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AI: the gateway to successful churn prevention and customer retention

Growth is one of the most important factors affecting a company’s valuation, attractiveness to investors and market position.

Aleksandr Peussa / October 14, 2021

Growth is one of the most important factors affecting a company’s valuation, attractiveness to investors and market position. Sales figures are at the heart of budgeting procedures and are monitored on a regular basis. For this reason, companies can prioritize new customers and new market opportunities over existing and well-established business that has already been “won”.

As a result, many companies experience an increased risk of churn, which leads to squeezed profit margins. According to Insvesp Consulting, it can cost five times more to attract a new customer than it does to retain an existing one. Moreover, according to the book Marketing Metrics, businesses have a 60 to 70% chance of selling to an existing customer while the probability of selling to a new prospect is only 5% to 20%.

The risk of credit risk mitigation

Historically, banks have always concentrated on credit risk mitigation, which leads to a situation in which churn event-related risks remain uncovered. However, based on current market conditions, churn could lead to even bigger losses than traditional credit losses.

Not surprisingly, profit losses due to customer churn are a big risk within consumer finance. In the Nordics and Baltics, this is driven by a highly competitive consumer loan market which is dominated by brokers offering customers a simple but effective product. By completing a single loan application, consumers can receive dozens of offers from different banks – so they no longer need to apply for loans at individual banks. Essentially, broker companies act as an expensive sales channel for banks who now have to pay broker commission for customers, customers who would previously come to them directly. And, if a customer churns, the bank in question pays twice in the sense that they lose the expected profits from the customer and incur the additional commission cost of acquiring a new customer to replace the churned one. Therefore; preventing churn is imperative.

Effective churn prevention

There are two important aspects of customers’ churn prevention:

  • Identifying customers with high churn risk
  • Taking appropriate actions to ensure customer retention

If a bank is unable to do these effectively, unnecessary operational costs are incurred. These include costly customer relation operations and lost revenues through offering better banking conditions to customers who have no intention of churning. This costly process can be minimized by using AI.

A new approach to churn

By analyzing your customer’s past and current behavior, our AI solution can identify existing high churn risk customers, several months before they actually churn. It includes powerful insight and traceability tools that provide personalized recommendations to retain these customers. Recommendations include lowering loan interest rates, increasing loan amounts, and reducing monthly payment instalments.

 

 

Additionally, our AI solution can help you identify the sweet spot between credit risk and churn risk, based on your risk appetite. This will depend on if your bank concentrates on:

  1. the low credit risk segment where credit losses are minimal but churn risk is high
  2. the high credit risk segment where churn risk is low and credit losses are higher
  3. somewhere in the middle.

If you want to turn your historical data into a competitive advantage, intensify portfolio profitability or just hear more about our AI-based solutions, please do not hesitate to contact us. Let’s have a data transformation journey together!

Aleksandr Peussa
Chief Data Scientist, Director Data Science, TietoEVRY

Aleksandr leads the data science domain development in Financial Services. He has worked in several international banks within risk management and business development, with a strong track record of business growth and intensifying corporate performance in Nordics, Baltics, and Poland.

Author

Aleksandr Peussa

Chief Data Scientist, Director Data Science, TietoEVRY

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