Diagnose Business Decline Using Key Data Metrics

Quick Overview

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.

Diagnose Business Decline Using Key Data Metrics

Company: Amazon

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### 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.

Overview: 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.

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Aug 4, 2025
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Diagnose Business Decline Using Key Data Metrics

Diagnose a Sudden Business Downturn Using Data

Context

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

  1. Outline a structured diagnostic approach to identify the root cause of the decline.
  2. List the key metrics you would examine at each layer (business, funnel, customer, supply/ops, marketing, product/tech).
  3. 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.

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?
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