Overview
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.
90%
Accuracy in Predicting Customer Churn
20–30%
Churn Reduction via Targeted Campaigns
3x
ROI Boost from High-Risk Segments
Customer Challenges
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.
Lack of a Predictive Framework to Identify and Prioritize At-Risk Customers
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.
Limited Visibility into Churn Drivers
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.
Generic Engagement Across All Customers
Communications were not tailored based on individual customer risk or behavior, leading to poor impact and missed opportunities to build loyalty.
Solutions
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.
01.
Behavioral Feature Engineering
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.
02.
Balancing the Dataset
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.
03.
Model Transparency with Feature Importance
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.
Contact Us
We’d love to hear from you.
Lets discuss how we can transform your business with AI. Talk to our AI expert team. Lets do AI journey together.
