Turn sales history into a CRISP-DM forecast plan
Apply CRISP-DM steps to prepare and evaluate a retail demand forecasting use case.
Forecast demand for back-to-school backpacks using three years of sales history without mistaking constrained sales for true demand. CRISP-DM: business understanding -> data understanding -> preparation -> modeling -> evaluation -> deployment Raw POS history undercounts demand when shelves were empty, promotions changed behavior, or substitutes captured sales. Business Decision: set order quantities for top backpack SKUs by store cluster six weeks before launch. Success: fewer stockouts on launch weekend without excess end-of-season markdown. The forecast now has a decision, timing, and business metric. Data Audit signals: POS sales, on-hand inventory, stockout days, promotion flags, school-calendar dates, returns, weather, and…
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