Abstract
We address the problem of optimising the intensity of online advertising. In contrast to the classical literature, we tackle the advertising interaction between the firm and the potential customer, for the sale of a onetime event, in a very limited time horizon. The problem is intrinsically dynamic due to two conflicting situations: the first arises when the customer is subjected to intense advertising pressure, which may lead to customer saturation and even irritation, while the second is the tendency for customers to forget if they are not reminded systematically through advertising. In order to determine an optimal event-advertising policy and develop an efficient enumerative shooting algorithm to solve the problem, we suggest a hazard rate-based approach to modelling the conflicting factors. Our analysis shows that the initial level of customer interest in the event has a non-trivial effect on the dynamics of the optimal advertising policy. In particular, this advertising policy consists of a monotonic increase over time prior to the event in the case of high initial interest and a concave, peak-wise form in the case of low initial interest.
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References
Aaker, D. A., & Bruzzone, D. E. (1985). Causes of irritation in advertising. Journal of Marketing, 49(2), 47–57.
Ahn, H., & Kim, K.-J. (2008). Using genetic algorithms to optimize nearest neighbors for data mining. Annals of Operations Research, 163(1), 5–18.
Amrouche, N., Martín-Herrán, G., & Zaccour, G. (2008). Feedback Stackelberg equilibrium strategies when the private label competes with the national brand. Annals of Operations Research, 164(1), 79–95.
Anderson, J. C., & Narus, J. A. (1998). Business marketing: Understand what customer’s value. Harvard Business Review, 76, 53–65.
Barucci, E., & Gozzi, F. (1999). Optimal advertising with a continuum of goods. Annals of Operations Research, 88, 15–29.
Berger, P. D., & Magliozzi, T. (1992). Optimal co-operative advertising decisions in direct-mail operations. Journal of the Operational Research Society, 43(11), 1079–1086.
Bitran, G. R., & Mondschein, S. V. (1996). Mailing decisions in the catalog sales industry. Management Science, 42(9), 1364–1381.
Bult, J. R., & Wansbeek, T. (1995). Optimal selection for direct mail. Marketing Science, 14(4), 378–394.
Chintagunta, P. K., & Dong, X. (2006). Hazard/survival models in marketing. In The handbook of marketing research: Uses, misuses, and future advances, 441.
Chun, Y. H. (2012). Monte Carlo analysis of estimation methods for the prediction of customer response patterns in direct marketing. European Journal of Operational Research, 217(3), 673–678.
Dayanik, S., & Parlar, M. (2013). Dynamic bidding strategies in search-based advertising. Annals of Operations Research, 211(1), 103–136.
Dubé, J., Hitsch, G. J., & Manchanda, P. (2005). An empirical model of advertising dynamics. Quantitative Marketing Economics, 3, 107–144.
Gonul, F., & Srinivasan, K. (1993). Modeling multiple sources of heterogeneity in multinomial logit models: Methodological and managerial issues. Marketing Science, 12(3), 213–229.
Helsen, K., & Schmittlein, D. C. (1993). Analyzing duration times in marketing: Evidence for the effectiveness of hazard rate models. Marketing Science, 12(4), 395–414.
Huang, J., Leng, M., & Liang, L. (2012). Recent developments in dynamic advertising research. European Journal of Operational Research, 220(3), 591–609.
Jain, D. C., & Vilcassim, N. J. (1991). Investigating household purchase timing decisions: A conditional hazard function approach. Marketing Science, 10(1), 1–23.
Jorgensen, S. (1982). A servay of some differential games in advertising. Journal of Economic Dynamics and Control, 4, 341–369.
Kumar, A., & Meenakshi, N. (2011). Marketing management (2nd ed.). New Delhi: Vikas Publishing House.
Kumar, V., Zhang, X., & Luo, A. (2014). Modeling customer opt-in and opt-out in a permission-based marketing context. Journal of Marketing Research, 51(4), 403–419.
Lilien, G. L., Kotler, P., & Moorthy, S. K. (1992). Marketing models. Englewood Cliffs, NJ: Prentice Hall.
Little, J. D. C. (1979). Aggregate advertising models: The state of the art. Operations Research, 27(4), 629–667.
Liu, Y., Liu, A., Liu, X., & Huang, X. (2019). A statistical approach to participant selection in location-based social networks for offline event marketing. Information Sciences, 480, 90–108.
Ma, S., Hou, L., Yao, W., & Lee, B. (2016). A nonhomogeneous hidden Markov model of response dynamics and mailing optimization in direct marketing. European Journal of Operational Research, 253(2), 514–523.
Mahajan, V., & Muller, E. (1986). Advertising pulsing policies for generating awareness for new products. Marketing Science, 5(2), 89–106.
Manchanda, P., Dubé, J. P., Goh, K. Y., & Chintagunta, P. K. (2006). The effect of banner advertising on internet purchasing. Journal of Marketing Research, 43(1), 98–108.
Moise, D., & Cruceru, A. F. (2014). An empirical study of promoting different kinds of events through various social media networks websites. Procedia-Social and Behavioral Sciences, 109, 98–102.
Naik, P. A., Mantrala, M. K., & Sawyer, A. G. (1998). Planning media schedules in the presence of dynamic advertising quality. Marketing Science, 17(3), 214–235.
Nerlove, M., & Arrow, K. J. (1962). Optimal advertising policy under dynamic conditions. Economica, 29(114), 129–142.
Piersma, N., & Jonker, J. J. (2004). Determining the optimal direct mailing frequency. European Journal of Operational Research, 158(1), 173–182.
Roberts, M. L., & Berger, P. D. (1999). Direct marketing management. London: Prentice Hall International.
Schmitt, P., Skiera, B., & Van den Bulte, C. (2011). Referral programs and customer value. Journal of marketing, 75(1), 46–59.
Seetharaman, P. B., & Chintagunta, P. K. (2003). The proportional hazard model for purchase timing: A comparison of alternative specifications. Journal of Business & Economic Statistics, 21(3), 368–382.
Wedel, M., DeSarbo, W. S., Bult, J. R., & Ramaswamy, V. (1993). A latent class Poisson regression model for heterogeneous count data. Journal of Applied Econometrics, 8(4), 397–411.
Wedel, M., Kamakura, W. A., DeSarbo, W. S., & Hofstede, F. T. (1995). Implications for asymmetry, nonproportionality, and heterogeneity in brand switching from piece-wise exponential mixture hazard models. Journal of Marketing Research, 32(4), 457–462.
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Kogan, K., Herbon, A. & Venturi, B. Direct marketing of an event under hazards of customer saturation and forgetting. Ann Oper Res 295, 207–227 (2020). https://doi.org/10.1007/s10479-020-03723-4
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DOI: https://doi.org/10.1007/s10479-020-03723-4