In this chapter, we use a database from a Portuguese bank, with data related to the behavior of customers, to analyze churn, profitability and next-product-to-buy (NPTB). The database includes data from more than 94,000 customers, and includes all transactions and balances of bank products from those customers for the year 2015. We describe the main difficulties found concerning the database, as well as the initial filtering and data processing necessary for the analysis. We discuss the definition of churn criteria and the results obtained by the application of several techniques for churn prediction and for the short-term forecast of future profitability. Finally, we present a model for predicting the next product that will be bought by a client. The models show some ability to predict churn, but the fact that the data concerns just a year clearly hampers their performance. In the case of the forecast of future profitability, the results are also hampered by the short timeframe of the data. The models for the next product to buy show a very encouraging performance, being able to achieve a good detection ability for some of the main products of the bank.
The huge amounts of data that banks currently possess about their customers allow them ...