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Online groceries and fresh foods shopping is relatively a new trend in e-commerce in Germany, with only 6.8% of total purchases being made online [Statista, 2016]. It’s expected to grow further. Developing a sustainable e-groceries platform faces different challenges. As an example, the associated delivery network may affect the freshness quality of the food due to delivery time. Also it can be hard to trust the product online, which may affect the purchase decision. In order to overcome these challenges, Smart Emma is instantiated as research project for a green e-groceries that is being developed in the context of smart cities in Aachen, Germany. The motivation behind this project is to empower the SME local grocery retailers against the competition of the expanding supermarkets chains, as they deliver superior products in quality and expertise. As a main goal, Smart Emma combines different retailers within one platform and creates synergies. Therefore a shopping module, a CO2-free logistics concept and a Business Intelligence (BI) solution are going to be developed.

A huge amount of data is expected to be generated from customers’ online activity and the highly frequent transactions due to the nature of the products. Ignoring this data or not analysing it probably will increase the project costs by an average of 30% [Mazenko, 2016]. Research in BI and Analytics is needed in order to increase the competitive advantage provided by these tools [Akter, 2016]. To achieve this, existing components need to be improved and new tools to be designed. This paper looks at the problem of designing and developing a Business Intelligence framework for local groceries retail stores in small cities such Aachen. It will derive the benefits from this solution to retailers and customers.

For the purpose of defining the framework requirements, sixteen local retailers were interviewed. The results were analysed using Mayring’s qualitative content analysis [Mayring, 2010]. The results show that a centralized BI framework needs to be designed. It will collect all user-generated content and activity in a central data warehouse. The components that should be developed include: data inquiry, data visualizations, data forecast and recommendation engine.It will provide the retailers with a knowledge management system where a set of Key Performance Indicators (KPIs) can be defined. In addition we introduce the new functionality of performance comparison relatively to the different aspects of the local market, partially or as a whole. In this paper, use case scenarios will be presented to illustrate the new developed functionalities and their ability to enhance the competitive advantage of targeted retailers using market information.

A beta version of the solution will be available and operating by the end of 2017. As enough real data will be generated, this work will offer an opportunity to study more closely market phenomena and concepts from a microeconomics point of view. These can be such as the diffusion of products, supply, demand, market equilibrium, pricing, and competition dynamics in the presence of symmetrical and asymmetrical information. The characteristics of this framework is that the parameters, number of variables and size of the data are better defined and more controllable than in the Big Data of worldwide top online sellers.