AI in E-shops has several uses with different rates of return. Quick payback comes from generating and translating product descriptions for large catalogues, where manual work would be unsustainable, and from improving catalogue search, which also understands imprecisely phrased queries. Medium-term benefits come from product recommendations and the sorting of reviews or customer queries. The greatest, but also the most demanding, value lies in demand prediction, which affects stock ordering and capital tied up in inventory – though it requires quality historical data. A shared condition is order in the product data. An e-shop with unfilled parameters and inconsistent naming will not achieve a good result in any of these areas. The first investment is therefore tidying up the catalogue, which pays off regardless of any further deployment of tools. Only after that does it make sense to address recommendations and predictions.
See also: Product description, Product recommendations, Inventory tracking and reservations.
