Fresh Relevance, the comprehensive personalization platform for digital marketers, today launched next generation artificial intelligence (AI) powered product recommendations. The new feature uses intelligent product similarity scoring to ensure customers are presented with the most relevant suggestions.
A powerful algorithm creates similarity scores across the product range based on a wide range of factors including product type, details and characteristics. This means when dynamic real-time product recommendations are deployed on the website or in an email, every shopper is only ever presented with suggestions that are likely to be highly relevant to what they have viewed, carted or previously purchased. Crucially, the engine is able to automatically create scores for new products, without the need for manual tagging or setup. This empowers marketers to immediately include recently added items in their product suggestions, even before customers have interacted with them.
Marketers have full control over their recommendations strategy with the ability to quickly and easily set rules to filter product recommendations and ensure maximum relevancy. A typical example would be to ensure only products with a similarity score of more than 50% are displayed. What’s more, marketers can also define fall-back product recommendations, in case not enough products match the set filter.
David Henderson, Chief Technology Officer at Fresh Relevance, explains: “The ability to present highly targeted product recommendations online and in emails is one of the most popular features within the Fresh Relevance platform. Now powered by our next generation AI engine, we are taking product recommendations to the next level, filling a gap in the digital toolbox that marketers have told us they want and need.”
AI-powered product recommendations are available now and the first innovation to be launched as a result of Fresh Relevance's close collaboration with a team of leading researchers at the University of Portsmouth.