You no longer have to choose between insight and impact
Marketing has long worked on the assumption that there are two paths to understand people and markets — often described as “nuance versus numbers.” Nuance wants to know how people feel, how brands are conceptualized and understood, what unknown drivers cause human behavior, and what drives business success. Numbers want to know exactly how big our markets are, what people buy, what price they pay, what path they took, and what drives business success.
The difference between these two points of view is most pronounced in Insights and Market Research, where individuals proudly describe themselves as “quant researchers” or “qual researchers.” While specialization is necessary, we often forget that the subjects of the research — people, products, and brands — are the same.
Imagine being a brand researcher given a file of one million tweets to extract insight from and use to move the business forward. If asked whether you’re doing quant or qual research, the question becomes a koan, where both answers are equally right and wrong. The solution is to un-ask the question, as the assumption of the question — the duality of the two types of research — is what’s wrong.
The thought experiment trips us up because we associate qual with depth and quant with scale. Yet, the data has both depth (the messiness of random human thoughts) and scale (the million observations). To get nuance, we need depth, interacting with individuals and using observations to spot patterns and form hypotheses. This inductive reasoning is the core of most qualitative approaches, requiring depth and being expensive to do and synthesize, which is why qual is usually associated with small sample sizes.
For numbers, we need scale, applying analytics and deductive reasoning, starting with a theory and hypotheses, and using statistical methods to confirm them. These techniques require large sample sizes and consistent data, which is why quant research has historically been dominated by the problem of scale.
The problem with dichotomies is that we accepted the division between qual and quant not because it was ideal, but because it was necessary. Many proponents of mixed-mode methods combine the approaches and build workflows that use the best of both. The rise of abductive reasoning is changing the quant/qual dichotomy, going beyond looking for patterns and asking, “What is the most likely explanation for this observation?”
Abductive reasoning requires deep observations, while Bayesian methods rely on data volume. However, new technologies and models allow us to have depth at scale. It wasn’t that qual needed small sample sizes — it was simply too expensive to collect observations and harder to synthesize results if there were too many subjects. Small sample sizes are not a definition of qual; they’re just a technological limitation.
Qual data collection at scale is now feasible, thanks to newer AI approaches, specifically Generative AI/LLMs, which can perform computational abduction, meaning it has the reasoning ability to perform the necessary synthesis. The qual/quant divide will fade over the coming years as we realize that the old depth vs. scale dichotomy is no longer a limitation. We will see a shift in how data is gathered, collected, and how insights are derived — without any trade-offs between depth and scale made. We will no longer define our research by these binary limitations but instead derive the truth about our customers, products, and brands from simultaneous depth and scale.
