Algorithmic Contract Design at Scale: Adaptive Peer Comparison for Enterprise Pricing

Abstract

In enterprise software, a contract commits the customer to a usage volume over a fixed term in exchange for discounted pricing. These contracts are individually negotiated across many dimensions -- size, duration, industry, product mix, usage history -- and without a data-driven reference point, discounts tend to be overly generous. Manual governance review enforces discipline but at days-scale per contract, with inconsistency across reviewers and no real-time feedback to sellers. We present \emph{Contract Scoring}, a peer-based grading system deployed on every contract at Databricks. The system identifies empirically similar historical contracts via adaptive nearest neighbors over ensemble trees, where shared leaf membership defines a data-driven similarity learned from the discount target. It returns a letter grade with per-product-line breakdown in seconds; the underlying peer set is available to the centralized review team for audit. Sellers treat the grade as a contract design ``exit criterion'', iteratively adjusting discount structures until the grade reflects their intended tradeoff. Deployment evidence shows measurable discount discipline across the scored portfolio, with a commercially significant impact on revenue.