Customer Analytics using Data Science: A Review of Segmentation, Churn Prediction, Recommendation and Explainable AI Techniques

The information age is dominated by massive data collection. Data on what customers buy, use and write are some examples. Manually processing and analyzing this volume of data is infeasible. Therefore, Data Science techniques are utilized to extract the value and patterns in the data. Customer Analytics, the science of analyzing customer behavior, is a classic example of this. We outline the data preprocessing, feature engineering, customer segmentation, churn prediction, recommendation systems, and Explainable AI (SHAP and LIME) steps of this process. We examine several recent papers in the domain of churn prediction with application in ecommerce, banking, telecommunications, and retail sectors. Churn prediction and Ensemble methods such as Random Forest and XGBoost are the standard in industry. While there is a wealth of literature on churn prediction using data from a single sector, there are many remaining challenges. These include data privacy, multi source data integration, bias, and cold-start problems. There is a need to get rapid, actionable insights. As a review paper, we don’t build a system to test our hypotheses.