Fast Simon, the leader in AI-powered shopping optimization, today announced Vector Search with advanced AI for eCommerce. Vector Search is able to handle longer search queries and reduce the return of “no results” compared to keyword search alone. This makes it easier for eCommerce sites to match buyer intent, personalize the shopping experience, answer questions and make product recommendations.
Rather than matching keywords like most eCommerce search engines, Vector Search uses natural language processing and neural networks to analyze a query. Vector embedding maps the words from the search to a corresponding vector to detect synonyms, intent and ranking, and it clusters concepts to deliver more complete results. For example, the search “fall wedding guest dresses for black tie event” would return relevant results for long dresses, dark colors and options for sleeves, even if the items weren’t all tagged with the exact keywords.
“While many eCommerce search queries today are just one to two words, Gen Z tends to search differently. They often use full sentences and look for contextual results that match their intent. This shift requires a new approach to search that goes beyond keywords to understand the meaning,” said Zohar Gilad, CEO of Fast Simon. “As the next generation of shoppers, meeting the expectations of Gen Z is crucial for retailers who want to stay relevant.”
Vector Search is Fast Simon’s latest innovation, helping eCommerce brands enhance the online shopping experience. Key benefits include:
- Natural language processing. Shoppers can search for any information in any way, and Fast Simon will process it and combine vectors and keywords to return the most accurate results.
- Automatic vectorization. Fast Simon automatically turns a query into a vector so it can be quickly compared to other vectors in a product database without a team of data scientists.
- AI optimization. Fast Simon’s advanced AI model learns from queries and shopper behavior, so results improve over time.