Most people think PM research starts with talking to users.
It doesn’t. It starts with reading your own data.
Our instructor asked the class what comes first — primary or secondary research. Almost everyone said primary. Wrong answer.
Secondary research comes first because it tells you who to talk to and what to ask. Without it, you end up interviewing the wrong people about the wrong problem, collecting clean data, and drawing a confidently wrong conclusion.
The Myntra example made this stick. Data shows women in Tier 1 cities scrolling the app between 6 and 7 PM, during their commute. High browsing, almost no purchases. If you want to move conversion, that’s the wrong group to ask about buying. If you want to move engagement, they’re exactly right. The data decides which question is worth asking.
The other thing I won’t forget: what users say and what users do are different things. A friend tells you “raise the UPI Lite limit to 5,000 rupees, I’d use that.” You check the logs. They’ve never opened UPI Lite. Not once. They gave you a feature request for a product they don’t use, because they wanted to be helpful.
We spent 40 minutes tearing apart survey questions for a hypothetical Swiggy feature for people with diabetes. Nearly every question the class wrote was leading, intrusive, or asked the user to design the product. The instructor’s model question never mentioned diabetes, never named a feature, anchored to a real past event (“when you last ordered, what was your biggest concern?”), and included an option that could kill the entire project.
That’s what a research question is supposed to do — give reality a way to say no.
I wrote a one-page research plan for Blinkit’s cart abandonment problem as my portfolio piece. The exercise forced me to sequence everything: secondary data first, then a hypothesis, then the exact cohort, then two methods designed so a negative result actually changes the decision.
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