Leveraging Deep Learning for Accurate User Journey Prediction and Revenue Enhancement on Online Platforms

The rapid evolution of online platforms has transformed how businesses engage with customers, making accurate user journey prediction crucial for enhancing revenue. This study investigates the application of deep learning techniques, particularly Multi-Layer Perceptron-Gated Recurrent Unit (MLP-GRU) models, to analyze and predict user behavior effectively. By leveraging a comprehensive dataset collected from e-commerce sales, we implement a systematic approach that includes data collection, preprocessing through Min-Max normalization, feature extraction using attention mechanisms, and revenue enhancement strategies. The attention mechanism allows the model to focus on significant user interactions, dynamically weighing the importance of various features to improve predictive accuracy. Our findings demonstrate that the MLP-GRU model not only captures intricate patterns in user behavior but also enables personalized recommendations that significantly boost conversion rates. By integrating deep learning with user journey analytics, businesses can optimize their marketing strategies, tailor user experiences, and ultimately drive higher revenue. The proposed method is implemented in Python and has an accuracy of about 99.11% which is higher than other existing methods like CNN, BiLSTM and LSTM. This research contributes to the growing body of knowledge in the field of artificial intelligence in e-commerce and provides actionable insights for practitioners aiming to leverage data-driven strategies to enhance customer engagement and satisfaction.