Picture the scene. The CMO has gathered the marketing team for a briefing on the next campaign. Someone needs to share audience research. And then comes the familiar, slightly pained admission: "We're still waiting on the agency to send that over. But they're working on it."
Matt Britton, Founder and CEO of Suzy, has heard some version of that exchange at companies across every industry for decades. It is, in his framing, the defining symptom of a market research model built for a world that no longer exists — one in which consumer insight was an expensive, episodic, agency-mediated process that took weeks to initiate, months to complete, and arrived packaged in a format that had already begun to decay before the first executive read it.
"Consumers change so much, and brands need to be able to harness those consumer-based insights quickly, not get results three months later when they're stale," Britton told Brand Innovators. "Often by that point, something else has happened — either a new technology came out or some new trend emerged — which made the original research results irrelevant."
The phrase he uses to describe what Suzy is doing — "disrupting the research supply chain" — is precise in a way that rewards unpacking. A supply chain is a system for moving something valuable from where it originates to where it is needed. In the traditional market research model, consumer insight originates from the consumer, travels through panel recruiters, survey designers, field agencies, data processors, analyst teams, and agency presenters before reaching the brand decision-maker who needed it. Every handoff adds cost, time, and the compounding risk that the insight, when it finally arrives, no longer accurately describes the consumer who has continued evolving throughout the process.
Suzy's disruption is to collapse that supply chain — combining survey design tools, a proprietary consumer panel, and research services in a single platform, priced as an annual SaaS license rather than a per-project service fee. The result is not merely faster research. It is a fundamentally different relationship between brands and consumer intelligence: continuous rather than episodic, in-house rather than agency-dependent, and responsive to the speed at which the consumer actually changes.
In a world where nearly 60% of researchers say their organizations depend on their team's insights more today than they did a year ago, and where research teams have evolved from data providers into strategic partners embedded in product and marketing decision cycles, that supply chain disruption has gone from competitive advantage to commercial imperative.
The gap between quantitative and qualitative research has always been one of the central tensions in consumer intelligence. Quantitative surveys produce numbers — statistically reliable measures of what percentage of your target audience holds a given attitude, intends a given behavior, or responds to a given stimulus. But numbers rarely explain why. The why lives in qualitative research: the focus groups, depth interviews, and open-ended conversations that surface the emotional logic, the hidden objections, the unarticulated needs that quantitative data cannot capture.
The traditional problem with qualitative research is that it has always been disproportionately expensive and slow relative to its value. Recruiting participants, scheduling and moderating sessions, transcribing hours of recorded conversation, coding themes, and synthesizing findings into a coherent narrative — the manual labor involved in turning qualitative data into usable insight could add weeks and substantial cost to any research program, pushing qualitative methods to the margins of marketing budgets that were already being squeezed.
AI has changed that calculus entirely. In 2023, Suzy hosted its AI First Summit in New York, where it launched Qual Analysis — an AI-powered qualitative research platform that automated the analysis of research interview transcripts. Britton was characteristically clear about what the technology accomplished and where the human remained essential.
"We had already provided the ability to have AI create transcripts of research interviews, but we still would need a human to come in and analyze the transcripts," he explained. "Now those transcripts are being analyzed on demand through AI. It allows us to give our clients what they're ultimately looking for, which is getting the insights that come out of those qualitative research studies quickly and more efficiently."
The commercial significance of this capability is substantial. AI-powered qualitative research delivers insights at roughly $45 per finding versus $180 for traditional methods — a 75% cost reduction — while enabling research cycles that would previously have required weeks to complete within hours or even minutes. Seventy-four percent of researchers who use AI regularly have seen an increase in qualitative research demand within their organizations; the bottleneck that cost and time created has been removed, and the appetite for qualitative insight was always there waiting.
What Britton was careful to preserve — and what distinguishes thoughtful AI deployment from naive automation — is the role of human judgment at the stages where it adds genuine value. AI handles the computational heavy lifting: transcription, theme identification, sentiment analysis, pattern recognition across large volumes of unstructured text. The human researcher remains in the loop at the stages requiring contextual interpretation, strategic synthesis, and the kind of judgment that cannot yet be automated without losing the nuance that makes qualitative research valuable in the first place.
This is not a cautious half-measure. It is the correct architecture for a research process that delivers both speed and reliability. The 71% of market researchers who expect AI to outperform humans in trend prediction by 2025 are not predicting the elimination of human researchers — they are describing a division of cognitive labor that allocates machine capabilities to the tasks machines do best and human capabilities to the tasks that require human judgment.
One of Britton's most incisive observations in the Brand Innovators interview was not about technology. It was about organizational psychology.
"Businesses used to make decisions on what we call the HIPPO," he said. "The highest paid person's opinion."
