By the time a customer churns, it's too late. A churn prediction model identifies at-risk customers while there's still time to intervene — with the right offer, at the right moment, to the right person.
Retention is almost always more profitable than acquisition. These numbers explain why.
Acquiring a new customer costs 5× more than retaining an existing one — making churn prevention one of the highest-ROI investments in your business
A 5% increase in customer retention can increase profits by 25–95%, according to Bain & Company research on customer economics
The probability of selling to an existing customer is 60–70%, vs. 5–20% for a new prospect — retained customers are your best sales channel
From raw behavioral data to a deployed scoring system in four stages.
I transform your raw transaction and engagement history into predictive signals — days since last purchase, purchase frequency decline, product category shifts, support contact patterns, and more.
Multiple models compared (Random Forest, XGBoost, LightGBM) and evaluated on precision, recall, and AUC. The model is validated on data it has never seen to confirm real-world performance.
Every customer gets a churn probability score (0–100%). You can filter, sort, and segment by score — immediately identifying your top 100 at-risk customers who need outreach today.
SHAP values explain why each customer is at risk — so your team knows whether to send a discount, a product recommendation, a support check-in, or a cancellation prevention offer.
A production-ready ML model with documented performance metrics — precision, recall, AUC, and confusion matrix on your actual data.
Every customer scored 0–100% for churn likelihood, exportable to CSV or integrated into your CRM for immediate action.
Feature importance and individual SHAP values showing exactly which behaviors predict churn — so interventions are targeted, not generic.
A live dashboard tracking churn risk trends, at-risk segment size, and intervention success rates over time.
Specific, data-backed recommendations for what to do with high-risk, medium-risk, and recently-churned segments.
Case studies showing churn and retention analysis in action.
Retention Analysis · Power BI
Identified 85% customer retention failure as the root cause of a 90% revenue collapse.
Customer Analytics · Python
Logistic regression and SHAP identifying the behavioral drivers of product returns.
Funnel Analytics · Path Analysis
9,935 sessions mapped to find where users disengage before converting.
Stop Losing Customers Silently
Tell me about your customer retention problem. First call is free — I'll tell you exactly what a churn model for your business would look like.
Book Your Free ConsultationRemote worldwide · adeyemi@adediranadeyemi.com