what are personalized recommendations?
Personalized recommendations enrich eCommerce sites by customizing a shopper’s experience with relevant upsells and cross-sells that result in increased conversion and higher sales. richrelevance’s unique “ensemble learning” approach displays over 15 different recommendation types, including:- People who purchased this also purchased
- People who viewed this also viewed
- People who viewed this eventually purchased
- Top sellers
- Recently viewed items
- Related new products
personalized recommendations for your site and emails
richrecs: upsell and cross-sells targeted products in real time
From the moment a shopper arrives at your site all the way through checkout, personalize their experience by adding recommendations to the following page types:
Product Search Home Category Cart Purchase- complete
richmail: add personalized recommendations to your emails
Bring shoppers back to your site for more by adding recommendations to the following email types:
Purchase Confirmation Shipping Confirmation General marketing (seasonal, weekly, etc.) Abandoned Cart
richrelevance delivers its products as a service, which means integration takes days, not weeks. Plus, pricing is entirely performance based, making it easy to get up and running.
The next generation of personalized recommendations
richrelevance achieves 10-15% higher lift than collaborative filtering
richrelevance is the exclusive provider of next generation personalized recommendations based on an innovative new approach called ensemble learning. This allows us to run not one but more than 15 different recommendation types, and then choose the best 2-4 types for each page based on the behavior of the shopper, similar shoppers, and other inputs.
In head-to-head split tests, richrelevance recommendations increased sales per visitor by 10-15% higher than collaborative filtering-based recommendations, the decade old status quo.
How? Collaborative filtering only looks at user behavior, while the richrelevance approach is all-inclusive:
- First, we build relationship maps of product inventory and attributes.
- Then, we capture consumer intent and combine it with the “wisdom of the crowds.”
- On page load, we run multiple recommendation types in real time to recommend the perfect products.
- Finally, we measure the performance of the recommendations, creating a real time feedback loop that constantly optimizes them on a per placement and per page basis.