Diagnosing Why More Walmart Orders Are Being Returned

Quick Overview

A product analytics case about Walmart's climbing return rate, asking what a product manager would examine, and in what order, to isolate the cause. It tests metric validation, return-lag and mix-shift reasoning, segmentation by channel, category and seller, and hypothesis-driven root-cause diagnosis.

Diagnosing Why More Walmart Orders Are Being Returned

Company: Walmart Labs

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

You are a product manager at Walmart. The share of orders that customers return has been climbing. What would you look into, and in what order, to isolate the cause? ```hint Check the number before the business Before listing possible causes, ask whether the metric itself moved for a real reason: how it is computed, and whether the returns, the orders behind them, or the mix of those orders changed. ``` ### Clarifying Questions - How is the return rate defined: returned orders over orders, returned units, or returned sales value, and is it counted by the date of the return or by the date of the original purchase? - Did it rise suddenly or gradually, and since when? Is it also higher than in the same weeks last year? - Which purchases are in scope: in-store sales, online orders shipped or picked up, grocery orders, and items sold by third-party marketplace sellers? - Has anything changed recently in the return policy, the ways customers can return items, or how returns are recorded? ### What a Strong Answer Covers - Validating the metric's definition and data before diagnosing anything. - Separating a change in returns from a change in orders, and a true rate change from a shift in the mix of channels or categories. - A segmentation checked in a sensible order (channel, category, seller, fulfillment path, region, customer cohort, return reason). - Internal and external hypotheses, each tied to the data that would confirm or rule it out. - How to confirm the root cause, size its contribution, and judge whether the rise is actually bad for the business. ### Follow-up Questions - Every category's return rate is flat, yet the overall rate went up. How is that possible, and what would you check? - The rise traces to a new, easier way to return online orders. Is that a problem, and how would you decide? - The extra returns are concentrated in a small set of third-party sellers. What do you do next?

Overview: A product analytics case about Walmart's climbing return rate, asking what a product manager would examine, and in what order, to isolate the cause. It tests metric validation, return-lag and mix-shift reasoning, segmentation by channel, category and seller, and hypothesis-driven root-cause diagnosis.

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Walmart Labs
Aug 18, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
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You are a product manager at Walmart. The share of orders that customers return has been climbing. What would you look into, and in what order, to isolate the cause?

Clarifying Questions Guidance

  • How is the return rate defined: returned orders over orders, returned units, or returned sales value, and is it counted by the date of the return or by the date of the original purchase?
  • Did it rise suddenly or gradually, and since when? Is it also higher than in the same weeks last year?
  • Which purchases are in scope: in-store sales, online orders shipped or picked up, grocery orders, and items sold by third-party marketplace sellers?
  • Has anything changed recently in the return policy, the ways customers can return items, or how returns are recorded?

What a Strong Answer Covers Guidance

  • Validating the metric's definition and data before diagnosing anything.
  • Separating a change in returns from a change in orders, and a true rate change from a shift in the mix of channels or categories.
  • A segmentation checked in a sensible order (channel, category, seller, fulfillment path, region, customer cohort, return reason).
  • Internal and external hypotheses, each tied to the data that would confirm or rule it out.
  • How to confirm the root cause, size its contribution, and judge whether the rise is actually bad for the business.

Follow-up Questions Guidance

  • Every category's return rate is flat, yet the overall rate went up. How is that possible, and what would you check?
  • The rise traces to a new, easier way to return online orders. Is that a problem, and how would you decide?
  • The extra returns are concentrated in a small set of third-party sellers. What do you do next?