Quality Assurance / Quality Monitoring Benchmarking
Hi! For those of you who are conducting quality checks on phone calls, chats, and tickets:
- How many are you doing each month for each channel?
- Are your counts based on the agent (e.g. 10 per agent per month) or by customer (X% per channel)
- What is the structure of your QA team - one pool of QA analysts to support all agents, QA analysts dedicated to a specific group or customer, or another structure?
I'd also appreciate any industry benchmarking you could point me towards!
Best Answer
-
Hi Kim,
We aim for 2% of total volume, divided by phone, chat, and email. Right now, we're completing 8 reviews per agent each month.
We used to have managers do the call reviews (2 per agent per month). The problem was that the managers didn't prioritize this, so some folks didn't do them and others rushed to complete them the last day of the month.
Then, we had managers reviewing 1 per agent per month and created pods of agents to co-review the calls within their group. The challenge here was that folks didn't always give objective feedback -- some were too lenient, some were too hard, and some had different scales in mind.
Now, we have a small group of 4 people who review cases for our teams. They calibrate regularly to provide consistent information and feedback, and the scoring has leveled out and is much more objective. The QA analysts rotate teams each quarter (based on Manager) but can fill in for each other as needed.
0
Answers
-
Hi @Kim Ahmer , we used to run 5% of total calls (support). When call volume increased, we concentrated on calls that took longer than 10min. During that time, we did not have a dedicated QA team; we worked in "squads" so one could listen to each other calls.
I also heard a comment from a CCO about leadership engagement to have Directors and VPs joining 5 calls in 50 days (here in this Podcast: https://www.tsia.com/podcast?wchannelid=g0p3e80cba&wmediaid=1kyj1tztbv).
One interesting thing now is to try using transcripts and AI-based text interpreters to gather insights from word-clouds and other analyses.
2 -
Thank you! These are very helpful answers!
0
