RichRelevance 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 in real-time and across the customer lifecycle.
FREMONT, CA: RichRelevance, a leader in experience personalization, announced the launch of first-of-its-kind 'Deep Recommendations,' a set of advanced personalization technologies that do not need historical events and behavioral data to generate relevant product recommendations immediately.
The new approach solves two problems:
• It removes constraints associated with traditional recommendations that don't work for retailers and brands with sparse data - seasonal products, fast-changing catalogs, and long-tail products.
• It helps product discovery by catching the user's preferences through a product's visual features and textual description.
With Deep Recommendations, retailers and brands that regularly introduce new products can instantly expose shoppers to them. Also, 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 deep composite 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 catalog images, behavioral 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 offers to fulfill their needs across their shopping journey.
Experience Optimizer (XO), the patented decision layer of Xen AI, is used to experiment continuously to predict the most favorable outcomes by mixing and matching traditional strategies, personalized strategies, and now, in-depth learning strategies.
"Deep Recommendations replicate how 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.