The capability of performing advanced analyses over a large amount of data is a key driver for innovating the assets of many companies operating in the complex value constellation that characterize the eCommerce, retail and marketing domain. Value constellation in this domain is characterized by the co-petition of several actors and stakeholders, such as eMarketplaces, retailers, comparison shopping engines, online advertising agencies, technology providers (for retailers and marketplaces), marketing agencies, marketing research and data vendors, and product data vendors.
The advent of big tech companies, with large-scale computing and cross-domain data infrastructures, market power, and data analytic competences from other geographical areas like the US and Asia, is threatening the business assets of several European small and large companies operating in this domain. European companies have the urge to transform this risk in the opportunity to innovate their services. Innovative services based on advanced data analysis techniques capable to leverage the large variety and volume of data from multiple sources , different sectors and across international markets characterized by different languages.
In particular, consumers and market data are often analysed without considering several contextual variables, which have a huge impact on consumer behaviour.
The environmental context, e.g., weather conditions, calendar events, e.g., holidays, and other kinds of global, national or even local events play a fundamental role in consumer choices. However, being able to better link these contextual variables to the analysis of consumer behaviour, or, even better, translating these analyses in actionable marketing strategies through the realization of innovative services, remains largely unexplored due to the cost and difficulty of integrating such external data with consumer-related data analysed by these companies.
Furthermore, European companies commonly operate in several diverse international markets either within the European Union or globally. The need to communicate with the customers as well as managing product information in a variety of different languages is a daily business reality for these companies.
Once these complex integration tasks are performed, integrated data can feed new descriptive and predictive analytical models. Nowadays several issues make the achievement of these tasks very costly and difficult: lack of shared or standard data models, lack of automatic support for reconciliation, large volumes of data, domain-specific data transformation operations.
The EW-Shopp project aims at deploying and hosting a data integration platform to ease these data integration tasks, by embedding shared data models, robust data management techniques and semantic reconciliation methods.