How to use artificial intelligence to increase sales? Recommendations for eCommerce

Nowadays, many online stores use recommendation algorithms to increase sales. Recommendation algorithms in eCommerce are based on the analysis of customer purchase data. The artificial intelligence system analyzes the products the customer has purchased and, based on this, suggests other products that may interest him. Data analysis is based on various factors, such as the customer’s purchase history, product preferences, store website behavior, and product features.

Methods to introduce recommendations on the website

Yosh.AI implements recommendation systems on websites and in the text assistant. One method is recommending products similar to those the customer has previously purchased. In this way, the algorithm takes into account personal preferences. Another form is to recommend products that other customers have purchased together with the product that the customer has already selected. In this way, the algorithm considers the purchase data of other users with similar preferences. By recommending complementary products, you can increase the value of the shopping cart. Recommended products can be displayed as a carousel in various places on the store’s website, such as on the home page, shopping cart, product page, or checkout process. A unique solution on the market introduced by Yosh.AI is recommendations in the chatbot. The algorithm recommends products through conversations with the user based on their previous queries and searches. Read more about the functionalities of our text assistant.

Recommendation system implementation

The recommendation engine basis on machine learning and artificial intelligence algorithms is specialization. Yosh.AI has experience in implementing a recommendation engine in cooperation with Google. In addition, we have built our own recommendation engine based on the BERT architecture. This approach is used with great success in the field of NLP – for text generation.

Accurate results of implementing the recommendation engine

We tested the quality and effectiveness of the recommendation engine. User profiles and their preferences were analyzed to carry out the experiments. Users of the text assistant tried to find similar products using a text query. We observed a time reduction in finding adequate products using the recommendation engine. In stores with high sales volumes, this provides significant financial benefits and satisfaction for the customer.

What are the benefits of using the recommendation algorithm in eCommerce?

The benefits of using the recommendation algorithm in eCommerce are multiple. First, recommendation algorithms allow a better understanding of customer preferences and needs. Thanks to this, the online store can adapt its offer to the individual needs of customers and better meet their expectations. Second, the recommendation algorithm can be an effective marketing tool for online stores and help increase the value of the average order. Third, thanks to accurate recommendations, customers who receive personalized product recommendations are more likely to make purchases and return to the store more often. Finally, introducing recommendation algorithms to the eCommerce strategy is a profitable step towards better customer service and increasing the online store’s revenue.

Carousel with recommended products basis user searches.