Deploy Deep Learning for High Value Results
Semantic Vector Search uses deep learning to associate products with queries in a shared semantic vector space. An encoder model learns from product discovery signals to encode products and queries as vectors. Incoming queries are encoded on the fly, and then products that are “near” the query in the shared vector space are returned.
Intercept Low-Performing Queries in Real Time
Semantic Vector Search addresses low-performing queries without requiring curation of lexical rules. This is a major breakthrough that allows merchandisers to focus on more strategic initiatives. It’s a shift from reactive rule curation to smarter search, and proactive merchandising.
Move Beyond Precision to Customer Goals
The traditional way of thinking about search recall and precision is based on a lexical concept of relevance – delivering explicit product matches to the terms entered. However, perfect precision often leaves dollars on the table. A semantic approach considers relevance from the perspective of the searcher’s goal, retrieving additional products to increase the value of the results to that shopper.
Case Study: Death of the No Results Query
How one of the world’s top five retailers used Lucidworks Never Null to increase AOV by 28%.
Semantic Vector Space Insights
Two products that are semantically similar may have dissimilar performance. Why does product A sell more often than product B? By comparing the KPIs of semantically similar products, merchandisers can dig into the data to gain insight into varying performance and make informed decisions. Perhaps product B is consistently shown below the fold, or product B is priced too high.
Think Inside the Search Box
Type-ahead suggestions are traditionally based on matching partial or complete query terms to popular searches, categories, and products. Use Semantic Vector Search to locate high performing queries and products that are nearby in the shared vector space. This powers a higher-converting experience that goes beyond understanding intent.
Power Smarter Recommenders
Semantic Vector Search can be combined with traditional filtering to find semantically similar products with specific features, such as a specific price range or high popularity and low return rate. Use this to power better recommendations and increase engagement.
Ecommerce Customer Expectations Are Changing
Sin City Improves Search
Las Vegas travel search engine Vegas.com increased page views and engagement by 63% . Bounce rates for the mobile site dropped 8%. Conversion rates from search to a reservation or ticket increased by 33%. The team has since built the desktop search experience with Fusion as well. Full case study…
Foot Locker Gets The Perfect Fit
The American sportswear and footwear retailer put Lucidworks to work and saw a 10% lift in add-to-cart, improved personalization, and better analytics to drive merchandising decisions for promotional events.
Explore More Key Features
Bring Your Own Model
Bridge the gap and invite your data science team to bring their custom developed models into production environments. Learn more
AI-Powered Product Discovery
Fusion AI learns from your customers to predict the next best experience, accelerate the path to purchase, and reduce operational burdens. Learn more
Recommendation Models
A diverse set of machine-learning models to create recommendations for any use case. Learn more
Lenovo’s annual revenue contribution through search increased 175% by switching to Fusion
We don’t have to go in and validate that results are good, our customers are telling us the results are good.“
Marc Desormeau
Global Search Lead
Lenovo
