marketing strategy

What Becomes Scarce When Insights Are Abundant?

As AI makes customer insights easier to generate, judgment becomes more valuable.

AI may be about to give marketers a new problem: not too little customer information, but too much.

I came away from Esomar Congress 2026 wondering whether we’re beginning to see the early signs of this. A recurring theme across many of the conversations and presentations was just how much easier AI is making it to work with customer information. Interviews that once required hours of manual review can be searched and synthesized quickly. Large numbers of open-ended responses can be analyzed in minutes. Customer reviews, call-center conversations, CRM records, social media, previous research and other sources can increasingly be brought together and interrogated at the same time.

But AI isn’t only making existing information easier to analyze. It is also making some kinds of customer information cheaper and easier to collect. Automated interviewing, adaptive surveys, rapid concept exploration, synthetic respondents and other emerging approaches can dramatically reduce the time and cost required to investigate a question.

  • In other words: more customer evidence × easier collection × easier analysis × cheaper questioning = dramatically more plausible insights.

That made me think about an earlier technological shift. When personal computers became widespread, many people assumed they would simply allow organizations to do the same work with fewer people. But productivity improvements don’t always work that way. When something becomes dramatically easier or cheaper, organizations often start doing more of it.

Something similar could happen with research. Imagine a marketer has a relatively modest question about customers. Today, they might decide it isn’t worth spending $30,000 and six weeks to investigate. But what if an AI-enabled approach can provide a useful first answer in a few hours? Suddenly, that question gets asked. And then another one. And another.

Competitors begin doing the same thing. Eventually, being able to investigate questions that once would have been considered too small or too expensive may stop being an extraordinary capability and become table stakes.

So perhaps the biggest effect of AI on research won’t be that companies need fewer answers. It may be that they start asking vastly more questions.

And that creates a different problem. When getting an answer becomes easy, the harder questions become:

  • Which evidence should we trust?
  • Which findings actually matter?
  • And, ultimately, what should we do?

That suggests a useful discipline for anyone working with AI-generated insight. Before accepting an answer simply because it arrives quickly and sounds convincing, ask three more questions:

  • What is this conclusion based on?
  • What evidence would make me question it?
  • What decision would change if it were true?

Those questions may matter more as AI gets better, not less.

For years, researchers have worried about information scarcity – not enough data, respondents, time or budget. AI may begin to reverse that equation. If customer information becomes abundant, the scarce resource may become judgment – deciding what deserves our attention, what deserves our trust, and what it means for the business. Need help deciding which customer inputs matter – and what they mean for your business? Contact me at info at bureauwest.com.

Posted on by Jay Zaltzman

When Hearing the Customer is No Longer the Hard Part

Why interpretation matters more as AI gets better at listening.

For a long time, market research required a great deal of work simply to capture and make sense of what customers were saying – recruiting participants, conducting interviews and groups, transcribing conversations, coding responses and identifying patterns.

AI is making much of that faster and easier. It can capture conversations, organize large amounts of customer input, identify recurring themes and summarize what people are saying – often remarkably quickly.

Which raises the question: If AI can capture, organize and summarize customer input faster and more cheaply, how can we researchers still provide value to our clients?

I think the answer lies in interpretation. Because knowing what customers said is not the same as understanding what it means.

Interpretation can take many forms. It might mean recognizing that two seemingly contradictory findings reflect legitimate needs pulling customers in different directions. It might mean realizing that the reason customers give for a choice isn’t necessarily the factor actually distinguishing one option from another. Or it might mean seeing that a frequently mentioned issue is less important to the decision than a quieter finding that changes how we understand the whole situation.

In each case, the value isn’t simply in identifying the pattern. It’s in putting forward an explanation of what the pattern means and why it matters. What needs, values or competing priorities might explain what we’re seeing? What is the underlying story? And, perhaps most important, does that interpretation help make a decision clearer?

One simple way to make that jump is to treat an interpretation as a hypothesis rather than a conclusion. Instead of asking only “What did we hear?”, ask “What would explain why we heard this?” Then look back across the research for evidence that supports, complicates or contradicts that explanation.

  • For example, imagine customers repeatedly say that a company’s range of options feels overwhelming. The obvious conclusion might be that they want fewer choices. But perhaps, across the interviews, you also hear people worrying about missing an important feature, comparing options repeatedly, or wanting reassurance that they’ve made the right decision. That suggests a different interpretation: the problem may not be too much choice itself, but anxiety about choosing incorrectly.
  • That distinction matters because it leads to a different response. Instead of simply reducing the number of options, the company might make choosing feel safer through better guidance, recommendations or reassurance.

A useful way to think about the process is:

  • What did we hear?
  • What might explain it?
  • What else in the research supports or challenges that explanation?
  • If we’re right, what should the client do differently?

That final question is an important test. If the interpretation doesn’t change how we understand the situation or what the client might do, we may still be describing the findings rather than interpreting them.

That’s also why interpretation is difficult to automate completely. AI can contribute, but interpretation requires judgment – deciding which patterns matter, testing possible explanations against the evidence, and taking a point of view about what the findings mean.

In other words, the researcher’s role may be shifting from primarily bringing the voice of the customer into the room to helping the organization understand what that voice is actually telling them.

I’ll be exploring that idea at the ESOMAR Congress 2026 in Valencia next week in a presentation called “Beyond the Voice of the Customer: The Rise of the Insight Interpreter.” It feels like an appropriate topic for a conference whose theme this year is Metamorphosis. Research is changing quickly. The opportunity is to make sure our role changes with it.

Do you have customer input that’s easy to summarize but harder to interpret? I’d be happy to help. Contact me at info at bureauwest.com

Posted on by Jay Zaltzman

If decisions are emotional, why do we still ask rational questions?

