Metrics question
You are a product manager for Returns and Cancellations at Flipkart. You see that the number of returns in the mobile category has been gradually increasing over the past 3 months. What would you do?
- Flipkart
- Metrics
- Medium
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Root-cause investigation for an operational metric: can you separate product, seller, and customer-behavior causes for a rising returns trend in one category.
How to approach it
- Confirm the metric: returns as a percentage of orders in mobile category specifically, rising over 3 months, versus other categories flat.
- Hypothesis: product-listing issues, e.g. inaccurate specs/images leading to mismatched expectations (wrong color, storage variant).
- Hypothesis: seller-side, a specific new seller or SKU with quality issues driving a disproportionate share of returns.
- Hypothesis: customer behavior, e.g. a promotional campaign or a new financing/EMI option enabling more impulse buys that get returned.
- Segment the data: which specific sub-category, brand, or seller is driving the increase, rather than treating 'mobile' as one bucket.
- Recommend the next step: pull return-reason codes (damaged, wrong item, changed mind) to confirm which hypothesis holds before acting.
What a strong answer includes
- Segments the 'mobile category' broadly to find the specific driver (one seller, one SKU, one region) rather than treating it as uniform.
- Uses return-reason codes as the key diagnostic data, since the fix differs completely for 'wrong item shipped' vs 'changed mind'.
- Considers an external trigger, like a new promo or financing option changing buyer behavior, not just a product/quality issue.
- Holds off recommending a fix until the root cause is confirmed with data.
Common mistakes
- Proposing a generic fix (better packaging) without segmenting to find the actual driver.
- Not using return-reason data, which is the fastest way to distinguish causes here.
Likely follow-up questions
- What would you do differently if the driver turned out to be one specific seller?
- How would you prevent this from happening again after you fix the immediate cause?
More metrics questions
- 70% of shoppers of an eCommerce site are reviewing the site's return policy page prior to shopping. What do you do?Shopify · Metrics · Medium
- How would you increase customer lifetime value for Swiggy?Swiggy · Metrics · Medium
- How would you measure the success of Gmail?Google · Metrics · Medium
- Google Maps had a 10% drop in daily active users. What do you do?Google · Metrics · Medium
- What is the most important metric for Uber Eats? Why?Uber · Metrics · Medium
- You are a PM for an e-commerce company. Due to some mistakes, the data team lost all key metrics. What are the metrics you will track as PM?Amazon · Metrics · Medium
More questions from Flipkart
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 14: Get the job: the AI PM interview loop