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Company focus

Too Good To Go

Why has Too Good To Go's user retention rate for the mobile app dropped by 15% over the past month?

Prepared by NextSprints

12 mins
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Data Analysis Problem Solving Strategic Thinking Food Tech Sustainability E-commerce Data Analysis User Retention Root Cause Analysis Mobile Apps Food Tech
Product Management Root Cause Analysis Question: Investigating Too Good To Go's app retention rate decline

Introduction

Too Good To Go's 15% drop in user retention over the past month is a critical issue that demands immediate attention. This analysis will systematically identify, validate, and address the root cause while considering both short-term fixes and long-term strategic implications for the app's success.

Framework overview

This analysis follows a structured approach covering issue identification, hypothesis generation, validation, and solution development.

Step 1

Clarifying Questions (3 minutes)

  • Looking at the timing, I'm thinking there might be seasonal factors at play. Has this drop coincided with any particular season or holiday period?

Why it matters: Seasonal patterns could explain temporary fluctuations in user behavior. Expected answer: No significant seasonal correlation. Impact on approach: If seasonal, we'd focus on cyclical retention strategies.

  • Considering user segments, I'm curious if this drop is uniform across all user types. Are we seeing differences in retention between new and long-term users?

Why it matters: Different user segments may require tailored retention strategies. Expected answer: The drop is more pronounced among newer users. Impact on approach: We'd prioritize onboarding and early engagement improvements.

  • Thinking about recent changes, have there been any significant updates to the app or changes in partner restaurants in the last 1-2 months?

Why it matters: Recent changes could directly impact user experience and retention. Expected answer: A major app update was released 6 weeks ago. Impact on approach: We'd focus on analyzing the impact of specific feature changes.

  • Regarding measurement accuracy, has there been any change in how we calculate or track retention rates?

Why it matters: Ensures we're addressing a real issue, not a measurement anomaly. Expected answer: No changes in measurement methodology. Impact on approach: Confirms we need to look at actual user behavior changes.

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Updated Mar 29, 2025