Case Study: a large B2B distributor boosts bid win rate by 10% with Ugam

Large B2B Distributor - Customer Case Study

A large B2B distributor faced challenges in efficiently responding to a high volume of bid requests. Their previous vendor’s manual process for product recommendations was only 60% accurate, could not scale beyond 600 products daily against a demand of over 1,500, and failed to provide alternate product choices. This led to a low bid win rate and forced their sales team to spend significant time verifying data. They partnered with Ugam to implement a solution leveraging machine learning and a large product database.

Ugam deployed multiple machine learning algorithms to deliver highly accurate (98%+) product recommendations and several alternate choices, normalizing for unit of measure to compare competitiveness. This allowed the distributor’s team to focus on pricing instead of data verification and easily handle over 6,000 product requests daily. As a result of Ugam's solution, the distributor increased its bid win rate by 10% over two quarters.