What are AI recommendations?
Online shoppers increasingly expect personalisation. Generic 'bestseller' lists convert considerably lower than personal recommendations. According to McKinsey, personalisation generates an average of 40% of revenue at e-commerce companies that use it effectively.
AI recommendation systems analyse the browsing and purchase behaviour of individual customers and show the most relevant products at the right moment in the customer journey.
How does it work?
Collaborative filtering and content-based filtering are combined into a hybrid recommendation engine. The system analyses what similar customers bought (collaborative) and which product characteristics suit the individual customer (content-based).
Recommendations are generated in real time and displayed on product pages, in the shopping cart, on the homepage and in email campaigns. A/B testing continuously optimises which recommendation strategies convert best.
What does it deliver?
Webshops report 10–30% higher average order value, 20% higher conversion rate on product pages and 15% more repeat purchases. According to Barilliance, 31% of all e-commerce revenue is attributable to product recommendations.