Let’s be honest: in the FMCG (Fast-Moving Consumer Goods) space, trade marketing budgets are almost always misallocated. Companies tend to fund invoice history rather than actual potential.
For the DataStorm 7.0 competition, our team, Stack Kings, decided to stop guessing and start modeling latent demand. We built an analytics pipeline and a Next.js field app to optimize a LKR 5M trade spend across 20,000 retail outlets in Sri Lanka. Here is the unvarnished breakdown of how we achieved a +253% lift over a naive budget allocation.
The "Empty Shelf" Problem (Right-Censoring)
The core data science issue here is a concept called right-censoring. The monthly sales volumes we observed in the 2.3M transaction records were just a lower bound. If a small shop shows low sales, it might just be under-stocked or credit-constrained, not lacking in customer demand.
Because of this, standard averages systematically underestimate a shop's true potential. Instead of using standard textbook models like Tobit—which require strict indicators of exactly when a shop ran out of stock (which we didn't have)—we built an ensemble model.
