qualitative research

“We’re in the business of influencing other people”

When insight fails, it’s rarely because the research was wrong. More often, it’s because the insight never quite fit how decisions actually get made inside the organization.

That reality came through clearly in my recent From Insight to Impact video interview with Jinghuan Liu Tervalon, a senior marketing and insights leader with deep experience across CPG, food and beverage, and omnichannel retail. Her perspective offered a useful reminder: the value of research and the value of the research partner are tightly intertwined.

  • For Jinghuan, truly valuable research is first and foremost actionable – but not in the abstract sense the word is often used. In practice, that means explicitly tying insights to growth strategy and translating high-level ambition into concrete business terms. Growing penetration, for example, only becomes actionable when it’s reframed as a specific number of households, regions, or behaviors the organization can rally around.
  • She also pointed out that executives are often presented with research that’s difficult to decipher – dense conclusions, large volumes of data, or findings that stop short of clear direction. When that happens, even strong insight can lose momentum. Actionability depends not only on what the research says, but on whether senior stakeholders can quickly grasp what it means for the business and what to do next. Translation and clarity, not just rigor, are what keep insight moving.

This emphasis on clarity connects directly to how Jinghuan thinks about the role of research partners. She spoke enthusiastically about partners who proactively schedule pre-meetings to understand strategy, stakeholders, and success criteria. “I absolutely love the pre-meetings,” she said, noting that skipping that step often leads to disappointment later – even when the research itself is sound.

What makes her perspective especially valuable is how candid she was about the internal dynamics surrounding qualitative research. Like many insights leaders, she regularly encounters skepticism: “that’s not representative,” or “that’s just one person.” At the same time, she sees how qualitative work uniquely brings consumers’ lives, motivations, and tensions to life in ways numbers alone cannot.

This is where the role of the qualitative research partner becomes inseparable from the value of the research itself. Jinghuan doesn’t just want partners who deliver insight – she values partners who help her influence. Insights teams don’t own activation; marketing and sales do. Their job is to persuade others to trust and act on what the research reveals.

  • “We’re not in charge of marketing activation. We’re here to provide data and insights and recommendations. Fundamentally we’re in the business of influencing other people. So the soft skills are really, really critical.”

When qualitative partners help convey the value of qualitative insight internally, they’re not overstepping – they’re sharing a very real burden.

A good research partner needs marketing fluency, organizational awareness, and an understanding of how stakeholders prefer to receive and challenge information. Partners who can speak the language of growth, brand, and activation dramatically increase the odds that insight won’t end up admired once – and then quietly shelved.

The takeaway is deceptively simple: research creates impact not just because it’s insightful, but because it’s designed to be understood, trusted, and acted on inside the organization. And in that sense, “valuable research” and a “valuable research partner” are often one and the same.

Want research that will be understood, trusted, and acted on inside your organization? Contact me at info at bureauwest.com and let’s talk through the best approach.

Source: “From Insight to Impact: Interview with Jinghuan Liu Tervalon,” 12/23/25

Posted on by Jay Zaltzman

Even experts need help choosing

Experienced customers are often unsure how to choose; the opportunity for marketers is to guide them with clarity.

In a recent project about how tennis players choose tennis balls, one of the most striking findings wasn’t about durability or bounce. It was about confidence. Even experienced players – people who have played for years and know the sport inside-out – told us they don’t really know how to choose a tennis ball. They’re confident on the court, but not in front of the shelf.

  • What they do know is which balls the pros use. And that becomes the anchor for their own choices. If the top-tier “professional” ball feels too expensive, they often pick the next version from the same brand, assuming it must be similar enough. The nuances of felt type, bounce characteristics, or durability rarely play a central role. Instead, players rely on borrowed expertise.

Tennis is just the entry point to a larger phenomenon: expertise in using a product does not necessarily translate into expertise in choosing it.

This gap is wider today than ever. Shoppers face more information than they can reasonably absorb across the many domains of their lives. Every category has its own specs, rankings, reviews, and jargon. People simply don’t have the bandwidth to stay on top of all of it. So even experienced users – people who know the activity well – may feel underqualified when navigating the marketplace. In that context, relying on shortcuts becomes not just common but rational. Borrowing the judgments of pros, brands, or other trusted sources helps buyers reach a confident decision quickly.

  • We see this across categories. A skilled home cook might pick pots and pans based on what a celebrity chef endorses. A long-time runner may choose shoes because elite marathoners wear them. A serious hobby photographer often selects equipment based on a professional’s recommendation rather than the features that truly match their own needs.
  • This same dynamic shows up in B2B. Business buyers are experts in their fields but not necessarily in the specific products they’re evaluating. A manufacturing executive may know operations inside-out yet still defer to analysts, integrators, or the industry’s “market leaders” when selecting software or equipment. A procurement team may lean on vendor tiering or Gartner rankings because it feels safer than decoding dense technical sheets.

The broader insight: buyers often want reassurance more than mastery. In a world of overwhelming information, feeling confident matters more than knowing every detail.

