Reduce wasted trade spend with demand prediction for FMCG trade promos

Executive Summary

FMCG teams lose millions to blunt trade promotions and manual forecasts. Build a SaaS demand prediction app that forecasts promotion lift and recommends trade spend allocation to increase ROI and reduce stockouts. Retailers and CPGs are digitizing POS and promotion data, making high-frequency historical promotion-response signals accessible. Stage 1 validation flags monthly workflow frequency and strong payer evidence, meaning decision cadence and budget ownership align with a subscription model. Advances in time-series ML and cheap cloud compute now enable near-real-time promotion lift estimation and scenario simulation that were impractical with legacy TPM modules and manual analysis. Combine ML time series models tuned for promotion lift with aggregated retailer POS and customer promotion history to produce actionable monthly spend recommendations. The product leans on proprietary promotion-response and POS datasets collected from early customers plus domain templates for FMCG trade cycles, enabling faster, measurable ROI than large TPM suites and spreadsheets. Stage 1 evidence shows budget owners and revenue impact are clear buyers and the workflow recurs monthly, enabling rapid adoption and retention.