Feature Selection in Customer Churn Analysis: Case Study in B2B Business

Customer churn analysis is one of the machine learning applications that has become a hot topic in businesses with the developing technology. Since the performance of fore-casting algorithms is directly affected by the abundance of data, the literature for B2C businesses has been developed faster. In B2B businesses, on the other hand, since customer dynamics are slightly different and the number of customers is not as high as in B2C, data mining studies have been carried out less frequently. Within the scope of BiletBank R&D studies, it is aimed to analyze the customer loss of BiletBank, a B2B company. In line with the customer loss analysis target, the categorical features of BiletBank customers, such as the city they are in, and their periodic interactions with the BiletBank system, have been converted into a data set. In this study, the evaluation of the features in the data set was carried out to create a source for customer loss analysis. Evaluation of features has been implemented by establishing nine different models, including statistical, wrapper, and embedded methods. It is aimed that the feature importance determined as a result of this study will be used in the customer churn analysis studies to be carried out from now on.