Retail & FMCG Analytics
Most FMCG planning teams are running weekly forecasts from data that's already four days old. In a market where promotions shift demand overnight, four days is the difference between a stockout and a writeoff.
What we hear from operators
The problems we solve
Demand planning runs on weekly data exports
The planning team exports sales data from SAP weekly. Loads it into the forecasting tool — or more often, into Excel. Applies the forecast model. Sends to supply chain. By the time supply chain acts on it, the underlying demand has already shifted. Promotions calendar isn't connected. Seasonality isn't modelled at SKU level. The forecast is always chasing reality.
Trade promotion ROI is measured after the fact, if at all
Most FMCG companies spend 15–25% of revenue on trade promotions. Most can't tell you which promotions drove incremental volume and which just accelerated existing demand. The promotion data is in one system, the sales data in another, and nobody has connected them with enough granularity to calculate a reliable promotional lift factor.
Distributor sell-out data arrives too late
Sell-in numbers are visible in real time from the ERP. Sell-out from distributors arrives weekly or monthly in a spreadsheet. The gap between what you shipped and what actually moved off shelf — the one that tells you whether you have a distribution problem or a demand problem — is often invisible until the next reorder cycle.
Who this is for
Who retail & fmcg analytics is built for
The roles that feel the problem first — and what we build for each of them.
Supply Chain / Demand Planning Head
The problem
The team runs weekly forecasts from SAP exports that are already four days old, with the promotions calendar disconnected and seasonality not modelled at SKU level.
What we build
SKU-level demand sensing in Microsoft Fabric with Azure Machine Learning, wired to daily sell-out feeds and the promotions calendar.
+15–25% forecast accuracy
Operations Director
The problem
You carry overstock on slow movers and stock out on fast movers at the same time, because sell-out data from distributors arrives weekly or monthly.
What we build
One integrated daily demand signal per SKU and channel in Power BI over Microsoft Fabric, with anomaly detection on unusual sell-out patterns.
Hollandia Dairy: 20–40% stockout reduction
Sales / Commercial Lead
The problem
Trade promotions eat 15–25% of revenue, but you cannot separate the promotions that drove incremental volume from the ones that just accelerated existing demand.
What we build
Promotional uplift modelled from historical promotion data in Power BI on Microsoft Fabric, so trade spend is informed before the promotion runs.
Hollandia Dairy: 5–15% sales uplift
CFO / Financial Controller
The problem
Trade spend is a large line with no measured ROI, and spoilage write-offs surface only after the stock has already been lost.
What we build
Promotional ROI and spoilage dashboards in Power BI over Microsoft Fabric, connecting sell-in, sell-out and promotion data at SKU level.
Hollandia Dairy: 10–30% spoilage reduction
Measurable outcomes
What changes after implementation
Specific shifts from delivered retail & fmcg analytics work — the before, and the after.
Forecast accuracy: typical 60–65% → 75–80% at SKU-week level
SKU-level statistical forecasting connected to real sell-out data consistently outperforms Excel-based planning by 15–20 percentage points on MAPE. The gap widens during promotional periods.
Promotional ROI visibility: post-event analysis → pre-event modelling
Trade spend decisions informed by modelled ROI before the promotion runs, not just reviewed after. Incremental volume separated from demand acceleration across historical promotions.
Distributor sell-out lag: weekly spreadsheet → daily automated feed
Sell-out visibility updated daily for distributors with API or SFTP capability. Weekly for the remainder. The planning team stops waiting for the monthly distributor report.
By market
Retail & FMCG Analytics — market-specific pages
Each page below covers what retail & fmcg analytics looks like specifically in that market — the local ERP landscape, compliance context, and the operational patterns we actually see there.
Singapore & Malaysia
United Kingdom
North America
By industry
Retail & FMCG Analytics — industry-specific pages
How retail & fmcg analytics applies to the specific systems, metrics, and operational challenges of each vertical.
Technology stack
Start with a conversation, not a proposal
First call is 30 minutes with Amit. We ask about your systems, your team, and your most pressing operational problem. You get a clear view of where the gap is and what closing it looks like. No slides. No pitch deck. No obligation to proceed.