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Differential Evolution Approach to Determine the Promotion Duration for Durable Technology Product Under the Effect of Mass and Segment-Driven Strategies

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

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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.

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References

  1. Weinstein, A.: Handbook of Market Segmentation: Strategic Targeting for Business and Technology Firms, 3rd edn. Routledge, New York (2013)

    Google Scholar 

  2. Buratto, A., Viscolani, B.: New product introduction: goodwill, time and advertising cost. Math. Methods Oper. Res. (ZOR) 55, 55–68 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Saak, A.: Dynamic informative advertising of new experience goods. J. Ind. Econ. 60, 104–135 (2012)

    Article  Google Scholar 

  4. 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)

    Article  Google Scholar 

  5. Rogers, E.: Diffusion of Innovations. Free Press, New York (1962)

    Google Scholar 

  6. Bass, F.: A New Product growth for model consumer durables. Manag. Sci. 15, 215–227 (1969)

    Article  MATH  Google Scholar 

  7. Dubé, J., Hitsch, G., Manchanda, P.: An empirical model of advertising dynamics. Quant. Market Econ. 3, 107–144 (2005)

    Article  Google Scholar 

  8. Sohn, S., Choi, H.: Analysis of advertising lifetime for mobile phone. Omega 29, 473–478 (2001)

    Article  Google Scholar 

  9. Hanna, R., Berger, P., Abendroth, L.: Optimizing time limits in retail promotions: an email application. J. Oper. Res. Soc. 56, 15–24 (2004)

    Article  MATH  Google Scholar 

  10. Çetin, E.: Determining the optimal duration of an advertising campaign using diffusion of information. Appl. Math. Comput. 173, 430–442 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  11. Jha, P., Aggarwal, S., Gupta, A.: Optimal duration of promotion for durable technology product in a segmented market. J. Promot. Manag. (in press)

    Google Scholar 

  12. Talke, K., Hultink, E.: Managing diffusion barriers when launching new products. J. Prod. Innov. Manag. 27, 537–553 (2010)

    Article  Google Scholar 

  13. Iyengar, R., Van den Bulte, C., Valente, T.: Opinion leadership and social contagion in new product diffusion. Mark. Sci. 30, 195–212 (2011)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Lee, S., Trimi, S., Kim, C.: Innovation and imitation effect s dynamics in technology adoption. Ind. Manag. Data Syst. 113, 772–799 (2013)

    Article  Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. Jun, D., Park, Y.: A choice-based diffusion model for multiple generations of products. Technol. Forecast Soc. Chang. 61, 45–58 (1999)

    Article  Google Scholar 

  18. Balakrishnan, P.S., Hall, N.: A maximin procedure for the optimal insertion timing of ad executions. Eur. J. Oper. Res. 85, 368–382 (1995)

    Article  MATH  Google Scholar 

  19. Aggarwal, P., Vaidyanathan, R.: Use it or lose it: purchase acceleration effects of time-limited promotions. J. Consum. Behav. 2, 393–403 (2003)

    Article  Google Scholar 

  20. 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)

    Google Scholar 

  21. Park, K., Jang, S.: Duration of advertising effect: considering franchising in the restaurant industry. Int. J. Hosp. Manag. 31, 257–265 (2012)

    Article  Google Scholar 

  22. Chiang, C., Lin, C., Chin, S.: Optimizing time limits for maximum sales response in internet shopping promotion. Expert. Syst. Appl. 38, 520–526 (2011)

    Article  Google Scholar 

  23. 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)

    Article  Google Scholar 

  24. 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

    Google Scholar 

  25. Storn, R., Price, K.: Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11, 341–359 (1997)

    Google Scholar 

  26. Price, K., Storn, R., Lampinen, J.: Differential Evolution. Springer, Berlin (2006)

    MATH  Google Scholar 

  27. Ali, M., Törn, A.: Population set-based global optimization algorithms: some modifications and numerical studies. Comput. Opera. Res. 31, 1703–1725 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  28. 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)

    Google Scholar 

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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

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_83

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  • Online ISBN: 978-981-10-0451-3

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