Case study: Pricing Optimization for a Fortune 500 Memory Manufacturing Company

About the Client

The client is a Fortune 500 memory manufacturing company and is considered to be among top 5 semiconductor companies in the world

Business Context

The memory market is highly volatile, and the probability of human error in predicting prices is high due to market dynamics and variations in supply and demand. Due to these challenges the client was looking to improve price intelligence for smarter pricing decisions. They intended to build an on-demand price intelligence solution for their sales representatives and also focus on margin enhancement by setting the right pricing recommendations.

BRIDGEi2i Solution

BRIDGEi2i developed and deployed a comprehensive approach to pricing that leverages a systematic, machine learning-based pricing engine – Quote Bot. The AI-powered Quote Bot was designed to provide real-time price intelligence and is capable of self-learning.

  • Historical distributors quote data was used with the SPOT prices
  • Data of customer and product was mapped to identify useful attributes
  • R was used for modeling, and SQL was used to automate real-time price recommendations using stored procedures
  • The AI Labs team developed Customer segmentation based on buying and quoting behavior
  • Pricing models were built based on customer segment, product attribute, and volume distribution across different customer regions and business units
  • Model refresh and scoring were automated by building scheduled store procedures in SQL
  • BRIDGEi2i’s AI labs deployed Quote Bot – a machine learning-based SQL application and was embedded into the client’s quoting system to provide real-time price intelligence to sales representatives
  • Distributors’ response in terms of win rates or margins were monitored automatically
  • The model health was monitored for stability as well as market dynamics based price changes, and the learning was fed into the Quote Bot

Business Impact

$ Revenue Increase
Adoption of Recommended Prices
Increase in Win Rate