Predicting customer satisfaction helps prioritize interactions and prevent churn
Last updated June 2, 2020
This report by Ovum, an independent research and advisory firm, features Zendesk’s new Satisfaction Prediction tool. The report is available for complimentary download for a limited time.
Some might say the ability to see into the future belongs to psychics and fortune tellers, but predicting customer satisfaction isn’t only for the clairvoyant (or someone doing manual ticket triage and making their best guess). It’s now something we can all do when armed with the right tools and data.
Many companies today measure customer satisfaction. It’s an important customer service metric, but one that typically isn’t being used to improve operations or help reduce customer churn. By applying machine learning and predictive analytics, you can now improve your customer relationships over the long-term, and can predict in the moment how likely a ticket is to receive a good or bad rating, allowing you to take action to ensure a positive outcome.
In the report, “Predicting customer satisfaction helps prioritize interactions and prevent churn,” you’ll learn from Aphrodite Brinsmead, Ovum’s Principle Analyst, Customer Engagement:
- Why customer satisfaction scores are so important
- How Zendesk is using machine learning to predict and assign customer satisfaction scores to interactions
- How Zendesk’s Satisfaction Prediction tool marks a change in the way analytics will be packaged and sold