Abstract:
The world after the Internet is a digital-first one, websites are vital marketing tools, anengagement and business enhancement tool . This study looks at how successful websites promoteabusiness by analyzing web performance metrics and using predictive modeling. Finding patterns inuser engagement and traffic dynamics was made possible by the analysis of the data, which comprisedunique visitors, page views, bounce rate, and session duration. Four forecasting techniques—ARIMA, Random Forest, Neural Network, and Long Short-TermMemory (LSTM)—were used to predict future website activity. Following data preparation, thesemodels were used to standardize and improve the accuracy of the data. Every algorithmwas evaluatedusing common performance metrics like R 2 score, RMSE, and MAE. Because of its capacitytorecognize intricate time-related patterns in time-sequential data, the LSTMmodel demonstratedthehighest accuracy among the applied algorithms in forecasting the number of visitors to the website. The findings show how beneficial intelligent analytics are to web strategy since they allowbusinessesto make well-informed decisions on promotions. It also looks at the long-termeffects of digital transformation on sustainability, the environment, and society. Overall, this study shows that abusiness can feel empowered to strengthen its online presence and improve the outcomes of itsactivity promotion by using predictive analysis of web traffic.
