Tom was Chief Data Scientist at Inscape and MarketCast. He founded data ventures later acquired by GfK, Ericsson, Verimatrix and MarketCast. His expertise includes AI, data fusion and measurement.
ExpertiseSurvey technology, data science and research automation.
Sample size was never the true bottleneck; AI and synthetic methods shift research constraints from respondent volume to hybrid model quality and calibration.
For the last thirty years, quant research has been built around a simple constraint: the tools were slow. Fieldwork took weeks. Cleaning and coding took longer. Logic was fragile, routing was manual, and every step required human oversight. Every research plan from brand tracking to message testing was shaped not by the question being asked, but by how much time and operational pain the system could tolerate.
Most of the commentary around synthetic data falls into two camps: uncritical hype or outright dismissal. The reality, as ever, is more practical. Done right, synthetic data can radically accelerate research workflows. Done badly, it becomes a hall of mirrors. Today, we’re launching synthetic data in our platform. And we’re doing it the right way.
Crude tactics no longer dominate fraud in quantitative research. It is technical, distributed, and increasingly difficult to distinguish from legitimate respondent behavior. The infographic highlights the most common fraud vectors we see in live data today, and the picture is clear: modern fraud blends in.
For decades, the economics of quantitative research have been defined by three constraints: cost, time, and respondent burden. Every part of the industry—methods, workflows, vendor models, even the calendar of insight—quietly assumes that research will always be slow, expensive, and difficult to execute repeatedly. AI automation directly challenges those assumptions.
For years, the ad testing world has been playing by the same rules: test your ad, wait 2-7 days for results (or longer if you’re working with a traditional agency), and consider that “fast.” But let’s be honest—business doesn’t move at that pace anymore.
Market research is undergoing a quiet transformation. Not the kind that comes with sweeping declarations, but one that’s reshaping workflows, mindsets, and results from the inside out.
Artificial Intelligence (AI) is one of the most talked-about innovations of our time, reshaping industries such as healthcare, finance, and even market research. Like any paradigm shift, AI brings with it skepticism and myths that cloud its enormous potential. These misconceptions often make market researchers hesitant to adopt AI tools, leaving them questioning the practicality, fairness, and value these tools hold.
Jevon’s Paradox is simple: when a resource becomes more efficient to use, we tend to use more of it—not less. Originally observed in the 19th century when improvements in coal-burning steam engines led to increased coal consumption, it has since become a powerful lens for understanding how efficiency drives demand. And today, it’s one of the most useful ways to think about the future of market research.
Web-based surveys have become a mainstay of market research for one big reason: efficiency. Yet, as organizations increasingly rely on online data collection, a new and growing threat has come into focus. Survey fraud actively undermines the reliability and value of research, putting both insights and business decisions at risk.
