Kraft Foods: trade promotion forecasting

Case study · Consumer goods

Kraft Foods deployed the RoadMap GPS Suite to replace sales-driven guesswork with statistical baseline forecasting, quantified promotional lift modeling, and retailer-level profit simulation across its trade planning process.

The challenge

Kraft Foods operated across diverse retail channels where trade promotions were a major revenue driver, yet forecasting methods did not consistently quantify incremental lift, cannibalization, forward-buying, or post-event volume decline. Sales teams submitted forecasts without statistical grounding, making it difficult to isolate true baseline demand.

Promotions were approved without structured profitability modeling. Finance and supply chain lacked visibility into retailer margin impact, and operational volatility spiked around every trade event. The organization needed a system that could unify commercial planning with operational feasibility.

The solution

We deployed the RoadMap GPS Suite as a centralized forecasting and planning engine. Time-series methods established seasonally adjusted baselines, removing promotional distortion from the demand signal. Regression-based lift models then quantified expected incremental volume, cannibalization between SKUs, and retailer-specific response rates for each promotional event.

A retailer profit and trade spend simulation layer allowed commercial teams to model net revenue impact, trade spend efficiency, retailer gross margin, and contribution margin before approving any program. Side-by-side scenario comparisons covered discount depth, duration, feature vs. display combinations, and timing, each dynamically recalculating volume, revenue, and supply chain demand.

• Statistical baseline forecasting (ARIMA and regression)
• Event-based promotional lift modeling
• Retailer profitability and trade spend simulation
• Scenario comparison planning
• Automated sales planning workflows integrated with existing ERP

Results

• Improved promotional forecast reliability across retail accounts
• Reduced bias in forecast overrides
• Greater trade spend visibility for commercial teams
• More disciplined promotional approval processes
• Improved retailer negotiation leverage through forward-looking financial projections
• Reduced supply chain disruption during event cycles
• Transition from reactive promotion planning to structured scenario-based forecasting

Key takeaways

• Accurate baseline forecasts are the foundation of effective promotion planning. Without separating baseline from lift, every forecast overstates or understates demand.
• Promotional lift must be modeled using historical elasticity, not assumed percentages. Each event should be treated as a quantifiable demand event.
• Retailer profitability forecasting changes the quality of trade decisions. When margin impact is visible before approval, unprofitable promotions get stopped early.
• Scenario modeling reduces financial and operational risk by ensuring approved promotions are both commercially justified and supply-chain executable.