Accounting for risk in the traditional RFM approach

Abstract

Purpose – The Purpose of this study is to provide an alternative way to create customer valuation metric while accounting for customer riskiness. Customer relationship management (CRM) emphasizes the importance of measuring customer value. Analytics has paved the way for innovation by providing companies valuable insights into the behavior of customers. Earlier models used to measure customer value do not take into account the types and level of risk posed by customers, such as probability of churn, regularity of purchases, etc. The authors put forth a new and innovative approach to measuring customer value while, at the same time, adjusting for customer riskiness. Design/methodology/approach – Using a non-parametric approach used in the operations research area, the authors create a risk-adjusted regency, frequency, monetary value (RARFM) score for each customer. These scores are used to segment the customers into two groups – customers with high and low RARFM scores. The authors then identify the underlying demographics and behavioral characteristics that separate the two groups. Findings – Findings of this paper indicate that customers who perform the best on the RARFM metric tend to be more experienced, and are more likely to exhibit behavioral tendencies that help them perform well in their jobs, such as purchasing promotional goods that act as sales aid and enhance their performance. Originality/value – The paper is innovative in its approach in terms of creating a new metric for calculating customer value. Few papers have proposed ways to handle and adjust for customer riskiness. Here, the authors propose three kinds of customer risk. Current paper provides a twist to traditional RFM analysis by creating a RARFM score for each customer, and provides a scientific way of assigning weights to RFM.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 10 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.