PepsiCo Deploys AI-Driven Pricing and Promotion Optimization at Scale

Effective pricing and promotion planning constitutes a central pillar of strategic revenue management for firms operating in highly competitive and dynamic markets. These planning activities require the simultaneous consideration of demand elasticity, competitor actions, channel and market specific constraints, and financial objectives. As the dimensionality and interdependencies inherent in these problems increase, manual or traditional approaches become suboptimal and insufficient. In this context, operations research provides a robust methodological foundation for scalable data-driven decision support systems that can optimize complex planning processes across large product and customer portfolios. This paper presents two large-scale optimization systems developed and deployed at PepsiCo to support revenue growth management initiatives: PromoAI and PricingAI. PromoAI integrates machine learning–based promotional forecasts with a mixed-integer linear programming model to optimize promotional calendars across trade channels. The system navigates through millions of product-promotion-timing combinations to find the one that maximizes PepsiCo and retailer revenues, subject to a wide range of customizable business constraints encoded in a modular, user-configurable interface. On the other hand, PricingAI focuses on the optimization of base prices across product portfolios over multiperiod horizons. The system employs Bayesian hierarchical models to estimate own and cross-price elasticities and captures competitive interactions at the product level. These elasticity estimates are fed into a nonlinear programming optimization engine that recommends price changes aligned with revenue and margin targets while incorporating operational constraints such as price thresholds, volume or profit margins, and channel- and market-specific business rules. Together, these systems demonstrate the feasibility and scalability of advanced optimization in large-scale enterprise environments. They highlight the value of integrating statistical learning with mathematical programming to enable enterprise-level automated decision making that is both data informed and aligned with strategic business objectives.

History: This paper was refereed.