CPG Glossary

What is Explainable AI (XAI) in CPG pricing and promotions, and why does it matter for retailer negotiations?

Explainable AI (XAI) in CPG pricing and promotions is an advanced machine learning framework that provides complete visibility into why a specific price point, promotional discount, or trade spending recommendation is generated. Unlike legacy "black-box" models, XAI reveals the underlying data drivers, such as store-level price elasticity, competitive price shifts, incremental volume lift, and category margin impacts, giving CPG Revenue Growth Management (RGM) teams the transparent logic needed to evaluate, trust, and confidently present recommendations during retailer negotiations.

What is revenue growth management in CPG?

Revenue growth management (RGM) in CPG is the practice of coordinating pricing, trade promotion, price-pack architecture, and product mix so that a consumer packaged goods brand grows net revenue and margin, not just volume. Instead of treating each lever separately, RGM manages them as one system, defending every dollar of revenue and making sure every dollar of trade spend earns its place.

What is trade promotion optimization?

Trade promotion optimization (TPO) is the use of data and predictive models to decide which trade promotions a CPG brand should run, at what depth and frequency, and with which retailers, so that promotional spend generates incremental revenue instead of subsidizing sales that would have happened anyway. It goes one step beyond trade promotion management (TPM): where TPM plans, executes, and tracks promotions, TPO tells you which promotions are actually worth running.

What is AI-powered pricing and assortment optimization for CPG?

AI-powered pricing and assortment optimization for CPG is the use of machine-learning models on store-level sales, competitive, and promotional data to recommend what a consumer packaged goods brand should charge and what should be stocked, at the store and channel level. Unlike descriptive analytics that report what already happened, these models predict how a price or assortment change will move demand, revenue, and margin before the decision is made.

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