In enKod, all recommendation algorithms can be divided into three groups: product-oriented, user-oriented, and self-driven.

For algorithms that focus on or depend on user behavior (Personal Recommendations,Cross-sales), we collect preference information on your site or app, namely:

1. Product or product category views;
2. Order content.

For product-oriented algorithms (Similar Products by Text, Similar Products by Image), we use your product catalogue for similarity in parameters between different products.

Self-driven algorithms (Popular Products, Popular Products in Category, New Products, Bestsellers) are calculated based on the history of orders and views on your site.

• History of product views (opens) for the last 60 days (for Popular)
• History of orders (purchases) for the last 60 days (for Bestsellers)

User-oriented algorithms analyze the purchase and viewing history of a particular user or all users on the site. Based on these attributes, products are selected that may also be of interest to a person.

Product-oriented algorithms analyse the interaction of all users with the products on the site and suggest the most viewed or most purchased ones.

In enKod you can use the following algorithms:

• Similar products by text
• Similar products by image
• Popular products
• Popular products in a category
• Personalised recommendations
• Related Products
• Novelties
• Bestsellers

To set up recommendations, the available display locations on the site are:

• Home page
• 404 page
• Personal cabinet
• Basket page
• Product card (selection of products of specific categories and subcategories is available)
• Category page (selection of products of specific categories and subcategories is available)

If your site has pages that are not included in the list above, you can place recommendations on them as well, but the following rules must be observed: each algorithm belongs to one of three types - either tied to a product (similar products, related products), or to user behaviour (personal recommendations), or not tied to any of the above (popular products, popular products in a category, new products). Each algorithm is allowed to be placed only in the place that corresponds to the logic of the output.

For example, you can't use the “Similar Products” algorithm on the 404 page, because there are no products on it that can be matched with similar ones. At the same time, it is possible to use “Personal recommendations” on a 404 page, because this algorithm is based on user behaviour and is not tied to a specific product.

Within one block of recommendations you can use up to two algorithms, between which there will be prioritisation. This is necessary in order to fill the entire block with products, even though the first algorithm may not pick up the required number of recommendations. For example, you want to use the personal algorithm, but for some visitors to the site has not yet formed a sufficient number of recommended products, because the client is new and has not yet shown enough activity on the site. In this case, you can put the popular products as the second priority. In case of lack of personal recommendations, the recommendation block will be filled with the most viewed products. Not all algorithms need to be supplemented: popular, popular for category and new items are self-sufficient, as they do not rely on user behaviour and always have enough positions to be displayed.

In addition, after personal recommendations, similar and related products should not be used as a second priority, as this may cause inconsistencies in the name of the recommendation block on the site and in the output.

When combining algorithms in a block, it is also necessary to comply with the conditions on the places of display, which were described above. I.e. if you want to place recommendations on the cart page, then both selected algorithms must be suitable for placement in this place. You don't have to worry about customisation, as the system will warn you if you try to combine mismatched algorithms and display locations.

Setting up recommendation blocks in email messages is almost no different from the site. The same combination restrictions apply to emails. The only difference is that you don't need to choose display locations, you only need to watch out for combinations of algorithms between each other: you can't use an additional algorithm for new and popular and similar and related algorithms in the second priority after personal ones.

Read more about dynamic content methods for substituting recommendations into emails in this section of the knowledge base.