How Marketing Theories Connect to Explain Brand Discovery and Decision-Making in the Age of AI Search: A Hamsterdam Marketing Lesson

By Ethan Lazuk

Welcome to a special 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 how several topics overlap to tell us one cohesive narrative about marketing in the AI search era.

Many of my Hamsterdam Marketing articles examine different parts of the same underlying process, which is how people recognize a need, discover brands, evaluate information, form preferences, and ultimately make decisions.

Perhaps most interesting is how AI search is changing several stages of that process, sometimes by reducing the effort required to research options and other times by influencing which brands consumers encounter in the first place.

A long story short …

Marketing concepts such as category entry points, associative networks, brand positioning, information foraging, signaling theory, and consideration sets aren’t isolated ideas. Rather, they describe different yet interconnected forces that shape consumer decisions.

By understanding the relationships between these concepts, SEO/GEO professionals can have a more complete framework for building brand visibility and influencing consumer choices.

Let’s delve into how different marketing topics connect to one another …

Rather than rehash each concept individually, we can compile them into five interconnected nodes.

1. Understanding the consumer

Segmentation identifies meaningful audience differences, personas help translate those differences into research and planning, and category entry points explain the situations that prompt people to seek a solution.

2. Building brand associations

Positioning establishes what a brand should mean. Differentiation establishes its distinctive value. Associative networks help explain how those meanings become linked in memory, while mental availability concerns whether a brand comes to mind in relevant situations.

3. Discovering and evaluating options

Information foraging describes the effort and expected value of finding information. Signaling theory describes how people judge quality when they lack complete information.

4. Forming preferences and shortlists

Consideration sets describe the limited number of options a consumer seriously evaluates. Construal level theory adds another dimension: psychological distance can influence whether consumers respond more strongly to abstract benefits or concrete details.

5. Building long-term brand value and growth

Brand equity captures the value associated with consumer knowledge and perceptions of a brand. Double Jeopardy Law adds a broader market-level perspective, explaining why reaching more category buyers is important for growth.

An example to tie the different marketing concepts together …

Imagine a marketing director at a mid-sized company who wants to understand how often their brand appears in AI-generated answers across platforms like ChatGPT, Google AI Overviews, and Perplexity. They’re considering investing in AI visibility software, also known as prompt-tracking tools.

Here’s how the concepts intersect:

1. Category entry point:

The marketing director notices that competitors are appearing in AI-generated recommendations while their brand is absent. This triggers a need to measure and improve AI search visibility.

2. Mental availability:

The marketing director recalls an AI visibility platform they’ve encountered through LinkedIn, industry newsletters, or conferences. They may also discover unfamiliar platforms through AI-generated recommendations.

3. Positioning and differentiation:

Some AI visibility platforms position themselves as enterprise solutions, while others emphasize affordability, ease of use, prompt-level analytics, or integration with existing SEO workflows.

4. Information foraging:

Instead of researching dozens of software vendors individually, the marketing director asks an AI assistant to compare AI visibility tools based on pricing, supported AI engines, reporting capabilities, and other relevant features.

5. Signaling theory:

Customer testimonials, recognizable client logos, published methodologies, transparent data collection practices, and independent reviews help the marketing director evaluate the credibility of competing AI visibility platforms.

6. Consideration sets:

After reviewing the AI-generated comparison and conducting additional research, the marketing director narrows the options to three AI visibility platforms and schedules product demonstrations.

7. Construal level theory:

Early in the research process, the marketing director may focus on abstract outcomes, such as gaining a competitive advantage in AI search. As the purchase decision approaches, their attention shifts toward concrete considerations, such as monthly subscription costs, prompt-tracking limits, data accuracy, reporting functionality, and implementation requirements.

A key insight into this process is that an AI assistant can influence several of these stages simultaneously. The assistant can introduce an unfamiliar AI visibility platform, explain how it differs from competitors, summarize evidence supporting its credibility, and recommend it as a potential choice.

Also interestingly, the AI visibility software vendors are competing for visibility within the same AI-generated answers their products are designed to measure. Each platform’s positioning, perceived credibility, and brand associations can influence how consumers evaluate it, while its discoverability and representation in AI-generated answers may affect whether consumers encounter and consider it at all.

Rather than viewing category entry points, mental availability, information foraging, signaling, and consideration sets as separate marketing concepts, we can see how they work together to shape a single purchasing decision, including one increasingly mediated by AI search.

A note worth remembering …

AI search makes the distinction between understanding keywords and understanding consumers even more important, because prompts can now expose richer information about people’s goals, constraints, and decision criteria.

Practical takeaways based on these marketing concepts …

By understanding how marketing theories connect, SEO/GEO professionals can more easily move beyond optimizing for individual keywords or prompts toward influencing the broader consumer decision-making process.

Here are actionable takeaways for SEO/GEO strategies:

• Identify category entry points, not just keywords: Research the situations, problems, and motivations that trigger demand for a product or service. Use these insights to develop content strategies and identify relevant prompts to monitor.
• Build consistent brand associations: Reinforce your brand’s positioning, differentiation, and core areas of expertise across your website, third-party content, and other marketing channels. The goal is to make your brand recognizable and relevant to specific customer needs.
• Optimize for consideration, not just visibility: Appearing in an AI-generated answer is only part of the opportunity. Evaluate whether your brand is included in recommendations and shortlists, how it’s described, and which competitors it’s compared against.
• Reduce the effort required to evaluate your brand: Apply information foraging theory by making product features, pricing, use cases, comparisons, and differentiators easy to find and understand. Structure information clearly so both consumers and AI systems can access relevant details.
• Provide credible signals of quality: Use original research, customer case studies, transparent methodologies, independent reviews, and verifiable credentials to support your brand’s claims. These signals can help consumers evaluate trustworthiness and provide evidence for AI-generated responses.
• Create content for different psychological distances: Apply construal level theory by addressing both abstract goals and concrete purchasing considerations. For example, an AI visibility software vendor might explain the strategic value of measuring AI search visibility while also providing detailed pricing, integrations, and reporting specifications.
• Measure how AI systems represent your brand: Beyond tracking mentions and citations, examine the attributes, strengths, weaknesses, and use cases AI systems associate with your brand. Compare these representations against your intended positioning and competitors’ representations.
• Connect AI visibility to broader brand-building efforts: SEO/GEO shouldn’t operate independently of PR, content marketing, social media, and other brand initiatives. These channels can help develop awareness, associations, and credibility that influence consumer decisions.
• Evaluate outcomes beyond prompt tracking: Record qualified referral traffic, branded search demand, conversions, and other business outcomes alongside AI visibility metrics. Look for relationships between these signals without assuming that increased AI visibility directly caused business growth.

A final takeaway …

SEO/GEO isn’t just about making a brand appear in search results or AI-generated answers. It’s about helping the right consumers discover, understand, trust, remember, and consider that brand. The individual marketing theories discussed above provide a framework for understanding how those outcomes are connected.

See you next time!

I hope you’ve found this broader discussion around the marketing concepts we’ve covered so far 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!