Investigate Why DAU Stagnates Despite High Downloads
Quick Overview
This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Why DAU Stagnates Despite High Downloads states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
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 interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Investigate Why DAU Stagnates Despite High Downloads states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Investigate Why DAU Stagnates Despite High Downloads
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:
Data cuts and instrumentation checks you would run.
Analyses (time series, funnels, cohorts) to diagnose where the gap occurs.
Key hypotheses that could explain the pattern and how you would test them.
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).
Clarifying Questions to Ask Guidance
Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.
What a Strong Answer Covers Guidance
A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.
Follow-up Questions Guidance
What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?