Skip to main content

In last week’s article I made the case for customer service without the annoying wait times.
Now let’s dive into five reasons why this is not happening already.
(On the contrary, wait times are rising in Europe and the US.)

1) The conviction it cannot be done

Many will argue it’s not possible to get rid of wait times entirely because of
unpredictable customer behaviour and limited resources.
Uncertainty in arrival patterns will prevent you from staffing to perfection.
Overcoming this uncertainty would require large amounts of staff, thereby increasing cost
and decreasing productivity.

And you would be right for a large part, these are contstaints to be taken seriously.
On the other hand, when stuff can’t be done, that makes it all the more interesting right?

Let’s look at some more obstacles that prevent us from reducing wait times.


2) The internal focus on efficiency

How many metrics do you know that track productivity as experienced by customers?
Yes, we track First Time Right, First Time Resolution / Fix, Average Speed of Answer,  Service Level, etc.
On the surface these metrics seem customer centric, but looks are deceiving.

These metrics are either about cost reduction or about cost control.
High rates of First Contact Resolution are great for the customer, but also great for reducing cost.
So we tend to aim as high as possible with these kinds of goals.

For Service Levels however, we try to hit a score as low as possible, above a set target.
And how to we set these targets? I bet you know the answer: just high enough so that we’ll get away with it.

What can you get away with?

So how do you decide on your customer’s tolerance for waiting?
Below method is currently considered best practice in contact centers:

You ask the geeks in your company to calculate at which service level the average customer starts to become unhappy. They present you the outcome, you round it to the nearest 10% and 10 seconds and there is your target.

You may want to be careful when aiming for ‘just not making customers unhappy’.
Also this is hard to keep track of over time.
One’s level of satisfaction is dependent on their internal framework of expectations.
What if other companies start raising the bar?
Adding to this, the least patient generation ever is already making up 40% of consumers.
Not taking the customers perspective will prevent you from looking further into reducing wait times.

3) Customer service being managed like a cost center

In the previous article I mentioned Coolblue using excellent customer service as their main USP.
Still, many organizations are managing their customer service like a cost center, leading to poor service.
This short-sighted approach to customer service fails to adress the hidden cost of poor customer service.
Consider the following annual churn percentages by industry:

Now what part of the above percentages do you think are a result of poor customer service?
financesonline points out that:

  • 33% of Americans would switch brands after one instance of poor customer service. (American Express)
  • 91% of customers would consider another purchase after positive customer service. (Salesforce, 2020)
  • 79% of business buyers have made a purchase decision based on customer service. (Salesforce, 2020)
  • In comparison, 69% of consumers did the same. (Salesforce, 2020)
  • Low customer effort when contacting customer service can drive loyalty. But only 29% of businesses measure customer effort. (The Northridge Group, 2020)

Add to this the 57% of consumers that are annoyed by wait times and you’ll get an idea of the true cost.
Customer Service is a profit center if you manage to not annoy your customers!

4) Primitive forecasting

Is your CS department ofen surprised by “unexpected events” and “external circumstances” leading to much higher / lower volumes?
Do you often experience long periods of long wait times to unforeseen increases in volume?
Much of that could have been prevented.
In the past 14 years I had the pleasure of working with workforce management teams across more than 30 companies.
I am sad to find the practice of forecasting customer interactions and required staffing levels has hardly improved over this period of time, if at all.

Spreadsheets are still the industry standard here.
Complicated spreadsheets, that are often maintained by individuals, thereby also creating single points of failure.
Too much energy is then directed towards explaining deviations and getting the story right, after the facts.
This in turn takes away energy from creating a sustainable and more accurate forecasting practice.

5) Islands of specialization

The last roadblock for reducing wait times is the way customer support departments are organized into islands of specialization.
While specialization can make us more productive, it also increases wait times in customer service.
It can also make your reps’ jobs really boring if their field of expertise is too narrow!

Have you ever experienced half the contact center being extremely busy, battling their way through a long queue of customers,
while the other half is playing games at their phones? (It’s a shame Work-from-home makes this largely invisible now.)
All these idle agents could be speaking to a customer, if they would have had the proper training and access.

In my experience we tend to understimate what agents are capable of. I have seen as many as 12 teams being merged together.
All while increasing customer satisfaction and needing less hours of training compared to the old situation.
The reps also became much happier with the increased diversity in their converations even though some were protesting before the change was made.

Of course there are many reasons why this is hard to do.
This requires both a decent CRM solution and a knowledge base that is well structured.
Also if your company is around for some time or has been through a merger you may find yourself in a complicated landscape of applications.



You may have noticed I left out the obstacle of finding sufficient staff.
I consider that to be a temporary market condition so I didn’t include it in the list.
Let me know if you think otherwise!

In next week’s article I will present some solutions and paint a picture of how things could be.


Thanks for reading!

Richard Zeelenberg
Founder @ AIM Forecasting