MNeuralTab: Integrating meta-modeling and neural networks for customer churn prediction in e-commerce

Customer churn poses a significant challenge to e-commerce businesses, impacting revenue and hindering long-term growth. Predicting customer churn is paramount for the long-term viability of e-commerce businesses, enabling proactive retention strategies and minimizing costly customer attrition. This research introduces a novel meta-model approach for e-commerce customer churn prediction. Our approach integrates multiple deep learning architectures, including TabNet, and leverages Neighborhood Component Analysis (NCA) to select the most relevant features for churn prediction. We evaluate our approach on two distinct e-commerce datasets: the Olist Online dataset and the REES46 dataset, which has not been previously used for churn prediction. By effectively combining the strengths of individual base models, our proposed approach surpasses the limitations of traditional models and accurately captures both feature-specific and hierarchical relationships within the data. Our proposed model obtained an area under the curve (AUC) of 0.98, an F1-score of 98.78%, an accuracy of 99.62%, a precision of 99.32%, Matthew’s Correlation Coefficient (MCC) of 0.987540 and a recall of 98.66% in the Olist dataset exceeding the results obtained from traditional models. An AUC of 0.95, an accuracy of 88.63%, a precision of 81.54%, a recall of 83.47%, an MCC of 0.731223, and an F1-score of 82.49% were obtained on the REES46 dataset demonstrating clear superiority over baseline models. Experimental results demonstrate superior performance on both datasets, with significant improvements over baseline and state-of-the-art methods. By enabling businesses to proactively identify at-risk customers, our model empowers them to implement targeted retention campaigns, optimize marketing spend, and ultimately enhance customer satisfaction and long-term profitability.