Abstract
Promotion plays an important role for the success of a product in the market and accounts for a large portion of the firm’s expenditure. This calls for judicious planning so that the promotion resources can be used efficiently at the same time creating maximum effectiveness. Firms use both mass and segment-driven promotion strategies to promote their product in a segmented market. Mass promotion spreads wider awareness of a product in the whole market with a spectrum effect while the segment-driven promotion is targeted to the distinct potential customers of the segments. This study proposes a mathematical model to determine the optimal length of a promotion campaign for a durable technology product, marketed in a segmented market under the joint effect of mass and segment-driven promotions. An application of the proposed model is demonstrated using real-life data. Differential evolution algorithm is used to solve the model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Weinstein, A.: Handbook of Market Segmentation: Strategic Targeting for Business and Technology Firms, 3rd edn. Routledge, New York (2013)
Buratto, A., Viscolani, B.: New product introduction: goodwill, time and advertising cost. Math. Methods Oper. Res. (ZOR) 55, 55–68 (2002)
Saak, A.: Dynamic informative advertising of new experience goods. J. Ind. Econ. 60, 104–135 (2012)
Jha, P., Aggarwal, S., Gupta, A., Kumar, U., Govindan, K.: Innovation diffusion model for a product incorporating segment-specific strategy and the spectrum effect of promotion. J. Stat. Manag. Syst. 17, 165–182 (2014)
Rogers, E.: Diffusion of Innovations. Free Press, New York (1962)
Bass, F.: A New Product growth for model consumer durables. Manag. Sci. 15, 215–227 (1969)
Dubé, J., Hitsch, G., Manchanda, P.: An empirical model of advertising dynamics. Quant. Market Econ. 3, 107–144 (2005)
Sohn, S., Choi, H.: Analysis of advertising lifetime for mobile phone. Omega 29, 473–478 (2001)
Hanna, R., Berger, P., Abendroth, L.: Optimizing time limits in retail promotions: an email application. J. Oper. Res. Soc. 56, 15–24 (2004)
Çetin, E.: Determining the optimal duration of an advertising campaign using diffusion of information. Appl. Math. Comput. 173, 430–442 (2006)
Jha, P., Aggarwal, S., Gupta, A.: Optimal duration of promotion for durable technology product in a segmented market. J. Promot. Manag. (in press)
Talke, K., Hultink, E.: Managing diffusion barriers when launching new products. J. Prod. Innov. Manag. 27, 537–553 (2010)
Iyengar, R., Van den Bulte, C., Valente, T.: Opinion leadership and social contagion in new product diffusion. Mark. Sci. 30, 195–212 (2011)
Peres, R., Muller, E., Mahajan, V.: Innovation diffusion and new product growth models: a critical review and research directions. Int. J. Res. Mark. 27, 91–106 (2010)
Lee, S., Trimi, S., Kim, C.: Innovation and imitation effect s dynamics in technology adoption. Ind. Manag. Data Syst. 113, 772–799 (2013)
Mahajan, V., Muller, E.: Timing, diffusion, and substitution of successive generations of technological innovations: the IBM mainframe case. Technol. Forecast. Soc. Change. 51, 109–132 (1996)
Jun, D., Park, Y.: A choice-based diffusion model for multiple generations of products. Technol. Forecast Soc. Chang. 61, 45–58 (1999)
Balakrishnan, P.S., Hall, N.: A maximin procedure for the optimal insertion timing of ad executions. Eur. J. Oper. Res. 85, 368–382 (1995)
Aggarwal, P., Vaidyanathan, R.: Use it or lose it: purchase acceleration effects of time-limited promotions. J. Consum. Behav. 2, 393–403 (2003)
Swain, S., Hanna, R., Abendroth, L.: How time restrictions work: The roles of urgency, anticipated regret, and deal evaluations. Adv. Consum. Res. 33, 523 (2006)
Park, K., Jang, S.: Duration of advertising effect: considering franchising in the restaurant industry. Int. J. Hosp. Manag. 31, 257–265 (2012)
Chiang, C., Lin, C., Chin, S.: Optimizing time limits for maximum sales response in internet shopping promotion. Expert. Syst. Appl. 38, 520–526 (2011)
Lo, A., Wu, J., Law, R., Au, N.: Which promotion time frame works best for restaurant group-buying deals? Tourism Recreat. Res. 39, 203–219 (2014)
Aggarwal, S., Gupta, A., Singh, Y., Jha, P.: Optimal duration and control of promotional campaign for durable technology product. In: Proceedings of IEEE IEEM 2012, pp. 2287–2289, Dec 2012
Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)
Price, K., Storn, R., Lampinen, J.: Differential Evolution. Springer, Berlin (2006)
Ali, M., Törn, A.: Population set-based global optimization algorithms: some modifications and numerical studies. Comput. Opera. Res. 31, 1703–1725 (2004)
Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE Congress on Evolutionary Computation, CEC2004, vol. 2, pp. 1980–1987 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer Science+Business Media Singapore
About this paper
Cite this paper
Arshia Kaul, Anshu Gupta, Sugandha Aggarwal, Jha, P.C. (2016). Differential Evolution Approach to Determine the Promotion Duration for Durable Technology Product Under the Effect of Mass and Segment-Driven Strategies. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_83
Download citation
DOI: https://doi.org/10.1007/978-981-10-0451-3_83
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-0450-6
Online ISBN: 978-981-10-0451-3
eBook Packages: EngineeringEngineering (R0)