A customer calls in to customer service, desperate to get his problem solved. He's been a loyal customer for a few years, but lately there have been a few issues. Now he’s on the fence about whether this is the right company to do business with.
Today’s call is a make-or-break situation.
There are two customer service reps available to take his call, Deron and Amy. If the call gets routed to Deron rather than Amy, the customer has an 11 percent greater chance of being satisfied.
Who will take the call?
The answer is decided by random chance. In this case, it probably goes to the rep who has been waiting the longest to receive a call. And if you're a customer service leader, you might be rolling the dice like this on every single customer contact.
Here's why service quality is so random and what you can do about it.
Why Service Quality is Random
A customer service team might have an 85 percent satisfaction rating, but that doesn't mean every rep is making 85 percent of their customers happy. There's often a lot of variation.
Some reps are consistently better than others.
Service agents are human, and have bad days like the rest of us.
Every problem is different, and some are very challenging.
I've even uncovered some research that suggest service quality naturally declines in the afternoon.
Here's a hypothetical example. The team's customer satisfaction rating is 85 percent, but the variation among the individual employees is extreme. Any customer who gets Kate will almost certainly be happy. More than one third of customers are dissatisfied when they work with Leo.
There are many reasons why service quality among employees could be inconsistent. Let's take a look at three common causes.
Tenure is one of the most common causes of inconsistent service.
Nearly every customer service team has a mix of veteran, moderately experienced, and newly hired reps. In the example at the beginning of this post, perhaps Deron had been with the company for several years while Amy had just completed new hire training.
According to data from the Zendesk 2019 Customer Experience Trends Report, the typical customer service rep with four years of experience delivers significantly better service than a newbie.
A customer service leader recently shared this challenge with me. Other departments frequently hired people from her team, which was good for the company, but it also meant she had to constantly hire and train new people.
The solution to this challenge is to speed up new hire training while simultaneously making it more effective. This may seem like an impossible task, but I've done it.
More than once, I've been able to reduce new hire training time by 50 percent, using a few simple techniques. One is called scenario-based training, which makes learning more closely mirror the actual job. You can drop me a line and I'd be happy to walk you through it.
Chronic Performance Issues
I've been fortunate to work with a lot of outstanding customer service professionals. The worst one, by far, was Brandon.
Brandon didn't want the job. He didn't care how he performed, and he definitely wasn't open to any feedback. He showed up when he felt like it and left when he wanted to.
He was hired because the CFO, my boss, told me to hire him. Brandon had just graduated high school, had no experience, even less ambition, but he was dating the CFO’s daughter. I think there was some pact between the CFO and Brandon's parents. In any case, I never would have hired him by choice.
Nearly every call was bad. He could barely handle the transactional stuff and any situation that had the slightest degree of difficulty would end in disaster.
Worst of all, I couldn't do anything about it. Brandon was allowed to slide on things no other employee could get away with until the CFO finally gave me the green to address his performance. He immediately quit.
Brandon also upset the rest of the team. They saw him getting preferential treatment. And they often had to handle the aftermath of Brandon’s poor service by taking the call from customers that he let down or was rude to.
Many of us have a Brandon on our team. Maybe not quite as bad, but someone whose service is chronically poor and who exhibits no signs of improvement. And if you don't do something about it, your Brandon will continue upsetting customers.
Situational Performance Issues
Some service problems are very situational.
For example, business-to-business support teams with weekday hours often get slammed on Monday mornings when all the problems that accumulated over the weekend come pouring in. This high volume might cause long wait times, which makes customers extra grouchy.
Any rep working the Monday morning shift might have lower scores than a rep who only works Wednesday through Friday afternoons, which are typically some of the slower periods for many companies.
Another example is the type of issue. Even experienced employees have their achilles heel. Going back to the example at the beginning of this post, Deron might be very adept at handling the customer's issue, while Amy is generally good but finds that particular issue to be a challenge.
Every employee on your team is an individual. So if you want to eliminate the randomness of service quality, you need to take an individual approach.
Let's go back to our hypothetical team:
The starting point is to look at the outliers. These are employees who are performing significantly better or worse than the average.
Kate and Steve are apparently crushing it. If they're on my team, I'd want to know why, so I can share that insight with the rest of the team. The easiest way to do that is to spend some time observing them serve customers.
Leo appears to be struggling. You might be tempted to jump to conclusions, but I'd start by spending time with him, too. Maybe he's new, or he gets all the difficult calls, or perhaps he's just not very good.
Service quality shouldn't be random. Spend time investigating outliers and you'll learn how to gain confidence that your customers will be happy, no matter if it’s Deron, Amy, or even Leo who takes the call.
You can learn more about investigating performance issues from my course on LinkedIn Learning.