"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 the path they took and what drives business success."
"Here's a thought experiment: Imagine you are a brand researcher, and I give you a file of one million tweets. Your job is to extract insight from this cache and use it to move the business forward. I then ask: "Are you doing quant or qual research?" Zen koans and the art of inductive/abductive reasoning The question is a koan because both answers are equally right and equally wrong. And, like many good koans, the solution is to un-ask the question."
Marketing often separates qualitative nuance and quantitative numbers, but both aim to explain the same people, products, and markets. A dataset can combine scale and messiness, requiring both pattern spotting and statistical measurement. Inductive and abductive reasoning generate hypotheses from observations, while predictive analytics and quant approaches test and scale those theories. Effective insight requires probing individuals for depth and using large samples for generalization. Specialization can help, but researchers should treat qual and quant as complementary tools rather than mutually exclusive identities.
Read at MarTech
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