Churn Prediction Model

Churn Prediction Model empowers your team to act before users leave. By analyzing historical cohort trends and behavioral signals, this tool identifies at-risk users and recommends personalized interventions to improve retention. It gives you the predictive edge needed to turn potential churn into long-term loyalty.

What is the Churn Prediction Model?

This app applies advanced predictive analytics and cohort-based segmentation to uncover:

• Which user groups are showing early churn signals
• What behaviors and patterns correlate with drop-off
• Where targeted save plays can generate the biggest retention wins

It generates actionable recommendations — not just risk scores — so your team can deploy win-back campaigns, in-product nudges, and outreach programs with precision and confidence.

Why use this tool?

• 🔮 Proactive Retention
  Identify user segments most likely to churn before it happens.

• 📈 Data-Driven Decisions
  Base your outreach strategies on real behavioral data and predictive models, not guesswork.

• 🎯 Targeted Interventions
  Design retention campaigns tailored to specific cohorts, usage patterns, or customer segments.

• 💡 Cohort Insights
  Learn how retention trends differ across signup months, product tiers, and engagement profiles.

How it works

1. Upload cohort and behavioral data
   Segment by signup date, usage patterns, last activity, satisfaction scores, and more.

2. Define churn signals and retention goals
   Input your indicators (e.g., inactivity, drop in usage, survey results) and target KPIs.

3. Get actionable retention strategy
   Receive risk cohorts, churn probabilities, and specific campaign tactics to close the retention gap.

What you'll need to provide

To generate accurate predictions and recommendations, please share:

• Cohort Data (e.g., signup month, usage frequency, last active date, subscription tier)
• Churn Indicators (e.g., inactivity thresholds, feature usage drops, satisfaction signals)
• Retention Goals (e.g., monthly rate improvement, tier-based retention benchmarks)

Who it's for

• Product managers aiming to increase user stickiness
• Growth teams focused on lifetime value optimization
• Retention marketers building proactive save campaigns
• SaaS founders tracking cohort health over time