Client: 

A leading global technology and asset financing firm sought to reduce churn across its B2B Small and Medium Business (SMB) customer segment.

Challenge: 

Annual churn had reached 18% of total assets financed, forcing reliance on expensive new customer acquisitions. The challenge was to leverage data science to predict churn risk and proactively retain customers, strengthening competitiveness against rival asset financing firms. With SMBs accounting for nearly 70% of its yearly portfolio the absence of a structured churn prevention strategy meant customer retention efforts were reactive, inconsistent, and ineffective. With no predictive insights into which customers were at risk, the business incurred substantial revenue leakage and higher acquisition costs — eroding profitability and long-term customer lifetime value.

Solution: 

eClerx partnered with the client to design a predictive churn prevention framework that turned data into actionable retention strategies. The engagement began with integrating multiple datasets — spanning revenue, customer history, Salesforce, transaction records, and even fraud and default lists — to create a holistic view of each customer. From over 250 raw variables, our data science team engineered more than 30 meaningful features that became the foundation for advanced analytics.

Through detailed exploratory analysis, we identified patterns and behaviors that signaled churn risk. Using decision tree models and churn propensity modelling, we segmented customers into risk groups and uncovered the specific drivers of attrition for each profile. This allowed the business to move beyond intuition and gain data-backed clarity on why customers were leaving.

Armed with these insights, we created actionable customer profiles and worked with client teams to design proactive outreach strategies. High-risk SMB customers were targeted with timely, personalized campaigns — ensuring that interventions reached them before they churned. By embedding these insights into everyday sales and service workflows, the client built a systematic, repeatable approach to customer retention that was grounded in data science.

Impact: 

The engagement unlocked significant business value:

• $340M upside identified in potential revenue loss prevention through proactive outreach
• 15% reduction in churn volume, translating to $120M retained from high-risk customers
• Strengthened retention strategy for SMB customers, reducing dependency on high-cost acquisitions
• Established a repeatable, data-driven churn prevention framework as a sustainable competitive advantage

By shifting from reactive responses to predictive, targeted interventions, the client secured stronger customer loyalty, improved profitability, and reinforced its competitive edge in the asset financing market.