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A leading Non-Banking Financial Company (NBFC) in Sri Lanka was facing rising customer churn and lacked the tools to effectively identify and act on early signs of risk. NeenOpal partnered with the client to develop a machine learning–driven churn prediction model that accurately identified at-risk customers, enabling timely and personalized retention strategies.
As part of their retention efforts, the client faced several challenges that made it hard to manage and reduce customer churn. These issues led to gaps in their strategy and execution, resulting in less effective outcomes.
Without a system to proactively identify potential churners, the client was unable to detect them early, reducing the impact of re-engagement efforts. Additionally, the absence of risk segmentation made it difficult to prioritize high-risk customer segments, leading to inefficient allocation of retention resources.
Despite a wealth of historical data, there was no clear understanding of what behaviors or patterns preceded churn, making it hard to design focused strategies.
Communications were not tailored based on individual customer risk or behavior, leading to poor impact and missed opportunities to build loyalty.
NeenOpal implemented a machine learning model to learn from the historical trends of Customer churn and predict the churn probabilities of the active customers. These Churn probabilities helped the client target the at-risk customers and optimize their retention strategies.
Customer behavior was analyzed and aggregated monthly to capture evolving trends — such as loan repayment consistency, transaction frequency, and engagement signals — giving the model a dynamic understanding of churn risk.
As churned customers represented a minority, data imbalance was addressed using suitable techniques to ensure the model could learn effectively from both churned and active customer classes.
To make the model explainable to business users, we extracted feature importance scores to reveal which behaviors or patterns were most predictive of churn. This not only improved trust in the model but also provided direct inputs for strategic decisions.
NeenOpal brings deep expertise in data science and AI-powered analytics to help businesses make smarter, faster decisions. Our tailored approach ensures every solution aligns with the client’s goals and industry needs. With a strong focus on measurable outcomes, we help companies unlock the full potential of their data, whether it’s reducing churn, boosting retention, or optimizing spend.
With NeenOpal’s predictive analytics solution, the client was able to transform their retention strategy from reactive to proactive. Here’s how our solution delivered tangible value:
The churn prediction model significantly strengthened the client’s customer retention strategy. By using data to anticipate churn and personalize interventions, the NBFC saw measurable improvements in customer satisfaction, campaign efficiency, and revenue continuity. More importantly, this initiative laid the groundwork for a customer lifecycle management system powered by data-driven intelligence.
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