Identify Issues and Redesign Customer-Conversion Chart
Quick Overview
Identify Issues and Redesign Customer-Conversion Chart 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.
Identify Issues and Redesign Customer-Conversion Chart
Company: Amazon
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
Data-visualization review during second-round interview.
##### Question
You are shown a customer-conversion chart. What issues do you notice, and how would you redesign or enhance the visualization? Discuss the pros and cons of your proposal.
##### Hints
Talk about chart type, axes, color, labeling, drill-downs, and how changes improve insight.
Quick Answer: Identify Issues and Redesign Customer-Conversion Chart 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.
Identify Issues and Redesign Customer-Conversion Chart
Critique and Redesign a Customer-Conversion Visualization
Context
Assume you are reviewing a chart shown during an interview that attempts to explain an e-commerce conversion funnel over the past 8 weeks. The typical funnel stages are: Visit → Product View → Add to Cart → Checkout → Purchase. The chart may include multiple traffic sources (e.g., Direct, Search, Ads) and show trends over time.
Task
Identify issues you commonly see in customer-conversion charts related to:
Chart type and layout (e.g., funnel vs. stacked bars vs. line/area).
Axes and scales (e.g., baselines, dual axes, truncated axes).
Color and accessibility (e.g., palette, meaning, contrast).
Labeling and annotations (e.g., missing denominators, unclear rates).
Segmentation and drill-downs (e.g., channel/device/geo, time granularity).
Propose a redesign that improves the ability to answer: Where are we losing users, how is this changing over time, and for whom? Include how you would:
Choose chart types for both overview and time trends.
Encode both absolute counts and conversion rates.
Handle segmentation, interactivity, and drill-downs.
Make uncertainty and data quality visible.
Discuss pros and cons of your proposed design choices and any trade-offs (e.g., simplicity vs. depth, static vs. interactive, single view vs. multiple small multiples).
State any assumptions you make (e.g., definition of "conversion", sessionization rules) and how they impact the visualization.
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?
