Artificial Intelligence-Based Digital Marketing for Discovering Shopping Possibilities and Enhancing Customer Experience

In the context of online grocery shopping, this research explores the potential applications of machine learning models for improving and predicting customer behavior. Using a large dataset of 10,939 data points, Artificial Neural network (ANN)., decision trees (DT), recurrent neural networks (RNN), and naive bayes (NB) were used to estimate the kind and timing of client transactions. When the models were evaluated and taught, they yielded incredible results. ANN found intricate patterns in the data with an astounding 97.6% accuracy rate. Decision trees demonstrated resilience and interpretability, scoring 97.3% and 97.8%, respectively, for precision and accuracy. RNN managed temporal dependencies with a 93.2% accuracy rate. Naive Bayes showed reliability with an accuracy rate of 91.2%. This information, together with recall, accuracy, F1 score, and confusion matrices, provide a complete picture of the benefits of each model. By better understanding customer behavior and maximizing targeted marketing efforts in the ever-changing online grocery shopping environment, the acquired insights improve the whole digital commerce experience.