Of all the myriad call center metrics, one that is universal is the “service level”. This metric is defined as a pair of numbers: a percentage value and a time value in seconds. So, for example, 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 that assumption is wrong and adhering to it blindly is not a wise choice. Sadly, the “myth” of the 80/20 standard is now deeply rooted in the industry, like an urban legend that can’t be debunked.
The Murky Origins of “80/20”
A year ago, I covered some of the “lore” surrounding the 80/20 standard in Why 80/20 is Probably the Wrong Service Level for your Call Center. Since then I’ve had one other conversation on these mysterious origins.
Analyst Donna Fluss (one of our Top Call Center analysts) explained that 80/20 was “hard-wired” into the original call center platforms made by Rockwell in the 1970s. Rockwell, which got out of the call center business in 2004, was one of the pioneers of the technology. (There’s debate over whether Ericsson or Rockwell were actually first to market with a true ACD.)
If anyone else has information on this, please let me know. Even if we’ve nailed it down to Rockwell, there’s still the question of why a Rockwell engineer picked that number.
The Danger of Service Level
The obvious danger in picking a service level arbitrarily and then trying to meet it is that resources get allocated the wrong way resulting in more harm than good.
But even if you’ve done all the right analysis (see below) and settled on a proper service level for your call center, there’s another danger. The very nature of the service level metric means that it hides variability “under the rug”. For example, if your agents successfully met 80/20 all day, you know that 80% of the calls were answered in under 20 seconds. But you know nothing about how bad the other 20% were. Were their wait times 30 seconds or 10 minutes? This variability is critical.
In his new book, The Executive Guide to Call Center Metrics, James Abbott states,
In today’s centers, variability is the largest driver of cost… the bigger the variation the more costly the operation.
(Hat tip to Susan Hash for brining my attention to the new book.)
Another danger arises when you have multiple skill groups in your call center. 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 in to skill groups. For more detail, and a quantitative example, see this blog post by Directly CEO Antony Brydon: How Erlang Formulas Killed Skill-Based Routing (and what’s bringing it back). As you can see from the title, that post talks about Erlang formulas, which are really the lynchpin in getting a handle on service levels.
Erlang to the Rescue
The only way to properly model the service level in your call center is to use the Erlang equations. It’s understandable that many call center managers today are not comfortable with Erlang, because when you look it up, you see equations like this:
Just look at that beast with its strange Greek letters and exclamation points… yeesh! (I recall having nightmares, in my university days, where I was being chased by a capital sigma.)
To get more comfortable with the concept, you can think of Erlang as a translator box, where you put in values like calls per hour, handle times, and number of agents available. These are “deterministic” values, in that they are easily measured. What you get as an output are the answers to “probabilistic” questions such as, “What are the odds a call will have to wait in a queue?” or “What are the odds a call will wait more than 30 seconds?”
Another important reason to be comfortable with Erlang is that it’s the only way you can do a proper analysis of call volume “spikes”. For a walk-through on that kind of analysis, check out this blog post.
Fortunately, there are many easy (and free) ways to get back that “feel”. For example, you can experiment with an online Erlang C calculator like the one here. Another option is the Excel macro you can download here.
Picking the Right Level
So now back to picking the right Service Level for your company. It really boils down to balancing your company’s desire to deliver customer satisfaction (or customer engagement or Net Promoter Score) versus the cost you’re willing to bear to achieve it.
The best explanation of the process that I’ve seen comes from Industry consultant Stuart Crutchfield who suggests the following:
- How do I want to prioritize my customers’ wait time? (This often can reflect Customer Lifetime Value or Propensity To Buy, where customers of greater actual, or potential, value are prioritized for a prompt answer.)
- Having segmented my customers by some measure of importance, after how many seconds in queue does their Abandonment Rate start to materially increase?
- What is the impact of increased Abandonment Rate on our customers’ satisfaction (compare higher abandonment groups to a control group)?
I hope this adds some clarity around the important topic of service level metrics.
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