Case study · FMCG
CPG Trade Promotion ROI & Baseline Demand Sensing
A global consumer goods brand division managing 240 SKUs across personal care and home care categories in 12,000+ retail storefronts.
Engagement record
- Client
- Consumer goods manufacturer
- Industry
- FMCG
- System
- Trade Promotion Effectiveness & Demand Sensing AI
- Timeline
- 6 Weeks
About this write-up
This case study describes the engagement as recorded. The client is not named; figures and outcomes are as reported by the client and have not been independently audited.
The challenge
The manufacturer allocated $28M annually to retail trade promotions and scan-down discounts, but over 45% of discount events generated negative incremental returns, merely subsidizing baseline shoppers. Simultaneously, promotional demand surges triggered 9.2% on-shelf stockouts during key sales weekends.
Objectives
- Ingest retailer POS scanner data, distributor EDI 852 feeds, and ERP TPM ledgers.
- Isolate true incremental promotional sales lift from organic baseline sales using causal AI.
- Deliver automated trade spend simulation and stockout prevention alerts to commercial teams.
Discovery
Econometric analysis revealed that deep discount promotions on large-pack SKUs cannibalized high-margin single-unit sales without bringing new shoppers to the category, resulting in net negative promotional ROI.
What was built
- Nielsen & Circana Syndicated POS Ingestion Connectors. Ingests weekly scanner data, pricing depths, and display feature flags across 12,000 retail stores.
- Causal Machine Learning & Price Elasticity Engines. Isolates true incremental sales lift while controlling for seasonality, advertising, and competitor discounts.
- SAP Trade Management & TPM Data Bridges. Synchronizes verified promotional performance with ERP commercial spending ledgers.
- Interactive Account Manager Deal Simulation Workbench. Provides account managers with real-time promotion ROI simulations and optimal discount depths.
Outcomes as reported
Reported summary: +15.4% Promotion ROI & $2.1M Reallocated.
| Figure | What it refers to |
|---|---|
| +15.4% Promo ROI | Return on commercial trade promotion expenditures increased across top brand lines |
| $2.1M Reallocated | In unprofitable trade discounts reallocated to high-growth, high-margin product categories |
| -44% Retail Stockouts | On-shelf stockouts reduced during high-volume promotional sales events |
| 5 Min Simulations | Joint Business Planning scenario modeling compressed from 2 weeks of manual analysis |
Figures as reported in the engagement record; not independently audited.
Lessons
- Isolating baseline volume allows account executives to negotiate discount depths with major retailers with complete confidence.
- High-frequency demand sensing connects commercial promotions directly with factory production scheduling.
Next steps
Integrating digital e-commerce retail media ad spend into the physical store trade ROI model.
Implementation questions
By controlling for seasonal trends, marketing ad spend, store foot traffic, and competitor pricing to isolate true causal lift.
We support SAP Trade Management, Oracle Demantra, Salesforce Consumer Goods Cloud, and Blue Yonder.
Historical POS scan data ingestion and baseline model calibration were completed in 4 weeks.
