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Predicting customer churn using machine learning

October 4, 2022

Using machine learning to predict customer churn has proved effective in identifying potential churners and developing successful retention strategies for the financial sector.

The process of digital transformation has opened up great new possibilities for banks and credit institutions. Online and mobile banking make it possible to reach every customer with an electronic device, no matter where they are in the world.

But as the market broadens, competition increases, any digital bank may potentially be a competitor. And since they are no longer obliged to stick to local banks, customers have a huge range of options when deciding where to open a bank account.

Furthermore, if a bank’s portfolio is uncompetitive in terms of offer and price, or its services are run poorly, it is likely to lose customers. This phenomenon is called churn.

Customer churn: facts and stats

In a nutshell, customer churn refers to customers who stop using a company’s products or services within a certain timeframe. If it happens over a short period, from several days to a month, we call it “hard churn”. It is known as “soft churn” if it happens between a couple of months and a year.

Customer churn has become a real struggle for international banks. According to recent stats, the annual attrition rate for banks in North America is about 11%. This means that banks spend considerable amounts of money and energy signing up new customers just to balance their books. Moreover, attracting and winning over new customers is extremely expensive, something like 6 or 7 times more than retaining existing ones.

In the digital era, banks must therefore focus their energies on developing an effective retention strategy to keep as many customers as possible. In this article, we’ll discuss what causes customers to leave and how to calculate churn prediction using machine learning.

Why customer churn happens

Customers may leave their bank for various reasons. Unfortunately, according to 1st Financial Training Services, 96% of unhappy customers don’t complain; 91% of them don’t explain why they are unhappy and simply leave. Based on our clients’ experience, we have drawn up a list of the main reasons why people decide to switch banks.

• Poor service

Good quality service is the basis for any solid business. But some businesses don’t understand how important it is until they start losing customers. A recent study reported that almost 9 in 10 customers abandon a firm because they experience poor customer service.

• Poor product-market fit

In the age of online banking, customers are constantly looking for better options. This means that if a bank can’t offer a good range of innovative, affordable products, not only will it be unlikely to find new customers, it will certainly lose existing ones.

• Slightly off-key product offers

Even when banks have a competitive product portfolio, they may not know their customers well enough and end up offering slightly off-key products, thereby lowering customer engagement.

• Difficult user experiences

Online banking websites that aren’t user-friendly and are difficult to navigate are a real pain for customers. Not to mention mobile banking apps that crash frequently, interrupting money transfers and online payments.

Predicting customer churn using machine learning

In today’s highly competitive market, successful banks are those which fully embrace digital transformation. Not only do they provide digital services and products, but they also use data-driven technology to enhance their decision-making process.

By exploiting the full potential of customer data, banks can better understand client behaviors and learn churn patterns from past records. This allows them to predict their customers’ future movements and respond accordingly.

Let’s see how.

• Managing customer data with advanced analysis tools

Banks need to smarten up their analytics if they want quick, accurate insights on their customers. But how? Spreadsheets alone aren’t enough to prevent banks from losing valuable pieces of information. They are ineffective when big volumes of data, from different sources and in multiple formats, are involved.

Equipped with an effective data analysis tool like Rulex Platform, banks can easily merge all their customer data from a wide range of databases into a single place in the same format. This facilitates data analysis, allowing banks to learn client behaviors and implement an effective retention strategy.

• Tracking churn patterns from historical records

Many different drivers cause customers to leave their banks, and they vary across life stages and demographics. Drawing on a bank’s historical data, machine learning models can generate insight on churn patterns. This helps business experts make predictions on possible future churners.

Rulex’s eXplainable AI (XAI) has proven particularly effective in the financial services sector. Rulex’s XAI quickly analyzes historical records and produces explainable outcomes regarding which customers are more likely to churn and why. On high-confidence predictions, Rulex technology’s success rate is between 90 and 99%.

• Increasing customer satisfaction with AI

Banks need to engage with their customers on a deeper level to improve customer retention. Knowing your clients means, for example, being able to offer them on-key products at the right time or enhance their experience. Once again, AI technologies like Rulex’s XAI can come in handy by suggesting the best ways to improve customer satisfaction.

After pinpointing customers who are likely to churn, Rulex’s XAI proposes corrective actions to prevent them from leaving. For example, for customers dissatisfied with service, Rulex’s XAI will suggest improvements to its quality. The corrective action will be to place customers who contact the support center twice or more into a special priority queue, so their calls are handled more quickly.

By using historical records and applying similar types of corrective action for all their customers, one of our clients was able to reduce churn rate by almost 3%, equal to a revenue increase of 11%.

If you feel it’s time for your bank to tackle the issue of customer churn, visit our Financial services page and get in touch with our experts for a free consultation.

Marketing Specialist • Sales and Marketing

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