Time Tracking Agent Productivity

2 Kommentare

  • Hannah Voice
    Hi Mike Jenkins 👋
    Such a great topic! We’ve had all these same conversations too, and I don’t think there is one perfect way to measure productivity but I’ll share what we’ve landed on for now.
    We don’t tend to look at tickets solved per agent because in some cases, an agent will just jump in to wrap up the conversation while another teammate is offline. This solve then gets attributed to them in Explore, when the reality is that the other teammate did most of the legwork. We look at:
    • Replies sent (“Public comments” in Explore)
    • Tickets replied to (“Tickets updated w/ public comment” in Explore)
    Similar to what you mentioned, we have a wide variety of questions and topics, and some tickets can be answered in a minute or two, while others can take much longer. Because of this, we tend to look at these metrics per week rather than per day or per hour. Most of our agents handle a variety of tickets in the week, i.e. it would be unusual for one agent to work on only complex tickets, while another works on only simple ones. So it tends to balance things out across the week.
    We have what we call “ticket commitments”, where there is a minimum number of replies sent per week that each agent should meet. This is not intended to be the number they’re aiming for (we have an individualised more ambitious goal for that), but simply the minimum. However, again similar to what you mentioned, we have some teammates who work on out-of-inbox initiatives. To account for this, we introduced a “modifier” system whereby a teammate might have for example a 25% modifier, which reduces their weekly minimum expectation. The idea is that if agents are hitting (and ideally exceeding) their minimum each week, then it’s a good indication that they’re on track.
    We also use the time tracking app and look at average update handle time for each agent. However, we do keep in mind that faster support isn’t always better support. It might be that an agent with the fastest handle times has the lowest CSAT score. So we use this as more of a data point than a singular way to track overall productivity.
    We did also consider looking at response times per agent but found that this doesn’t work well in Explore. Response times and resolution times are tied to tickets, not assignees. The first reply time would report correctly for an agent, only if that ticket is still assigned to them when we run the report. If another agent solves the ticket, it would then report the first reply time for that secondary agent, because the time is tied to the ticket rather than the assignee. The same goes for requester wait time and full resolution time.
    Would really love to hear from others on this topic too!
  • Frank Ferris

    Awesome discussion and ideas here! I work for a company that helps automatically import ZD tickets data into spreadsheets, where it can be easier to write custom formulas and define your logic around what makes a "productive rep". Many of our users are solving a similar case.

    Free to use (with paid premium features like data automations) if it seems like a fit https://coefficient.io/integrations-google-sheets/zendesk


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