Value segmentation of remotely acquired customers in banking: a model-based approach

Innovations in information technologies lead to significant changes in the banking sector. While consumers' adaptation to digital services is accelerating, there are developments in the areas of customer experience and expectations. The integration of banks with high technology to meet customer expectations ensures that new products and services are emerging in the digital banking. Remote customer acquisition has become one of the most important developments in digital banking, enabling banks to acquire new customers by overcoming geographical restrictions and without any physical interaction. The possibility that digital channels will be the most important customer acquisition channels for banks soon makes remotely acquired customers have a strategic importance for banks. It is important for banks to get to know these customers better acquired through digital channels without any physical interaction. Efforts to bring these customers to a value segment that will create more value for the bank are increasing significantly. In this study, it has been tried to emphasize the strategic importance of remote customer acquisition and online account opening process within the scope of digital transformation in the banking sector. Using data obtained from a private bank operating in Turkey, various machine learning models were applied to estimate the value segment of customers opening remote accounts and the results of the models were compared. Random Forest was the best performing machine learning model, which predicted customers' value segment with 76% accuracy.