Setting Up Machine Learning Part 2
Brands are updating and redesigning their sites, and although this gets their messages in front of people, it’s not enough.
Relevance is the most important part of a customer’s digital experience.
Online users say that relevant experiences are extremely important to them. People want to be recognized and understood with tailored services and information. They want it faster and distributed across a greater variety of devices and channels.
Join us for Part 2 of our Learning Series where we’ll talk about how to further optimize your Coveo Machine Learning models.
In this webinar, we will show you what active ART and QS Machine Learning Models look like and take a deeper dive into the analytics that can be derived from them.
You will learn how to:
- Create personalized experiences for your users through context
- Understand Machine Learning effectiveness for your organization using templated Coveo reports
- Evaluate the performance of queries and query pipeline changes using the Machine Learning Query Suggest and detailed Summary Usage Analytics report templates
If you missed Part 1, we covered how to activate and test Query Suggest (QS) and Automatic Relevance Tuning (ART) machine learning models enabled in your Coveo platform. You can see the recording here.
Machine learning is at the forefront of virtually all major enterprises, as they harness ways to maximize productivity and relevance through AI that works in real-time.
One of the biggest online challenges is understanding individual customers so your content meets their needs. So give them relevance that makes them feel like they’re the only person browsing your site.
