🤖 AI Summary
This study addresses the challenges of excessive discounting, inefficient manual review, and inconsistent pricing decisions in enterprise software contract negotiations, which often stem from a lack of data-driven benchmarks. To overcome these issues, this work proposes an adaptive nearest-neighbor-based peer comparison scoring system that embeds dynamic peer benchmarking directly into the contract design workflow. By leveraging tree-based ensemble models to learn similarity from historical contracts and defining data-driven proximity through shared leaf nodes, the system generates real-time letter-grade ratings and product-line-level insights for new contracts. The approach enables auditable, real-time pricing decisions and centralized oversight, significantly enhancing discount discipline post-deployment and driving commercially meaningful revenue uplift across scored contract portfolios.
📝 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.