CPG Demand and Promotion Optimization

This application area focuses on optimizing core commercial decisions in consumer packaged goods—specifically demand forecasting, pricing, trade promotions, and inventory planning—using data-driven, automated analytics. Instead of relying on slow manual analysis and intuition, CPG companies use advanced models to predict consumer demand across channels, determine the right price points, and decide which promotions to run, where, and when. These systems integrate data from retail partners, e‑commerce platforms, marketing campaigns, and supply chain operations to continuously refine recommendations. It matters because CPG margins are thin and execution complexity is high, especially in digital commerce and omnichannel retail. Poor forecasts and suboptimal promotions lead directly to stockouts, excess inventory, wasted trade spend, and missed growth opportunities. By systematizing and automating demand and promotion decisions, CPG firms can improve forecast accuracy, trade ROI, shelf availability, and overall profitability—while freeing commercial and revenue growth teams from manual reporting to focus on strategy and execution.

The Problem

Forecast demand and optimize CPG price/promo plans with measurable ROI

Organizations face these key challenges:

1

Forecasts miss promo spikes and post-promo dips (lift and cannibalization not modeled)

2

Trade spend is allocated by habit, not ROI (weak promo attribution and learning)

3

Chronic stockouts during promos and excess inventory after them

4

Slow planning cycles: weeks to update plans when retailers/e-comm conditions change

Impact When Solved

Enhanced SKU-level demand forecastingOptimized trade spend for higher ROIReduced stockouts and excess inventory

The Shift

Before AI~85% Manual

Human Does

  • Manual spreadsheet updates
  • Periodic S&OP meetings
  • Estimating promo impacts based on last year

Automation

  • Basic historical average calculations
  • Simple trend analysis
With AI~75% Automated

Human Does

  • Interpreting AI recommendations
  • Final decision-making on strategic plans
  • Monitoring market conditions for adjustments

AI Handles

  • Granular SKU-store-week forecasting
  • Causal promo modeling
  • Optimization of pricing and inventory decisions
  • Continuous learning from backtested outcomes

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

AutoML Demand Baseline for SKU-Week Forecasts

Typical Timeline:Days

Stand up a baseline forecast for high-volume SKUs using POS history and a basic promo flag to produce weekly forecasts and simple error metrics. This validates data availability, forecastability, and the business value of improved accuracy before deeper promo/price modeling.

Architecture

Rendering architecture...

Key Challenges

  • POS vs shipments mismatches and missing weeks
  • Cold-start for new SKUs and pack changes
  • Forecast granularity tradeoffs (SKU-store vs SKU-region)
  • Separating base demand from promo effects (only crude flags at this level)

Vendors at This Level

Smaller regional CPG brandsEmerging DTC CPG companies

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