Streaming Analytics—Real-Time Customer Satisfaction in Brick-and-Mortar Retailing

Abstract

The manifold changes in retailing in recent years has led to the scenario where competition is mainly driven by price. However, this one-sided focus on price as the only competitive instrument has become a significant problem for many retailers due to the increase in competition and reduction in scope for price differentiation. For brick-and-mortar stores in particular, however, customer satisfaction within the store is also a decisive factor, although this can currently only be assessed manually by employees as there are no analytical processes in place. Active evaluation and control of overarching measures is technically and economically not yet feasible. The aim of this research is to sketch a sentiment analytics model to analyze customer satisfaction for brick-and-mortar retailing. Using the presented Customer Satisfaction Streaming Index (CSSI), a mathematical model is developed that is tailored to the characteristics of the available data sources. In a second step, a framework for conducting big data analyses based on a standard retail system architecture is demonstrated, and a prototypical implementation is demonstrated. The preliminary results show that this is a suitable method for brick-and-mortar retailers. As the quality of social media sources might not be fully sufficient, alternate resources are discussed.