Customer churn prediction for invoicing software

Abstract

Customer Churn, also known as customer attrition occurs when customer quit using the product or stops doing business with the company. The companies are always interested in identifying those customers as customer acquisition is costly than customer retention. This thesis attempts to predict the churners using machine learning models in an invoicing software made by Isolta Oy.

The actions performed by the user while using the invoicing software are tracked and stored in the data store. The data is then retrieved and cleaned for further processing. Exploratory data analysis, transformations and aggregations are performed on data to make it ready for applying machine learning models. The machine learning algorithms used in this thesis are Random Forest, K-nearest Neighbor, XGBoost and Decision Trees with boosting. XGBoost has better prediction results as compared to the other three algorithms with accuracy up to 77% and f-score of 0.77.