Understanding E-Commerce Consumer Behavior with CNN-LSTM: A Data-Driven Approach

Companies operating in India must recognize consumer behavior patterns to succeed in its continuously changing varied market environment. This research introduces an advanced deep learning system which uses both convolutional neural networks and long short-term memory architecture to study and predict shopper actions. Behavior modeling benefits from the new model because it uses CNN networks to collect spatial features from consumer transaction data alongside search patterns meanwhile, LSTM networks help recognize time-based behavioral trends. Training and testing applied to a large-scale dataset which included an 80-20 train test separation and high-performance GPU hardware and TensorFlow PyTorch frameworks for implementation. The experimental data confirms that the new CNN-LSTM model achieves superior performance against both conventional machine learning approaches and independent deep learning models. By reaching 92.4% accuracy our model outperformed logistic regression that scored 73.8%, random forest with 81.2%, and a hybrid CNN-SVM that achieved 89.3%. The model shows dependable predictions for consumer behavior with performance results from precision to F1-score at 91.8% to 92.2% and 92.7%. Churn prediction accuracy reached levels 18 percentage points higher than baseline models and an AUC-ROC score of 0.92 validated the model as an effective discriminator. Feature importance heatmaps in subsequent analysis revealed major determinants of consumer behavior including how often they buy and spend as well as their demographic features. Businesses now have practical recommendations from the study to refine their marketing activities while elevating customer interactions. The complex Indian consumer behaviors become understandable through the proposed CNN-LSTM model which demonstrates both strong accuracy and robust performance for applications in personalized marketing and strategic business planning.