Enhancing Sales Forecasting in E-Commerce Through Advanced Predictive Analytics and Deep Neural Networks

Within this context, this research is aimed at enhancing the sales forecasting in e-commerce using the newer approaches of predictive analytics and deep neural networks. There usually is significant variability of results as compared to traditional approaches that are based on historical data and simple econometric models. This research evaluates how deep learning approaches, LSTM and Transformer models, improve precisely the forecast by noticing the behaviour dynamics and more complex patterns in the business environment. The actual approach includes sales history data, understanding customers, trends, and related promotions to train as well as test neural networks. The basic hybrid approach integrates deep learning and statistical methods to forecast the maximum potential outcomes. The performance of models in this study is evaluated by Mean Absolute Error (MAE), Root Mean Squared Error(RMSE) and R-Squared to compare the forecasted sales values with the actual ones. It is established that incorporating the deep learning models that have been described enhances the forecast precision concerning the baseline and conventional methods of approach significantly, especially the hybrid model. The precise accuracy, and factors that cause it to fluctuate from one product category to another, and the impact of promotional activities on the forecast's precision, are addressed. Thus, the study contributes to the knowledge that adopting innovative techniques such as advanced predictive analytics and deep learning to improve sales forecasting can potentiate strategic business courses of action and the firm's performance in today's cut-throat e-commerce marketplace.