Generic customer segments and business models for smart grids

Abstract

The implementation of smart grids—one of the urgent goals to meet international policy expectations for energy efficiency and CO2 reduction targets—is not a technological issue alone, as it also requires social acceptance by various stakeholders (Wolsink, Renew Sustain Energy Rev, 2011). It is of particular interest that smart grid products and services provide value to the customer. On the one hand, customer value of smart grid technologies is crucial to customer acceptance. On the other hand, as customer value is a key driver for economic value creation and competitive advantage (DeSarbo et al., Strategic Manag J 22:845–857, 2001; Porter, Competitive advantage, 1985), it is also important for companies and investors and thus will affect market acceptance of smart grid technologies. In the literature, business models address the bridge between customers and company needs and serve as mediators between technology and economic success by providing a value proposition to customers and a revenue model for companies (Chesbrough and Rosenbloom, Ind Corp Chang 11:529–555, 2002). However, we know from the literature that a one-size-fits-all business model may not lead to the best results as it might fail to address heterogeneous customer value perceptions (DeSarbo et al., Strategic Manag J 22:845–857, 2001; Morris et al., J Bus Res 58:726–735, 2005; Ruiz et al., Serv Ind J 27:1087–1110, 2007; Wiedmann et al., Psychol Mark 26:625–651, 2009). Thus, different business models providing different customer value propositions need to be developed to fit the different market segments in an optimal way. On the basis of a cross-European country study, we explore three generic B2C customer segments for smart grid products and services based on different value perceptions (Supporters, Ambiguous and Skeptics). Based on the segmentation we conceptually derive four generic business model designs with different customer value propositions best suited for approaching those segments (Saver, Smart +, Trader, Smart Camouflage). Implications for energy policy, research and smart grid management are derived from the findings.