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Retail & CPG · Predictive AI

Separating real promotion uplift from noise

A major retailer

Baselinetrue organic demand isolated across thousands of products
Promo ROI

Quantified per campaign

A/B

Validated in market

Faster

Campaign planning

The challenge

Frequent promotions across thousands of products and stores made it impossible to tell genuine uplift from sales that would have happened anyway. Discounts subsidised loyal customers and hid the true cost of acquisition.

What we built
  • A model trained on non-promotional history forecasts what would have happened without each campaign.
  • Behavioural auditing finds categories that have become dependent on discounts.
  • Control and treatment groups validate predictions against real-world results.
The results
  • The retailer can cut wasteful discounts and invest in campaigns that work.
  • Automated forecasting shortened campaign planning cycles.
  • Promotion decisions now rest on measured uplift.
Similar challenge?

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