Zendesk Introduces Feature to Spot Poor Customer Service

US-based customer service platform Zendesk has introduced a machine learning and predictive analytics feature called Satisfaction Prediction, which uses historical customer survey results to predict which individuals are likely to provide negative feedback.

Headquartered in San Francisco, with additional operations in the US, Europe, Asia, Australia, and South America, Zendesk provides a cloud-based customer service platform, also called Zendesk, that includes ticketing, self-service options, and customer support features. In addition, its Help Center allows customers to access a knowledge base and community forum, searchable through a central customer portal.

The firm's new Satisfaction Prediction solution uses an early warning system for poor customer satisfaction, which predicts and pinpoints the customer conversations most likely to lead to high or low satisfaction. By analysing customer signals, the system is able to generate a simple 0-100 score each time a customer service ticket is created or updated. Satisfaction Prediction learns from signals that may precede negative customer satisfaction, such as the amount of effort involved to solve a ticket, or language used within a ticket, paired with the customer's satisfaction rating of the ticket. The model then learns from these signals, enabling the feature to predict the probability that a ticket will receive a good satisfaction rating.

Adrian McDermott (pictured), SVP of Product Development, comments: 'This feature enables organizations to better anticipate a customer's level of frustration before bad interactions can occur. By introducing 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.'

Web site: www.zendesk.com .

All articles 2006-23 written and edited by Mel Crowther and/or Nick Thomas, 2024- by Nick Thomas, unless otherwise stated.