Enhancing Retail Sector Customer Engagement Through the Application of Modern and Intelligent AI-Driven Analytical Techniques

Abstract

The modern digital ecosystem, retail organizations collect huge amounts of data on customer interaction on the internet, and it is possible to gain further insights into customer behaviors and make strategic choices. Artificial Intelligence (AI) and Machine learning (ML) has changed this situation, offering the ability to spot patterns, make recommendations customized to the customer, profile the customer, and engage in proactive strategies. This paper describes an end-to-end predictive model of the analysis of the customer behavior based on the e-commerce dataset with real transactional records. The approach involves data collection, preprocessing, features transformation, normalization, and selection after which model training, validation and test are carried out. Sequential relationships in customer and transactional patterns are modelled using a bidirectional long-short term memory (Bi-LSTM) model. Use the Mean Squared Error (MSE), the Mean Absolute Error (MAE), and the coefficient of determination (R2) to evaluate the model’s performance. The Bi-LSTM demonstrated strong learning and prediction capabilities with an R2 value of 95, an MSE of 0.93, and an MAE of 0.97. These findings prove the possibility of deep learning as a behavior modelling in retail. The results also indicate that the Bi-LSTM can be used to learn to predict temporal patterns much better than traditional machine learning methods, thus retailers can enhance engagement planning, customer retention, and the decision support.