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).
-
Statistical clarity (e.g., sample size, uncertainty, significance).
-
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.
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
-
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
-
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
-
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?
