Data Science: In EMEA retail, the brands that win are the ones that see clearly. We eliminate the black box between your HQ and your European retail performance. nonplusultra's Data Science service gives consumer electronics brands full visibility into distributor activity, retailer performance, sell-through rates, and inventory levels across every market we manage. We build real-time dashboards to your brand's KPIs — sell-out by retailer, RTS by store, inventory by market. Your leadership team acts on data from last week, not last quarter. Our reporting infrastructure aggregates data from multiple sources across the retail ecosystem into a single, actionable view. Also called: retail analytics; sell out reporting; demand forecasting; sell-through reporting Key capabilities: >98% accuracy in advanced sales forecasting: AI-based demand prediction across retailer × SKU × calendar week; Sell-out, stock, field visit, and marketing data: integrated into one unified structure with automated weekly insight reports; Sales, demand, and stock risk forecasting: AI-driven and predictive, not just retrospective; Hot Spot / White Spot analysis: classifies stores by performance tier and drives targeted field action plans; Ready to Sell Score (RTS) analytics: weekly tracking of store-level execution quality (0–25 points) against sell-out uplift; Triple Baseline Reporting: Actuals vs. Forecast vs. Goal, delivered weekly and reviewed at every QBR; Three-layer Data Science Maturity Model: from data integration and quality through insight delivery to ML-driven optimisation and A/B testing One view of what happened — Every retailer reports differently. We take the fragments and build one number the brand can act on. Sell-out, stock, field and marketing data in one structure; Dashboards built on the brand's KPIs, not on a template; Ready to Sell analytics, store by store, against sell-out; Hot Spot and White Spot classification per region A forecast you can produce against — A rolling 52-week horizon that moves forward every week, so Q1 of next year is never a blind spot in December. Three layers: Goal, Forecast and Production Forecast, never mixed up; AI-based demand prediction per retailer, SKU and calendar week; Stock-risk and out-of-stock forecasting, not just sales; A/B scenario testing on price, promotion and field investment How the number gets made: Integrate — Weekly sell-out by SKU and retailer, stock in channel, listing status, price and promotion calendar. The first tier has to be in place, the second makes it robust. Run — Wednesday the data is pulled, Thursday the model runs and the account lead adds the override layer, Monday it is published to the brand. Steer — Next week is fixed, weeks 2 to 13 roll, weeks 14 to 52 stay directional for production. Accuracy is measured on next week only, so nobody hides behind the long horizon. Close the gap — The value sits between Goal and Forecast. That gap becomes named actions: a marketing push, a distribution extension, a promotion slot, a range extension. Sources → Integration → Dashboard → Forecast run → Monday publish → Accuracy review XGIMI — every decision evidence-based: Store selection, range and field investment were argued from the data rather than from the loudest opinion in the room. 5+ European markets in year one, 120 stores in the flagship programme, 3–5× sell-out improvement, client-measured Meta — one reporting layer across 15 countries: Field visits, POS status and sell-out from 15 markets, read as one picture instead of fifteen spreadsheets. 97% POS compliance across EMEA, 15+ countries in one view, 50,000+ field visits reported per year Frequently asked: Is this a dashboard, or a reporting service? A retail dashboard built to your KPIs, and the people who keep it true. Retail reporting fails on the input, not on the chart: sell-out arrives in six formats from six retailers, and somebody has to normalise it every week before the view means anything. How accurate is the forecast? Above 98 % forecast accuracy on retailer × SKU × calendar week. A demand forecast is only worth the model if the distributor can act on it, so it lands in the replenishment conversation rather than in a slide. Where does the data come from? Retailer sell-out and stock feeds, distributor reports, our own field visits and the marketing calendar, integrated into one structure. The field data is what most reporting lacks: without it, a drop in sell-out has no explanation attached to it. Do you replace our BI team? No. We supply the European retail layer your business intelligence cannot see: distributor activity, retailer performance, sell-through and stock by market. Most brands pipe it into what they already run, and the Data Science Maturity Model describes the three steps from integration to ML-driven optimisation.