Attention-enhanced LSTM for high-value customer behavior prediction: Insights from Thailand’s E-commerce sector

The rapid growth of e-commerce in emerging markets like Thailand has presented businesses with both opportunities and challenges. One critical challenge lies in accurately identifying high-value customers amidst vast amounts of transactional data. Effective predictive models must not only deliver high accuracy but also provide transparency to guide actionable business decisions. Predicting high-value customers is particularly important in these markets due to evolving consumer behaviors and increasing competition. This study introduces an attention-enhanced Long Short-Term Memory (LSTM) model to predict high-value customer behavior in Thailand's e-commerce sector, addressing the challenges of achieving high predictive accuracy while ensuring interpretability. The novelty of this research lies in integrating an attention mechanism within the LSTM framework, enabling the identification of key customer behaviors—such as total purchase amount, purchase frequency, and monthly purchase frequency—that significantly influence high-value customer classification. By leveraging transactional data from a leading Thai e-commerce platform, the model delivers outstanding predictive performance with accuracy rates of 99.75 % (training), 99.77 % (validation), and 99.83 % (testing), coupled with low error metrics (RMSE: 0.0391, MAE: 0.0039). The attention mechanism enhances model transparency by identifying influential behavioral features, thereby enabling actionable insights that align with customer segmentation and targeted marketing strategies. Compared to traditional LSTM models, this approach demonstrates superior predictive power and interpretability, making it an effective tool for e-commerce platforms seeking to optimize customer retention and engagement strategies. This study significantly contributes to advancing machine learning applications in e-commerce by showcasing how attention mechanisms can address the dual needs of predictive accuracy and transparency. The practical benefits of this model are particularly relevant for emerging markets like Thailand, where consumer behaviors and competitive dynamics are evolving rapidly. Future research should investigate the scalability of this approach across diverse datasets and markets, incorporating additional data sources such as demographic and social media information, to further enhance its applicability and robustness.