Validate Possibly Biased Cancellation-Reason Data and Present It to Leadership

You are a product manager at an e-commerce platform, and the current cancellation rate is x%. Customers who cancel choose a reason from a list, and the team reports how cancellations break down by reason. Senior management doubts that breakdown: they suspect many customers just pick one of the top two reasons on the list because it is the quickest way through, so the numbers may not show why people really cancel. How would you get to cancellation-reason data you can present to management with confidence, given their distrust of the current numbers and the chance that customers choose reasons that are not their true ones? ```hint Test the doubt before defending the data Think about how you could measure whether a reason's position on the list changes how often it is picked, and what other evidence could confirm or contradict the reason a customer gives. ``` ### Clarifying Questions - Whose cancellations are these: shoppers cancelling orders, or merchants cancelling their subscription to the platform? - Is the reason list always shown in the same order, is a reason required, and can customers pick more than one or write their own? - Which decisions will management make from the breakdown, and how precise does it need to be for them? - What other data exists for cancelled orders or accounts, such as delivery estimates, price changes, payment failures or support conversations? ### What a Strong Answer Covers - Concrete checks on existing data that would reveal position bias, rushed answers or misleading reasons - Changes to the reason capture, tested in an experiment, that measure and reduce the bias without making cancelling harder - Triangulation of stated reasons with behavioral and operational evidence and with qualitative research - A presentation that shows estimates with their uncertainty, known limits and the decisions they support, instead of claiming perfect accuracy - Handling management's skepticism directly, including agreeing up front on what would make the data trustworthy enough ### Follow-up Questions - Your test shows one reason's share falls sharply once it no longer appears first. What do you tell management about the figures they have been using? - Leadership wants a "true" breakdown next week, before any experiment can finish. What do you present? - How would you get closer to the real reason when customers prefer a polite answer, such as "changed my mind", over the actual one?