Diagnose Business Decline Using Key Data Metrics evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Scenario
Diagnosing an unexpected business downturn using data analysis.
##### Question
How would you use data to diagnose a business decline? List the key metrics you would examine and the data sources needed.
##### Hints
Think funnels, cohorts, external factors, and leading indicators.
Quick Answer: Diagnose Business Decline Using Key Data Metrics evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
You are the data scientist supporting a large consumer marketplace. Leadership reports a sudden decline in topline performance over the last 2 weeks. You need to rapidly diagnose the problem using data.
Task
Outline a structured diagnostic approach to identify the root cause of the decline.
List the key metrics you would examine at each layer (business, funnel, customer, supply/ops, marketing, product/tech).
List the internal and external data sources you would pull to support the investigation.
Hints: Incorporate funnels, cohorts, external factors, and leading indicators. Make minimal assumptions as needed.
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?
