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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In this episode of Signal and Noise, hosts Brian Lamar and Andrew DeCilles sit down with Samuel Cohen, Ph.D., CEO of Fairgen, to explore how generative AI and advanced statistical modeling are reshaping the way consumer research is conducted, validated, and scaled.

Samuel shares his international academic journey, from studying mathematics and synthetic data research at Oxford to working with AI in industry labs before launching Fairgen. He explains how rising costs and declining data quality in traditional market research led him to build a platform focused on generating reliable, usable insights from limited samples.

The conversation breaks down the difference between partial simulation and full simulation, clarifying how Fairgen uses statistical models to amplify real survey data rather than replacing it outright. Samuel walks through real-world applications, including how enterprise clients use data amplification to unlock granular insights across small or hard-to-reach segments without dramatically increasing budgets or field time.

The hosts and Samuel also discuss where AI works well and where it falls short, particularly in high-stakes research, governance-driven projects, and complex quantitative methods like advanced conjoint analysis. The episode closes with a forward-looking perspective on how budgets, decision risk, and organizational governance will shape the future role of simulated data in consumer research.

Key Takeaways:

• Generative AI can amplify small samples into enterprise-level insight without multiplying cost or field time.

• There is a critical difference between simulating data and strengthening real data, and most people get it wrong.

• Fairgen’s approach shows how math and models can reveal patterns humans cannot see at scale.

• Not all research should use AI, especially when decision risk and governance are high.

• The future of insights may be fewer surveys, smarter modeling, and faster strategic confidence rather than bigger sample sizes.

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

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In this special New Year episode of Signal and Noise, Brian Lamar and Andrew DeCilles return with a candid conversation about what 2026 may hold for the market research and insights industry. Drawing from months of conversations with researchers, technology leaders, and clients, the hosts share a grounded but optimistic view of an industry entering a period of economic repositioning, technological acceleration, and rising expectations for data quality.

The discussion opens with reflections on personal and professional goals before shifting into a wide-ranging outlook on volatility, global and political uncertainty, and how those forces shape research budgets and decision-making. Brian and Andrew explore why market research is well positioned to benefit from artificial intelligence, even as pricing pressure, consolidation, and competition continue to intensify.

Key Takeaways:

• The economic and political factors shaping research budgets in 2026

• Why artificial intelligence may reduce cost and time while raising expectations for quality

• The rising importance of verified and high-quality online samples

• The growth of synthetic data and hybrid research approaches

• How the role of the researcher is shifting toward insight and strategy

• Industry consolidation and what it means for agencies and panels

• Why innovation and experimentation will define competitive advantage in 2026

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In this episode of Signal and Noise, hosts Brian Lamar and Andrew DeCilles sit down with Craig Alter, an experienced consumer insights leader at Perfetti Van Melle, to explore how consumer behavior, impulse buying, and innovation research intersect inside the world of candy, gum, and mints.

Craig shares his nontraditional path into market research, explaining how early experience in finance, marketing, and brand management shaped his ability to connect subtle consumer insights to measurable business outcomes. He discusses why many professionals discover research later in their careers and why diverse business backgrounds are a strength for the insights industry.

Craig also discusses innovation and product testing as one of the most rewarding areas of consumer research. He walks through central location tests, flavor development, texture evaluation, and how research can serve both product refinement and selling stories with retail buyers. Throughout the discussion, he highlights how qualitative and quantitative methods increasingly blend together to solve real business problems.

The episode concludes with a thoughtful discussion on the role of artificial intelligence in research. Craig offers a pragmatic perspective on where AI can add speed and efficiency, such as summarization and early screening, and where human nuance remains irreplaceable, particularly in humor, taste, emotion, and impulse-driven behavior.

Key Takeaways:

• Impulse-driven categories like candy are difficult to research because consumers often cannot explain why they buy in the moment.

• Observational and in-context research is critical for understanding real shopper behavior, especially at the shelf or checkout.

• Consumer behavior changes significantly by channel, so insights must be tailored for grocery, convenience, club, and digital environments.

• Innovation research works best when qualitative and quantitative methods are combined to refine products and tell compelling business stories.

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

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In this episode of Signal and Noise, hosts Brian Lamar and Andrew DeCilles welcome Ellen Houston and Jordan Harper from Qualtrics Edge, marking the first time the podcast has featured guests from Qualtrics. The conversation dives into the future of AI, synthetic data, and the evolution of modern research inside one of the most influential insights platforms in the world.

Ellen, who leads the Edge Center of Excellence, outlines how her team focuses on the intersection of market research and artificial intelligence, particularly in developing synthetic respondents and next-generation research tools. Jordan, a senior principal thought leader, brings a scientific and strategic perspective shaped by his background in astrophysics, engineering, technology, and agency leadership. Together, they explain how Qualtrics Edge is working across product, engineering, delivery, and customer teams to establish a rigorous foundation for AI in research.

Throughout the episode, the conversation highlights the opportunities and challenges of AI, including research design, niche audience modeling, accuracy signals, and the role of synthetic respondents in uncovering deeper truths and exposing issues in survey construction. Both guests share examples of experiments, such as priming tests and concept evaluations, that reveal how synthetic respondents behave compared to humans and how these differences can expand the insight landscape.

Key Takeaways:

• Qualtrics Edge is focused on using AI to advance market research, especially through synthetic respondents.

• Synthetic respondents are meant to support human research, not replace it.

• The Qualtrics model is trained on decades of real survey data, giving it a unique advantage.

• Synthetic respondents help reveal issues in survey design and respondent behavior that humans may hide or overlook.

• Future developments include niche synthetic audiences and expanded AI tools across the entire research process.

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In this episode of Signal and Noise, hosts Brian Lamar and Andrew DeCilles sit down with Brandon Richard, Senior Vice President at The Link Group and a long-time AI enthusiast who has been leading multiple AI initiatives across qualitative and quantitative research. Together, they unpack what is actually happening in the research industry as companies race to understand and apply artificial intelligence.

The episode also explores the limits of AI-generated synthesis, the need for trust and human verification, the challenges of capturing nuance in qualitative work, and why the industry must avoid falling into the trap of faster and cheaper at the expense of true insight. Brandon highlights the importance of friction in the research process, explaining that many of the valuable ideas and breakthroughs come from the messy and human parts of research, not simply the final deliverable.

Key Takeaways:

• Where AI synthesis supports analysts and where it still falls short

• Practical examples of AI being used before and after qualitative work

• A balanced view of synthetic samples, digital twins, and personas

• Why accurate forecasting and real-time insight remain difficult for AI

• The risks of creating research that is fast and cheap but not meaningful

• Why friction in research often produces the best insights

• What the future of human plus AI collaboration should look like

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