No bank wants its customers to churn. Machine learning and predictive analytics can be used for churn risk prediction, to retain more revenue for banks.
Banks know that AI and Machine Learning (ML) can help reduce churn through early intervention. However, building propensity-to-churn models takes time and expertise, and many banks find that not all of their product lines get the attention they deserve due to resource constraints. This lack of bandwidth can often lead to less than optimal customer retention outcomes.
If you're interested in learning more, check out our blog on how predictive analytics is transforming banking.
The AI & Analytics Engine can give bandwidth back to data teams so that more banks can maximize the output of their teams to prototype and productionize ML models, so retention strategies can be planned. For other relevant use cases, check out our article on the Top 18 essential AI Use Cases in Leading Industries!
Customer churn in banking is when a customer closes their account and switches to another bank. Over their customer lifetime, customers generate fees on transactions, banking fees, credit cards, home loans, and personal loans. Churn is a problem, because the bank loses revenue, and it costs more to acquire a new customer than retaining an existing one.
The landscape is rapidly changing for the banking and finance sectors. Previously, customer churn rates were relatively low. However, with the increasing options in the market and the proliferation of fintech, customer churn is an increasingly important battleground for financial institutions.
Traditionally, simple churn analysis uses rules based on known behaviors to identify churn risks. Rules-based systems can be inflexible and overlook customers who do churn and generate false positives. This means expensive incentives are provided to customers who are at low risk of churning.
Machine Learning (ML) for customer churn risk prediction is ideal, particularly for banks. This is due to the problem consisting of complex data over time, and many interactions between diverse customer behaviors. Without ML, it can be difficult and inefficient for bank employees to identify and act in a systematic way.
The cost of acquiring a new customer can be higher than that of retaining a customer by as much as 700%. And, increasing customer retention rates by a mere 5% could increase profits by 25% to 95%.1
1https://www.bain.com/insights/retaining-customers-is-the-real-challenge/
The AI & Analytics Engine provides a complete ready-to-deploy ML solution with its solution templates. These templates automatically build complete pipelines from start to finish, resulting in consumable outputs that are ready for integration into production systems.
There is a template that is tailored specifically for “customer churn prediction”. The goal of the customer-churn prediction template is to create a model that can predict, on a periodic basis, the likelihood of churn (the risk) for the active customers of a company, based on their transactional data. The Engine also offers solution templates for other business use cases including “customer lifetime value prediction”, “next-best offer optimization”, “demand forecasting” etc., which are targeted at specific industries such as banking.
The templates require users to provide only non-technical inputs – such as the business problem definition (e.g. how do you define churn), domain-knowledge inputs (where/in which time frames can potential signals about customer’s future intent be found), and connections to their data sources (e.g. files, database/data warehouse/data lake connections, etc.).
The Engine then automatically builds a full end-to-end solution that is ready for consumption.
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An illustration of how the AI & Analytics Engine builds an end-to-end solution with the customer churn prediction template
Watch this video to see our Data Scientist, Dr. Kathy Sarafrazi, demonstrate how easy it is to use the churn prediction template.
It is completely auditable, repeatable, and maintainable
It speeds up the process of bringing ML to production and get to predictive insights and value out of data rather quickly
It increases productivity for both non-technical and technical users:
It automates mundane tasks in the ML workflow
It allows technical users to focus on high-value tasks such as:
Business-problem formulation
Applying domain expertise
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