Analytics and Operations Research Increases Win Rates for IBM’s Information Technology Service Deals

Aly Megahed (), Taiga Nakamura (), Mark Smith (), Shubhi Asthana (), Michael Rose (), Maja Daczkowska () and Sandeep Gopisetty ()
Additional contact information
Aly Megahed: IBM Research–Almaden, San Jose, California 95120;
Taiga Nakamura: IBM Research–Almaden, San Jose, California 95120;
Mark Smith: IBM Services, Skeffington, Leicestershire LE7 9YB, England;
Shubhi Asthana: IBM Research–Almaden, San Jose, California 95120;
Michael Rose: IBM Services, Owensboro, Kentucky 42301;
Maja Daczkowska: IBM Services, Dublin D15 HN66, Ireland
Sandeep Gopisetty: IBM Research–Almaden, San Jose, California 95120;

Interfaces, 2020, vol. 50, issue 1, 50-63

Abstract: In part of its business, IBM Services competes in a tender process to win complex information technology service contracts worth multimillion dollars each. In response to a client’s request for proposal (RFP), IBM Services and other service providers prepare and submit solution proposals to the client. Clients short list a number of providers and engage with them through due diligence and intense negotiations to select a final winner for the bid. IBM Research has partnered with stakeholders in IBM Services and developed an innovative analytical ecosystem of tools that automatically and cognitively read an RFP by extracting client requirements and mapping them to IBM offerings; perform accurate costing and recosting, pricing and repricing, and market benchmarking of the bid; and predict the status over time of the various deals being pursued to effectively manage the sales pipeline and align salesforce resources. By using these tools, IBM has increased its win rate (a business impact of about $350 million). The tools can be transported to multiple similar industries with a tender process, such as the construction industry, the financial services industry, and the medical services industry.

Keywords: IT deals; win prediction; contract costing; B2B pricing; market benchmarking; RFP analytics; structured and unstructured data; revenue management (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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