Data analytics for online content management

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Web site traffic analysis and optimization often involves Web click streams, the recordings of user clicks during a Web site visit. There are also mobile application click streams and advertisements (ads) click streams, which record clicks in applications and clicks on ads. Those click streams are valuable resources for any online business because they show how users interact with online content. However, although beneficial, stand-alone analysis of click streams has limited potential because the behaviour of online users is impacted by external factors such as weather and user location. The main objective of this research project is to design a generic and extendable software framework for online content management. The framework will improve online content effectiveness, increase sales, reduce manual processes, and ultimately increase revenue from online sources. Two main components will be developed: the first component, integrated data analytics services, will include analytics of click streams with other data such as weather and user location; the second, a supply-side platform for advertising, will include a highly automated process for pricing online inventory. To achieve these goals, the project will explore and advance integrated data analytics, click-through rate prediction, user segmentation, and the supply-side platform for online advertising. The results from this project will directly benefit the industrial partner Pelmorex by enabling them to extract new insights and increase business value from existing data. A sophisticated supply-side platform to be developed in this project will increase revenue from the online advertising inventory. The project will push the boundaries in the area of data analytics related to click streams, supply-side platforms, and online advertising. As a result, it will contribute to growth in these areas. Furthermore, the HQP involved in this project will gain valuable experience working in data analytics and online advertising with Big Data, which will make them valuable to any related data analytics, online advertising, or Big Data job market.