsterdam Marketing Lesson

By Ethan Lazuk

Welcome to another edition of Hamsterdam Marketing! 🐹

This is a project where we examine marketing topics to learn about their fundamental concepts and apply them to the context of SEO/GEO.

This time, we’ll be looking at category entry points.

If keywords tell us what someone searched, then category entry points help explain what happened in their life or business that caused them to search in the first place.

Category entry points are particularly interesting in the context of AI search because people can express the context for an entire situation in a single prompt or a conversation.

What Are Category Entry Points?

Category entry points (or CEPs) are the situations, needs, or occasions that cause someone to think about buying from a product or service category.

We’ve talked about mental availability and salience before. CEPs tie directly into that:

• Mental availability is how easily a brand comes to mind in relevant buying situations.
• Salience is how noticeable or mentally accessible a brand is when one of those situations occurs.
• Category entry points are the specific situations or cues that trigger that recall.

For an SEO/GEO client, potential category entry points could be situations like:

• “Our organic traffic has been declining and we don’t know why.”
• “Competitors are showing up in ChatGPT and Google AI Overviews, but we aren’t.”
• “We’re launching a new website and want to make sure search visibility doesn’t drop.”

CEPs are associated primarily with Jenni Romaniuk and Byron Sharp of the Ehrenberg-Bass Institute for Marketing Science. In a 2004 paper, they argued that traditional “top-of-mind awareness” was too narrow and instead they described brand salience as the likelihood that a brand will be noticed or retrieved from memory in a buying situation. They also argued that buying situations contain different cues, including the situation, benefit sought, context, location, activity, etc. That created the theoretical basis for CEPs.

The concept of CEPs was formalized by the authors in 2016 in a book that talks about mental availability, where CEPs function as the cues through which buyers mentally enter a category.

Today, the Ehrenberg-Bass Institute describes CEPs as the building blocks of mental availability, or the thoughts buyers have as they transition toward making a category purchase.

What’s interesting about CEPs is the theory isn’t about advertising so much as memory. Essentially, memories are interconnected, and cues can activate connected information.

Imagine someone’s brain containing associations like:

Running late → hungry → need lunch → something quick → nearby → Chipotle / Sweetgreen / McDonald’s / deli

The person doesn’t necessarily begin with “I want Sweetgreen.”

Rather, the situation activates the category, and then the category/situation helps retrieve possible brands.

That’s the CEP idea.

How CEPs Fit into AI Search

We must distinguish between keywords, prompts, and CEPs:

Concept	Example
Category Entry Point	“I need something quick to eat between meetings.”
Traditional search query	quick lunch near me
AI prompt	“I have about 20 minutes between meetings in Midtown. What’s somewhere nearby where I can get a healthy lunch for under $20 without waiting long?”

In short, the CEP exists before the query or prompt — it’s what triggers it as the situation creating demand.

If we’re talking about traditional web search, a single CEP could produce dozens or hundreds of queries.

The same person could express the same CEP very differently:

• fast lunch midtown
• healthy takeout near me
• where should I eat between meetings
• quick lunch under $20
• best healthy lunch near Grand Central

That’s why with AI search, CEPs are especially interesting, because their context can be fully explained in a single prompt, for example:

“I have a meeting at 1:30 and only about 25 minutes for lunch. I’m near Grand Central and want something healthy, preferably high protein, for less than $20. What are my best options?”

We also have evidence that people are expressing more context in search. Google reported in May 2026 that the average AI Mode query in the U.S. had become three times the length of a traditional Google Search query. Google also reported strong growth in planning and brainstorming queries.

Take this prompt/query, for example:

“things to do in nashville this weekend with friends…”

It includes location, time, companions, interests, and desired experience, which are several dimensions that appear similar to the dimensions marketers use when researching CEPs

Traditional search often forced someone to compress the CEP into a few keywords, but AI search lets them describe more of the CEP as a prompt.

Thus, CEPs can play a role in prompt-tracking and covering the “answer market.”

