In this quick-hit episode of Signal and Noise, Andrew catches up with Brian fresh off the floor of SampleCon 2026, recording from the Delta Sky Club in Seattle. No studio setup, no guests, just a real-time debrief from one of the industry's most dedicated annual gatherings.

Brian shares his firsthand impressions of a conference that felt noticeably different this year. SampleCon is evolving. What once centered on panel standards and supplier partnerships is now leaning hard into technology, AI implementation, and a wave of new faces and companies that would have felt out of place at the event just a few years ago. Brian compares the vibe to a smaller IIEX, and that is not a small compliment.

The conversation covers the headline moment of the conference, a point-counterpoint keynote debate between Patrick Comer and Melanie Courtright on human versus synthetic respondents, the growing industry consensus shifting from "should we use synthetic?" to "prove to us that it works," and a standout session from Walmart's research team making a public case for better respondent care and panel investment.

Key Takeaways:

• How SampleCon is evolving from a supplier networking event into a technology and innovation conference

• What one keynote debate revealed about where the industry stands on synthetic research

• Why the conversation around AI and synthetic data has shifted from "should we?" to "prove it works"

• Why conferences held at resort-style venues create a different and arguably more productive networking environment

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In this episode of Signal and Noise, Brian and Andrew sit down with Lenny Murphy, one of the most recognized voices in the market research industry. As the founder of GreenBook, co-founder of the Insight Innovation Exchange (IIEX), and a Gen2 Advisors partner with deep roots in M&A and investment, Lenny brings a perspective that is equal parts historical lens and boots-on-the-ground reality.

The conversation covers the accelerating collision between artificial intelligence and the market research industry. Lenny reframes AI not as an apocalypse, but as a restructuring, drawing parallels to the printing press, the automobile, and the industrial automation of the 1980s. The discomfort is real, the displacement is real, but so is the opportunity.

The trio digs into where the industry has already been commoditized and where it went wrong, why the real asset in research has always been the connection to consumers, and how agentic AI is already reordering business models faster than most companies can respond. Lenny also shares a candid take on why many established firms are caught in the crosshairs, not because of a lack of talent, but because of a failure to adapt before the wave arrived.

The episode also touches on the future of live events and in-person connection, the upcoming IIEX conference in Washington, DC, and a new IIEX West launch in San Francisco, with Lenny making the case that human gatherings become more valuable, not less, as automation takes over routine work.

Key Takeaways:

• Why Lenny views AI as a restructuring event comparable to the printing press and industrial automation, not an ending

• How the research industry commoditized its own most valuable asset and what that means now

• Why the shift from process-based pricing to impact-based pricing is both necessary and overdue

• What the "reckoning still to come" looks like for established firms that have not adapted fast enough

• Why live events and in-person connections are poised to become more critical in an AI-driven world

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In this episode of Signal and Noise, Brian and Andrew sit down with Tim Matthews, who heads up the in-person research and support team at ROI Rocket. Tim brings over 20 years of marketing experience, starting from tactical brand activations at the Winston Cup Series to building a research firm rooted in creative, human-centered consumer engagement.

The conversation centers on a growing challenge in the research industry: the erosion of trust in online quantitative data, the difficulty of reaching low-incidence and niche populations, and why in-person recruiting is anything but old-fashioned.

Tim reframes the concept of the "intercept" entirely. Forget the clipboard in the mall. His team operates more like a creative, agile engagement deployment squad, going where consumers live, work, shop, and play to find respondents that online panels simply cannot reach. From private golf clubs to luxury dinners at the Playboy Mansion, Tim shares the strategies and stories behind some of his most memorable and creatively ambitious recruiting projects.

The episode also features a live "Can You Find Them?" game, where Brian and Andrew throw out increasingly niche audience targets, and Tim explains how his network and methodology could actually reach them, from nuclear plant operators to doomsday bunker owners.

Key Takeaways:

• Why online panels fall short for low-incidence and hard-to-reach populations

• How Tim's team uses experiential incentives (not just cash) to recruit elusive respondents

• The value of storytelling over transactional survey-taking

• What the "six degrees of Kevin Bacon" has to do with finding niche research targets

• Why stated vs. actual behavior gaps make in-the-moment observation so powerful

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In this episode of Signal and Noise, Brian and Andrew sit down with Tom Anderson and Spencer Imel, co-founders and partners at The Langston Co, a quant-focused consumer insights firm known for deep category expertise and rigorous research standards.

Tom and Spencer share the origin story behind Langston, from their early days working together at a fast-growing Denver startup to building a research firm rooted in reliability, trust, and methodological discipline. What began as two social scientists exploring how to understand consumers evolved into a firm now operating at the intersection of AI, category intelligence, and scalable insights.

The core of the conversation centers on a bold idea: AI has made speed cheap, but trust is still expensive.

Langston describes its journey from early skepticism about AI to building a sophisticated internal Insights Assistant that sits on top of category-level survey ecosystems. Instead of treating AI as magic, the team invested heavily in understanding how large language models actually work, including context windows, tool calls, and failure points. By combining structured survey data, industry context, and documented research methodologies, they created a system that accelerates analysis without sacrificing rigor.

