Fathimah Al-Ma'Shumah, Dony Permana, Kuntjoro Adji Sidarto
Customer Lifetime Value is an important and useful concept in marketing. One of its benefits is to help a company for budgeting marketing expenditure for customer acquisition and customer retention. Many mathematical models have been introduced to calculate CLV considering the customer retention/migration classification scheme. A fairly new class of these models which will be described in this paper uses Markov Chain Models (MCM). This class of models has the major advantage for its flexibility to be modified to several different cases/classification schemes. In this model, the probabilities of customer retention and acquisition play an important role. From Pfeifer and Carraway, 2000, the final formula of CLV obtained from MCM usually contains nonlinear form of the transition probability matrix. This nonlinearity makes the inverse problem of CLV difficult to solve. This paper aims to solve this inverse problem, yielding the approximate transition probabilities for the customers, by applying metaheuristic optimization algorithm developed by Yang, 2013, Flower Pollination Algorithm. The major interpretation of obtaining the transition probabilities are to set goals for marketing teams in keeping the relative frequencies of customer acquisition and customer retention. © 2015 AIP Publishing LLC.
Statistics Research Division, Faculty of Mathematics and Natural Science, Bandung Institute of Technology, Indonesia; Statistics Study Program, Faculty of Mathematics and Natural Sciences, Padang State University, Indonesia; Industrial and Financial Mathematics Division, Faculty of Mathematics and Natural Science, Bandung Intitute of Technology, Indonesia
