An Explainable AI Enable Approach to Reveal Feature Influences on Social Media Customer Purchase Decisions

The use of social media is widespread in modern culture. People are making purchases on social media sites. Compared to more conventional approaches, digital marketing has shown to be successful. Both academics and researchers can use it to understand better how to use social media to influence consumers’ purchasing decisions. In this study, we proposed a machine learning (ML)-based customer purchase decision prediction system with model explainability. We use eight well-known ML algorithms for prediction and SHAP, SHAPASH, and LIME for model explainability. Among the models, the RF shows its superiority by achieving 93.52% accuracy with 93% precision, 92% recall, and 93% F1 score. We apply the XAI tools on RF and reveal the behind story of customer purchase decisions. We show both the global and local explainability of the model to find the actual cause of online purchases and how the features influence the customer to purchase. This study helps online marketers grow their businesses, and customers can also be benefited from it.