Churn Detection and User Classification via Machine Learning in the Food and Beverage Sector

In the modern business world, detecting and predicting customer behavior is one of the key factors for any operation to achieve their goals. Customer churn is one of these behaviors of interest, which makes churn detection and prediction a hot topic in the Machine Learning domain. The customer data that was studied is obtained from a global food and beverage company’s operations in Turkey: Their gift-based mobile application rewards customers who buy their products, and the user data of many sorts is stored within its database. In this study, the unlabeled customer data of a large scale was analyzed and classified via the combination of various supervised and unsupervised ML methods such as K-Means Clustering, Random Forest, Support Vector Machines, Logistic Regression, XGBoost. Then, a score-based churn detection & prediction algorithm is developed after picking the best performing models based on their performance metrics.