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Operations Research Improves Sales Force Productivity at IBM

Author

Listed:
  • Rick Lawrence

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Claudia Perlich

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Saharon Rosset

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Ildar Khabibrakhmanov

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Shilpa Mahatma

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Sholom Weiss

    (IBM Corporation, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598)

  • Matt Callahan

    (IBM Corporation, Business Performance Services, Armonk, New York 10504)

  • Matt Collins

    (IBM Corporation, Business Performance Services, Armonk, New York 10504)

  • Alexey Ershov

    (IBM Corporation, Business Performance Services, Armonk, New York 10504)

  • Shiva Kumar

    (IBM Corporation, Business Performance Services, Armonk, New York 10504)

Abstract

In 2004, IBM introduced a set of broad operations research-based initiatives designed to improve the efficiency and productivity of its global sales force. The first solution, OnTARGET, provides a set of analytical models designed to identify new sales opportunities at existing IBM accounts and at noncustomer companies. The second solution, the Market Alignment Program (MAP), optimally allocates sales resources based on field-validated analytical estimates of future revenue opportunities in operational market segments. IBM Research developed the operations research models and initial internal websites for both solutions. The IBM Software Group initially implemented OnTARGET, which was subsequently made available to over 13,000 sales representatives across IBM sales organizations worldwide. The IBM Sales and Distribution organization deployed MAP as an integral part of its sales model to better align sales resources with the best market opportunities. We describe the development of both analytical models, and the underlying data models and websites used to deliver the solutions. We conclude with a discussion of the business impact, which we estimate as hundreds of millions of dollars annually for the combined initiatives.

Suggested Citation

  • Rick Lawrence & Claudia Perlich & Saharon Rosset & Ildar Khabibrakhmanov & Shilpa Mahatma & Sholom Weiss & Matt Callahan & Matt Collins & Alexey Ershov & Shiva Kumar, 2010. "Operations Research Improves Sales Force Productivity at IBM," Interfaces, INFORMS, vol. 40(1), pages 33-46, February.
    • Handle: RePEc:inm:orinte:v:40:y:2010:i:1:p:33-46
    DOI: 10.1287/inte.1090.0468
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    Cited by:

    1. Carlos Fernández-Loría & Foster Provost, 2022. "Rejoinder to “Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters”," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 23-26, April.
    2. Michael J. Davis & Yingdong Lu & Mayank Sharma & Mark S. Squillante & Bo Zhang, 2018. "Stochastic Optimization Models for Workforce Planning, Operations, and Risk Management," Service Science, INFORMS, vol. 10(1), pages 40-57, March.
    3. Meyer, Anne & Glock, Katharina & Radaschewski, Frank, 2021. "Planning profitable tours for field sales forces: A unified view on sales analytics and mathematical optimization," Omega, Elsevier, vol. 105(C).
    4. Ossi Ylijoki, 2018. "Guidelines for assessing the value of a predictive algorithm: a case study," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 19-26, March.

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