##### 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.

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.

How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Adobe.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Adobe during technical interviews.

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).

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

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

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

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?