Today, most retail companies have access to very large amounts of data - so-called 'Big Data'. This data can consist, for example, of customer, product, transaction, sales channel, geospatial, text and image data (observed over time). The interest in Big Data among retailers is huge for obvious reasons, and with adequate analytical methods, e.g. consumer behavior can be predicted.

However, the shortcomings in both theory and methodology, among both users and researchers, regarding retail data and predictive analytics are evident. In fact, much of the information value of Big Data is not utilized, and often people simply do not know what to do with all the data. Predictive analytics based on Big Data also faces additional challenges with the upcoming EU General Data Protection Regulation [GDPR] and how data may de facto be used in the future. The need for research on how theory and method can be combined to extract the informational value of Big Data, under GDPR, is thus extremely urgent.

To address this need, an interdisciplinary project has been initiated at the Stockholm School of Economics, where leading researchers from the Center for Retailing [CFR] and the Center for Economic Statistics [CES] collaborate. The project is unique in the sense that access to Big Data is provided by leading retail companies in Sweden. The project team also includes two PhD students.