Customer Analytics Capabilities in the Big Data Spectrum: A Systematic Approach to Achieve Sustainable Firm Performance

Customer analytics plays a vital role in generating insights from big data to improve service innovation, product development, personalization, and managerial decision-making; yet, no academic study has investigated customer analytics capability through which it is possible to achieve sustainable business growth. To close this gap, this chapter explores the constructs of the customer analytics capability by drawing on a systematic review of the literature in the big data spectrum. The chapter\u27s interpretive framework portrays a definitional aspect of customer analytics, the importance of customer analytics, and customer analytics capability constructs. The study proposes a customer analytics capability model, which consists of four principal constructs and some important sub-constructs. The chapter briefly discusses the challenges and future research direction for developing the customer analytics capability model in the data rich competitive business environment

Tackling Lack of Motivation in Aspirational Analytics Companies : SME Examples from the Manufacturing Industry

Establishing business intelligence analytics (BIA) in small- and medium-sized manufacturing enterprises is a pervasive problem. SME’s - the majority of businesses - play an important role in creating jobs, but research is primarily focused on large corporations. The authors worked with small manufacturing companies at the aspirational capability level but found that their motivation to introduce BIA was low. They had many business challenges but perceived the obstacles (primarily cost and effort) as too great, and their priorities were with operational issues. A two-phase approach based on a well-known analytics maturity model was devised to help raise company motivation. The article describes three studies in different companies using variations of the approach. Comparative analysis of the cases shows that demonstrating a clear path to improved functional efficiency is key to improving motivation, and that simple, easy to learn tools can provide these insights at little cost.EISBN13: 9781522566922BISONMM

Searching for Herbert Simon

Since Herbert Simon’s seminal work (Simon, 1957) on bounded rationality researchers and practitioners have sought the “holy grail” of computer-supported decision-making. A recent wave of interest in “business analytics” (BA) has elevated interest in data-driven analytical decision making to the forefront. While reporting and prediction via business intelligence (BI) systems has been an important component to business decision making for some time, BA broadens its scope and potential impact in business decision making further by moving the focus to prescription. The authors see BA as the end-to-end process integrating the production through consumption of the data, and making more extensive use of the data through heavily automated, integrated and advanced predictive and prescriptive tools in ways that better support, or replace, the human decision maker. With the advent of “big data”, BA already extends beyond internal databases to external and unstructured data that is publicly produced and consumed data with new analytical techniques to better enable business decision makers in a connected world. BI research in the future will be broader in scope, and the challenge is to make effective use of a wide range of data with varying degrees of structure, and from sources both internal and external to the organization. In this paper, we suggest ways that this broader focus of BA will also affect future BI research streams