Recovering $20M with Predictive Churn Modeling for a Distribution Company
Client Overview
A leading distribution company was struggling with high customer churn, especially among mid-sized and high-value clients. Each lost customer represented a significant loss in recurring revenue and lifetime value, yet by the time churn was noticed, it was too late to act.
They needed a proactive solution — one that could identify at-risk customers early and give their sales and retention teams the opportunity to re-engage before it was too late.
The Challenge
The client had vast amounts of transactional data, but no way to extract predictive insights from it. Churn signals were subtle and varied across customer segments. Traditional KPIs like recent order volume or support tickets didn’t tell the full story — and reactive strategies were falling short.
They brought us in to build a machine learning model that could predict churn before it happened, using historical behavior to detect the earliest signs of disengagement.
Our Solution
We developed a custom churn prediction model that analyzed detailed order patterns and behavioral trends across the customer base. The model’s goal was to assign a real-time churn risk score to each customer, enabling the business to intervene while there was still time to make an impact.
Key Features of the Model:
Order Pattern Analysis: We modeled frequency, volume, product diversity, and timing irregularities in historical orders to detect subtle shifts in behavior.
Segment-Aware Predictions: Customers were clustered based on business type and purchasing behavior, allowing the model to tailor its churn risk predictions to different segments.
Actionable Outputs: We integrated the churn scores into the client’s CRM, creating real-time dashboards and alerts for sales reps and account managers.
Results
The business impact was significant and measurable:
Over $20 million in at-risk revenue recovered within the first year by proactively engaging high-risk customers before they churned.
Retention team efficiency increased, with interventions focused only on customers most likely to leave.
Model accuracy and trust grew over time, enabling the client to expand its use across new customer segments.
Conclusion
Churn prevention is most powerful when it's predictive, not reactive. By transforming order data into meaningful churn signals, we helped this client stay one step ahead — turning potential losses into retained revenue. With our machine learning solution in place, they no longer guess who might churn — they know.