Marketing Research veterans Brian Lamar and Andrew DeCilles bring you the honest conversations that the research industry needs. From trends to breaking news to ugly conversations others won’t touch; no subject is off limits. Join us for an unfiltered take on mrx with storied guests speaking their minds, expert takes on the hottest topics, and tales from those who’ve been in the trenches. Marketing Research has never been in such a season of change and outcry—we’ll help you separate the signal from the noise.
What Should Market Research’s Biggest Priority Be in 2027? | Signal & Noise Ep 45
Brian spotted an Insights Association poll asking one simple question: what should the insights industry’s biggest priority be in 2027? The four options were responsible use of AI, data quality and trust, proving the value of insights, and building next-gen talent. He sent it to Andrew, and they decided to just talk through all of it on the pod.
What followed was one of their more honest episodes in a while. They work through each priority on its own terms: what it actually means and whether the industry is actually equipped to tackle it. The responsible AI conversation turns into a sharp critique of conference culture, where nobody is getting on stage to talk about where AI breaks. The data quality section goes deep on why transparency is still the industry’s biggest structural gap. And the next-gen talent discussion raises a question nobody has a clean answer to: how do you build the next generation of researchers when the job itself is changing faster than any curriculum can track?
Key Takeaways:
Why responsible AI use is meaningless without someone willing to get on stage and talk about where it fails
Why data quality and trust are two different problems that keep getting bundled into one unanswerable priority
What happens to the industry if a billion-dollar AI company acquires a major traditional research firm and applies VC growth expectations to it
If you loved the episode, have comments, or want to appear on the show, connect with us down below!
Connect with us:
LinkedIn (https://www.linkedin.com/company/roirocket/) (ROI Rocket)
LinkedIn (https://www.linkedin.com/company/signal-and-noise-podcast/) (Signal & Noise)
YouTube (https://www.youtube.com/channel/UCnmhaTMIktkMgdhVz3Bpbnw)
ROI Rocket (https://www.roirocket.com/)
15 Sept 2026
Top 10 Best Practices for Sampling in Brand Tracking Studies | Signal & Noise Ep 44
After a small break, Brian and Andrew are back. Both came with independent lists, sat down, and went back and forth for an hour on something they have been doing together for over a decade: running brand tracking studies the right way.
Between them, they have overseen the in-field transition of more than ten large continuous tracking programs, launched dozens more, and spent years studying exactly how panel bias shows up in longitudinal data in ways that most researchers never catch. This episode is the playbook they wish existed when they started.
The conversation covers how to think about blending panels for a tracker, why your anchor panel is the most important decision you will make, how inherent panel bias gets built in through recruiting practices that panels will never volunteer to you, why over-engineering the blend is its own form of data corruption, and why NPS in a tracker is a trap waiting to spring.
If you run a tracker, manage a tracker, or are about to set one up, this one is worth a full listen.
Key Takeaways:
Why your anchor panel is the single most important structural decision in a blended tracker
How panels develop inherent brand bias through their own recruiting, and why they will never tell you about it
Why NPS is dangerous in a tracker and how to use it without blowing up your trend line
If you loved the episode, have comments, or want to appear on the show, connect with us down below!
Connect with us:
LinkedIn (https://www.linkedin.com/company/roirocket/)
YouTube (https://www.youtube.com/channel/UCnmhaTMIktkMgdhVz3Bpbnw)
ROI Rocket (https://www.roirocket.com/)
11 Aug 2026
What Does Sampling Look Like in 5 Years? | Signal & Noise Ep 43
No guest, no agenda, no notes. Brian and Andrew just riffed for an hour on one of the biggest questions in the industry right now: what does sampling look like in 5 years?
Brian is presenting on the future of sampling at the Future of Insights Summit in Athens, Georgia, and this episode is the unfiltered, longer version of that talk. The current model is broken and nobody serious is arguing otherwise. The real question is what replaces it, and who wins when it does.
They work through where today's panels fall short, what synthetic data can and cannot actually do right now, and why the first truly useful synthetic products will likely be built inside brands, not sold to them. P&G, Google, and Meta are sitting on behavioral data sets that dwarf anything a panel company owns. That has consequences.
The conversation also gets into why the brands that build proprietary synthetic tools will have a moat that nobody can buy, why psychological data will never fully be replaced, and why the next 5 years might be the most interesting and terrifying stretch in the history of this industry.
Key Takeaways:
Why the current online panel model is broken and what the industry is not doing about it
Why the first useful synthetic products will be built by brands using their own data, not by research suppliers
Why human panels are not going away, and what role they play in a world increasingly run by synthetic models
If you loved the episode, have comments, or want to appear on the show, connect with us down below!
Connect with us:
LinkedIn (https://www.linkedin.com/company/roirocket/)
YouTube (https://www.youtube.com/channel/UCnmhaTMIktkMgdhVz3Bpbnw)
ROI Rocket (https://www.roirocket.com/)
Reach and audience
Public platform figures. Ratings count people who left a rating, not total listeners.
Apple Podcasts (US)
5.0 / 5
1 ratings
Spotify
3.7 / 5
3 ratings
Contact Signal and Noise
Guest appearances
Books guests
Based on episode analysis; this does not confirm that the show is currently accepting guests.
Pod Engine is an independent podcast discovery and analytics service and is not affiliated with or endorsed by this podcast. Artwork and show content belong to their owners. Full legal notice.
Explore this show Podcast research with Pod Engine
