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Building Marketing Models that Make Money

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  • Leonard M. Lodish

    (Marketing Department, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104-6371)

Abstract

Building models that help marketers make productive decisions and that they actually use is hard. I learned some lessons in my 30+ years of building and applying models for sizing and deploying sales forces, for estimating brand health, and for estimating the impact on revenue of marketing mix and of the attractiveness to consumers of product attributes. One is that building scientific models that improve productivity is an art. Other lessons include these: to balance model complexity versus ease of understanding and estimation; to involve managers in any subjective estimates for models they will implement; to make measures available to managers when they need them and at the level of organization they need; to use the predictive validity of a hold-out sample to persuade managers of a model's credibility; and to recognize that even for empirical models, subjective estimates about the future may be necessary. Productive marketing models may have different attributes than those published in prestigious academic journals.

Suggested Citation

  • Leonard M. Lodish, 2001. "Building Marketing Models that Make Money," Interfaces, INFORMS, vol. 31(3_supplem), pages 45-55, June.
    • Handle: RePEc:inm:orinte:v:31:y:2001:i:3_supplement:p:s45-s55
    DOI: 10.1287/inte.31.3s.45.9681
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    File URL: http://dx.doi.org/10.1287/inte.31.3s.45.9681
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    References listed on IDEAS

    as
    1. Magid M. Abraham & Leonard M. Lodish, 1993. "An Implemented System for Improving Promotion Productivity Using Store Scanner Data," Marketing Science, INFORMS, vol. 12(3), pages 248-269.
    2. Leonard M. Lodish & Ellen Curtis & Michael Ness & M. Kerry Simpson, 1988. "Sales Force Sizing and Deployment Using a Decision Calculus Model at Syntex Laboratories," Interfaces, INFORMS, vol. 18(1), pages 5-20, February.
    3. John D. C. Little, 1970. "Models and Managers: The Concept of a Decision Calculus," Management Science, INFORMS, vol. 16(8), pages 466-485, April.
    4. Gary L. Lilien, 1975. "Model Relativism: A Situational Approach to Model Building," Interfaces, INFORMS, vol. 5(3), pages 11-18, May.
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    Cited by:

    1. Harikesh S. Nair & Sanjog Misra & William J. Hornbuckle IV & Ranjan Mishra & Anand Acharya, 2017. "Big Data and Marketing Analytics in Gaming: Combining Empirical Models and Field Experimentation," Marketing Science, INFORMS, vol. 36(5), pages 699-725, September.
    2. Gary L. Lilien & John H. Roberts & Venkatesh Shankar, 2013. "Effective Marketing Science Applications: Insights from the ISMS-MSI Practice Prize Finalist Papers and Projects," Marketing Science, INFORMS, vol. 32(2), pages 229-245, March.
    3. Preyas S. Desai & David Bell & Gary Lilien & David Soberman, 2012. "Editorial --The Science-to-Practice Initiative: Getting New Marketing Science Thinking into the Real World," Marketing Science, INFORMS, vol. 31(1), pages 1-3, January.
    4. Anderl, Eva & Becker, Ingo & von Wangenheim, Florian & Schumann, Jan Hendrik, 2016. "Mapping the customer journey: Lessons learned from graph-based online attribution modeling," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 457-474.
    5. Edward I. Brody, 2001. "Marketing Engineering at BBDO," Interfaces, INFORMS, vol. 31(3_supplem), pages 74-81, June.

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