The concept of a “North Star” metric has become a fashionable discussion among start-up culture. The idea is that companies should find a single, simple metric to focus on and, if they choose well, it will align everyone on the team with the actions needed for success. Check out this deck on that topic from Sean Ellis, who popularized the “growth hacking” phenomenon.
Call centers are highly focused on metrics too. If most call center teams had to pick a “North Star” it would be service level, which measures the length of time callers wait to reach an agent. It’s kind of surprising that, despite all the enormous changes that have occurred in customer service technology, the dominant metric remains service level. Even more surprising is that most call centers would name the same target value for that metric: the magical “80/20”. Why is this so?
Service level is always given as a pair of numbers: a percentage value and a time value in seconds. An “80/20″ service level means 80% of calls answered in 20 seconds. That exact combination is considered by many to be an industry standard.
Many people assume this standard is based on careful analysis which revealed that 80/20 was a good target to set, but in reality, it appears 80/20 was arbitrarily chosen in the early days of call center technology. The original logic behind it is lost in the mists of time. (See Why 80/20 is Probably the Wrong Service Level for your Call Center.) Despite this murky origin, it has served as a focal point for call center teams.
There is some value in the fact that it’s a common goal that everyone across the industry understands. So when colleagues are discussing service levels at an industry event (say, the excellent ICMI Call Center DEMO conference this month, which Fonolo will be attending) they can properly compare notes and trends.
Service Level Pitfalls
There is also a danger to sticking to the default 80/20 target since this isn’t the right number for every organization. If you strive to meet the wrong target, resources get allocated the wrong way. The next section will address how to choose the proper service level. But before we get to that, two notes of caution:
- The very nature of the service level metric means that variability can get swept “under the rug”. Let’s say your call center successfully met your 80/20 target all day. So you know that 80% of the calls were answered in less than 20 seconds, but what you don’t know is how bad the other 20% were. Were their wait times 30 seconds or 10 minutes? This variability is critical.
- If you have multiple skill groups in your call center, another issue arises. Averaging across the groups to get a single service level can obscure problems in specific groups. Measuring each group separately is smarter, but that leaves you with too many numbers to interpret. This is actually an argument against dividing your workforce into skill groups.
Picking the right service level for your company is a matter of balancing your company’s desire to deliver high customer satisfaction (Net Promoter Score); versus the cost you’re willing to bear to achieve it. That’s not an easy process, but here are some questions to get you started:
- How important is it to minimize your customers’ wait time? (Do you have data connecting that to Customer Lifetime Value or Propensity to Buy?)
- After how many seconds in queue do your customers start to hang-up? (In other words, when does Abandonment Rate start to rise?)
- Can you map Abandonment Rate to customer satisfaction or lost revenue?
Fortunately, there is a large amount of writing on this topic. (That’s one benefit of “service level” having such a long history.) Here are some recent books we can recommend:
- Optimize by Patrick Botz and Dick Bucci
- Call Center Management on Fast Forward by Brad Cleveland
- Call Center Rocket Science by Randy Rubingh
Here is some further reading from our blog:
- Why 80/20 is Probably the Wrong Service Level for Your Call Center
- Call Center Metrics: 4 Pitfalls You Need to Avoid [Whitepaper]
- The Danger of the 80/20 Service Level Continues
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