Analytical integration and data-driven decision making in complementary and alternative medicine

Abstract

Customer Lifetime Value (CLV) measures the success of an organization by estimating the net value its customers contribute to the business over the lifetime of the relationship. How can organizations assess their customers’ lifetime value and offer strategies to retain those prospects and profitable customers? The first part of this dissertation offers an integrated view of methods to calculate CLV considering scenarios ranging from finite-and-infinite customer lifetimes to customer migration and Monte Carlo simulation models. In addition to the CLV models, customer segmentation is considered the fundamental marketing activity assisting enterprises to gain a deeper understanding of their customers’ characteristics and needs and, consequently, develop appropriate strategies to strengthen the relationship between them and their customers. Many segmentation models proposed in the literature have been based on specific criteria or attributes such as psychology, demography, or behaviors. At present, the recency (R), frequency (F), and monetary values (M) and cluster analysis models are two popular methods used to create data-driven behavioral segmentation. One of the limitations of those two methods is that most studies focus on transaction-based data, that is, past customer behavior. Therefore, the second part of this dissertation presents a case for integrating CLV and the probability of customer migration, also called the probability that a customer will return in the future, in the segmentation models. The first scenario uses a slightly modified RFM model, replacing the monetary value (M) with CLV. The second scenario integrates recency, frequency, CLV, length of relationship (L), and the probability of migration in the k-means clustering technique. Both CLV, cluster analysis, and RFM models are validated in the context of the healthcare industry, particularly in the area of complementary and alternative medicine (CAM), which refers to practices for people or patients who seek alternative treatment or illness prevention along with or instead of conventional medicines. The results show that understanding CLV and improving customer segmentation models can help the organization develop strategies to retain valuable customers while maintaining profit margins. In addition, Appendix A illustrates a teaching case study on the application of business intelligence and marketing analytics to making proper decisions in a competitor-oriented pricing environment in Complementary and Alternative Medicine (CAM) Industry. This case study helps conceptualize the nature of the complementary and alternative medicine (CAM) Industry, understand the concept, pros, and cons of price wars, outline what factors/criteria are needed to get more insights about customers, utilize the RFM model and cluster analysis to segment customers based on their similar characteristics, illustrate how to calculate customer lifetime value (CLV), utilize the business intelligence framework to justify the decision choices, and finally, understand how to make decisions in competition-oriented pricing situations.