Predictive Analysis For Ecommerce Sales

The growth of Online Shopping has been exponential, allowing organizations the ability to track customer data and utilize the data for predictive analysis towards strategic decision-making. In this study, machine learning approaches such as decision tree or random forest classifier will be employed to predict e-commerce sales and optimize inventory management for better targeting of customers. These analyses are conducted through actual analytics platforms, like Google Analytics, AWS Forecast, and Salesforce Einstein. A number of statistical validation techniques include cross-validation and residual analysis besides performance metrics RMSE and MAE is an effort to affirm the reliability of the model. Besides, the research also addresses the real-world feasibility through incorporation of predictive models in the e-commerce platform of Shopify, Magento, and WooCommerce. Data privacy and security issues are reviewed, including considerations of GDPR compliance and protocols for encryption. The results indicate that predictive analytics can have a very strong positive impact on business performance and provide a competitive advantage over time in the e-commerce sector.