An ontology-based data integration approach for web analytics in e-commerce

• A semantic approach to represent and consolidate web analytic data is proposed.
• An OWL ontology for web analytics in e-commerce is designed and proposed.
• The proposed approach is validated with tracking data of 15 real-world e-shops.
• Obtained semantized data successfully train advanced data mining algorithms.
• We provide actual e-shops with tools to enhance their commercial activities.

Web analytics has emerged as one of the most important activities in e-commerce, since it allows companies and e-merchants to track the behavior of customers when visiting their web sites. There exist a series of tools for web analytics that are used not only for tracking and measuring web traffic, but also for analyzing the commercial activity. However, most of these tools focus on low level web attributes and metrics, making other sophisticated functionalities and analyses only available for commercial (non-free) versions.In this context, the SME-Ecompass European initiative aims at providing e-commerce SMEs with accessible tools for high level web analytics. These software facilities should use different sources of data coming from digital footprints allocated in e-shops, to fuse them together in a coherent way, and to make them available for advanced data mining procedures. This motivated us to propose in this work an ontology-based approach to collect, integrate and store web analytics data, from many sources of popular and commercial digital footprints. As article’s main impact, we obtain enriched and semantically annotated data that is used to properly train an intelligent system, involving data mining procedures, for the analysis of customer behavior in real e-commerce sites. In concrete, for the validation of our semantic approach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocated in 15 e-shops of different commercial sectors and countries (UK, Spain, Greece and Germany), throughout several months of activity. The obtained results show different perspectives in customer’s behavior analysis that go one step beyond the most popular web analytics tools in the current market.