How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Amazon.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Amazon during technical interviews.

Identify Issues and Redesign Customer-Conversion Chart

Quick Overview

This question evaluates competency in product analytics, data visualization, and conversion-funnel analysis, focusing on chart selection, axes and scale interpretation, color and labeling accessibility, segmentation strategies, and statistical clarity.

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.

Quick Overview

Context

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.

Identify Issues and Redesign Customer-Conversion Chart

Quick Overview

Critique and Redesign a Customer-Conversion Visualization

Context

Task

Solution

Comments (0)

Identify Issues and Redesign Customer-Conversion Chart

Quick Overview

Critique and Redesign a Customer-Conversion Visualization

Context

Task

Solution

Comments (0)