Predicting Customer Churn at a Swedish CRM-system Company

Abstract: This master thesis investigates if customer churn can be predicted at the Swedish CRM-system provider Lundalogik. Churn occurs when a customer leaves a company and is a relevant issue since it is cheaper to keep an existing customer than finding a new one. If churn can be predicted, the company can target their resources to those customers and hopefully keep them. Finding the customers likely to churn is done through mining Lundalogik's customer database to find patterns that results in churn. Customer attributes considered relevant for the analysis are collected and prepared for mining. In addition, new attributes are created from information in the database and added to the analysis. The data mining was performed with Microsoft SQL Server Data Tools in iterations, where the data was prepared differently in each iteration. The major conclusion from this thesis is that churn can be predicted at Lundalogik. The mining resulted in new insights regarding churn but also confirmed some of Lundalogik's existing theories regarding churn. There are many factors that needs to be taken into consideration when evaluating the results and which preparation gives the best results. To further improve the prediction there are some final recommendations, i.e. including invoice data, to Lundalogik of what can be done.