Abstract
Abstract The purpose of the paper is to evaluate the feasibility of business-to-consumer (B2C) customer relationship analytics in the industrial business-to-business (B2B) context, in particular spare part sales. The contribution of the paper is twofold; the article identifies analytics approaches with value potential for B2B decision-making, and illustrates their value in use. The identified analytics approaches, customer segmentation, market basket analysis and target customer selection, are common in the B2C marketing and e-commerce. However, in the industrial B2B marketing, the application of these approaches is not yet common.. The different kinds of analytics under examination in this paper use machine learning (ML) techniques. The examination takes into account the applicability and usefulness of the techniques as well as implementation challenges. The research suggests that the identified analytics may serve different business purposes and may be relatively straightforward to implement. This requires careful examination of the desired purposes of use in a particular business context. However, the continuous and real-time use of such analyses remains a challenge for further examination also in information systems research. Keywords: Business analytics, B2B decision-making, Machine learning, Data mining, Artificial intelligence, CRM
Recommended Citation
Stormi, Kati; Laine, Teemu; and Elomaa, Tapio, "FEASIBILITY OF B2C CUSTOMER RELATIONSHIP ANALYTICS IN THE B2B INDUSTRIAL CONTEXT" (2018). Research Papers. 61.
https://aisel.aisnet.org/ecis2018_rp/61