Product recommendation engine types – Ometria Help Centre

There are seven different product recommendation engines.

Some engines are designed for use in automation campaigns, others for broadcast campaigns, as indicated below.

Note: Product recommendations in automation campaigns generate at the point of send. Recommendations in broadcast campaigns generate at the point of open.

Icon	Engine description	Type
	Top products - Recommends the best-selling product. Choose to recommend the best-selling products in the past 7, 14 or 28 days. This calculation is based on the number of products sold (including those in invalid orders). These products display from most popular to least popular in your email template blocks.	No context recommendation engines These can be generated for any contact, without needing to know which products they have interacted with. You can use these engines for both broadcast and automation campaigns.
	Latest products - Recommends the products most recently added to your online store. Products are only ‘latest products’ the first time they are created in Ometria. If you update a product on your site (e.g. to add new colours to the product line) the date created will stay the same and the product will keep its position. This means that in most cases an updated product won't show in a 'latest products' block.
	Similar products - Recommends products most similar to the products the contact has interacted with, based on common attributes. For automation campaigns, the type of interaction depends on the triggering event. E.g. If the trigger is an order, then the recommendations are based on the product purchased. If the trigger is a visit to a webpage, the recommendations are based on the products viewed.	Product based recommendation engines These can be used where a contact has interacted with a product/set of products, e.g. abandoned basket or ordered. These are known as context products. These engines only work correctly in automation campaigns, as they require data from the contact in order to generate useful recommendations. If you add these engines to broadcast campaigns, the fallback model will be used.
	Bought this, bought that - AKA 'product up-sell'. Recommends the most commonly purchased products by customers that purchased any of the context products.
	Viewed this, bought that - AKA 'product cross-sell'. Recommends the most commonly purchased products by other customers. This engine takes into account contacts who: viewed the context product(s) but didn't order (abandoned browse/visit session) have the context product(s) in their basket viewed context product(s) that dropped in price placed an order for the context product(s)
	Recently viewed - Recommends products that the contact has viewed in the past 7, 14 or 28 days. These recommendations display in order of most recent.	Profile based recommendation engines These engines generate recommendations based on the contact's behavioural history (products they've purchased or viewed). These engines will only work correctly when used in automation campaigns. Note: You can use these engines in broadcast campaigns, but contacts without the relevant data available (e.g. recently viewed/recently purchased) will receive the fallback engine you choose.
	Personalised - Recommends products with attributes the contact has shown most interest in (by viewing or purchasing a product with those attributes). See also: Personalised product recommendations

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