Articles

Due to influence of Internet, this e-commerce sector has developed rapidly. Most of the online retailing or selling businesses are seeking for way for predicting their products demand. Sales forecasting may help retailers develop a sales strategy that will enhance sales and attract more money and investment. The current research work puts forward a machine learning framework to forecast E-commerce sales for strategic management using a dataset of E-commerce transactions. With 70 percent of the data for train and 30 percent for test, three models were produced, namely, Random Forest, Decision Tree, and XGBoost. In order to evaluate the models, performance measures inclusive of R-squared (R²) and Root Mean Squared Error (RMSE) were employed. Thus, the XGBoost model was the most accurate in marketing predictive capabilities for E-commerce sales with the R² score of 96.3%. This has demonstrated the increased capability of XGBoost algorithm to forecast E-commerce monthly sales more accurately than other models and can assist decision makers for managing inventory and arriving smart and quick decisions in this rapidly growing E-commerce market. The findings reiterate the importance of using advanced analytics in order to drive effectiveness and customer experience within E-commerce sector.

Technological advances have had a transformative impact on the retail landscape. Challenges arise with guaranteeing technological changes lead to, rather than detract from, increased efficiency and positive experiences. First, integrating technology into the supply chain in an aggressive way is costly. It requires vast changes to existing systems and developments of cross-industry communication protocols. Secondly, the public is often quick to reject technological changes or slow to become users. Finally, ensuring that technological advancements do not only benefit the top few retailers and are accessible to those of any size poses a challenge, as has been seen in the fate of only a handful of radical changes in retail technology. On the other hand, an integral aspect of technology, particularly that used for big data collection and processing, is that it can account for these and other variables. It can predict the success of ventures into modernizing or developing new systems and can identify more effective and efficient ways to do so. Of course, the concerns of job loss or technological monopoly still loom. But, it would seem, the continued advancement of technology in the retail landscape is inevitable.