In today’s highly competitive financial services landscape, customer retention is more critical than ever. With consumers having endless options, financial institutions must find ways to better understand their customers and predict churn risk. This is where Machine Learning (ML) can be a game-changer.
ML-powered customer retention models allow banks and credit unions to gain granular insight into the factors driving attrition. By leveraging ML algorithms, institutions can analyze various customer attributes and behaviours to determine the propensity to leave. The algorithms continuously learn from data to uncover the underlying patterns and relationships.
Some of the critical indicators an ML model can detect are:
-Change in deposit or transaction behaviour;
-Loss of regular payroll deposits;
-Customer demographics and life stage transitions;
-Reduction in product holdings; and
-Unusual or decline in credit card transactions.
As soon as the model identifies customers at high risk of churn, proactive measures can be taken to retain them. This could include personalized promotions, fee waivers, improved and directed customer service, etc. The key is addressing issues before the customer leaves.
ML predictions enable financial institutions to target retention campaigns accurately. Broad-based offers waste resources and have minimal impact. Implementation strategies of customer retention models are typically targeted at customers with a low-mid to mid-high likelihood of leaving because those are the customers most easily swayed. For example, it is unlikely that a campaign or offer would persuade someone with a 100% predicted probability of churning to stay with the institution.
The benefits of ML-powered customer retention models include:
-Increased retention rates and customer lifetime value;
-Reduced churn and attrition costs;
-Improved targeting of retention programs;
-Higher campaign response rates;
-Enhanced customer satisfaction and loyalty; and
-Better allocation of human capital.
The bottom line is that ML allows financial institutions to move from reactive to proactive retention strategies. By understanding which customers are likely to leave – and why – proactive measures can be implemented early. This results in longer-lasting and more profitable customer relationships.
In today’s fiercely competitive landscape, leveraging ML is becoming a necessity to predict and prevent churn. Financial institutions can gain a strategic advantage over competitors with insightful and accurate ML retention models. The superior customer experiences and lifetime value will ultimately drive business success. ML-powered churn prediction is a game-changer that no financial institution can ignore.