With our machine learning platform one can find relevant content based on user behavior, textual content, or other structured data, and use the data to show that you care about the customers’ interests and want to add value for them. 

Typical data used to create relevant data for the customers is what the customers buy, click on, search for, and add to the shopping cart. Within domains such as media and entertainment, text analysis is also used to find relevant content.

Advanced personalization
An online store will typically be able to use the machine learning platform to recommend relevant products for its customers. Examples of recommendations are "trends now", "recommended for you" and "those who bought this, also bought this."

Using the platform, it is also possible to set up a very advanced search engine for your own website, including a self-developed plugin for the search engine Solr.

Easy implementation
The machine learning platform is very easy to integrate and has an API which makes it so the platform can be taken into use quickly. The API can be used from the server side, but the most common way to integrate it is through a client integration. This means that the users enter a javascript on their site and that the browser communicates directly with Sannsyn through this.

This technology allows for more advanced and important opportunities, such as being able to verify if a recommendation has been visible to a customer - not just shown on the website. To the extent that you want special adaptations of the Sannsyn platform, Pearl's developers have very good knowledge of the solution and are able to effectively assist you.

Counseling
Our experience is that many companies want to be more data driven in their approach to the market, but struggle to get started. In conjunction with Sannsyns business advisors, data scientists and technicians, we at Pearl can give you a good understanding of the subject area, as well as success factors and potential pitfalls. Together with your internal resources, we can develop a forward-looking and robust data strategy.