Customer Churn Chronicles - Predictive Analytics Game

📱

StreamFlix Plus

You're the new Chief Analytics Officer at StreamFlix Plus, a streaming service losing $2.3M monthly to customer churn. Your mission: Build a predictive model that catches churners before they leave—but without annoying happy customers with retention offers they don't need.

Monthly Churn Rate

8.2%

Avg Customer Value

$35/mo

Support Tickets

12,450

⚡ Your Three-Stage Challenge

Stage 1: Text Mining 🔍
Analyze support tickets to identify complaint patterns and sentiment

Stage 2: Time Series Analysis 📈
Spot usage decline patterns and engagement drops

Stage 3: Combined Prediction 🎯
Integrate insights to predict churn with confidence scores

🔍 Stage 1: Support Ticket Analysis

Analyze customer support tickets to identify complaint themes and sentiment patterns. High-risk keywords: "cancel", "terrible", "unacceptable", "disappointed"

📊 Sentiment Summary

😡 Angry

😤 Frustrated

😐 Neutral

😊 Satisfied

🏷️ Detected Themes

📈 Stage 2: Usage Pattern Analysis

Examine customer usage patterns over the last 90 days. Look for declining engagement, irregular patterns, or sudden drops that signal churn risk.

🔍 Identify Usage Patterns

📊

Steady Usage

Consistent engagement

📉

Declining

Gradual decrease

🎢

Cliff Drop

Sudden decrease

⚡

Sporadic

Irregular usage

🌊

Seasonal

Cyclical patterns

💤

Inactive

No recent activity

📊 Key Metrics

30-Day Average

Trend Direction

Days Since Last Use

🎯 Stage 3: Final Churn Prediction

Combine text and time series insights to make your final prediction. Remember: False alarms cost $50 in unnecessary retention offers!

📊 Customer Churn Chronicles

⚡ Your Three-Stage Challenge

🔍 Stage 1: Support Ticket Analysis

📊 Sentiment Summary

🏷️ Detected Themes

📈 Stage 2: Usage Pattern Analysis

🔍 Identify Usage Patterns

📊 Key Metrics

🎯 Stage 3: Final Churn Prediction

Make Your Prediction

📊 Customer Churn Chronicles

⚡ Your Three-Stage Challenge

🔍 Stage 1: Support Ticket Analysis

📊 Sentiment Summary

🏷️ Detected Themes

📈 Stage 2: Usage Pattern Analysis

🔍 Identify Usage Patterns

📊 Key Metrics

🎯 Stage 3: Final Churn Prediction

Make Your Prediction

🎉 Results