Tracking the amount of time it takes from when a ticket is opened to when it is closed — often referred to as “time to close” (TTC) or “mean time to repair” (MTTR) — is one of the most commonly measured service-desk metrics. Many organizations tie this metric to an SLA and guarantee customers certain performance based on it (for example, all Priority 2 tickets will be closed no later than 6 hours after they’re opened). While knowing what this metric is serves as a good starting point, the real value is understanding the underlying reason why this number is what it is — and more importantly, what improvements you can make in order to reduce it.
With that in mind, consider your current reporting capabilities and whether or not you can answer the following questions:- Which customer contributes the most delays to your TTC?
- How does your TTC vary by analyst? By assignment group?
- Does your TTC vary by day or week of the month? By year?
- Are there certain assets or categories that skew your TTC metric?
By being able to quickly view your standard TTC metric through a variety of different life-cycle lenses such as these, you can pinpoint which segments of tickets in your service desk are the biggest influencers of your overall TTC.Subsequently, you can target those specific segments for improvement, and once you’ve done that, just imagine … All those pesky penalties you pay when you miss your SLA targets? Gone. Those unhappy customers waiting and waiting — and complaining and complaining — to be back up and running? Gone. Your boss hearing about how satisfied clients are with the service you’re providing instead? Check. Saving the company money because the help desk is performing better? Check. Suddenly getting more funding for your department? Checkmate.
