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Personalization mechanisms for fashion segment stores to take into account the size of the user’s clothes or shoes in recommendation blocks. Secondly if stores consider each size and color as a separate product recommendations for similar products may show other sizes/colors of the same product since they look as similar as possible to the algorithm. For example for a ring of the same size as alternatives recommendations are made for exactly the same rings but of a different size. In the Retail Rocket platform this is solved with the help of group products.

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Change product ID or properties If the ID or other properties of a product USA Phone Number List change too frequently for example the product is constantly changing from category to category the quality of the recommendations can drop noticeably. If at the time of calculating the recommendations of related products the product was in one category and at the time of the recommendation request it had already moved to another the search result may turn out to be not relevant and it will take some time for the algorithm to re-form the links between products and categories. In addition this may occur due to a violation of business.

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Rules for example when conditions are prescribed not to recommend products of one category for products of another. When the product or category id changes the business USB Directory  rules break down and need to be re-written in order to build high quality recommendations. Reconciliation of data from analytics systems Another important indicator by which you can check the completeness of the data and the correctness of their transmission is the match between the numbers in the sales report of our platform and the client’s web analytics system for example Google Analytics. We compare the number of orders and the amount of revenue the difference should be minimal.