Zendesk Gets Predictive; Scoring Customer Satisfaction with Machine Learning

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It's increasingly difficult for enterprises to identify and prioritize conversations to achieve optimal customer satisfaction - at least not without the helping hand of technology.


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Zendesk this week released Satisfaction Prediction, a machine learning and predictive analytics feature that leverages historical satisfaction survey results to predict conversations at risk of bad customer satisfaction before they occur, enabling enterprises to take a truly data-driven approach to eliminating potential service problems.

The solution uses predictive analytics to create a score each time a customer service ticket is created or updated, allowing agents to prioritize workflows, drive business rules, or trigger downstream integrations based off a dynamic rule that combines the intelligence of thousands of customer signals. A machine learning model is automatically generated, through big data analysis of live account data, to create a unique, personalized customer service prediction model for each Zendesk customer.

“Zendesk’s satisfaction prediction capabilities bring a sixth sense to increasingly complex customer conversations, empowering organizations to better anticipate a customer’s level of frustration before bad interactions can occur,” said Adrian McDermott, SVP of Product Development at Zendesk. “By introducing cutting-edge machine learning technology to Zendesk’s customer support platform, we’re using data-driven insights to help organizations build better long-term relationships with their customers.”

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