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?

Introduction

The gradual increase in mobile returns at Flipkart over the past three months is a concerning trend that requires immediate attention. As the product manager for Returns and Cancellations, I'll approach this issue systematically to identify the root cause, validate our findings, and develop both short-term and long-term solutions.

Step 1

Clarifying Questions (3 minutes)

• Looking at the timeframe, I'm wondering if there have been any recent changes to our mobile product listings or return policy. Have we made any significant updates in the last 3-4 months?

Why it matters: Recent changes could directly impact return rates. Expected answer: Information about recent updates or confirmation of no major changes. Impact on approach: If changes were made, we'd focus on those specific areas first.

• Considering user segments, I'm curious about the distribution of returns across different mobile price ranges. Are we seeing a higher increase in returns for budget, mid-range, or premium devices?

Why it matters: Helps identify if the issue is specific to certain product segments. Expected answer: Breakdown of return rates by price category. Impact on approach: Would guide our focus on specific product segments or overall strategy.

• Thinking about seasonality, I'm wondering if this increase aligns with any particular events or shopping seasons. Have we seen similar patterns in previous years during this period?

Why it matters: Helps distinguish between cyclical trends and new issues. Expected answer: Historical data on return rates during similar periods. Impact on approach: If seasonal, we'd focus on optimizing for known patterns; if not, we'd investigate new factors.

• Considering our metrics, I'm curious about the definition of "returns" in this context. Are we including both refunds and exchanges in this metric, and has this definition remained consistent?

Why it matters: Ensures we're analyzing the correct data and comparing apples to apples. Expected answer: Clarification on what constitutes a "return" in our metrics. Impact on approach: Would help us focus on the right data and potentially uncover definitional issues.