Spending money to acquire customers that leave the bank is not only frustrating but, it's expensive! Once effective onboarding and cross-selling systems are installed, banking institutions need a way to predict which customers are at risk of leaving so they can take decisive actions to keep attractive customers. It's a three-step process:
- Predict who is at risk of leaving
- Trigger retention activities
- Measure the effectiveness of retention activities
In this blog - we'll talk about a few things that machine-learning can tell us about who is likely to leave the bank.
Figure 1 plots a few characteristics that differentiate customers who are at high and low risk of attrition.
The plot shows the following common characteristics of customers at high risk of attrition:
- Few accounts
- Few services
- Rarely have direct deposit
- Low or negative checking balance
- Low tenure
The findings from machine-learning square with what a banker would intuitively expect so, we are confident that we have a good machine here that will systematically process customer data and identify customers at-risk of leaving so notifications can be sent to the front lines to execute retention treatments.
Summarily, it's imperative to install effective onboarding and cross-selling systems to reduce attrition risk. Simultaneously, banks need to leverage machine-learning to identify attractive customers at risk of attrition and take immediate and aggressive steps to retain those customers, which is only possible with machine-learning. If you'd like to learn more about how to retain more customers, get in touch with us -- we'll show you what we've learned.