Churn Prediction
Using AI and analytics to identify customers likely to stop using a product or service, enabling proactive retention efforts.
In-Depth Explanation
Churn prediction uses machine learning to identify customers at risk of leaving before they actually churn. This enables proactive retention interventions that are far more effective than reactive win-back campaigns.
Churn prediction signals:
- Declining engagement/usage
- Support ticket patterns
- Payment issues
- Negative sentiment
- Competitor activity
- Contract milestones
ML approaches:
- Classification models: Predict churn probability
- Survival analysis: Predict when churn might occur
- Deep learning: Pattern detection in complex data
- Ensemble methods: Combining multiple models
Implementation considerations:
- Define churn clearly for your business
- Choose prediction window (30, 60, 90 days)
- Balance precision and recall
- Integrate with CRM/action systems
- Measure intervention effectiveness
Business Context
Acquiring new customers costs 5-25x more than retaining existing ones. Churn prediction enables efficient retention investment.
How Clever Ops Uses This
We implement churn prediction systems for Australian subscription and service businesses, typically reducing churn by 15-25% through early intervention.
Example Use Case
"A SaaS company identifying that customers who don't log in for 14 days have 60% churn risk, triggering automated re-engagement campaigns."