AI-Optimized CPQ-Dynamic Discounting Based on 12-Quarter Customer Purchase Histories
Keywords:
Quoting automation, AI powered pricing, Dynamic discounting, Predictive pricing models, Customer purchase history, Real-time discount logic, Base Unit Price (BUP), BUP – Actual, BUP– Normalized, BUP – Recommended, pricing normalization, pricing intelligence, Discount automationAbstract
In this study, we propose an AI-driven approach to transforming the Configure - Price - Quote (CPQ) process by introducing dynamic discount logic tailored to a customer’s 12-quarter purchase history of a given product. By embedding a machine learning model within Salesforce CPQ, the solution is designed to replace static discounting with intelligent, real-time pricing predictions factoring in historical buying patterns, product mix, deal velocity, and engagement trends. The model incorporates normalized and actual Base
Unit Prices (BUP) to recommend optimal pricing at the quote line level. This initiative is expected to dramatically reduce manual quoting effort, enhance pricing consistency, and improve revenue recognition, setting the stage for scalable and data-driven sales operations.
