RichRelevance Launches ‘Deep Recommendations’: The Next Generation of Advanced Commerce Personalisation
An industry-first solution using deep learning AI that generates up to 80% higher attributable sales from product recommendations RichRelevance, a leader in experience personalisation, today announced the launch of first-of-its-kind ‘Deep Recommendations’, a set of advanced personalisation technologies that, unlike traditional recommender engines, does not need historical events and behavioural data to immediately generate relevant […]
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An industry-first solution using deep learning AI that generates up to 80% higher attributable sales from product recommendations
RichRelevance, a leader in experience personalisation, today announced the launch of first-of-its-kind ‘Deep Recommendations’, a set of advanced personalisation technologies that, unlike traditional recommender engines, does not need historical events and behavioural data to immediately generate relevant product recommendations.
The new approach solves two problems:
(a) it removes constraints associated with traditional recommendations which don’t work for retailers and brands with sparse data – seasonal products, fast-changing catalogues and long-tail products, and
(b) it helps product discovery by catching user’s preferences through a product’s visual features and textual description.
With Deep Recommendations, retailers and brands that regularly introduce new products can expose shoppers to these new products instantly.
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In addition, categories such as fashion and home furnishings where shoppers look for ‘visually similar’ or ‘visually complementary’ products can break through the clutter with highly relevant and high conversion visual AI-based recommendations.
RichRelevance Deep Recommendations are enabled by Xen AI, the most advanced machine learning engine in the space and the only one with composite deep learning, an industry-first approach that blends all known data and decisions to predict the next best experience.
Xen AI extracts and combines feature vectors (the “DNA”) found in product text descriptions and catalogue images, behavioural data, derived affinities and stated preferences and matches in real-time with shopper intent to create highly relevant high-conversion recommendations.
This helps your customers not only get what they are initially looking for but also inspires them to discover contextual recommendations to fulfil their needs across their shopping journey.
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Experience Optimiser (XO), the patented decision layer of Xen AI, is used to continuously experiment in order to predict the most favourable outcomes by mixing and matching traditional strategies, personalised strategies and now, deep learning strategies.
Results from its over 30 early adopters and customers have revealed spectacular results, with Xen AI Deep Recommendations creating an average lift of 40% in engagement and 80% higher attributable sales, in comparison to standard recommendations prevalent in the industry today.
“We instinctively knew that using visual aspects of a product for recommendations is effective in fashion and lifestyle business – it’s much closer to the expertise of our merchandisers.”
“I am excited with early results – our engagement is up 40% over our merchandising rules, and revenue per 1000 impressions has increased by 19%, compared to the other recommendation models,” said Sylvain Lys, Head of Omnichannel Customer Experience at Promod, France.
“Deep recommendations is our top-performing strategy right now and is delivering average attributable sales of Eur 10.68 per click. The results are scarily good.”
“Without RichRelevance, these innovative AI technologies wouldn’t have differentiated us, and helped us grow”, said Anton Paasi, Head of Ecommerce, Verkkokauppa.com, a leading Finnish online retailer.
“Deep Recommendations replicate how to store assistants help a shopper with their purchases, by interpreting their likes through a combination of language cues and visual attributes revealed in the shopping journey, along with an understanding of their past affinities to a brand or price point.”
“The relevancy will continuously improve as deep learning algorithms gather more volumes, and Xen AI learns from how users interact with these recommendations”, said Mark Buckallew, VP, Product Management at RichRelevance.
RichRelevance was recently named a leader in Gartner’s 2020 Magic Quadrant for Personalisation Engines.
To learn how RichRelevance Deep Recommendations work, and to read more client successes, visit richrelevance.com/deeprecs.
About RichRelevance
RichRelevance is the global leader in Digital Customer Experience Personalisation, driving digital growth and brand loyalty for 200 of the world’s largest B2C and B2B brands and retailers including REI, Walmart, Burberry, CDW, ShopDirect, ATEA, Komplett and Coop.SE. The company leverages advanced AI technologies to bridge the experience gap between marketing and commerce to help digital marketing leaders stage memorable experiences that speak to individuals – at scale, in real-time, and across the customer lifecycle. Headquartered in San Francisco, RichRelevance serves clients in 44 countries from 9 offices around the globe.