The episode includes a live walkthrough of their AI-powered Insights Assistant, demonstrating how a brand manager could instantly explore brand performance across age groups within a facial skincare category. The conversation then zooms out to tackle bigger questions about the future of research, including:

• Will chat-based AI become the new dashboard?

• How should firms balance automation with human interpretation?

• What happens to the role of the researcher in an AI-forward world?

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Brandon Richard returns to Signal and Noise for his second appearance, joining Brian and Andrew in person at The Link Group’s Durham, North Carolina, office. The conversation picks up where their previous AI discussion left off, diving deeper into what has actually changed in the past few months and what has not.

As an AI enthusiast and healthcare research leader, Brandon shares how The Link Group approaches AI pragmatically. Rather than chasing every new tool, the team focuses on understanding core AI categories such as conversational surveys, synthetic data, knowledge management, and generative productivity tools. The number of vendors may be exploding, but the foundational capabilities remain relatively stable.

The episode also explores a bigger existential question for the research industry. If AI can generate longitudinal synthetic respondents and analyze business questions directly, what role does market research play? Brandon argues that the value of research lies not just in answers but in the collaborative process. The refinement of business questions and the strategic intuition researchers bring to the table are difficult to replicate with a single AI prompt.

Key Takeaways:

• AI tools are multiplying fast, but the core research use cases have not changed much.

• Synthetic data still makes the most sense as augmentation, not replacement.

• The magic of research is in the process, not just the answer.

• AI is most powerful as a thinking accelerator.

• If research becomes “question in, answer out,” the industry is in trouble.

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In this special in-person episode of Signal and Noise, the hosts sit down with Beth Finn, CEO, and Jason Ebbing, COO of The Directions Group, for a wide-ranging conversation on the future of insights leadership.

Beth and Jason share their career paths, how The Directions Group approaches integrated intelligence, and what it means to move beyond siloed research toward clearer signals that drive real business action. The discussion also explores data quality, speed to insight, pricing research based on value, and how artificial intelligence should support rather than replace human thinking.

This episode offers a candid look at modern leadership in the insights industry and how organizations can stay relevant as decision-making accelerates and expectations rise.

Key Takeaways:

• Clients are overwhelmed with data but still struggle with clarity and decision-making

• Integrated intelligence works best when multiple data sources are planned together from the start

• Research teams gain influence when insights are tied directly to business actions

• Data quality must be usable, credible, and actionable to create real value

• AI should amplify human judgment, not replace it, especially in strategy and leadership

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In this episode of Signal and Noise, Brian Lamar sits down with Frank Kelly, one of the most experienced voices in panel management, sampling, and respondent engagement, to unpack what is actually broken in online research and how the industry can fix it.

Frank reflects on nearly four decades working across every major panel model, from postal and telephone panels to online access panels at Nielsen, Kantar, Ipsos, and now Virtual Incentives. He explains why today’s fraud and data quality challenges are not new problems, but the result of incentives, engagement, and trust being systematically undervalued for years.

A central theme of the conversation is compensation. Frank makes the case that low incentives drive fraud, disengagement, and professional respondents, while fair and meaningful incentives expand the pool of real people willing to participate. He challenges the assumption that higher incentives automatically increase fraud and explains why the opposite is often true.

The discussion also explores how conversational AI, video, and smarter profiling can radically improve panel quality if paired with the right incentive strategy. Frank outlines a future where premium panels support deeper qualitative work, smaller samples, and AI-powered synthesis, all while maintaining higher standards of validation and trust.

Key Takeaways:

• Low incentives shrink the respondent pool and invite fraud

• Fair compensation expands access to real, engaged participants

• Incentive strategy is as important as fraud detection technology

• Conversational AI and video can improve quality when paired with better pay

• Premium panels will be essential as big qualitative research grows

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In this episode of Signal and Noise, Brian Lamar and Andrew DeCilles are joined by leaders from CASE4Quality for a candid and deeply informed conversation on the state of data quality in market research.

The discussion features Mary Beth Weber, founder of CASE4Quality, alongside Tia Maurer - Data Quality Guru, Efrain Ribeiro - Godfather of Sampling, and Karine Pepin - Data Fairy. Together, they unpack how the industry arrived at its current data integrity challenges and why progress has been slower than many expected.

The episode explores the realities of online sampling, fraud, and professional respondents, including how the promise of unlimited and inexpensive sample has distorted incentives across the ecosystem. The guests explain how aggregation, lack of transparency, and pressure for speed and cost reduction have quietly undermined confidence in research outputs.

The conversation also addresses the growing tension between AI-driven research and poor-quality input data. The panel warns that synthetic data and advanced analytics cannot solve quality problems if the underlying data is flawed. Throughout the episode, the group emphasizes the need for transparency, accountability, and brand-led standards to restore trust and long-term viability to the research industry.

Key Takeaways:

• The industry operates under the false assumption that unlimited high quality sample exists

• Speed and low cost have consistently been prioritized over data integrity

• Fraud and professional respondents remain widespread and often undetected

• Aggregated and opaque sampling practices make validation nearly impossible

• AI and synthetic data amplify risk when built on compromised data foundations

• AI can improve efficiency in research, but human judgment is still essential for understanding emotion, taste, humor, and nuance.

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