01
Fundamentals and data foundation
The guide explains which historical sales, inventory, campaign, and product data are needed for forecasting.
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Demand forecasting guide for e-commerce
The core guide to AI forecasting, data foundations, forecast models, OOS correction, and e-commerce implementation.
Why this matters
The guide is the conceptual entry point into demand forecasting for e-commerce. It explains which data truly matters: historical sales, availability, campaigns, product attributes, variants, seasonal patterns, and external peaks such as Black Friday.
Correcting stockout bias is especially important. If a bestseller is unavailable for three weeks, a naive forecast only sees low sales. A good forecast recognizes that demand did not disappear; it could not be served.
Implementation is not just about training a model. Teams need an operating routine: review the forecast, evaluate risk, derive order quantities, prioritize purchase orders, and later measure whether OOS and overstock actually decline.
Chapters and takeaways
02
Models and common mistakes
It covers model types, overfitting, missing out-of-stock correction, and false interpretation of zero sales.
03
E-commerce implementation
Forecasts become buying decisions, inventory reach, reorder priorities, and operating routines.
Operator checklist
Check data sources and data quality
Correct stockout bias
Define forecast granularity at SKU/variant level
Model campaigns and seasonality
Translate forecast into replenishment process
FAQ
Should I start with the guide or an audit?
The guide is ideal for understanding. An audit directly shows where your assortment has OOS or overstock risks today.
Is the guide relevant for Shopify brands?
Yes. It is especially relevant for Shopify and DTC brands with many variants, launches, seasonal demand, and recurring reorders.