A small paradox at the heart of customer research – and what it reveals about how people really make decisions

Over the past few decades, behavioral economics and behavioral science have shown something most researchers recognize immediately: people don’t fully understand the true drivers of their decisions.

Yet in customer research, we often design our questions as if rational explanations will reveal the answer. Ask someone why they chose a particular product and the response usually sounds perfectly logical: “Price.” “Features.” “Convenience.” “A good deal.”

Those explanations aren’t necessarily wrong. But they’re rarely the whole story. I remember an interview years ago with a woman who told me she would never switch car brands. When I asked why, she talked about reliability, resale value, and service quality. It sounded like a textbook rational decision.

Then, almost as an afterthought, she added: “It was the first car I bought after my divorce. It made me feel like I could start over.”

In that moment the decision made sense – not because of the features, but because of what the purchase meant to her. She wasn’t really buying transportation. She was buying a sense of independence and renewal.

Moments like that happen constantly in qualitative research. Participants explain their choices in rational terms, yet the emotional driver of the decision emerges indirectly – in stories, metaphors, or moments that seem almost incidental.

The interesting question isn’t why decisions are emotional. Behavioral science has demonstrated that repeatedly. The more interesting question is why people so often describe those decisions in rational language.

Part of the answer, I’ve come to believe, is cultural. In the United States especially, people feel a strong pressure to present themselves as confident, rational, and self-directed. Decisions are expected to look intentional and logical, even when the deeper motivations are emotional. So when we ask people why they chose something, the answer we hear is often the explanation that feels most acceptable to say out loud.

Over years of research interviews, I began to notice that participants were often answering a different question than the one I had asked.

This dynamic doesn’t just affect research interviews. It shapes how insights get interpreted inside organizations. When customer explanations sound rational, teams often focus on functional improvements – better features, lower prices, more convenience. But if the real driver of the decision is emotional or cultural, those improvements may miss what actually matters to customers.

When we ask, “Why did you choose this?” the answer might really be responding to a deeper, unspoken question such as:

  • Will this make me feel competent?
  • Does this reflect the kind of person I want to be?
  • Will people like me choose this too?

Those hidden questions often explain far more about a decision than the rational explanation that appears on the surface.

Seeing that pattern repeatedly led me to write a short book I’ve just finished: The American Customer: The Hidden Forces That Shape Choice.

The book explores how cultural stories – about independence, reinvention, belonging, and possibility – shape the way American customers interpret their decisions and explain them to others. It also introduces a few practical lenses I’ve found useful for decoding motivations that participants don’t always articulate directly.

The book will be published in late March, 2026. I’m especially interested in whether the ideas resonate with your own experience in research or marketing. If you have thoughts, questions, or reactions, I’d love to hear them. Contact me at info at bureauwest.com.

Posted on by Jay Zaltzman

When AI Optimizes Everything, What Makes a Brand Different?

A closer look at where brand distinctiveness truly lives when optimization becomes table stakes.

In a recent TED Talk, Vinciane Beauchene asked a provocative question: If AI could take over all your team’s tasks tomorrow, who would you keep – and why? The talk reframes the “Will AI take our jobs?” anxiety, but as a marketer, that made me think of another question: If you removed your humans tomorrow, would your customers care… and why?

AI is rapidly becoming excellent at functional execution. It can optimize pricing, personalize recommendations, automate workflows, generate content, and coordinate across systems without fatigue. In many industries, that covers a surprising share of what we traditionally think of as marketing work. When everyone can execute at that level, functional excellence stops being a differentiator. And when optimization becomes table stakes, what makes customers choose you?

To answer that, it helps to step back and look at value in layers. Every company competes across three levels:

  • Functional value – speed, convenience, price, performance
  • Emotional value – how the experience makes customers feel
  • Identity value – what choosing you says about them

AI will compress functional advantages first. It will increasingly simulate emotional ones. The real strategic question is: where does your identity and trust equity live?

Let’s look at Costco as an example. At first glance, Costco looks purely functional. Low prices. Tight SKU selection. Supply chain efficiency. Those are all areas where AI can and will optimize aggressively.

But look deeper: emotionally, Costco creates the thrill of discovery. The treasure hunt effect. The feeling that you are getting access to something special and well curated. Customers feel smart shopping there. They feel protected from being ripped off.

At the identity layer, it goes further. Being a Costco member signals something. You are savvy. Practical. Not flashy, but informed. You belong to a tribe that values value. The membership card itself reinforces that identity.

Now imagine Costco becoming a perfectly frictionless, fully automated purchasing engine. No humans. No curated surprises. No in-store serendipity. Just optimized bulk fulfillment. It might be more efficient. But would it feel the same?

This is the risk many organizations face as they pursue AI. The danger isn’t that AI makes them worse. It’s that it makes everyone equally good at the functional layer, while unintentionally eroding the emotional and identity layers that drive loyalty.

Before automating aggressively, companies should ask:

  • Where does our differentiation truly live?
  • Which parts of our experience build emotional equity?
  • Where does human presence increase trust?
  • What would customers actually miss if it disappeared?

This isn’t a workforce exercise. It’s a customer understanding exercise. In the age of AI, the companies that win won’t simply automate more. They will automate wisely, while deliberately protecting and strengthening the human moments that anchor identity and trust.

If you’re exploring AI in your organization and want to understand where your differentiation truly lives across the functional, emotional, and identity layers, I’d be happy to talk about how we apply the Decoder Lens to uncover what your customers actually value – and what must stay human to protect it. Contact me at info at bureauwest.com.

Source: “Will AI take your job in the next 10 years? Wrong question,” Vinciane Beauchene, TED@BCG, October 2025

Posted on by Jay Zaltzman