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Investigate Why DAU Stagnates Despite High Downloads

Last updated: Mar 29, 2026

Quick Overview

This question evaluates proficiency in product analytics, funnel and cohort analysis, instrumentation validation, attribution and incrementality assessment, and experiment design within a data science context.

  • medium
  • Adobe
  • Analytics & Experimentation
  • Data Scientist

Investigate Why DAU Stagnates Despite High Downloads

Company: Adobe

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Adobe Express averages 1 M daily downloads, yet daily active users remain flat. ##### Question Daily downloads are high but DAU is not growing. How would you investigate the discrepancy? Describe the data cuts, analyses, hypotheses, and experiments you would run. ##### Hints Consider funnel drop-offs, activation and retention cohorts, organic vs paid installs, device/platform issues, user feedback.

Quick Answer: This question evaluates proficiency in product analytics, funnel and cohort analysis, instrumentation validation, attribution and incrementality assessment, and experiment design within a data science context.

Related Interview Questions

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Adobe logo
Adobe
Aug 4, 2025, 10:55 AM
Data Scientist
Onsite
Analytics & Experimentation
4
0

Investigating High Daily Downloads but Flat DAU

Scenario

Adobe Express reports approximately 1M daily downloads, yet daily active users (DAU) remain flat.

Assume:

  • Daily downloads = app store installs on iOS/Android (unique devices), inclusive of organic and paid.
  • DAU = unique users who generate a qualifying in-app event per calendar day (e.g., app open or key activity), de-duplicated by user/account where available.

Task

Daily downloads are high but DAU is not growing. Outline a rigorous plan to investigate and resolve this discrepancy.

Your answer should cover:

  1. Data cuts and instrumentation checks you would run.
  2. Analyses (time series, funnels, cohorts) to diagnose where the gap occurs.
  3. Key hypotheses that could explain the pattern and how you would test them.
  4. Experiments or causal methods to validate fixes and measure impact.

Hints to Consider

  • Funnel drop-offs: install → first open → permissions → sign-up/login → activation event → repeat use.
  • Activation and retention cohorts (D0/D1/D7/D28).
  • Organic vs. paid installs; incrementality of paid channels.
  • Device/platform/app version issues (crashes/ANR, app size, OS compatibility).
  • User feedback (reviews, CS tickets, surveys) and store listing performance.
  • Reinstalls, duplicate devices, cross-platform cannibalization (mobile vs. web).

Solution

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