A New Vision for Quality Management

Late last month Forrester Research published the latest contact center-as-a-service (CCaaS) Wave report. One of the most interesting things I took away from that experience is how much quality management (QM) is changing.

Quality management systems were initially built for large contact centers that could afford to have a team of specialists spending their days listening to call recordings. Forms were used to review various elements of agent performance. This technology has been very successful and has become an indispensable application for larger contact centers.

At the same time, though, there are many things about this traditional approach to QM that are less than perfect. Agents often chafe at the idea of being scored based on a review of one or two calls per month selected randomly. It's hard for agents to trust that a single bad call won't skew their performance scores. Companies have been challenged trying to keep the folks who do the agent reviews out of a siloed ivory tower. Many is the time I asked QM managers at tradeshows what they used to route their calls and they had no idea.

These specialized QM teams bought their software from specialized workforce optimization (WFO) vendors such as NICE or Verint. Over time, the lines have blurred between WFO vendors and the CCaaS vendors with which they integrate. For example, NICE bought InContact, creating a company that spans the full gamut of WFO and CCaaS. In this recent CCaaS wave, I was surprised by the number of vendors that have built their own QM solutions.

The solutions that CCaaS vendors have built are different from what has come before. These systems bring new value and approaches to QM by leveraging artificial intelligence and providing simpler, more flexible approaches to call reviews. While different vendors approach the solutions differently, these solutions share a couple of key characteristics, including the following:

  • They use AI for better insights with less effort. Transcription, semantic understanding, and machine learning combine to allow AI-driven QM solutions to review every customer interaction on any channel. For simplicity's sake, I'll talk about calls here. AI can be used to identify calls of interest based on the reasons for the call, such as cancellation. AI can track customer sentiment on calls based on tone and/or the words said. It can also notice where sentiment changes to help locate problem areas. AI can pre-fill agent review forms, reducing workloads for quality management teams. Nuanced reviews from AI are not yet an option, AI can capture some things very well without human intervention. These include checking to see whether agents asked the customers' names, whether agents gave their own names clearly, and whether compliance statements were handled properly.
  • They bring QM to more companies. CCaaS vendors are providing tools and approaches that allow smaller teams to benefit from QM. Lower deployment and storage costs and simpler and smarter tools are making QM practical for contact centers with as few as 20 agents. Many of the CCaaS vendors continue to support partnerships with QM providers for their largest customers who need some of the scale and functionality that these systems uniquely provide.

Newer QM solutions include some interesting twists. In many cases, call recordings analyzed by AI are now included in reporting and analytics systems. Supervisors can drill down within their reports to see the actual waves of conversations. Interesting tidbits can be overlaid on these waves, including customer sentiment at various points in interactions, and various topics of discussion. This makes it easy for supervisors to get to point of interests in calls and listen to the recordings.

This is the start of a new approach for QM. Very soon we will see contact centers relying on AI to score every customer interaction for insight into the performance of different groups and individual agents. Supervisors will have tools to easily find examples that will help them coach on specific situations where agents need help with some of the more nuanced aspects of customer interactions.

This partnership of automated and human coaching will deliver better outcomes for contact centers through better feedback and fairer scoring.

Max Ball is a principal analyst at Forrester Research.