A Theory of Customer Valuation: Concepts, Metrics, Strategy, and Implementation

Customer value refers to the economic value of the customer's relationship with the firm. This study approaches the topic of customer value for measuring, managing, and maximizing customer contributions by proposing a customer valuation theory (CVT) based on economic principles that conceptualizes the generation of value from customers to firms. The author reviews the established economic theories for valuing investor assets (e.g., stocks) and draws a comparison to valuing customer contributions. Furthermore, the author recognizes the differences in the guiding principles between valuing stocks and valuing customers in proposing CVT. Using CVT, the author discusses the concept of customer lifetime value (CLV) as the metric that can provide a reliable, forward-looking estimate of direct customer value. In addition, economic models to estimate CLV, ways to manage CLV using portfolio management principles, and strategies to maximize CLV are discussed in detail. The author extends the customer value concept by discussing ways that a customer can add value to the firm indirectly through incentivized referrals, social media influence, and feedback. Finally, the benefits of CVT to multiple constituencies are offered.

Accounting for risk in the traditional RFM approach

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.