Web traffic is a vital metric for online businesses and organizations. This research paper proposes a web traffic analytical application that helps website owners monitor and analyze their website traffic. This data can be used to identify areas of the website that need improvement and to create targeted marketing campaigns. We examine the different types of data that can be collected, such as page views, unique visitors, time spent on the page and login counts and discuss the various methods used to analyze this data, including data visualization, predictive modeling, statistical analysis techniques and machine learning to provide real-time analytics on web traffic. The paper discusses the different types of data collected by these applications, the tools and technologies used for data collection, and the methods used for data analysis. The paper also discusses the challenges and limitations associated with web traffic analytical applications and the future directions of research in this area. The effectiveness of the application was evaluated through a case study, where it was used to analyze the traffic of an e-commerce website. The results showed that the application was able to provide valuable insights into user behavior and website performance, which can be used to improve the overall user experience and increase website engagement. The application will enable website owners and marketers to optimize their online presence and improve their digital marketing efforts. Keywords: web traffic, analytical application, data collection, data analysis, data visualization, predictive modeling, machine learning, digital marketing.