Optimizing CLV Predictions with RFM, NPS, CES, and ATV: An Ensemble Approach

Abstract

Correct estimation of CLV will help in optimizing the business-customer relationship management effectively.  A market research analyst would do this. It shows the new method for estimating CLV using the needed Ensemble Learning technique such as the Stack Method with XGBoost, Random Forest, and Gradient Boosting. It achieves this by integrating key customer metrics like Recency, Frequency, Monetary, or RFM, Net Promoter Score, or NPS, Customer Effort Score, or CES, and Average Transaction Value, or ATV. The methodology is applied to a dataset of 15,000 customer profiles of a well-known e-retailer operating worldwide. This present study explores how these metrics all relate to each other and influence the predictions of CLV and business performance. Carrying an in-depth investigation into RFM, NPS, CES, and ATV metrics and then applying an ensemble method approach. The model was validated on a 10 percent test set to prove its excellence at capturing complex and nonlinear relationships between predictors and CLV.  The generalizability of the proposed model was revealed in the improvement of performance for both training and test datasets. The wide explanations of variance and the high R-squared proved that these factors showed their predictive strength. While NPS and CES were highly influential regarding the accuracy of the model, the importance of ATV was much smaller, yet still meaningful in the predictions. A multi-metric approach is important in the prediction of CLV because it will enable integration such as important metrics like NPS and CES, which are extracted from customer feedback. Integration by businesses enables them to get insights for correct customer segmentation, efficient resource allocation, and effective strategic growth. This study opens more horizons for further research in making more cost-effective predictions of CLV, considering larger areas such as the expansion of datasets, enrichment of customer profiles, and optimization of marketing strategies paper provides a clear framework that helps any organization to better business-customer relationships and strategic decisions through using an ensemble learning method.