Fast Simon’s Personalization AI Embeddings and Sophisticated AI Models Significantly Improve Conversion for Recommendations, Search and Collections on ECommerce Sites
Fast Simon, the leader in AI-powered shopping optimization, today announced Personalization AI Embeddings to significantly improve shopper experience and conversion on eCommerce sites. Personalization AI Embeddings codify the shopper’s journey by creating vectors that feed Fast Simon’s AI model. Using these vectors, the AI model predicts what the shopper is looking for to deliver more relevant product recommendations, personalized search results and personalized collections.
According to Salesforce research, 73% of customers expect better personalization as technology advances. This trend may be fueled by the recent popularity of generative AI, which has significantly increased the public’s awareness and expectations of the benefits of AI-powered technology. Fast Simon has been a pioneer in AI for more than a decade and uses the latest models to constantly improve product recommendations. For years, the company has offered best-in-class personalization with AI-powered audiences and segmentation to recommend products based on customer intent and actions. Now, it’s taking personalization to the next level with the introduction of Personalization AI Embeddings.
Personalization AI Embeddings leverage complex logic and multiple AI inputs, including color, category, image matching, text descriptions, customer activity and location, to improve shopper experience and increase conversions for eCommerce merchants. Personalization AI Embeddings can improve search results, optimize collection assortment and suggest product categories for the shopper while giving merchants granular control over merchandising. Here’s how:
- Context-sensitive recommendations are based on the customer’s action and where the recommendation is made. For example, different recommendations can be made on collection pages versus in the shopping cart.
- Recommendations can be narrowed based on affinities, including color, category and more, to improve relevancy and conversions. For example, black shoe polish will be surfaced alongside black leather shoes and boots, but not alongside brown leather shoes or black fabric sandals.
- Multimodal AI inputs, including categories, text and images, are considered when recommending a product. For example, “Shop the Look” will consider images, written product descriptions and more when recommending complementary items.
“Our Personalization AI Embeddings deliver extremely relevant recommendations to delight shoppers and help merchants increase conversions,” said Zohar Gilad, founder and CEO of Fast Simon. “Our goal is to exceed shoppers’ expectations of AI-powered eCommerce experiences and deliver a personalized journey that makes them feel understood by the merchant.”
The post Fast Simon Launches Personalization AI Embeddings to Further Improve Conversion first appeared on AI-Tech Park.
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