Hedging The Commodity Spike

Why Commodity Volatility Demands a New Approach

Since 2020, CPG companies have relied heavily on price increases to drive growth, with more than 90% of sales growth coming from pricing rather than volume. But this strategy has hit a wall. Consumer resistance is mounting with 60% of consumers saying they'll buy fewer snacks and confectionaries if prices stay high or climb further.

Meanwhile commodity price volatility has averaged 10-20% annually over the past four years, with some commodities swinging up to 70% in a single year. The drivers of this volatility are diverse and interconnected:

• Persistent inflation from post-pandemic economic shifts
• Supply chain disruptions affecting availability and costs
• Energy shocks from geopolitical events and supply-demand imbalances
• Climate events impacting agricultural yields (85% of major food commodities face increased drought exposure)
• Policy changes including tariffs and trade restrictions

Take cocoa as an example: extreme weather and disease outbreaks in West Africa drove prices to $6,000 per metric ton, forcing chocolate manufacturers to completely rethink their cost structures and promotional strategies.

From Reactive to Strategic: The Modern Trade Promotion Management Imperative

Leading CPG companies are replacing post-mortem analysis ("what happened?") with forward looking decision-making ("what will happen if we do this?").

Capabilities That Deliver ROI

1. Trade Promotion Optimization (TPO)
Predictive modeling recommends alternative promotions based on baseline forecasts, price elasticity, and current commodity conditions. Teams can select recommended promotions knowing the financial impact before execution. When cocoa prices spike, the system might suggest promoting a less cocoa-intensive SKU or adjusting the promotion depth to maintain margin.

2. Trade Spend Management
The right trade spend management solution brings relief to the sales team managing trade budgets. Built-in checkbook management enables KAMs to know how much they’ve spent, what their balance is and enables them to react quickly to drive volume. Additionally, if fluctuating volumes create changing prices (and therefore trade funding dynamics and rates), a KAM should be able to mass update their remaining calendar and immediately understand the impact.

3. Dynamic, Integrated Scenario Planning
Instead of hoping for a volume lift, planners model how changes in price, duration, and promotion type affect revenue and margin at the SKU, account, and day level. Promotions are approved or rejected based on clear ROI, not assumptions or outdated commodity pricing. Learn more about “Human in the loop” AI driven scenario planning in our blog here.

AI-Driven Intelligence: Transforming Volatility into Advantage

71% of CPG leaders adopted AI in 2024, up from 42% in 2023, with major companies reporting significant ROI within 90 days of implementation. Those that properly implemented AI achieved 69% revenue growth and 72% cost reduction.

Prescriptive Analytics in Action

Modern Trade Promotion Optimization has gone beyond descriptive and diagnostic ("what happened and why") to predictive ("what will happen") and prescriptive ("what should we do"). You can read more about all 4 types of business analytics in our in depth blog here.

Descriptive → Diagnostic → Predictive → Prescriptive

Companies using prescriptive analytics report it takes approximately 12 weeks to transform their RGM departments from reactive to proactive. The payoff is substantial: avoiding underperforming promotions and reallocating spend to better ones can yield 1-2% of revenue improvement straight to the bottom line.

According to recent research, most revenue managers currently use a combination of predictive and descriptive analytics, with only 23% focusing on prescriptive analytics. This indicates a significant opportunity for growth as companies that effectively use RGM see annualized gross margin gains of 4 to 7 percent.

Beyond Supply Diversification: Portfolio Optimization

Many CPG companies are deploying AI-enabled systems of intelligence (SOIs) for supply chain management that anticipate and optimize across multiple dimensions simultaneously.  SOIs are a new solution architecture designed to serve as a company's centralized "brain" for value chain optimization, demand forecasting and decision-making. SOIs integrate with an organization's existing systems of record and engagement, fusing human-like reasoning and generative AI with transparent glass-box machine learning and nonlinear optimization to automate the analysis of large, complex datasets. These systems analyze TPx data, product margins, commodity usage, consumer preferences, and market dynamics to identify opportunities for portfolio evolution that reduces exposure to volatile materials while maintaining revenue growth. 

For example, when wheat prices spiked in 2022, leading food manufacturers could have used AI to:

• Identify SKUs with the worst margin impact from commodity increases
• Model consumer acceptance of reformulations using less volatile ingredients
• Optimize promotional calendars to emphasize products less exposed to grain price swings
• Reallocate trade spend from products bleeding margin to more profitable alternatives

Major brands like Unilever increased ice cream sales by 30% in key markets using weather-based AI demand forecasting systems, while Colgate-Palmolive employed machine learning and prescriptive analytics to run billions of scenarios, achieving strong results with major retailers.

Practical Strategies: Making Every Trade Dollar Count

Strategy 1: Elasticity-Based Promotion Design

With commodity costs fluctuating, understanding price elasticity becomes critical. By combining digital shelf analytics with Electronic point of sale (EPOS) data, companies can:

• Reduce promotion depth without compromising results
• Pare back promotion duration when margins tighten
• Shift to promoting more profitable SKUs when specific commodities spike
• Use virtual shopper modeling to test unlimited price combinations before execution

Strategy 2: Integrated Trade Promotion and Retail Media

One of the biggest wastes in trade promotion is running a promotion plus paid search placement plus retail media. This can lead to “double paying” for the same customer. While EPOS data is a key, and core, starting point for modeling, it is important that models are not source-constrained, and can incorporate multi-variant data sources.

Strategy 3: Agile Contracting and Dynamic Pricing Models

When commodity prices swing rapidly, rigid annual promotional calendars become liabilities. Progressive manufacturers are adopting:

• Flexible contracting arrangements with retailers
• Dynamic pricing models that account for commodity fluctuations in real-time
• Promotion approval processes that require current commodity cost validation
• Quarterly rather than annual promotion planning cycles

Strategy 4: Demand Forecasting Enhanced by Commodity Intelligence

Advanced analytics and AI-driven forecasting tools help businesses anticipate demand fluctuations and adjust both procurement and promotional strategies accordingly. Major food manufacturers employ commodity hedging alongside trade promotion optimization, creating a coordinated approach to managing cost volatility.

Conclusion: The New Competitive Reality

The CPG industry has entered an era where commodity volatility is expected. Consumer price resistance has eliminated the easy option of simply passing costs through to shoppers. Thus trade promotion management becomes the critical lever for maintaining margins while driving volume.

Companies that continue managing trade promotions based on last year's results or gut instinct are wasting billions of dollars. Those that integrate commodity intelligence, AI-driven optimization, and strategic portfolio management into their TPx processes are transforming volatility from a threat into a sustainable competitive advantage.