Systembolaget wanted to learn how they could use AI to solve real business problems increasing their service level towards customers. Together with Findwise part of Tietoevry, Systembolaget has taken the idea of AI driven recommendations based on taste all the way into production.
Similar wine, is available as a new function to test in Systembolaget's mobile app. The function uses an Artificial Intelligence model to give the customer suggestions for new wines with a similar taste as their favorite wine. When the customer has specified a wine whose taste they like and asks for suggestions for similar wines, suggestions are given on other wines with similar taste and aroma patterns. The products proposed are of the same size so that the customer is not tempted to buy more than intended.
We know that many customers find it difficult to choose wine. Now we are trying to reach out with our advice in a new way. It will be exciting to see how it is received by our customers, says Frida Jarlbäck, business developer at the Strategy and Offering department at Systembolaget.
The function Similar wine, has been developed with the help of Systembolaget's beverage experts, evaluated by their knowledgeable store staff and now available for customers to try this new way of getting beverage advice.
The project started out as a proof of concept to explore how AI can be used to define and develop a data model for similar wines and future digital services. Findwise worked from the beginning together with Systembolaget in an agile and cross functional team. Using an iterative approach we delivered an AI-powered application which recommends similar wines based on taste.
The result of the PoC was very successful and Systembolaget chose to go further with a production version in their labs concept, first as a tool for store employees and now as a service for the end customers.
Please contact us if you want to know more about the technology used, learn to rank in Elasticsearch using a decision tree model, or if you want to learn more about how we brought a successful AI-PoC into production using an AI-as-a-service model in a public cloud environment.
