United StatesManufacturing2025 – ongoingSteelcase
AI pricing engine that recommends discounts on complex sales projects
Steelcase raised auto-approved pricing requests from 35% to nearly 50% with a machine-learning discount engine
Problem
Steelcase sales staff had to submit pricing requests for complex projects for review. Each submission took about 20 minutes, which slowed responses to customers.
Approach
Steelcase built a pricing engine on the Databricks platform on Azure. Random forest models, served through real-time inference, recommend discounts and send them to the internal Pricing and Contracts Management application through API endpoints. A feature store holds more than 70 predictive features, refreshed weekly.
Results
Takeaway
Letting a model approve routine discount requests freed sales hours that had gone into manual submissions. The company did not say how it measured the hours saved.
Categories
- Size
- Large enterprise
- Industry
- Manufacturing
- Business function
- Marketing and sales
- Innovation type
- Process innovation
- AI technology
- Predictive analytics and ML
- Adoption
- Off-the-shelf tool or platform
- Evidence
- Vendor case study
Sources
Source summaries
Databricks describes how Steelcase built a machine-learning pricing engine that recommends discounts to its internal pricing application. Auto-approvals rose from 35% to nearly 50%, and more than 1,500 sales hours a year were reclaimed. Pricing submissions had previously taken about 20 minutes each.
Full details are in the original sources.