Measuring your Support Queue

Last week I wrote about how we use a dedicated “SWAT Team” to handle the inevitable unplanned work which threatens to creep in and disrupt our sprints. This week I want to talk about how we measure our SWAT Team, what KPIs we use and how we know whether we are doing a good job.

There are hundreds of posts out there which discuss the merits of KPIs for developers and how the wrong ones encourage behaviour such as cherry picking or incorrect prioritisation. I agree with them entirely, that’s why it’s important that the measurements you do take should reflect the customer experience, rather than the team’s performance.

For example, counting the number of tickets solved would be a poor metric because only quick wins would be looked at. Equally timing how long was spent on each ticket would also encourage people to rush in order to get better stats. However, recording how long a customer waited (from raising a ticket to resolution) measures the customer experience – which is, after all what we should be more concerned about!

In my team we use two main statistics to measure how well we’re performing. The first is one I’ve mentioned before. Average Support Ticket Age is crucial for us because it places a numeric value on how long customers are currently waiting for our help.

The second metric we use is Cycle Time, this is the time between a Ticket being passed to the team and it being resolved. In other words, how long is it taking to solve tickets passed to us This is taken as an average of tickets closed over the last six weeks.

The reason we like these two values is not only that they give a customer’s perspective on our work, but because they balance each other nicely. If we measured Cycle Time alone then we’d get fantastic results by simply solving tickets as they come in but longer running and challenging tickets would be left behind. Equally by focusing only on the older tickets it’s likely we’re missing urgent tickets and quick wins which could be resolved quickly. It’s only by continuously improving both values do we provide a good service.

You’ll notice that we don’t worry too much about the volumes of tickets. I find this doesn’t actually matter, the number of tickets being raised varies from month to month, from customer to customer, and will change as customers leave and (much more ideally) join us. If the team is becoming overloaded with tickets then this will be highlighted in the metrics we already have (as we won’t solve the tickets as quickly). A measure of tasks in the queue is less import than whether the team is keeping up with the required workload.

A final point to make us where we start and finish timing. If you’ve ever read The Goal you’ll know that one of Eli Goldratt’s key points is whether you are measuring the right thing (he points out that efficiency increases do not necessarily produce an increase in profit). The decision you have to make with your KPIs (particularly when a ticket was opened) is whether to start your timer when the customer raises the ticket, or when it’s passed to your team. There is no perfect answer here, if you want to understand the entire customer journey then you need to look at Customer to Customer timings, however – if your team only plays a small part in that journey (as in our development team’s case as we have several support teams before us in the process) then your metrics will be less valuable if they include areas outside your control. Consider what you’re measuring, but never forget that you may only play a small part of the customer’s overall journey.

Hopefully I’ve given you a few ideas? Do you agree with my views? How do you measure the performance of your support teams?