The HIPPO — Highest Paid Person's Opinion — is one of the most durable failure modes in organizational decision-making, and one of the least discussed. It describes the dynamic in which research data, survey results, and consumer feedback are systematically subordinated to the preferences and intuitions of whoever has the most institutional authority in the room. The CMO who believes the new logo is wrong overrides the focus group data that shows consumers strongly prefer it. The CEO who has a gut feeling about the product positioning overrides the survey evidence that the positioning doesn't resonate with the target audience. The HIPPO wins, the research is cited selectively or not at all, and the company makes the decision it was going to make anyway.
The solution to the HIPPO problem is not, in Britton's framing, simply better research. It is better research literacy — the organizational capability to read, interpret, and apply consumer insight across the teams that actually make the decisions that research is meant to inform.
"As more and more people in the enterprise need to understand research, it dawned on us that we need to make sure that we're investing in education, so more people can learn the art of doing it properly," Britton said. This recognition drove Suzy's investment in Suzy Academy, its online training platform, and its partnership with the Market Research Institute International (MRII) to offer a certified advanced learning course for marketing professionals.
The scope of what "doing it properly" means is broader than most marketing teams appreciate. Understanding how to formulate survey questions that don't lead respondents toward predetermined answers. Knowing the right size for a focus group panel in order to have statistical confidence in the results. Recognizing when quantitative data is being asked to answer a qualitative question and vice versa. Understanding how social desirability bias can distort responses when people answer with what they think they should say rather than what they actually believe — a problem Britton flagged as affecting everything from consumer research to political polling.
Research teams that embrace innovation are already reaping organizational rewards. Teams identifying themselves as "cutting edge" in research methodology are more likely to report increases in influence, budget, and demand for their work than teams using traditional approaches. The correlation is causal: when the research function produces insights that are faster, more reliable, and more actionable, it earns a strategic seat at the decision-making table rather than being summoned to provide post-hoc rationalization for decisions already made.
The HIPPO persists in organizations where research is treated as an external service rather than an internal capability. When every research need flows to an agency and returns in a packaged report that no one in the marketing team fully understands how to interrogate, the highest-paid person's opinion fills the vacuum by default. When marketing teams are trained to formulate research questions, interpret results, and apply consumer intelligence to actual decisions, the HIPPO loses its structural advantage.
One of the persistent challenges facing research platforms and their advocates in enterprise budget conversations is the difficulty of demonstrating return on investment in conventional financial terms. Unlike a paid media campaign, which can be attributed to clicks, conversions, and revenue with increasing precision, the value of market research is often preventive rather than generative — it prevents bad decisions rather than directly producing revenue. And preventive value is notoriously difficult to quantify.
Britton's answer to this challenge is a metaphor that reframes the entire question.
"It's almost like, what's the ROI of a seatbelt? You won't know unless you get into an accident, so you wear it every day. I think that brands really understand that."
The seatbelt analogy is genuinely clarifying, because it accurately identifies the category of value that consumer research provides. The question "what is the ROI of this research study?" is the wrong question in the same way that "what is the ROI of this seatbelt?" is the wrong question. Both miss the point. The seatbelt's value is not in what it earns you — it is in what it saves you from. And the research study's value is not always in the campaign it enables — it is just as often in the Pepsi Kendall Jenner ad it prevents.
Britton has returned to that ad as a canonical example throughout his speaking career: a campaign that cost Pepsi not only the production investment and the advertising spend, but a decade of brand perception damage — all of which would have been prevented by a research process capable of surfacing the consumer reaction before launch rather than after. The fact that the exact revenue impact of that avoidance is unmeasurable does not mean the value is zero. It means the measurement framework is inadequate.
The research budget survived economic turbulence in 2023 better than many discretionary line items precisely because brand-side marketers had internalized this seatbelt logic. The pipeline for creating and marketing new products is long, and the cost of launching something that fails to resonate — in production, distribution, marketing, and brand equity damage — is high enough that the research investment looks rational even without a precise ROI calculation. Marketers understand, in Britton's assessment, that the risk of not knowing is more expensive than the cost of finding out.
The fourth and perhaps most philosophically sophisticated theme in Britton's Brand Innovators conversation is his thinking about how research capability should be distributed across the enterprise — and what the right level of sophistication actually is for most users.
"If you look at the iPhone, everyone's now a good photographer," Britton observed. "And even more so because we also have services like Instagram and its filters. But you still have to know where to point the camera."
The iPhone metaphor is doing significant intellectual work here. Before the iPhone, photography required mastery of technical parameters — aperture, shutter speed, ISO, depth of field — that most people had neither the time nor the inclination to learn. After the iPhone, those technical parameters became largely automated, and the skill requirement shifted from "master the camera's manual settings" to "know where to point the camera and what moment to capture."
