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A Choice-Based Approach to the Diffusion of a Service: Forecasting Fax Penetration by Market Segments

Author

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  • S. Weerahandi

    (Bellcore)

  • S. R. Dalal

    (Bellcore)

Abstract

Motivated by poor performance of standard estimation methods in our application, the problem of modeling the diffusion of a new service (or a product) is considered without the assumption of a homogeneous population. The model consists of a two-stage procedure where customers receive purchase occasions according to a conventional diffusion model and at each purchase occasion they buy according to a binary choice model. This approach permits explicit incorporation of individual customer demographics and product attributes, whence one can study changes in diffusion as a function of changes in product attributes, prices, advertising, and customer demographics. The model has provided excellent results in a number of applications including the one on fax penetration reported here. In our applications this approach has been useful in market segmentation, studying the effect of marketing strategies, and in evaluating the effects of new service features. A limited validation is carried out to judge the forecasting performance of the model used in the fax example. A simulation study is carried out to compare the proposed solution with the result obtained by fitting the Bass model to a set of market segments separately. The simulation indicates that the proposed approach substantially improves over the aggregate fitting.

Suggested Citation

  • S. Weerahandi & S. R. Dalal, 1992. "A Choice-Based Approach to the Diffusion of a Service: Forecasting Fax Penetration by Market Segments," Marketing Science, INFORMS, vol. 11(1), pages 39-53.
  • Handle: RePEc:inm:ormksc:v:11:y:1992:i:1:p:39-53
    DOI: 10.1287/mksc.11.1.39
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    Cited by:

    1. Peters, Kay & Albers, Sönke & Kumar, V., 2008. "Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables," EconStor Preprints 27678, ZBW - Leibniz Information Centre for Economics.
    2. Hongmin Li, 2020. "Optimal Pricing Under Diffusion-Choice Models," Operations Research, INFORMS, vol. 68(1), pages 115-133, January.
    3. Fouad El Ouardighi & Gustav Feichtinger & Gila E. Fruchter, 2018. "Accelerating the diffusion of innovations under mixed word of mouth through marketing–operations interaction," Annals of Operations Research, Springer, vol. 264(1), pages 435-458, May.
    4. Yair Orbach & Gila Fruchter, 2014. "Predicting product life cycle patterns," Marketing Letters, Springer, vol. 25(1), pages 37-52, March.
    5. Orbach Yair & Fruchter Gila E., 2010. "A Utility-Based Diffusion Model Applied to the Digital Camera Case," Review of Marketing Science, De Gruyter, vol. 8(1), pages 1-28, June.
    6. Bas, Javier & Zofío, José L. & Cirillo, Cinzia & Chen, Hao & Rakha, Hesham A., 2022. "Policy and industry implications of the potential market penetration of electric vehicles with eco-cooperative adaptive cruise control," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 242-256.
    7. Emmanouilides, Christos J. & Davies, Richard B., 2007. "Modelling and estimation of social interaction effects in new product diffusion," European Journal of Operational Research, Elsevier, vol. 177(2), pages 1253-1274, March.
    8. Fildes, Robert & Kumar, V., 2002. "Telecommunications demand forecasting--a review," International Journal of Forecasting, Elsevier, vol. 18(4), pages 489-522.
    9. Vardit Landsman & Moshe Givon, 2010. "The diffusion of a new service: Combining service consideration and brand choice," Quantitative Marketing and Economics (QME), Springer, vol. 8(1), pages 91-121, March.
    10. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    11. Kim, Namwoon & Srivastava, Rajendra K. & Han, Jin K., 2001. "Consumer decision-making in a multi-generational choice set context," Journal of Business Research, Elsevier, vol. 53(3), pages 123-136, September.
    12. Ozer, Muammer, 2007. "Reducing the demand uncertainties at the fuzzy-front-end of developing new online services," Research Policy, Elsevier, vol. 36(9), pages 1372-1387, November.
    13. Anders F. Jensen & Elisabetta Cherchi & Stefan L. Mabit & Juan de Dios Ortúzar, 2017. "Predicting the Potential Market for Electric Vehicles," Transportation Science, INFORMS, vol. 51(2), pages 427-440, May.

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