Queue Measurement- Best Practices

When I saw queue management trending in business forums and on social networks, I kind of nodded, because it needed addressing. It needed it quickly and succinctly, and I felt I had accomplished that. I was foolish to think that people wouldn’t expect entire single pieces on things like queue measurement as well. Even though there’s practically nothing to say on those that merits this.

Is It Important:

Oh yes, queue measurement is important just like how managing this is, because the most detrimental period during service and support is having to sit and wait to be served, when you already have enough problems, hence your need for help. So, managing the order of people in line by severity and duration, and making less onerous depending on these are very, very important things to do.

But, measurement, which is critical to managing it, is actually very, very straightforward. This is one of those things that’s crucial to have a good handle on, but isn’t difficult to see to. If your business climate, internally, has made this a complex thing, then there are problems beyond this which do need addressed, first.

The Basic Analytics:

So, let’s go ahead and cover this and you’ll see why this is so simple and quick.

First, we’re talking about any channel other than call centers which just work the way they do, and measurements are just lines holding for a specific time for different departments. There’s nothing that can be done for that for now, and it sucks.

For everyone else, there are basically two analytics to track in real time during operational hours, and as histo-gram reports for measurements of performance and effectiveness.

The first one is going to be how critical an issue is. Help desks are very transparent about obtaining this information by just asking the user to provide it. Everyone else seems to shy away from that, and I don’t see why. The user isn’t put off by being asked how bad their problem is.

The other is obtained simply by tracking open tickets in any support channel. When a ticket is open, the system will track its time open or on hold as a duration unresolved.

Obtaining Obfuscated Criticality:

I really don’t know why you all make this part so hard on yourselves when not using help desks. But, if you’re unable to prevent that, which I could see happening when higher ups demand it … it’s not so bad.

It usually just ranks it by if a few recorded questions didn’t help, and by the inquiries made by the user in the forms and/or the department they wind up in finally.

Using This to Measure:

Using this in measuring depends on what you consider the right way of handling your queue overall. But, you basically want a threshold for all tickets/calls/contacts by way of duration. Nobody must wait longer than this threshold at all times.

Along with that, most companies have a prioritization set based on criticality of a contact. The urgent ones get handled immediately, whenever less urgent ones aren’t very close to the ultimate threshold of wait time.

When the urgent issues are all resolved, they prioritize the less urgent ones by order of severity, all the while keeping the truly non-critical ones as a watch for threshold proximity.

A Caveat:

When using prioritization and dynamic queue measurement like this, be sure to inform customers upon initial contact that they are handled in order of reception and by level of urgency.


Amy Clark is the Lead Author & Editor of IWantItNow Blog. Amy established the Customer Engagement blog to create a source for news and discussion about some of the issues, challenges, news, and ideas relating to customer service, support and engagement.
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