RFM: A Business Analytics Case for All; No Statistics Required

Businesses and other organizations across the globe are becoming more and more data-driven, using a combination of descriptive, diagnostic, predictive and prescriptive analytics to gain a strategic advantage through understanding the past, what we hope to happen in the future, and the ability to accurately predict future outcomes. These forms of analytics span from basic statistical summaries and data visualization to artificial intelligence models. Many organizations are now requiring new job applicants, new hires, and existing employees to be data literate. As such, it is becoming incumbent on teachers, students, and practitioners to possess some basic knowledge or experience in business analytics, at least within their educational and functional domains. Current best-practice in business school curriculum embeds some form of analytics across the curriculum. Unfortunately, many business colleges do not have the experience or resources to do so, hence teachers are unprepared to teach, and students are not prepared to enter the business world being data literate. While higher levels of analytics can be statistically intimidating, there are numerous applications of analytics that do not require statistics or higher-level models. This paper introduces one such technique practiced within marketing education and industry since 1995 and is called RFM. RFM has long been known in marketing curriculum and practice but has seen virtually no exposure in business schools outside of marketing major courses. This reflects an unintended consequence of teaching and learning within "functional" silos. It is hoped that teachers and students across the business curriculum, as well as workforce participants, can use this case to gain an appreciation of data literacy and analytics toward application within any functional area of business. The purpose of this paper is to avail those outside of marketing education and practice with an effective, easy to understand, easy to apply model, with no statistics involved. The goal is to facilitate increased data literacy and interest in understanding and/or applying analytics to other functional arear of business. RFM is not unique to this paper but is aimed at broadening teacher, student and workforce participant experience and knowledge of business analytics.