An Explainable AI Framework for Marketing Customer Segmentation and Campaign Response Prediction

The segmentation of clients and the forecast of marketing campaign response are essential for optimising marketing expenditures and enhancing customer participation. This work suggests an XAI approach for segmenting the clients, predicting the responses using machine learning, and targeting them based on profit. Specifically, 2,055 cleansed customer records were extracted from the Kaggle Customer Personality Analysis dataset through preprocessing. We combine multiple clustering methods using consensus clustering to find the different segments of consumers, using Principal Component Analysis (PCA). The performance of seven machine learning classifiers is assessed using five-fold cross-validation, considering several performance metrics. The best accuracy (ACC) is 87.59% for Gradient Boosting (GB) and the ROC-AUC is 0.909 for Logistic Regression. The segment integration yields an improved recall (REC) rate of 79.4% and F1 score (F1) of 0.649, compared to the previous situation of REC value 73.0% and F1 value 0.622. The resulting profit-optimised solution is a projected profit of 61.1 currency units per 100 consumers. Moreover, elements such as catalogue purchases, recency, and campaign history are identified as elements that are significant via the techniques of SHapley Additive ExPlanations (SHAP) and permutation feature significance. The proposed approach will enable more accurate client segmentation, targeted marketing campaigns, and efficient use of resources. More testing is needed on a variety of real-life datasets to ensure greater generalisability, but the outcomes are encouraging.