Explore the evolution from keyword search to natural language and now conversational search, a shift fueled by technological innovations like semantic vectors, large language models (LLM), and vector search engines, marking a major leap in e-commerce product discovery.
Learn how Google’s Vertex AI Search for Retail uses three AI-driven capabilities as its secret sauce—catalog enrichment, smart matching with Google Shopping data, and conversion-focused ranking—to guide customers to precisely what they need by asking the right questions and presenting the most relevant products.
The outcome? When a search engine effectively uses rich product data to match and rank products well, customers find what they need faster, leading to higher conversion rates, increased average order value, and stronger customer loyalty.
The conversational search secret sauce
The richer the product data, the better the search
Generate rich catalogs using multimodal foundation models to analyze and harmonize product data from user manuals, descriptions, customer reviews, user-generated content, and images, ensuring accuracy and adherence to company photo standards—eliminating the need for marketplace sellers to manage tedious cataloging tasks.
Match products to customer intent
Show the right products in response to a user’s query by leveraging extensive customer behavior data from Google Shopping to match products in the catalog with the customer’s shopping intent.
Rank products by conversion likelihood
Rank all matching products using Google’s ranking algorithm, which predicts conversion propensity by analyzing product information and on-site customer behavior, ensuring that the matched products are optimized to meet the customer’s intent.