Aevah.

CPG Pricing Intelligence

Playbook Series

The CPG PricingIntelligencePlaybook

How to model market factors before your competitors do. The 7 external signals your pricing model is ignoring and a framework for connecting them to real-time elasticity, scenario planning, and defensible margin decisions.

Audience CFO · CCO · CMO · VP RGM

Read Time 12 to 15 minutes

Includes Self-Assessment + Template

By Aevah · aevah.com

6x

Faster pricing decisions in AI-governed orgs vs. quarterly review cycles

6–8pt

Gross margin outperformance for CPG brands with real-time pricing analytics

90 days

Average signal-to-response lag in traditional CPG pricing organizations

Aevah. 02 of 05 · CPG Pricing Intelligence Playbook

The Core Problem

The 7 market factors your pricing model is not ingesting

These signals affect your competitor's pricing capacity, your retailer's margin pressure, and your consumer's price sensitivity simultaneously. A model built on last quarter's data is already wrong before you execute the decision it recommends.

01

Fuel and Freight Costs

Diesel price movements ripple through logistics costs for both you and your competitors, changing who can afford to hold price and when the competitive ceiling shifts.

Signal lag: 3 to 8 weeks. Model it forward, not backward.

02

Commodity Input Indices

Raw material costs drive cost-of-goods volatility across the entire category. Competitor cost pressure is your pricing window.

CRB Commodity · PPI Food and Beverages · USDA

03

Retailer Margin Pressure

When retailers are under margin pressure, their tolerance for CPG price increases compresses. When they are in growth mode, the dynamic reverses.

Track: Retailer earnings calls · Private label share

04

Consumer Sentiment and Trade-Down Risk

Consumer confidence indices and private label penetration rates are leading indicators of price sensitivity. A 5-point drop in confidence can increase elasticity meaningfully.

University of Michigan Consumer Sentiment Index

05

Competitor Promotional Velocity

When a competitor runs a deep promotional event, your everyday elasticity temporarily increases. When they are absent from promotion, your pricing ceiling rises. Most models miss this entirely.

Syndicated POS data · Trade press · Retailer scan

06

Channel Mix Shifts

eCommerce growth, club channel penetration, and DTC expansion all change the effective price architecture. Channel-specific elasticity differs significantly from aggregate elasticity.

Grocery vs. club cannibalization risk

07

Macroeconomic and Regulatory Signals

Interest rate environment, import tariffs, and minimum wage increases affect both consumer spend and your cost structure, yet few pricing models ingest macroeconomic variables as live inputs. Most models treat macro signals as manual overrides, not as dynamic model drivers. This is a structural gap.

Aevah. 03 of 05 · CPG Pricing Intelligence Playbook

Portfolio Risk

Cross-SKU cannibalization: why single-SKU models lie

Even a perfectly calibrated elasticity model fails if it treats each SKU in isolation. Cross-portfolio cannibalization is invisible in most pricing decisions and it is where the biggest margin surprises originate.

When a CPG brand raises the price on a premium SKU without modeling the elasticity interaction with its mid-tier SKU, the result is often counterintuitive: overall category revenue may be flat or growing while gross margin is declining. Volume shifts to the lower-margin SKU. The model said the price increase was right. It was, in isolation.

The Cannibalization Trap

Three scenarios that fail on the portfolio P&L:

Premium price increase accelerates trade-down to your own mid-tier SKU
Promotional depth on a flagship pulls volume from a higher-margin adjacency
Price-pack architecture change increases per-unit revenue but destroys household penetration economics

The fix is not a better single-SKU model. It is a cross-portfolio elasticity model with scenario planning that runs before the decision, not after the period closes.

The Solution

A framework for market-factor-integrated pricing

Moving from quarterly static models to real-time signal-integrated pricing does not require replacing your analytics stack. It requires a governed integration layer built on top of what you already have.

1

Clean Product Master (MDM)

One unified SKU hierarchy across ERP, trade systems, and BI tools. The non-negotiable foundation.

2

Governed Baseline Pipeline

Separates baseline volume from promotional lift. Baseline contamination is the number one cause of model failure.

3

Market Signal Integration

Automated ingestion of fuel, commodity indices, and competitor activity as live model variables.

4

Cross-SKU Elasticity Model

Accounts for substitution effects, promotional interactions, and channel-specific elasticity across the portfolio.

5

Scenario Planning Interface

Commercial-team-accessible. Pricing decisions are modeled before execution with full data lineage.

Aevah. 04 of 05 · CPG Pricing Intelligence Playbook

Execution Tool

Quarterly Pricing Cycle: Scenario Planning Template

Stress-test pricing assumptions against current market signal conditions before the decision is made, not after.

Scenario	Market Signal Trigger	Cross-SKU Risk	Recommended Action
Fuel cost spikeOver 10% in 8 weeks	Competitor logistics margin compressed. Window to hold or increase price.	Low. Competitor capacity constraint limits trade-down risk.	Hold or Increase Price
Consumer confidence declineOver 5 points	Price sensitivity increasing. Trade-down risk rising.	High. Premium SKUs vulnerable to mid-tier substitution.	Hold Price Consider Promo
Competitor promo surge	Temporary demand diversion. Elasticity coefficient increases.	Medium. Depends on depth and channel overlap.	Targeted Response
Commodity input spikeOver 15%	Cost pressure shared across category. Window exists but is narrow.	Medium. First mover on justified increase often wins.	Act Within 4 to 6 Weeks
Private label share growthOver 2 points	Value equation under pressure. Price gap must be managed actively.	High. Premium SKUs most vulnerable to permanent trade-down.	Hold Price Invest in Value

Self-Assessment

Pricing Analytics Maturity Assessment

Each checked item = 1 point. Your total identifies the highest-priority gap to close before your next pricing cycle.

✓

We have a single, consistent SKU hierarchy across ERP, trade systems, and BI tools.

If different systems have different SKU definitions, cross-SKU modeling is unreliable.

✓

Our elasticity model uses separate coefficients for everyday pricing and promotional pricing.

Conflating the two is one of the most common elasticity model failures in CPG.

!

Our pricing model ingests at least one external market signal as a live variable.

Most models do not. This is the single biggest gap between reactive and predictive pricing.

~

We can model cross-SKU cannibalization effects before executing a price change.

Pre-decision scenario modeling, not post-period analysis.

!

Our pricing analytics outputs are consistent with the numbers our CFO and finance team use.

If the CFO runs a different number, the pricing model will not be trusted at the board level.

~

Our RGM team can run scenario models without requiring a data science team to rebuild the model.

Commercial-team accessibility separates a pricing intelligence platform from a data science project.

✓

We can trace every pricing recommendation to its underlying data with full lineage.

Board defensibility requires that every price move is justifiable with governed, verified data.

0 to 2

Foundation Stage

Significant risk exposure on current pricing decisions.

3 to 5

Developing

Key gaps creating avoidable margin risk each quarter.

6 to 7

Advanced

Likely outperforming peers. Optimization is the priority.

Aevah. 05 of 05 · CPG Pricing Intelligence Playbook

Next Step

See what your pricing model is missing in 30 days.

Aevah's LISN platform deploys on your existing stack and delivers governed, market-factor-integrated pricing intelligence in 30 days. No rip-and-replace. No data science rebuild. Just a governed integration layer that makes your model predictive before your next pricing cycle.

Book a 20-Minute Pricing Analytics Assessment →

Deploys on Snowflake · dbt · Databricks · and your existing BI stack