Instead of generating 100 paraphrases of “best CRM software,” you could cover different CEPs:

CEP	Prompt family
Outgrown existing system	switching/replacement prompts
Team expanding	scaling prompts
Budget pressure	affordability prompts
Reporting problem	analytics prompts
Integration problem	compatibility prompts
Implementation concern	ease-of-use prompts
New company	first-CRM prompts

Then vary the wording within each situation.

That’d give you a more representative prompt selection for your target audience’s potential buyer’s journey.

How to Formulate CEPs for AI Search Research and Content Strategy

Identifying the CEPs of your target audience can be a great starting point for keyword or prompt research.

Modern CEP research commonly uses Jenni Romaniuk’s 7W framework to discover buying situations:

CEP dimension	What you’re trying to uncover	Potential AI-prompt language
Why	Motivation/problem	“I need…” / “I’m trying to…”
When	Time/occasion	“before work,” “this weekend,” “at year end”
Where	Location/context	“near my office,” “while traveling”
While	Concurrent activity	“while commuting,” “while working”
With whom	Other people	“with my kids,” “for my team”
With what	Complementary products/services	“works with Salesforce,” “with dinner”
How feeling	Emotional state	“I’m overwhelmed,” “I want to celebrate”

Let’s do an example using the common topic of CRM software.

A traditional keyword strategy might uncover:

• best CRM software
• CRM for small businesses
• Salesforce alternatives
• cheap CRM

But CEP research might uncover something more like this:

CEP	Possible AI prompt
Company is growing	“Our sales team has grown from 5 to 30 people and spreadsheets aren’t working anymore. What CRM should we consider?”
Existing software is too expensive	“Our Salesforce renewal is coming up and the cost has gotten hard to justify. What alternatives work well for a 50-person SaaS company?”
Need something simple	“What’s a CRM my sales reps will actually use without weeks of training?”
Leadership needs reporting	“We need better pipeline forecasting before our next board meeting. What CRM platforms have strong reporting?”
Integrating a stack	“What’s a CRM that works well with HubSpot, Slack and our existing Google Workspace setup?”
Frustration	“I’m tired of manually updating spreadsheets every Friday. Is there a lightweight CRM that can automate most of this?”

Those prompts aren’t merely variations of “CRM software,” but rather they represent different situations in which the CRM category becomes relevant. Plus, each situation may require different evidence from the companies competing to appear in the answer. That’s the basis for a GEO strategy.

CEPs trickle into query fanout territory, as well.

Imagine this CRM prompt:

“Our 30-person sales team has outgrown spreadsheets, but Salesforce seems too complicated. We need something easy to implement that integrates with Slack.”

That one prompt could trigger information needs around:

• CRM for growing businesses
• CRM implementation difficulty
• Salesforce alternatives
• CRM Slack integrations
• CRM team size
• CRM onboarding time
• CRM pricing
• CRM reviews

So we could say that the CEP creates the human information need, the prompt expresses that need, while query fanout decomposes that need into information retrieval tasks.

As for practical implications for content strategies, rather than starting with a keyword, we can start by asking “What situations cause people to need this category?” That’s followed by “What questions arise in that situation?” and then lastly “What information would an AI system need to confidently answer those questions?” Those three simple questions give us a richer foundation for content work based on CEPs rather than keywords.

This ties back to our work on segmentation, which says that different situations can create different information needs, i.e., the need for different content pieces.

Thus, we’re no longer creating content simply because a keyword exists. Rather, we’re trying to make the brand relevant to real buying situations.

Takeaways

Category entry points are a useful marketing concept for thinking about AI search because they shift our attention from keywords to the real-world situations that cause people to search.

As AI assistants enable people to express increasingly detailed needs, constraints, and context, those buying situations are becoming more visible in the prompts themselves.

So, as a practical takeaway, don’t optimize for a collection of prompts based on keyword variations. Instead, understand the situations that generate those prompts, the CEPs.

See you next time!

I hope you’ve found this discussion around category entry points helpful for your SEO/GEO strategy.

Stay tuned for a new Hamsterdam Marketing lesson soon.

And if you want a hand with your SEO/GEO strategy, get in touch with me. I’m an independent consultant based in New York City.

Until next time, enjoy the vibes:

Thanks for reading. Happy optimizing!