Recommendation Engine
Click-worthy recommendations
Predict what people need, before they even know they need it. Engage and convert with data and AI.
Recommend at every step of the journey
Context-aware recommendations
Show what’s most relevant to a person based on where they are in their journey – from pre-purchase to post-purchase, from seeking answers to exploring content.
Ready-to-go machine learning models
Choose the algorithm that fits your scenario. Coveo provides recommendation models that are easy to set up – including interest-based, frequently viewed and bought together, popular items, and more.
Journey and data stitching
Needs change over time, so do recommendations with Coveo. Connect current context, user history, and more, from across all your data platforms to serve the right content at the right moment.
Powerful, flexible machine learning
All content types
Define which content types to pull in or create recommendation streams across various content types, and let the recommender carve the most relevant path for each user.
Data set selection
Choose which data sets should train a recommendation model. Focus on a certain region or use inputs from a specific interface – it’s up to you.
Model correction
Draw insights from and respond to your changing product catalog, content, or user behavior. Coveo lets you know when it’s time to retrain your models.
Recommendation experiments
Want to verify which strategy works best? Preview or A/B test different configurations, then optimize and refine.
Start simple and grow your relevance
Recommendations from Day 1
Provide search, content, and product recommendations even when data about an item or individual is sparse.
Rich user profiles
Drive ever-more-relevant recommendations as user profiles are dynamically enriched by actions and affinities.
Scale <em>and</em> precision
Massive content libraries and catalogs with millions of SKUs are no problem with Coveo. Our recommendation system zeros in on the right thing for the right person.
Predictive recommendations means that before the customer has even asked a question, we’ve anticipated potential answers for them, to get them more quickly to their solution.
Jeff Harling
Area Vice President, Global Self-Service, RingCentral
We found that people who use auto-populated suggestions were almost 20% more likely to click through to that recommendation.
Amy Gath
Vice President, North America Marketing & Strategy, Formica
I just recently looked at the analytics and recommendations had eclipsed what [automatic relevance tuning] was doing. So what we’ve been noticing is that for every one document in the [knowledge base] that our users are looking at, they’re choosing to look at the recommendations 2 more times.
Mike LaStella
Former Sr. Manager, Online Services, Palo Alto Networks
Gartner Magic Quadrant for Insight Engines
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