Investigate Causes of Driver WOW Score Drop

Quick Overview

Investigate Causes of Driver WOW Score Drop 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.

Investigate Causes of Driver WOW Score Drop

Company: Lyft

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Lyft notices its driver WOW (satisfaction) score has dropped 10% quarter-over-quarter. ##### Question How would you investigate the causes behind the driver WOW score drop? What data would you pull, which segments would you examine, and what hypotheses would you test? Propose an experiment or product change to improve the WOW metric and explain how you would measure success. ##### Hints Think metric definitions, funnel breakdowns, cohort analysis, A/B test design, and measurable KPIs.

Quick Answer: Investigate Causes of Driver WOW Score Drop 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, 10:55 AM
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Investigate Causes of Driver WOW Score Drop

Investigating a 10% QoQ Drop in Driver WOW (Satisfaction)

Context

Assume WOW is a standardized driver satisfaction metric collected via in-app surveys (e.g., 0–10 or 1–5 Likert), aggregated weekly and reported quarterly. The latest quarter shows a 10% relative decrease in the mean WOW score. Your task is to diagnose causes and propose a data-driven fix.

Tasks

  1. Clarify the metric and verify the drop (definitions, weighting, response rates, significance, seasonality).
  2. Identify what data to pull and how to break down the driver journey/funnel.
  3. Specify segments to examine and hypotheses to test.
  4. Propose an experiment or product change to improve WOW and define success metrics and guardrails.

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?
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