Analytics-driven solutions for customer targeting and sales-force allocation

  • Authors:
  • R. Lawrence;C. Perlich;S. Rosset;J. Arroyo;M. Callahan;J. M. Collins;A. Ershov;S. Feinzig;I. Khabibrakhmanov;S. Mahatma;M. Niemaszyk;S. M. Weiss

  • Affiliations:
  • IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Softwere Group, Carmel, Indiana;IBM CHQ, Enterprise on Demand, Armonk, New York;IBM CHQ, Enterprise on Demand, Somers, New York;IBM CHQ, Enterprise on Demand, Somers, New York;IBM CHQ, Enterprise on Demand, Armonk, New York;IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Research, Division Thomas J. Watson Research Center, Yorktown Heights, NY;IBM Software Group, Bedford, New Hampshire;IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY

  • Venue:
  • IBM Systems Journal
  • Year:
  • 2007

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Abstract

Sales professionals need to identify new sales prospects, and sales executives need to deploy the sales force against the sales accounts with the best potential for future revenue. We describe two analytics-based solutions developed within IBM to address these related issues. The Web-based tool OnTARGET provides a set of analytical models to identify new sales opportunities at existing client accounts and noncustomer companies. The models estimate the probability of purchase at the product-brand level. They use training examples drawn from historical transactions and extract explanatory features from transactional data joined with company firmographic data (e.g., revenue and number of employees). The second initiative, the Market Alignment Program, supports sales-force allocation based on field-validated analytical estimates of future revenue opportunity in each operational market segment. Revenue opportunity estimates are generated by defining the opportunity as a high percentile of a conditional distribution of the customer's spending, that is, what we could realistically hope to sell to this customer. We describe the development of both sets of analytical models, the underlying data models, and the Web sites used to deliver the overall solution. We conclude with a discussion of the business impact of both initiatives.