The iPhone did not produce worse photography by democratizing access to it. It produced more photography, more people engaging with photography, and a substantial number of genuinely exceptional photographs from people who would never have become traditional photographers but who had something visually compelling to say. What it did not do — and what Britton's parallel makes clear — is eliminate the need for human judgment about what to photograph and when.
"We're not trying to teach everybody in the organization how to kind of go into the equivalent of a camera's manual settings, which would be that very sophisticated research functionality," Britton said. "But we do want to teach them how to point that proverbial camera in the most customer-centric way possible. That's where I think market research is ultimately going."
The strategic vision embedded in this metaphor is important and not widely articulated in the research industry. Most research platform thinking still falls into one of two camps: highly sophisticated tools for research experts, or simplified consumer-grade products that sacrifice depth for accessibility. Suzy's ambition, as Britton describes it, is something more nuanced: a platform with professional-grade research capabilities under the hood, presented through interfaces and educational scaffolding that allow non-specialist marketing professionals to use it well without needing to understand the manual settings.
The competitive advantage this creates is significant. An enterprise that has equipped its entire marketing team with the ability to point the research camera correctly — to formulate questions that get at what they actually need to know, to interpret results without falling into the HIPPO trap, to know when they are looking at a quantitative answer to a qualitative question — has built a consumer intelligence capability that no agency engagement can replicate. It is distributed, continuous, integrated into the actual decision-making process, and not dependent on waiting for someone else to send over the results.
The traditional market research supply chain routes consumer insight through a chain of intermediaries — panel recruiters, survey designers, field agencies, data processors, and agency analysts — before delivering packaged results to the brand teams that need them. Each handoff adds cost, time, and decay: by the time the research arrives, the consumer has often moved on. Suzy's disruption is to collapse this chain into a single vertically integrated platform, enabling brands to conduct, analyze, and act on consumer research internally, in near real time, at a fraction of traditional cost. The result is not just faster research — it is a fundamentally different relationship between brands and consumer intelligence.
AI has transformed the most labor-intensive parts of qualitative research — transcription, coding, theme identification, and sentiment analysis — from manual, days-long processes to automated workflows that deliver results in hours. Suzy's Qual Analysis platform, launched in 2023, applies AI to the analysis of research interview transcripts, enabling clients to surface qualitative insights on demand rather than waiting for human analysts to complete the coding process. The human researcher remains involved at the synthesis and strategic interpretation stages, ensuring that AI efficiency is applied to the right tasks without sacrificing the contextual nuance that makes qualitative research valuable.
HIPPO — Highest Paid Person's Opinion — describes the organizational dynamic in which research data is systematically overridden by the preferences of whoever holds the most institutional authority. It persists when research is treated as an external service that produces reports rather than an internal capability that informs decisions. Research training addresses it by distributing the ability to formulate good research questions, interpret data honestly, and recognize common biases across the marketing teams that actually make decisions. When more people in the organization can read the research critically, the HIPPO has fewer opportunities to substitute personal preference for evidence.
Consumer research is best understood as risk management rather than direct revenue generation. The value of knowing what your consumers actually think before launching a product, campaign, or positioning is primarily preventive — it avoids the brand damage, production waste, and market failure that result from decisions made without evidence. Britton's seatbelt analogy captures this precisely: you cannot calculate the ROI of a seatbelt except in the context of the accident it prevents. Brands that treat research as a line item subject to ROI calculation on a per-study basis are systematically underestimating its value; the correct frame is continuous risk management infrastructure, not episodic investment.
The cumulative picture Britton described in his Brand Innovators conversation is of a fundamental repositioning of consumer intelligence — from an external service purchased from agencies, to a core internal competency built into how enterprise brands operate.
The technology enabling this repositioning — AI-powered qualitative analysis, SaaS-priced research platforms, natural language survey tools, real-time consumer panels — has matured rapidly. The organizational change required to take full advantage of it is moving more slowly, because it requires not just new tools but new habits, new training, and a new understanding of what consumer insight is actually for.
The HIPPO still wins a lot of meetings. The agency is still working on it. And the research results are still arriving three months after the decision was made.
But in the organizations that have genuinely rebuilt their consumer intelligence capability — inside the enterprise, continuously, at the speed the market actually moves — those dynamics are being replaced by something closer to what Britton has always envisioned: brands that know what their consumers think before they need to guess, teams that can point the camera at the right question and get an answer before the moment passes, and marketing decisions made with evidence rather than authority.
For more on how the next generation of consumers — Generation AI — is emerging with entirely new relationships to brands, data, and authenticity that will require the most sophisticated consumer intelligence organizations to continuously upgrade their understanding, Generation AI is the essential guide. And for ongoing conversations with the CMOs and insights leaders navigating these questions every day, The Speed of Culture podcast is where those discussions happen.
To see what on-demand consumer intelligence actually looks like in practice, Suzy's enterprise platform is where brands are building the capability Britton describes.
