Abstract:
In the competitive landscape of retail e-commerce, understanding and predicting customer behaviour is challenging for
business success. This study introduces the Retail Deep Neural Network (Ret-DNN) model, a novel approach of advanced deep learning
techniques to enhance predictive analytics in the e-commerce domain. The Ret-DNN model excels in predicting various aspects of
customer behaviour, providing deep insights into shopping habits, and transaction distribution, and identifying popular products based
on sales data. The proposed model also offers a detailed analysis of customer purchase frequency and transaction patterns by country,
enabling a comprehensive understanding of customer engagement. By accurately predicting behaviours, the Ret-DNN model equips
businesses with the tools to optimize marketing strategies, improve customer satisfaction, and drive significant growth in the retail
e-commerce business. The proposed Ret-DNN model minimizes prediction errors and increases prediction precision, demonstrating
performance with the lowest Validation Mean Absolute Error (MAE) of 0.2531 and Root Mean Square Error (RMSE) of 0.3575, along
with high accuracy rates of 0.91, 0.90, and 0.92 for validation, test, and training phases, respectively. Overall, this novel Ret-DNN
model achieves an average accuracy of 91%, highlighting its effectiveness in predicting customer behaviour in retail e-commerce. A
future research direction is also presented as a concluding remark.
