Read this article about IBM, one of our finalists, in the category Best Achievement in Technology Enabled Process Automation, selected by the independent judging panel, for the 2019 Business Transformation & Operational Excellence Industry Awards program. Click here to read articles on other finalists.

Using Artificial Intelligence (AI), IBM has created a sales opportunity passing solution that has exponentially improved the process, resulting in $32M in additional revenue over the first half of 2018.

Much of IBM’s business is conducted through our wide network of Business Partners (BPs) who are highly invested in IBMs solutions. Historically, the responsibility of selecting a BP for a specific sales opportunity fell on the IBM seller, who often has limited knowledge about which BPs represent the best choice for a given opportunity, resulting in a delayed process and revenue impact.

Opportunity passing has historically been a very manual process. Business rules were prescribed to determine which leads should be passed and notifications were then sent to the Opportunity Owners (OOs), who then had to select a BP for an opportunity.  While many process improvements were implemented across the past decade attempting to increase the volume and speed of passing, none contributed to a sizable impact.

IBM’s Chief Analytics Office (CAO) partnered with IBM Research Ireland to create a solution that provides recommendations for BP selection. Called the Smarter Cognitive Opportunity Recommendation Engine (SCORE), this engine uses AI techniques that allow it to learn from sales data and operational feedback from IBM Sellers, BPs and clients to recommend the optimal BP to receive each lead. 

To allow for quick and effective development and implementation of the system, the previous opportunity passing system was retro-fitted with the new cognitive models.  As a result, no additional development was needed to facilitate the lead passing – simply the development and integration of a new recommendation engine and a mechanism to automatically pass opportunities to BPs.

Prior to SCORE, BP recommendations were based on a model that was limited in scope and based predominantly on business rules.  To improve this model, an Elastic Search algorithm was developed focusing on the main characteristics of a successful business partner: (1) Historical Performance; (2) Product Experience, (3) Customer Experience; (4) Industry Experience; (5) Deal Size; (6) Proximity to the Deal; and (7) Social Network of Sellers and BPs.  This algorithm was then enhanced with a feature that incorporates feedback from sellers to continuously improve recommendations.  The model is retrained weekly to account for the recent activities of BPs.  Once the recommendations were considered trustworthy, a system to automatically pass the opportunities to BPs was implemented. 

SCORE’s results over the past eight months demonstrate improved access to the BP ecosystem, positioning IBM to better tap into the full potential of BPs.  Many changes have already been put into effect that would not have been possible under the previous infrastructure and new improvements are made continuously to the model and the overall process because of the cognitive system that is in place. 

With SCORE, the time to pass a sales opportunity to a BP was cut in half when comparing 1H2017 to 1H2018.  In addition, process improvement benefits have driven an incremental $32M of transactional revenue for IBM in the first half of 2018.