Every bank wants to retain as many of its customers as possible to sustain its business. Because it costs a lot more to bring in a new customer than it does to retain an existing one.
Banks benefit from knowing what leads a customer to decide to leave the company. Preventing churn allows companies to develop loyalty programs and retention campaigns to retain as many customers as possible. In addition, banks are constantly analyzing a lot of information about their customers:
- Credit rating;
- The length of time of using the bank’s services;
- Age of the client;
- Balance of funds in the account;
- Number of banking products purchased;
- Use of loyalty programs;
- Complaints and overall level of satisfaction;
- Approximate expected annual income of the client and more.
Using Python machine learning, it’s possible to predict bank customer churn before it happens. By analyzing a variety of data points such as transaction history, online activity and customer demographics, algorithms can determine which customers are most likely to switch banks.
With this information in hand, banks can take proactive measures to keep these customers by offering targeted promotions or better customer service. And for those customers who do leave, banks can use this information to pinpoint the reasons why they left so they can make improvements in those areas. This way, banks can not only retain their existing customers but also improve their overall service based on insights gleaned from the churn prediction models.
As for me, I just enjoy analyzing user experience and building models for predicting customer behavior. So when I came across a dataset with data from 10,000 bank customers, I just couldn’t resist. And built a model that predicted customer churn with 99.9% accuracy!
