Diagnose Heavy Return-Policy Page Traffic Ahead of Browsing on an E-commerce Store

Quick Overview

A product manager interview question about an online store where 70% of shoppers open the return policy page before they shop. It tests whether the candidate can decide if the behavior hurts the business, segment the visitors, check root-cause hypotheses with data, and choose actions with sound metrics and guardrails.

Diagnose Heavy Return-Policy Page Traffic Ahead of Browsing on an E-commerce Store

Company: Flipkart

Role: Product Manager

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Other

You are a product manager at an online store. Its analytics show that 70% of shoppers visit the store's return policy page before they shop. What would you do? Work out whether this behavior is a problem for the business, explain what is likely driving it, and say what you would change, if anything. ```hint Symptom or problem? Reading a policy page costs the store nothing by itself. Before you list causes, decide which business outcome would have to suffer for this to matter, and which group of shoppers you would compare against. ``` ### Clarifying Questions - What does the store sell: items where fit or feel matters, such as clothing and shoes, or goods that are easier to judge online? Are any categories final sale? - Does "before they shop" mean before viewing any product, or after browsing but before adding to the cart or paying? - Do shoppers who read the policy convert, spend and return items at different rates from shoppers who do not? - Is the pattern long-standing or recent, and is it concentrated among new customers, one device or one region? ### What a Strong Answer Covers - Treating the 70% as a symptom: naming the outcomes (conversion, order value, return rate, net revenue) that decide whether it is a problem, and checking the data before reacting. - Segmenting the visitors and their path to the page (entry point, tenure, category intent, device, traffic source) before forming hypotheses. - Hypotheses that span shopper doubt about fit or quality, unclear or strict policy terms, earlier bad return experiences, site design that routes people to the page, gift buying, and return abuse, each paired with the evidence that would confirm it. - Actions matched to each confirmed cause, including the option to change nothing, validated with an experiment. - Success metrics with guardrails such as return rate and return cost per order. ### Follow-up Questions - Policy readers convert better than other shoppers but also return far more items. Is that a problem, and what would you do? - The legal team will not let the policy terms change. How else could you reduce the uncertainty that sends people to the page? - How would you test whether showing return terms on product pages raises conversion without raising return costs?

Overview: A product manager interview question about an online store where 70% of shoppers open the return policy page before they shop. It tests whether the candidate can decide if the behavior hurts the business, segment the visitors, check root-cause hypotheses with data, and choose actions with sound metrics and guardrails.

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Flipkart
Nov 24, 2024
mediumProduct ManagerOtherAnalytics & Experimentation
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You are a product manager at an online store. Its analytics show that 70% of shoppers visit the store's return policy page before they shop. What would you do? Work out whether this behavior is a problem for the business, explain what is likely driving it, and say what you would change, if anything.

Clarifying Questions Guidance

  • What does the store sell: items where fit or feel matters, such as clothing and shoes, or goods that are easier to judge online? Are any categories final sale?
  • Does "before they shop" mean before viewing any product, or after browsing but before adding to the cart or paying?
  • Do shoppers who read the policy convert, spend and return items at different rates from shoppers who do not?
  • Is the pattern long-standing or recent, and is it concentrated among new customers, one device or one region?

What a Strong Answer Covers Guidance

  • Treating the 70% as a symptom: naming the outcomes (conversion, order value, return rate, net revenue) that decide whether it is a problem, and checking the data before reacting.
  • Segmenting the visitors and their path to the page (entry point, tenure, category intent, device, traffic source) before forming hypotheses.
  • Hypotheses that span shopper doubt about fit or quality, unclear or strict policy terms, earlier bad return experiences, site design that routes people to the page, gift buying, and return abuse, each paired with the evidence that would confirm it.
  • Actions matched to each confirmed cause, including the option to change nothing, validated with an experiment.
  • Success metrics with guardrails such as return rate and return cost per order.

Follow-up Questions Guidance

  • Policy readers convert better than other shoppers but also return far more items. Is that a problem, and what would you do?
  • The legal team will not let the policy terms change. How else could you reduce the uncertainty that sends people to the page?
  • How would you test whether showing return terms on product pages raises conversion without raising return costs?