Diagnose Decline in User Engagement and Experience Quality

Q: Diagnose Decline in User Engagement and Experience Quality

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 Diagnose Decline in User Engagement and Experience Quality states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Q: 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.

Q: What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at TikTok.

Q: What role is this question designed for?

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

Question

Diagnose Decline in User Engagement and Experience Quality

Product Metrics Deep-Dive and Causal Inference (TikTok)

Context

You are a data scientist working on TikTok’s core product. Over several weeks, daily active users (DAU) have been declining. Separately, a manager wants to understand how users’ network speed affects TikTok usage, but only observational data are available.

Tasks

a) DAU has been steadily declining. Which metrics would you define, and what experiment(s) or analysis would you design to diagnose the issue?

b) To quantify the causal impact of users’ network speed on TikTok usage with observational data, describe a suitable causal-inference approach and how you would explain its validity to a non-technical stakeholder.

Hints: Consider retention, cohorts, segmentation, and methods like instrumental variables (IV) or propensity weighting. Communicate assumptions clearly.

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 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?
What decision would you make if metrics disagree?

Diagnose Decline in User Engagement and Experience Quality

Quick Overview

Diagnose Decline in User Engagement and Experience Quality

Diagnose Decline in User Engagement and Experience Quality

Product Metrics Deep-Dive and Causal Inference (TikTok)

Context

Tasks

Constraints & Assumptions

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer