iTWire - Zendesk applies machine learning to predict customer satisfaction

It's increasingly common for organisations to ask their customers how satisfactory their latest interaction was, often by asking them to participate in an automated survey at the end of a phone call, or by inviting them to complete an online feedback form after a physical interaction such as an airline flight or a virtual one such as using the company's web site. But by that time, it's too late to head off any negative outcomes - the interaction is complete, so all you can do is try to stop problems occurring again or provide the customer with some sort of compensation to make up for it. So customer service provider Zendesk has added its new Satisfaction Prediction feature to the enterprise plan, but only for clients who receive at least 500 satisfaction ratings a month. The feature has been in beta test for five months ago, and in that time has analysed more than 1.82 million customer interactions. Satisfaction Prediction uses hundreds of signals - including text description, number of replies and total wait time - to calculate the likelihood of a positive satisfaction rating.