Before Elevate can match a customer with the right product, it first needs to understand both the product and the search intent. To do this, Elevate analyzes and enriches your product data, organizes it into a structured knowledge model, and uses that understanding to interpret searches and recommendations. This article explains the core components that transform product data into product intelligence and power search and discovery across Elevate.
From product data to search results
Before Elevate can return a search result or recommendation, it needs to understand both the products in your catalog and what the customer is looking for.
To achieve this, Elevate processes your product data through several connected systems. Product information is analyzed and enriched, structured into a knowledge model, and then matched against customer searches. Finally, the most relevant products are ranked and returned to the shopper.
At the heart of this process is the Elevate ontology - a retail-specific knowledge model that helps Elevate understand products, attributes, and concepts in the same way shoppers do. Rather than relying only on keywords, the ontology enables Elevate to understand product types, relationships, and customer intent.
If you'd like to learn more about the ontology itself, see Understanding ontology in Elevate.
The diagram below shows how product data moves through Elevate, from initial analysis to the final search results shown to the customer.
The process starts when product data is sent to Elevate. The Classifier analyzes and enriches the data before placing products into the Knowledge Graph, where they are connected to product types, attributes, and concepts. When a customer performs a search, the Query Analyzer interprets the search intent and matches it against the structured product data. Finally, Product Ranking determines the order in which relevant products are shown.
The Elevate Classifier
The Classifier automatically analyses and enriches your product data when it is sent to Elevate. It interprets the information in your product feed and maps each product to a structured product model within the Knowledge Graph.
By extracting attributes from structured fields, product descriptions, and sometimes images, the Classifier ensures that Elevate understands what each product is and how it should behave in search. The quality of the classification depends heavily on the quality and consistency of your product data.
Learn more about how the classifier works
The Elevate Knowledge Graph
The Knowledge Graph structures products, attributes, and concepts into a connected model that allows Elevate to understand meaning rather than only text. It organizes product types into hierarchies and connects related concepts so that search queries can be interpreted more accurately.
Because these concepts are linked, customers can search at different levels of specificity and still receive relevant results. The Knowledge Graph also supports multilingual search by connecting equivalent concepts across languages.
Learn more about how the knowledge graph works
The Query analyzer
The Query analyzer interprets your customers’ search queries in real time. Instead of matching text directly, it breaks each query into meaningful concepts such as product type, brand, color, and contextual attributes.
These concepts are then matched against the classified product catalogue. If no exact match exists, Elevate can automatically relax less important parts of the query to return the most relevant results instead of showing an empty search page.
Learn more about the Query analyzer
Product ranking
Product ranking determines the order in which products appear in search results and product listings. Once relevant products have been identified, Elevate evaluates how likely each product is to convert and ranks them accordingly. Ranking considers factors such as customer interactions, recent trends, stock availability, price changes, and product lifecycle signals. This ensures that customers see products that both match their search and perform well commercially. Read more here.