Abstract
Relationship marketing considers customers as the main asset of organizations and the source of their profitability. Allocation of marketing budgets to these valuable assets is critical; because the way companies allocate their marketing budgets to customers affects their profitability. The purpose of this research is to provide a new approach to allocate marketing budgets to customer segments in a long-term view and a dynamic process. The contribution of the model is as follows: (1) unlike the previous modeling efforts where a predefined budget level was assigned to acquisition and retention, the suggested model sets and allocates marketing budgets simultaneously; (2) the model is multi-period which means the strategies for setting and allocation of marketing budgets may be different in each period. The proposed approach aims at maximizing customer equity in a long-term planning horizon with several periods. Decision variables determine how the budgets should be assigned to customer acquisition and retention in each period. Formulation of the model is followed by a numerical illustration. Since the model is nonlinear and non-convex, none of exact mathematical programming approaches may solve it. Therefore, genetic algorithm and simulated annealing approaches were employed. The results showed the effectiveness of the model compared with other extant models.
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References
Albadvi A, Koosha H (2011) A robust optimization approach to allocation of marketing budgets. Manag Decis 49(4):601–621
Bazaraa MS, Sherali HD, Shetty CM (2006) Nonlinear programming. Wiley, Hoboken
Berger PD, Bechwati NN (2001) The allocation of promotion budget to maximize customer equity. Omega Int J Manag Sci 29(1):49–62
Blattberg RC, Deighton J (1996) Manage marketing by the customer equity test. Harv Bus Rev 74(4):136–144
Chan SL, Ip WH (2011) A dynamic decision support system to predict the value of customer for new product development. Decis Support Syst 52(1):178–188
Ching WK, Ng MK, Wong KK, Altman E (2004) Customer lifetime value: stochastic optimization approach. J Oper Res Soc 55(1):860–868
Chuang YF, Chian SH, Wong JY (2013) Customer value assessment of pharmaceutical marketing in Taiwan. Ind Manag Data Syst 113(9):1315–1333
Dong W, Swain SD, Berger PD (2007) The role of channel quality in customer equity management. J Bus Res 60(12):1243–1252
Dréo J, Pétrowski A, Siarry P, Taillard E (2006) Metaheuristics for hard optimization. Springer, Berlin
Fischer M, Albers S, Wagner N, Frie M (2011) Dynamic marketing budget allocation across countries, products, and marketing activities. Mark Sci 30(4):568–585
Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York
Hand DJ (2001) Modelling consumer credit risk. IMA J Manag Math 12(2):139–155
Hogan JE, Lemon KN, Rust RT (2002) Customer equity management: charting new directions for the future of marketing. J Serv Res USA 5(1):4–12
Holthausen DM, Assmus G (1982) Advertising budget allocation under uncertainty. Manag Sci 28(5):487–499
Hyun SS (2009) Creating a model of customer equity for chain restaurant brand formation. Int J Hosp Manag 28(1):529–539
Kim AJ, Ko E (2012) Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand. J Bus Res 65(10):1480–1486
Klein R, Kolb J (2015) Maximizing customer equity subject to capacity constraints. Omega 55:111–125
Koosha H, Albadvi A (2015) Allocation of marketing budgets to customer acquisition and retention spending based on decision calculus. J Model Manag 10(2):179–197
Kumar V (2006) CLV: the databased approach. In: Bejou D, Keiningham TL, Aksoy L (eds) Customer lifetime value: reshaping the way we manage to maximize profits. Best Business Books, Binghamton
Kumar V, Bhagwat Y, Zhang XA (2015) Regaining “lost” customers: the predictive power of first-lifetime behavior, the reason for defection, and the nature of the win-back offer. J Mark 79(4):34–55
Lew A, Mauch H (2007) Dynamic programming: a computational tool. Springer, Berlin
Lin CWR, Hsiau HJ (2010) A genetic algorithm approach for optimizing chemical towers construction project scheduling with dynamic resources constraints. Int J Ind Eng Theory 17(2):128–141
Little JDC (1970) Models and managers: the concept of a decision calculus. Manag Sci 16(8):466–485
Meyer-Baese A, Schmid V (2014) Pattern recognition and signal analysis in medical imaging. Elsevier, Oxford
Miao C, Du G, Xia Y, Wang D (2016) Genetic algorithm for mixed integer nonlinear bilevel programming and applications in product family design. Math Probl Eng 16(1):1–15
Mitchell M (1999) An introduction to genetic algorithms. MIT Press, London
Nocedal J, Wright SJ (2006) Numerical optimization. Springer, New York
Payne A (2005) Handbook of CRM: achieving excellence in customer management. Elsevier, Great Britain
Pfeifer PE, Haskins ME, Conroy MR (2005) Customer lifetime value, customer profitability, and the treatment of acquisition. J Manag Issues 17(1):11–25
Reinartz W, Thomas JS, Kumar V (2005) Balancing acquisition and retention resources to maximize customer profitability. J Mark 69(1):63–79
Sastry K, Goldberg DE, Kendall G (2013) Genetic algorithms, search methodologies. Springer, Berlin, pp 93–117
Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Heidelberg
Swain SD, Berger PD, Weinberg BD (2014) The customer equity implications of using incentives in acquisition channels: a nonprofit application. J Mark Anal 2(1):1–17
Tan CM (2008) Simulated annealing. In-Teh, Croatia
Thomas JS (2001) A methodology for linking customer acquisition to customer retention. J Mark Res 38(2):262–268
Tirenni GR (2005). Allocation of marketing resources to optimize customer equity. PhD Thesis, University of St. Gallen, Switzerland
Tirenni G, Labbi A, Berrospi C, Elisseeff A, Bhose T, Pauro K, Pöyhönen S (2007) Customer equity and lifetime management (CELM): finnair case study. Mark Sci 26(4):553–565
Tsafarakis S (2016) Redesigning product lines in a period of economic crisis: a hybrid simulated annealing algorithm with crossover. Ann Oper Res 247(2):617–633
Venkatesan R, Kumar V (2004) A customer lifetime value framework for customer selection and resource allocation strategy. J Mark 68(4):106–125
Venkatesan R, Kumar V, Bohling T (2007) Optimal customer relationship management using bayesian decision theory: an application for customer selection. J Marketing Res 44(4):579–594
Verhoef PC, Donkers B (2001) Predicting customer potential value an application in the insurance industry. Decis Support Syst 32(11):189–199
Yang XS (2008) Nature-inspired metaheuristic algorithms. Luniver Press, University of Cambridge, United Kingdom
Yang XS (2014) Nature-inspired optimization algorithms. Elsevier, Amsterdam
Yoganathan D, Jebarajakirthy C, Thaichon P (2015) The influence of relationship marketing orientation on brand equity in banks. J Retail Consum Serv 26:14–22
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Koosha, H., Albadvi, A. Allocation of marketing budgets to maximize customer equity. Oper Res Int J 20, 561–583 (2020). https://doi.org/10.1007/s12351-017-0356-z
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DOI: https://doi.org/10.1007/s12351-017-0356-z