This article focuses on the use of machine learning to prevent customer churn. Using the example of one client, we will show how to identify a need, a problem, a solution, and key benefits.
Customer churn is the term used when an existing customer stops using a company and / or stops buying its products. In other words, the client decides to sever their ties with the company. Some types of churn cannot be prevented – for example, moving to another country. This outflow is classified as a non-targeted outflow. In this article, we’ll focus on another class of customer churn – addressed churn.
Problem: A leading multinational bank focuses on private banking. The bank has faced an increased outflow of clients over the past period due to increased competition in the market. The bank possesses a large volume of customer data, but does not use it effectively.
In addition, the bank wants to understand the factors affecting capital outflows so that they can be more proactive in addressing such issues, and not just react after the fact. The real challenge and need is to reduce churn, stabilize the business, and increase profits.
Solution: Using an AI-based platform, the bank began to detect characteristics that caused customer churn. These patterns were identified automatically by the sophisticated machine learning algorithms underlying them. In addition, the customer churn profile has identified high-yield customers at risk. Proactive campaigns are now running regularly to ensure they can retain such customers before they leave.
As we saw in the example above, one of the leading multinational banks was able to use an AI-powered platform to understand why their customers are leaving. Now they can actively fight capital flight. Note that all insights were discovered automatically, without spending 100 hours manually discovering the data or writing a single line of code.
The system used the underlying machine learning algorithms to answer specific business questions and discover hidden insights in the data.
Bank employees can now clearly understand the profiles of customers who leave. They can reach out to existing customers who fit that profile and take proactive steps to reduce churn.