Fast Simon Launches Vector Search With Advanced AI for eCommerce
Fast Simon, the leader in AI-driven shopping optimization, announced advanced AI Vector Search for e-commerce. Vector Search can handle longer search queries and reduce “no results” return rates compared to keyword-only searches. This makes it easy for e-commerce sites to respond to shopper intent, personalize the shopping experience, answer questions, and make product recommendations.
Instead of matching keywords like most e-commerce search engines, Vector Search uses natural language processing and neural networks to analyze the query. Vector integration matches search terms with a matching vector to detect synonyms, intents, and ratings, and groups concepts together to provide more complete results. For example, searching for “fall wedding wear for a black tie event” will return relevant results for long dresses, dark colors, and sleeve options, even if all entries are not. tagged with 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,”
“As the next generation of shoppers, meeting the expectations of Gen Z is crucial for retailers who want to stay relevant.”
Zohar Gilad, CEO of Fast Simon.
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.
