Personalized product recommendations to power your eCommerce We increase stores' revenues by offering their customers personalized suggestions prepared by Artificial Intelligence. For each store, we create an individual shopping model based on the collected history of user behavior. Such a model predicts the most interesting products for each user of the website in real-time. Confirmed results Our behavioral recommendations cause a double-digit increase of revenues. Each implementation is measured using A/B tests, thanks to which the store owner can see the effectiveness of our solution. Machine learning algorithms can understand the characteristics of user behavior based on the analysis of millions of events collected on a website. Behavioral recommendations are personalized offers for online store users based on their behavior. Simple integration with the store is done by adding Qanuk in Google Tag Manager. Based on the collected data, our Data Science team trains a model predicting products for customers, maximizing the effectiveness of prediction. Recommendations on the website are placed by adding the Qanuk Carousel tag in GTM and indicating the place where they should be displayed. The recommendation carousel automatically connects to our model and presents users with personalized products. The appearance of the carousel can be easily customized in the GTM settings. We have created Qanuk.ai based on many years of experience in implementing online stores on the Magento platform, which we have carried out at Snowdog. We've combined them with recommendation research and machine learning to create a tool for online stores that will increase their revenues and at the same time will be effortless to integrate.