AI-Driven Trade Promotion Optimization (TPO)
30% higher promotion ROI in a defined engagement, with end-to-end lineage across the in-scope data path — governed uplift and segmentation models on a Databricks medallion architecture.
Buyer Objection
While serving as Field CTO for a data and AI platform pursuing CPG and retail accounts, trade spend sat as the second-largest line on the P&L — managed by spreadsheet post-mortems, with McKinsey estimating 72% of promotions fail to turn a profit. The buying committee — finance, sales, and IT — needed one auditable ROI story. Mode: light-touch, high-leverage.
Architecture Thesis
Trade spend is the second-largest line on the P&L, managed by spreadsheet post-mortems. A governed medallion architecture unifies ERP, CRM, and POS feeds so every ROI figure traces to source transactions — and the value case survives CFO scrutiny.
Reference Architecture
Swipe to explore the diagram
Disparate enterprise feeds — SAP ECC product masters, Oracle EBS transaction history, Salesforce CRM loyalty tiers, and retailer POS data — are unified into a Databricks Lakehouse architecture. Raw data lands in the Bronze staging layer, standardizes within the Silver layer (Sales Fact, Product Master, Customer Profile), and is enriched by machine learning models. The Sales Uplift Model predicts baseline vs. incremental promotional volume, feeding validated metrics into Gold KPI tables accessible via Power BI dashboards with end-to-end lineage across the in-scope data path — lineage events in OpenLineage format, landed in the customer's catalog.
Preconditions & Assumptions
- Source feed access (SAP ECC, Oracle EBS, Salesforce, retailer POS) contracted and stable.
- Uplift-model experimental design agreed — baseline, holdout logic, and evaluation metric defined before modeling.
- Promotion history and deduction data quality assessed.
Control Model
- End-to-end lineage across the defined in-scope data path, emitted as OpenLineage events into the customer's catalog.
- Model governance: versioned models, documented ground truth, drift monitoring.
- Human review of reallocation recommendations before spend moves.
Validation Plan
- Uplift models evaluated against holdout periods with documented baselines.
- ROI attribution stress-tested against seasonality, assortment, pricing, distribution, competitor behavior, and retailer execution.
- Dashboard figures reconciled to source ERP transactions in sampled audits.
Quantified Outcomes & Methodology
Benchmark source: McKinsey & Company — trade-promotion profitability research (72% of promotions fail to turn a profit).
Scope & Methodology
Methodology: promotion-ROI uplift measured within the engagement's experimental design, which distinguished architecture contribution from external factors (seasonality, assortment, pricing, distribution, competitor behavior, retailer execution) — controlled for, not eliminated. The McKinsey 72%-of-promotions figure is an external benchmark. Scope: in-scope retail channels and promotion types.
Risks & Non-Goals
- Attribution is the hard problem: models inform allocation, but external factors share credit for any uplift.
- POS data quality varies by retailer; lineage exposes gaps but cannot close them.
- A governed model still requires merchants to act on it.
Rollout Approach
- Stage 1: governed data foundation with lineage, before any modeling.
- Stage 2: uplift models in shadow mode against holdouts.
- Stage 3: reallocation recommendations live with human approval gates.
Executive Takeaway
Particularly effective when the value case must survive CFO scrutiny — every ROI figure traces back to source transactions.