Analytical integration and data-driven decision making in complementary and alternative medicine
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2019
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2562
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eng
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b211052
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Kessara Kanchanapoom (2019). Analytical integration and data-driven decision making in complementary and alternative medicine. Retrieved from: https://repository.nida.ac.th/handle/662723737/6035.
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Analytical integration and data-driven decision making in complementary and alternative medicine
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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.
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Thesis (Ph.D. (Business Administration))--National Institute of Development Administration, 2019