Customer Retention & Win-back
MediumRetaining customers and winning back lapsed ones.
The Pain
Retention efforts are reactive. Churn is identified after customers have left.
What's Possible
AI can predict churn risk and enable proactive retention. Lapsed customers are systematically targeted.
Signals This Is Worth Exploring
Churn prediction is limited
Retention is reactive
Win-back is not systematic
Customer lifetime value is not maximised
Impact
Lower customer churn
More proactive retention
Higher win-back success
Improved customer lifetime value
Typical Approach
Assess
Analyse churn patterns and retention efforts.
Pilot
Apply predictive retention.
Scale
Extend across customer base.
What to Watch Out For
Prediction has uncertainty
Not all churn is preventable
Intervention has costs
Customers have varying value
Questions to Think About
Before we talk, you might want to consider:
What is your churn rate?
How do you identify at-risk customers?
What retention programs do you have?
How do you win back lapsed customers?
Build On This
Once the basics are working, you can expand:
Value-based retention
Prioritise high-value
Intervention testing
Optimise retention tactics
Win-back automation
Systematic re-engagement
Want to explore if this fits your organization?
Book a 30-minute call to discuss your situation and whether this use case makes sense for you.
Book a 30 min call