For marketers, this insight creates several opportunities:

  • Make the choice architecture intuitive. Use naming, packaging, and product tiers that clearly signal who each option is for.
  • Provide simple heuristics. Offer clear starting points: “If you value X, start here.”
  • Translate expert logic into everyday language. Emphasize what each feature does for the customer.
  • Clarify your product ladder. Highlight step-up benefits and trade-offs.

The takeaway: Your customers don’t need to become experts. They need to feel they’re making a smart, safe choice – and they’ll gladly borrow your expertise if you offer it in a clear, human way.

Want to help your customers make the right choice? Email me at info at bureauwest.com.

Posted on by Jay Zaltzman

Insights that make an impact

I’m excited to announce the launch of my video series “From Insight to Impact” – conversations with leaders in marketing, strategy, and research on how to ensure research delivers meaningful business value to client companies.

My first guest was James Forr, Head of Insights at Olson Zaltman, the firm founded by Dr. Gerald Zaltman (Harvard Business School) and Dr. Jerry Olson (Penn State University). I asked James how we can provide more value to clients, especially in the age of AI. Two of his points really stood out:

  • Ask clients better questions
  • Ask research participants better questions

On the client side, James said “it’s our job many times to try to help clients think about their problems a little bit more deeply. Sometimes the question that you as a client want to answer is not necessarily the question that you should ask.”

  • He gave an example where a client was looking to reverse a decline in sales of their breakfast cereal. They originally wanted to conduct research to understand their brand better, but James and his team suggested learning more about the context in which the brand plays, specifically, breakfast. The client knew that breakfast was an important bonding time for mother and child, so they designed research to learn more about that relationship. They found there were developmental steps children go through, and moms frequently would discover the child went to the next step (e.g., made a friend independently) over breakfast. Those insights helped the client create highly effective marketing.

Beyond reframing client questions, James also emphasized listening more deeply to participants. He gave an example of a project where a couple consumers said that it’s important to eat healthy “these days.” While it would be easy to skip over the mention of “these days,” James thought there was something behind that. Subsequent probing found that people had concerns about “big food” and “big pharma” – concerns that were shaping consumer behavior in new ways. 

AI can process vast amounts of data, but it doesn’t catch those subtle moments. Human researchers add value when we slow down, notice unexpected comments, and follow up. I’m reminded of an experience I had facilitating a board strategy session. One board member said something that made everyone else laugh uncomfortably. I continued on, because I had a lot to cover. But then I caught it and circled back. That issue turned out to be the crux of the problem of how the organization saw itself and the basis for how it would move forward. It can be difficult to note these kinds of statements because we tend to have packed discussion guides. But it’s worth making that extra effort!

You can view the full interview with James, as well as future interviews, on my YouTube channel here.

Let’s talk about how to maximize the impact of your next research project. Contact me at info at bureauwest.com.

Source: “From Insight to Impact: Interview with James Forr,” Bureau West, 9/24/25

Posted on by Jay Zaltzman

Better Qualitative Analysis with AI

We know AI can save researchers time. It can generate transcripts in minutes, cluster quotes into themes, and even draft summaries. That’s useful, but even more important, can AI make the analysis better and more valuable to clients? I think it can.

Here’s how:

Moving beyond speed to depth. Traditional analysis often stops at description: “Here’s what participants said.” With limited time, it’s easy to get stuck at that level. AI can free us from the mechanical work so we can push deeper – to why certain comments matter, what patterns emerge, and where tensions lie.

Seeing what humans might miss. AI excels at scanning vast amounts of text and surfacing signals we might overlook. It can flag contradictions, unusual word choices, or subgroup differences that don’t immediately catch the eye. The researcher then brings judgment and context to interpret what these signals mean. Together, it’s a more robust analysis than either could do alone.

  • Of course, we need to know to ask AI for these things. For example, “were there any situations where a participant contradicted something they said previously?” Or “were there any differences of opinion between segment x and segment y?”

Challenging assumptions more systematically. One of the most valuable roles of qualitative research is to challenge client assumptions. But – though I hate to admit it – we researchers also have assumptions and biases. AI can help us make sure we aren’t swayed by the most memorable participants and don’t forget some of the quieter exchanges.

  • I ask AI to check my assumptions as well as those of my clients. I might ask “did most participants prefer x?” and be surprised when it turns out that I was just remembering a vocal minority and many participants actually didn’t feel that way. And of course, AI analysis can easily back up these statements with quotes. So if we need to tell our clients that the findings did not match their assumptions, we can provide proof!

Strengthening implications. Clients don’t just want findings; they want implications. AI can assist by drafting preliminary “implication statements” from the data clusters it identifies. The researcher then sharpens these into strategic recommendations. This combination helps ensure that insights aren’t just descriptive, but actionable.

  • I like to work with AI as a kind of brainstorming partner and challenge its suggestions. That back-and-forth discussion can result in stronger implications.

When AI takes on the mechanics of coding and clustering, the researcher’s role shifts upward: from data wrangler to meaning-maker, from summarizer to strategist. That shift is where qualitative research delivers its greatest value. The key is not to let AI do the thinking for us, but to let it clear space and surface signals so we can do the thinking that matters most.Let us provide our thinking for your research needs! Contact me at info at bureauwest.com and let’s discuss how to best answer your research questions.

Posted on by Jay Zaltzman