Zansa › Answers › Forecasting and data science
How can e-commerce stores forecast demand across many products and channels?
Forecast at the level you buy and fulfil, using sales history by product and channel, promotions, price, traffic, seasonality and stock availability. For large catalogues use methods that learn across products and group similar items, and treat new, slow and bestselling items with different approaches.
E-commerce sales are strongly affected by marketing activity, so record campaigns, email sends and ad spend. Also record out-of-stock periods and page views, which indicate unmet demand.
Additional points:
- Returns affect net demand and should be modelled.
- Event days such as Black Friday behave differently from normal days.
- Review accuracy by product tier, using WAPE and bias.
A machine-learning model across the catalogue can beat per-item methods if you have enough data, but test against seasonal baselines.
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