Product recommendations are one of the most effective ways to inspire your customers by helping them discover new items they might like.
We’re always looking for new ways the Fresh Relevance platform can help you deliver the most relevant product recommendations across channels while meeting your unique business goals.
The latest enhancement enables you to suggest products according to how similar they are to the product viewed, based on information available in the product details.
Finding similar products
The Fresh Relevance platform already enables marketers to flexibly blend over 20 different types of recommendations, such as crowdsourced suggestions and personalized recommendations based on a shopper’s history.
Our latest feature allows you to enhance your strategy with recommendations based on product similarities.
Fresh Relevance's Artificial Intelligence (AI) engine automatically generates a list of products similar to the one viewed. The list is generated by calculating a similarity score for each related product, without the need for any manual tagging or setup.
The percentage score is a measure of how similar the AI thinks the related product is - a higher score indicates a more similar product.
Displaying the most relevant products
Just like other types of product recommendations, you can incorporate AI product suggestions into dynamic email and web content using Fresh Relevance SmartBlocks.
Marketers can set rules to ensure that only the most similar product suggestions are displayed. For example, you could choose only to show products with a similarity score of more than 50%.
In case there are not enough products to match the set filter, you can set a fallback product recommendation type - for example, frequently purchased products - to make sure customers are always served the most appropriate content
Enhancing your recommendations strategy
Recommending similar products works particularly well for shoppers at the browsing stage, as it allows them to see alternative products which might suit their needs. This can also be effective in shopping recovery emails: you can show shoppers relevant alternatives to the product they already looked at or carted but didn’t purchase.
This type of content-based recommendation is especially useful for retailers who regularly add new products. Marketers can immediately include recently added items in their recommendations strategy without any manual tagging, and before customers have even interacted with them.
Improving customer experience with AI
This latest feature is a result of our collaboration with a team of leading researchers at the University of Portsmouth. It is just one of the ways we’re leveraging the power of AI to enhance the customer experience.
With Fresh Relevance’s powerful AI product recommendation engine, you can combine personalized recommendations with crowdsourced suggestions and similar products, in whatever way best suits your business needs. Marketers have more options than ever to drive engagement and increase conversions with timely, relevant content.