From the moment shoppers arrive on your site, Fresh Relevance tracks every product they browse in real-time. This means the Fresh Relevance system knows what's selling right now. It combines that knowledge with your Slot Rules and decides what products to display.
In addition to our standard Product Recommendation option, Fresh Relevance now offers three new recommendation types, "People like you buy", "Frequently bought with this" and "After viewing this, people buy" based on a machine learning approach. The cross channel rules engine allows recommendations to follow browsers across different interaction points and targetting of different customer types with different recommendations.
This looks back at the history for each individual shopper and what products they've browsed. It then looks back at other people who browsed those products and recommends the products which they ended up buying. This is a great general purpose recommendation type for use on many different types of web pages and helps improve your Website Personalisation.
This looks at the product on the current page. It then looks back at what people who bought this product have ended up buying with it. This is great as a cross sell recommendation tool for use on product pages and/or cart pages, or in Cart and Browse Abandon Emails. It would also work well as an additional "did you forget" page on the checkout, after the cart page.
This promotes the highest converting product which is bought by people viewing the current product. It's a great way for an eCommerce store to show a larger amount of their product inventory to web browsers.
Different product recommendations can be aligned with different stages of the customer journey. For example, new customer acquisition can focus on special offer product recommendations whilst existing customers can be targeted with recommendations based on past purchases.
Fresh Relevance is very quick and easy to implement and also allows the user to set up advanced personalization. No integration project or product feeds are required as all content is collected in real-time.