team at The Deetken Group.
Our approach is to combine leading edge analytical techniques, including machine learning and economic modelling, with deep experience in behavioural economics (i.e. the economics of how firms and individuals make choices). This approach allows us to surface keen insights to develop strategies and solutions that ultimately improve services.
One common issue was a “false close”.
This means that the service provider agent at the call centre would mark an issue - say slow login time or consistent freezing of an application - as resolved, even if the issue persisted for the user.
The service provider had a standard set of ticket data related to the calls they were receiving and how they were being addressed.
But these were not providing all the insight we needed to figure out why these shortcomings - these “service failures” - were persisting.
At the Intersection of Machine Intelligence and Behaviour Economics
Many people work in the area of advanced analytics and a lot of firms have great skills in economic modelling and in the broader area of behavioural economics - the economics of how firms and individuals make decisions. But these worlds rarely collide.
Our work revealed that the service provider was incentivized to close a ticket as quickly as possible because their performance was being measured against how quickly they could address an issue. In other words, speed was one of their performance metrics.