Since the creation of contact centers, managers and QA teams have been asking one big question: “How do we make sure we’re keeping our customers happy?”
That was the birth of quality management for contact centers: overseeing tasks and conversations to ensure agents are properly trained to help customers.
Quality management has changed significantly over the years, as new technologies allow managers and QA teams to gain new insights and information from each call and interaction. With that in mind, let’s look at how quality management has changed, and how companies like MiaRec are shaping its future.
Manual Call Scoring
The oldest version of quality management is the most time-consuming method. It requires supervisors or quality assurance teams to listen to call recordings, then assess the agent’s performance and assign scores. This is a manual process taken one call at a time, usually done on Excel spreadsheets, although there are also tools like the Agent Evaluation functionality (found in MiaRec’s Conversational Intelligence Platform) that can help with the process.
This method tends to be great for scenarios where having a real person provide insight is typically more accurate than automated tools. It also helps supervisors gain a personal understanding of their agents and customers and lets them accurately score the calls themselves.
However, there are several downsides to this method, the biggest one being the time it takes to go through the process. The evaluator must listen to the entire call, manually note every positive or negative element, and assign overall scores (which can be impacted by human bias). This takes longer than the call itself, which is simply not an efficient use of time.
As a result, only about 2-3% of all calls get scored this way. That doesn’t provide nearly enough information to get an accurate impression of any agent’s performance, and can’t give supervisors any insights into compliance, like overall script adherence. Small samples simply can’t provide enough data to make sure agents are handling every call properly, so manual scoring is typically only efficient for smaller teams with low call volumes.
Keyword-Based Scoring
What if we were to let software analyze the calls before passing it on to a supervisor? That brings us to keyword-based scoring, where a program scans a conversation’s transcript for specific words.
The targeted keywords typically relate to call scenarios, such as ensuring the agent reads compliance statements. The scoring algorithms can identify key phrases (or even similar phrases) to ensure agents are sticking to the script and customers are satisfied with the support they’re receiving. Unlike manual scoring, this can be used for every call without taking up a supervisor’s time.
However, there are still some challenges with this scoring system, and it’s not just because focusing on keywords is a rather narrow criterion. Setting up, configuring, and maintaining keyword-scoring software can be an endeavor, and it’s an inflexible method that can’t account for context.