Reinforcement Learning and Gradient Boosting for Dynamic Pricing in Configure-Price-Quote Systems: A Multi-Vertical Empirical Study
Corresponding author: Rajesh Soma
Abstract
Most enterprise Configure, Price, Quote (CPQ) deployments still run on deterministic rule engines designed for a simpler era of product catalogs and stable pricing environments. As catalogs expand and buyer expectations shift, these systems increasingly become a source of friction rather than velocity. This study builds and evaluates Machine Learning (ML)-CPQ, a six-layer system that tackles CPQ's three core bottlenecks: configuration accuracy, pricing intelligence, and approval latency using a combination of gradient boosting, Proximal Policy Optimization reinforcement learning, and transformer-based Natural Language Processing (NLP). The study trained and tested the system on a synthetic dataset of 14,200 sales quotes constructed from calibrated statistical distributions derived from published CPQ failure-mode rates and practitioner benchmarks, spanning the manufacturing, enterprise SaaS, and telecommunications verticals, and compared ML-CPQ against representative rule-based baselines for each vertical. The improvements were substantial and consistent: quote generation time dropped by 51.7%, configuration error rate declined from 8.0% to 2.89%, approval cycle time shortened by 61.9%, and average revenue per closed-won deal increased by 4.6%. These results are reported in detail, including vertical-level breakdowns and an ablation study that isolates each component's contribution. In addition, the study documents practical obstacles in data quality, model explainability, and sales team adoption as these obstacles are often underreported relative to headline performance numbers.
Keywords:
configure price quote, CPQ automation, machine learning, dynamic pricing, reinforcement learning, sales automationReferences
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