In a typical contact center, human quality assessors are able to monitor one to two percent of all calls handled. Speech Analytics, on the other hand, enables the quality manager to gain an insight on what is happening in every call.
It also gives the quality manager the chance to liberate himself from the stereotypical quality management process where his quality monitors would listen to one or more calls per agent per week, give them feedback and then wait until the following week to see how well they had acted on their feedback.
Speech Analytics can be used in quality improvement projects to measure and address specific issues and take focused, effective action.
Here's how a quality manager might address Average Handling Time using Lean Six Sigma methodology and Speech Analytics.
"D" for "Define"
The first step is to define the project. The problem to be addressed needs to be defined concisely but also specifically. It may be useful to calculate the Cost of Poor Quality (COPQ) and so define the financial impact of excessive Average Handling Time. This is when decisions are made about the scope of the project, which include which operational processes will be covered, where the process in questions begins and ends and who needs to be on the project team.
"M" for "Measure"
The "Measure" phase is likely to take longer than the "Define" phase, because the first part involves establishing what needs to be measured and how, while the second involves data collection itself. A good place to start is by making a detailed map of the process. In contact centers, it is very tempting to rely on the process maps the IT department will have of the call flow. It is a good idea, however, to involve agents to get their perception of the process and ensure that any unofficial workarounds are also documented.
It is also important to conduct a Voice of the Customer exercise to get the views of the end customer of the process. When companies decide to speed up processes at the expense of customer satisfaction, the result is often a loss of brand value and customer loyalty. Customer opinions should then be transformed into the expression of customer needs and further quantified as Critical To Quality (CTQ) indicators which can be measured.
Once this has been done, the next stage is root cause analysis, where the most likely root causes related to each CTQ are selected to be validated by the collection of data.
Data collection is the point where Quality Management and Speech Analytics applications can be used to measure these root causes in terms of whether the agent is following the scripted elements of the call, whether s/he is giving the customer the correct information and meeting the customer's requirements.
"A" for "Analyze"
Once the results are in, they need to be analyzed to validate the root causes. Analytical techniques will include control charts to establish the process's stability and capability.
In this example, if call lengths prove to be widely variable and include data points which are more than 3 standard deviations either side of the average, then the process will be considered to be unstable. This makes it very difficult to predict what is going to happen next. The process becomes very difficult to manage and future outputs difficult to estimate.
Where there are customer mandated specifications for handling time, for example, it is not possible to complete the call flow in less than 4 minutes, then any calls which are shorter or longer than these limits are considered to be "defects". Process capability is a measure of how many defective outputs a process produces. It also makes sense to analyze the correlation between the results for each root cause and the CTQ in question using either scatter plots or, if necessary, correlation and regression calculations.
"I" for "Improve"
The results of the analysis will be used as the input for the "Improve" stage. Possible solutions to improve the CTQ indicators will be brainstormed and the results whittled down using various prioritization techniques to establish practical, feasible and effective solutions.
To minimize the risk, it is a good idea at this point to run a pilot project, perhaps using a small team of agents to compare their results against the larger "control" group. Assuming the results of the pilot project are as expected, then full implementation can take place.
"C" for "Control"
Once the new solution is running, the same data collection plan that was developed in the "measure" phase will be used here to check if the solution is working as expected.
The pilot project may also have highlighted the need for further measurements to properly manage the new process. Once again, this is where Quality Management and Speech Analytics applications can be used to monitor the process and ensure that the agents are progressing through the workflow in the most efficient and effective manner.
The use of Speech Analytics as a measurement tool enables the user to take much larger and more significant samples of data. This means that root causes can be verified quickly and accurately and then addressed with laser like precision.
Liam Anderson, Senior Business Consultant