By: Rob McDougall, CEO, Upstream Works Software
Reporting on the performance of conversational AI applications is often challenging, as they must be tailored to a specific business and its needs. As a result, it is difficult to gauge the success of a chatbot in interacting with and meeting the needs of customers.
To understand this issue, it’s helpful to look back at the history of Interactive Voice Response (IVR) systems. IVRs were generic systems that ran customized business applications – applications that were designed to meet a specific self-service need for that business. As a result, a ‘standard’ IVR report didn’t know anything about the actual application, and standard reporting was limited to port usage statistics. One of the outcomes of this became known as “IVR hell,” where businesses created applications that met their own needs but had no information on whether or not they met the customer’s needs. Without accurate reporting, it was hard to improve the IVR’s performance and customer satisfaction.
Reporting on the performance of chatbot applications can be similarly challenging, as these conversational AI applications (CAI) are tailored to a specific business and its needs. While ChatGPT has made amazing inroads into natural language processing, the underlying technology still needs to be integrated into your specific business in the same fashion that an IVR had to. The same goes for any other vendor on the market.
As a result, the same IVR reporting problem exists today with CAI.