In business-to-business selling the last month of the year is always slow, so I’ve been going through some old stuff. Normal people would take this time to look at pictures of loved ones, photos from old trips and so forth. But I’m a mathematician so I look at graphs and equations instead. Yes, this sounds pretty sad, but in this case I’ll impart some wisdom from a very old piece of stuff I came across.
Call centers have two fundamental time measurements that they pay close attention to: average talk time, and average queue time. What does it mean when we say, “our average talk time is 2 minutes”? It does mean that some calls last less than 2 minutes and others last longer, but averaging all them we get 2 minutes. But, how are the call lengths distributed around that average? Does this mean that the probability of some calls lasting 1 minute is the same as the probability of some calls lasting 3 minutes? Is the probability of call lengths distributed on a normal bell-shaped curve with 2 minutes right in the middle like this graph?
You might think so, but you’d be wrong if you did. Caller behavior and the way calls get handled create something different. Here is an old graph dating all the way back to 1961 that shows the probability of call talk time lengths with an average talk time of 2 minutes.
It’s from some old AT&T data. It shows that call lengths follow an exponential distribution rather than a bell curved shaped distribution. Because the average is 2 minutes, the area under the graph to the left side of the 2 minute mark is equal to that under the right. The greatest probability is that calls are handled pretty quickly under two minutes, but there are a lot of calls that take longer and stretch out to 9 or 10 minutes, but with decreasing probability as the times get that long. You’ll notice right at the very beginning of the graph near 0 minutes there’s a spike and and then a dip after which it then settles into near perfectly following an exponential distribution (the dashed curve). This was caused by people hanging up right away because of having called the wrong number, or nowadays pressing the wrong selection to an IVR prompt.
The time spent in queue time follows the same pattern because the highest probability for when someone is likely to abandon from queue is the very moment they are placed in queue or soon thereafter. There’s no certain way to avoid this because a lot of people have little to no tolerance for being placed on hold. If there is any alternative such as a company competitive to you, or they’ve called on an impulse, a larger number of people will be of the no tolerance type. Try increasing your ring delay, the amount of time your ACD gives a ring tone to callers before it seizes the call — callers don’t feel they are delayed when hearing a ring.
Its been said by others many times, and I’ll say it again. As a call center manager you can’t gauge how well your service levels are by looking at how “busy” your agents are. Intuitively, we would think that if you drop your agent levels at any moment by 10% that your callers will experience a 10% increase in the time they have to wait for service. 10% less agents means about 10% higher queue times, right?
No, absolutely not! Small decreases in agent levels will not make make the agents that much more busy, but it will make the callers experience steep increases in queue time. Stated more precisely:
1. If the agents on staff are reduced in a linear manner, this gives a slightly greater occupancy rate, and some small savings in staff expense.
2. But, the average speed to answer for the callers (your customers) increases in an exponential manner.
3. This is just a fact of the mathematics of queuing theory, and the fact that calls arrive in a random distribution over time. Do you want small linear savings, or large exponential revenues?
Here’s an example. Let’s say I’m a call center manager, and let’s look at the following table from a simple Erlang C calculation:
In the parameters at the top, I’ve selected 30 minutes as a time period over which I expect 430 calls to arrive with an average talk time of 2:30, and an average after call work time (wrap-up time) of 20 seconds. From my experience and historic reports, I think callers will, on average, wait for up to 3:00 in queue before hanging up (abandoning), and I want to maintain a service level goal of answering 80% of the calls within 20 seconds. In this calculator, I then click the Calculate button. In the staffing calculations at the bottom, the famous Erlang C formula tells me a level of 46 agents is best for achieving my goal.
Now, look closely at the columns labeled AvgSpdAns (average speed to answer), Occupancy (the percentage of time agents are on a call) and Abandon (the percentage of callers who hang up while waiting in queue). At 46 agents the occupancy rate is 88%, and indeed, when I look over the cubicles, I see my agents seem to be idle for over 10% of their time. But I’m a draconian call center manager! I want to crack the whip, make my agents sweat, and pinch every penny when it comes to payroll! I think I can drop the agent levels by 4 or 5 agents, about 10%, and save a lot of money and make management happy by having my agents work at near 100% occupancy! What a great idea!
Uh … no, I should be fired on the spot. Look what would happen if I were to drop from 46 agents down to to 41 or 42 agents. Sure, my agents get a little bit more busy, but the average speed to answer increases from 10 seconds to a minute and a half at 42 agents, and an utterly disastrous 6:46 at 41 agents. Now, let’s say my call center is a catalog order center. Every caller is waiting, credit card in hand, just eagerly wanting to give my company their hard earned money. But we won’t get that revenue, because 10 to 62 percent are going to be abandoning, and they will be so ticked off they will never, ever, call again. They will abandon me; I will be rejected, dumped, and be “standing in the shadows of love, getting ready for the heartaches to come” as the Four Tops sang. (I really like how we use that term “abandoned” in our industry. It has such a sad, heartbreaking finality to it.)
Sure, you’re saying “this is an extreme example, and anyway we have real time displays, reader boards and such that warn us before things go haywire, and we’ve heard this sort of advice before”. Well, very often the first things learned are the first things forgotten, and a surprising number of call center managers do manage, to one degree or another, by looking at and perceiving the activity levels of their agents. This is especially true in smaller call centers. I guess it’s just human nature.
Also, as a caller yourself, have you ever noticed the attitude and behavior of agents you talk to is so perfectly correlated with the amount of time you’ve spent in queue? The longer the queue time, the grouchier the agent is, and the grouchier you are. The agent is being worked too hard, just as your patience is. Last month I spent over an hour in queue when calling the US Internal Revenue Service (that’s our our tax agency here in the US that provides the wonderful “service” of sticking a vacuum cleaner hose into our bank accounts). In any other interaction, I’m quite sure the agent and myself would be very pleasant people, but the conversation we had was as unpleasant as the long wait before it, and it was all because of high occupancy and average speed to answer.
So, I’m never going to interact with the IRS again; I have abandoned them! Well maybe not … death and taxes are the only certainties, so I’m sure I’ll be waiting forever in queue again with that particular call center operation. But, I’m really hoping the queue for St. Peter is a little more quick as that would be a another queue not to abandon! 😉
Do you know of any stats or surveys that answer the question of how many call center managers really pay attention to the call volume forecasts generated by their WFM software based on historical data?
My informal research indicates that about two thirds of them view the forecasts as inaccurate, and create their own based on side knowledge, hunches, selective use of historical reports, and a feel for what’s going to happen in the future rather than following an algorithm in their WFM software. This is usually because a company’s offerings, business climate, campaigns and so forth are always changing. As with most things, the past is not always a good indicator of the future.
I had a manager tell me a few weeks ago, “our call forecasting is as much a seance as it is a science”. Great quote!
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