Project Overview

An exploratory analysis of user engagement and churn patterns in the Waze app, highlighting behavioral factors that influence retention and identifying opportunities for churn mitigation.

Objectives

• Clean and explore user activity data
• Handle missing values and drop unlabeled rows
• Engineer behavioral features (e.g., days_since_last_trip, drives_weekend_pct)
• Visualize retention vs churn and uncover underlying drivers
• Provide actionable business and product insights

Key Insights

• Churn rate is ~72%, indicating aggressive criteria or engagement challenges
• Users with recent activity and higher total drive counts are more likely to be retained
• Weekend driving behavior is a positive retention signal
• Dataset includes more newer users, inferred from lower overall engagement

Featured Visual

Figure: Churn vs Retention behavior by distance driven per day