Killing a 32% Monthly Churn Rate with ML & CRM

A SaaS platform was losing customers at 32% monthly churn, not because the product failed, but because users never reached their value moment in the first 30 days.

What I built:

• ML models analysing 47 behavioural signals to predict churn 21 days in advance
• Trigger-based campaigns engaging at-risk users with personalised value demonstrations
• Milestone-based onboarding redesign driving 156% feature adoption increase
• Referral programme turning satisfied users into new acquisition channels

Results:

• 44% churn reduction (32% → 18%)
• $1.8M annualised revenue impact
• 13% new subscriber growth via referrals

#CRM #Retention #ChurnReduction #MachineLearning #SaaS #LifecycleMarketing