The product recommender has gained great importance in recent years and its application in companies such as Netflix is well known. its application in companies such as Netflix, Amazon or Spotify although they are also widely used in other commercial areas.
These product recommenders allow us to filter all the customer's information to predict what will be of value to him within the catalog of products offered by the company, thus maximizing the value for both the customer and the company.
In order to make an optimal recommendation we must analyze information about the customer (age, sex, socio-demographic aspects, etc.) and about the products (type of product, price, brand, etc.), brand, etc.), with all this we try to predict which product can be interesting for the customer and for the company.
A product recommender can be based on two important aspects:
This type focuses on user information, i.e., different customer information is analyzed, such as previous purchases, preferences, ratings given to other products, average price consumed by the customer, etc.
With all this you seek out other customers with similar characteristics, therefore, products that have been successful with similar customers are likely to interest the new customer as well.
Based on product
The product is the basis of the prediction. The aim is to look for similarities between what has been consumed or liked in the past, thus offering the consumer products with similar characteristics in terms of price, brand, etc.
This model is simpler than the previous one because it only requires information from the product and not from the consumer.
These algorithms are more profitable in companies with a high number of products to offer and a large number of customers, thus achieving greater accuracy.
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