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Rating Customers According to Their Promptness to Adopt New Products

Author

Listed:
  • Dorit S. Hochbaum

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

  • Erick Moreno-Centeno

    (Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas 77845)

  • Phillip Yelland

    (Google Inc., Mountain View, California 94043)

  • Rodolfo A. Catena

    (The SPHERE Institute, Burlingame, California 94010)

Abstract

Databases are a significant source of information in organizations and play a major role in managerial decision-making. This study considers how to process commercial data on customer purchasing timing to provide insights on the rate of new product adoption by the company's consumers. Specifically, we show how to use the separation-deviation model (SD-model) to rate customers according to their proclivity for adopting products for a given line of high-tech products. We provide a novel interpretation of the SD-model as a unidimensional scaling technique and show that, in this context, it outperforms several dimension-reduction and scaling techniques. We analyze the results with respect to various dimensions of the customer base and report on the generated insights.

Suggested Citation

  • Dorit S. Hochbaum & Erick Moreno-Centeno & Phillip Yelland & Rodolfo A. Catena, 2011. "Rating Customers According to Their Promptness to Adopt New Products," Operations Research, INFORMS, vol. 59(5), pages 1171-1183, October.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:5:p:1171-1183
    DOI: 10.1287/opre.1110.0963
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    References listed on IDEAS

    as
    1. Barry L. Bayus, 1998. "An Analysis of Product Lifetimes in a Technologically Dynamic Industry," Management Science, INFORMS, vol. 44(6), pages 763-775, June.
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    3. Dorit S. Hochbaum, 2004. "50th Anniversary Article: Selection, Provisioning, Shared Fixed Costs, Maximum Closure, and Implications on Algorithmic Methods Today," Management Science, INFORMS, vol. 50(6), pages 709-723, June.
    4. Warren Torgerson, 1952. "Multidimensional scaling: I. Theory and method," Psychometrika, Springer;The Psychometric Society, vol. 17(4), pages 401-419, December.
    5. Michael Brusco & Stephanie Stahl, 2005. "Optimal Least-Squares Unidimensional Scaling: Improved Branch-and-Bound Procedures and Comparison to Dynamic Programming," Psychometrika, Springer;The Psychometric Society, vol. 70(2), pages 253-270, June.
    6. P. J. F. Groenen & W. J. Heiser & J. J. Meulman, 1999. "Global Optimization in Least-Squares Multidimensional Scaling by Distance Smoothing," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 225-254, July.
    7. Dorit S. Hochbaum & Asaf Levin, 2006. "Methodologies and Algorithms for Group-Rankings Decision," Management Science, INFORMS, vol. 52(9), pages 1394-1408, September.
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    Cited by:

    1. TANASE, George, 2016. "The Future of Marketing in 2016: Trends in the New Digital Age," Romanian Distribution Committee Magazine, Romanian Distribution Committee, vol. 7(2), pages 20-25, July.

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