AI/ML-based Carrier Pricing Solution For A Large Logistics Company Results In 2.5 % Lift In Margin

Case Study

AI/ML-based Carrier Pricing Solution For A Large Logistics Company Results In 2.5 % Lift In Margin

Business Objective

Our client is a leading North American 3PL (Third Party Logistics) company that has a strategic partnership with Tiger Analytics for their digitization and analytics transformation journey (Roadmap 2023).

As part of their Digital Freight Matching platform program, the client wanted to use AI/ML for the “Carrier Buy Price” recommendation to improve the carrier price discovery.

Challenges

  • Lack of adequate modeling data due to outliers and incorrect data
  • High variation in carrier pricing for routes < 150 miles

Solution Methodology 

  • Utilized load, carrier, and market-rate data and other external factors (macroeconomic, fuel rate) to develop the model
  • Built and deployed a robust, scalable, and cost-effective pricing recommendation engine to predict/recommend the optimal “Carrier Buy Price”

Business Impact

  • 2.5 % margin lift due to pricing engine usage, which resulted in improved sales conversion and significant EBIDTA
  • Pricing model enabled agents to negotiate better deals and provided detailed insights to improve operational and agent performance
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