Sales Promotion Analytics That Predicts Uplift Before You Launch
Promotion Analytics forecasts the real incremental impact of every sales promotion (uplift, cannibalization, halo effects, and net ROI) before budget is committed. We build AI models on your POS, loyalty, and trade-spend data so commercial teams move from gut-feel calendars to evidence-based decisions across categories, regions, and channels.
Stop funding promotions that look successful but quietly destroy margin. See which campaigns actually grow the business.
- Predictive uplift and baseline modeling per SKU, store, and channel
- Cannibalization and halo-effect detection across adjacent products
- Trade-spend ROI attribution and promotion mix optimization
- Price elasticity and discount-depth simulation
- Post-event measurement with automated learning loops
Why Most Sales Promotions Lose Money Without Anyone Noticing
Are your promotions growing the business, or just pulling forward demand you already had? Most retailers and CPG brands run dozens of overlapping promotions each quarter but measure them against flat year-ago baselines. That hides cannibalization, masks forward-buying, and rewards deep discounts that erode margin, so teams keep repeating losing mechanics because the surface data looks positive.
Architecture Built for Retail and CPG Scale
Ingestion from POS, ERP, loyalty, syndicated scan data (Nielsen, IRI, Circana), weather, and trade-promotion management systems.
Reusable features for price, promo depth, seasonality, weather, and competitor activity.
Gradient-boosted baselines, causal uplift models, Bayesian elasticity, and mixed-effects models for long-tail SKUs.
What-if promo calendars with expected P&L ranges and confidence intervals.
APIs and dashboards into your TPM, category planning, and BI tools (Power BI, Tableau, Looker).
Model monitoring, drift detection, and automated retraining after each promo cycle.
How It Works
We audit POS, loyalty, trade-spend, and syndicated data, map promotion history, and agree on priority categories and KPIs. Output: data readiness report and use-case shortlist. (1-2 weeks)
We build baseline, uplift, and cannibalization models for the priority category and validate against historical campaigns. Output: back-tested models with measured accuracy vs actuals. (4-6 weeks)
We deploy a planning workspace where your category managers simulate mechanics, depths, and calendars with predicted P&L. Output: live simulation tool used in the next planning cycle. (2-4 weeks)
We connect post-event actuals, automate retraining, and roll out to more categories, regions, and channels. Output: quarterly uplift reports and expanding model coverage. (ongoing)
Benefits You Can Measure
3-7 pp margin improvement on promoted volume
10-20% reduction in unproductive trade spend within the first year
15-30% higher forecast accuracy on promoted SKUs vs legacy baselines
2-5x faster promotion planning cycles through simulation
Who This Is For
What We Deliver
A predictive platform for sales promotion planning, execution, and measurement, built on your own POS, loyalty, and trade-spend data.
Frequently Asked Questions
Sales promotion analytics uses statistical and machine-learning models to predict, measure, and optimize the incremental impact of promotional campaigns. It quantifies true uplift against a counterfactual baseline, detects cannibalization and halo effects, and attributes trade spend to profit, replacing flat year-over-year comparisons with causal measurement.
BI dashboards describe what happened; promotion analytics predicts what will happen and explains what was incremental. Standard BI compares promoted-week sales to a prior period, which ignores baseline drift, cannibalization, and forward-buying. Our models isolate the causal effect of each promotion so decisions rest on incremental profit, not raw volume.
At minimum: 18-24 months of daily or weekly POS data at SKU-store level, a promotion history log (mechanic, depth, dates), and price history. Loyalty data, syndicated scan data, weather, and competitor pricing improve accuracy but are not required for the first model. We run a data-readiness audit in week one.
Typical back-tested accuracy is within 8-15% MAPE on promoted SKU volume in stable categories, improving as more post-event data flows back into the models. We publish measured accuracy per category during back-testing, so you see performance before go-live with no black-box claims.
Yes. We integrate with major TPM platforms (SAP TPM, Exceedra, Accenture CAS, Blacksmith) and custom systems via APIs or event streams. Predictions, simulations, and post-event scorecards flow back into TPM so planners work in their existing tools.
10-14 weeks from kickoff to the first optimized promo cycle with measured incremental profit. Discovery takes 1-2 weeks, model build 4-6 weeks, and the simulation MVP 2-4 weeks; the first post-event measurement completes one promo cycle after go-live.
Both. Retailers use it to optimize their own promo calendars and loyalty mechanics; CPG brands use it to plan trade spend with retail partners and defend pricing in joint business planning. The modeling approach is the same; the data sources and activation workflows differ.
Start Measuring What Your Sales Promotions Actually Earn
Book a 30-minute, no-obligation review. We will look at one recent campaign with you, show how a predictive baseline changes its measured ROI, and outline what a pilot would look like in your environment.
Discovery call
A 30-minute, no-obligation review of one recent campaign and where its measured ROI really sits.
Pilot scoping
We outline what a pilot would look like in your environment, with priority categories and data needs.
Model build & go-live
We build and back-test the models, then deploy a planning workspace for your next promo cycle.