AI-Enabled Customer Retention Strategies Using K-Nearest Neighbors (KNN) And SVM

Abstract

The retention of the current customers is more important for a company compared to acquiring new customers. This paper attempts to explore how machine learning models, such as K-Nearest Neighbors (KNN) and Support Vector Machine (SVM), can be used through Artificial Intelligence (AI) to make predictions on customer retention. Historical data of customers, such as their demographics, transaction history, and engagement scores, are analyzed to foresee whether the customers stay or go. The findings show that both models can predict the customer's retention effectively, but SVM has a higher accuracy (89.2 % vs 85.3 %), higher precision (90.5 % vs 87.1 %), higher recall (86.2 % vs 81.8 %), and higher F1 score (88.3 % vs 84.4 %) compared to KNN. The results suggest that AI can be a valuable tool for enhancing customer retention through tailored services and targeted promotions. By using these AI models, businesses can predict customer churn before it even happens, thereby lowering the risk of losing valuable customers and boosting their customer lifetime value. However, this will depend on how complicated the data set is; SVM works better with complicated and big datasets. The study provides useful information about the real-world application of AI in customer retention, giving companies the means to enhance their customer engagement tactics. The use of more advanced AI technologies like ensemble learning or deep learning models could enhance the model's interpretability or predictive performance.