One of the first things to do when setting up Klaus is to create your filters. These determine the conversations you’ll be reviewing. See some of the ways our clients are using this now.
Set up a filter for a specific agent as an assignee and a dynamic timeframe for every agent whose work you are reviewing. This helps you concentrate on the review process and you’ll develop an understanding of this specific agents performance, their strengths and weaknesses, common mistakes and overall style.
If your goal is to understand the team performance relating to a specific channel, you could set up a filter to look at all conversations from that specific channel and all the agents who are working in that channel. Instead of focusing on the agent, you’ll get an overview of your performance and processes relating to that channel.
Negative CSAT rating
Do you track CSAT with Zendesk? You could concentrate your review efforts specifically in places where customers have felt and expressed a negative feeling about your service. This helps you not only to get a good understanding of the main issues customers are having but also which specific issues or internal processes lead to the most negative customer experiences.
Refund/Return/New product line related conversations
Is there a new process/product you have launched, or maybe an update to an existing internal policy that you’d like to check upon? In terms of how the customers react, but also adherence from the agents' side. Create a filter using a Zendesk tag or a Custom field for this specific process/product.
Ie. we just launched a new refund process and I want to make sure all agents are using it correctly. I’d create a filter for all tickets with a Zendesk tag “refund” or a custom field that we use called ‘Ticket category=Refund’. This way I could keep an eye all tickets related to refunds from the day we launch the process and work on any refinements as soon as I see it not performing as expected.
I might want to check on the feedback the QA team/CS Managers are giving our agents - I can also create a filter for that. This way I could make sure that the feedback agents get is helpful, constructive and fair - and the time we spend on conversation reviews is supporting agent development.