The future of generative AI in conversation intelligence
Tethr is testing GenAI applications to help contact center leaders find and act on conversation insights even more efficiently.
October 31, 2023
December 7, 2020
Want to understand what’s causing your customers to call back repeatedly? In this post, we discuss how you can work to eliminate customer recontacts by evaluating call data.
“Have I fully resolved your issue today?”
In the book, The Effortless Experience, my CEB (now Gartner) co-authors and I argue that this question is, hands down, the worst question a service rep can ask a customer. This is unfortunate because, as you’re reading this post, chances are pretty good that your reps are asking your customers this question (or something like it, such as “Is there anything else I can help you with?”) at the end of every service interaction.
Two reasons: First, it sends the customer the message that you’re trying to wrap things up so you can get to the next call in the queue (which it does). But as bad as that is, it’s actually the lesser of the two reasons. The second, more important reason that it’s such a bad question to ask is that it doesn’t accurately capture what it’s intended to capture—namely, whether the service rep resolved the customer’s issue.
In the book, we showed that while companies think they do an admirable job resolving customer issues (companies we surveyed said they get to resolution roughly 76% of the time in customer interactions), their customers tended to disagree. Specifically, customers told us that companies only managed to resolve their issues 40% of the time.
Clearly, in the eyes of customers, there is a significant opportunity to improve the way that companies handle issue resolution. And it makes a ton of sense for companies to close the performance gap here: repeat contacts or customer recontacts are, by far, the most insidious of all sources of customer effort.
When a customer has to recontact the company to chase down the resolution of their issue, it increases the probability that the customer will be disloyal at the end of the service experience by 2.5X. Customer recontacts figure prominently in the story of recurring effort we discussed in our last post on the Four Flavors of Customer Loyalty (the moment the customer “breaks” from the sheer exhaustion of doing business with the company).
In no uncertain terms: Customer recontacts are toxic to the customer experience.
The problem is that eliminating repeat contacts is much easier said than done. Most CX and customer care leaders already know what a disastrous effect customer recontacts have on the customer experience and have been hard at work—in many cases, for years—trying to eliminate this friction point for their customers but, for one reason or another, have struggled to put a dent in this seemingly intractable problem.
A big part of the reason companies struggle to fix their customer recontacts issue is because they don’t understand what causes it to begin with. CX and service leaders tend to assume that repeat contacts are strictly a function of not successfully resolving the issues customers contact them about—which, of course, stands to reason. But, it turns out, failing to resolve the issue a customer contacts the company about (what we call in the book “explicit issue failures”) only accounts for half of total callbacks.
The rest of the time, customers call back for reasons sometimes only tangentially related to the issue they called about in the first place.
Often, they call back because they experienced some disconnect with the rep—they were confused by the answer they received from the first rep or maybe just didn’t like the answer from the first rep and so, they called back.
And sometimes customers will call back because the first rep solved their problem…but doing so raised another question for the customer (or perhaps caused a new issue) that the customer only realized after hanging up the phone. In the book, we call these sorts of tangentially related callback drivers “implicit issue failures.”
While this all makes sense in theory, it can be a lot harder for a company to really pin down what’s causing both explicit and implicit issue failures in their own organization. When I present research from The Effortless Experience, this is one of the most frequent questions I get.
In the book, we profiled one organization—a Canadian telecom carrier—that put a team of business analysts to the task of analyzing a year’s worth of call and transactional data to try to figure out why customers would call them back repeatedly. Most companies don’t have the luxury of putting a team against it, so we would often suggest (partly in jest) a much cheaper alternative: buy some pizza and beer, pull your best reps into a conference room after work and ask them to figure it out.
Technology has come a long way since 2013 when CEB published the book. Today, we have the ability to use AI and big data analytics to listen and understand at scale—effectively delivering the benefit that a team of business analysts would, but without the cost or time required to do the work manually. And today, we're bringing that knowledge to bear on the issue of customer recontacts.
Over the past year, one of our data scientists at Tethr, Gerardo De La O, has been looking at large samples of calls from a wide variety of Tethr customers to see if he could quickly figure out what took that Canadian wireless carrier a year to discover…with a bit more precision than what you’d get over a pizza and beer-infused rep brainstorming session.
When conducting this sort of analysis using call data, it turns out there are only 2 pieces of information required to identify customer recontacts:
With these 2 pieces of information, we were able to identify how many times any single individual called into a company and across what time period.
The first thing we did was to identify all recontact interactions for the companies whose data we were analyzing—in other words, instances in which there were at least two calls from the same customer across any time period—and compared those calls to instances where there was no recontact.
The customer recontacts time period can vary for different companies—for instance, those on a monthly billing cycle may want to look at a 30-day callback period whereas companies in the financial services industry (banks and credit card issuers in particular) can be much shorter, often less than 72 hours. Given the companies in our study, we considered any callback within 7 days to be a recontact situation.
The second thing we did was build a recontact rank to establish the particular order a call occurred in—for instance, the third contact in seven days. Once we did this, we were able to answer a few basic (but important) questions:
When we ran the analysis, we found that for the companies in our study, roughly 12% of all calls ended up in a recontact situation within one day and that number jumped to more than 20% when looking at a seven-day window. As an aside, about one-third of the total sample of customers recontacted at some point during the year—so, as you can see, the bulk of recontact happens within seven days.
What’s more interesting is that a high percentage of these customer recontacts seem to be controllable, presenting an opportunity to save money and reduce customer effort at the same time.
We found three specific categories of opportunity among the companies in our study:
First, in terms of process and call handling, we unearthed something rather surprising: a huge percentage of customer recontacts were being driven by long holds and transfers.
Why? As we examined some of the specific calls where this was happening, the cause and effect became pretty clear.
In short, customers have other things to do and, at some point, will just disconnect and call back at a later time. For instance, one customer called back the day after her initial call and explained to the rep that she’d been on hold for more than ten minutes and had to go to a meeting, so she just decided to hang up and call back the following day. We saw the same thing with transfers—which have the same effect as long holds. Upon re-contacting, customers reported that they’d been transferred on the prior call and didn’t have time to wait to speak to another agent.
These are the “explicit issue failures”—the company just failed to resolve the issue the first time around (because the customer ran out of time, as it turns out). The proposed fix? Train agents to ask customers—before putting them on hold or transferring them—whether the customer was running tight on time and, if so, to proactively ask for a time when the company could call them back to continue the discussion. This can lead to lower rates of customer recontacts.
In terms of the second category of callback drivers—experiential issues—we found two behaviors reps needed to avoid demonstrating (because of their likelihood to drive repeat contacts) and two that reps should use more often given their ability to forestall repeat contacts.
Namely, we found that when reps use what we call “powerless to help” language (i.e., when reps hide behind policy saying things like “we’re not allowed to do that”) or when reps demonstrate uncertainty with the customer, there was a significantly higher probability that the customer will hang up and recontact—often within the same day.
Conversely, when reps use advocacy-based language (e.g., “let me get this fixed for you” or “here’s what I’d recommend”) and when they offer guidance and education to the customer (i.e., when the rep takes time to suggest next steps or provide more details on specific products or services), there was a significantly lower probability that those customers would call back.
These issues fall into the category of “implicit issue failures”—the company may have concluded that the first rep successfully resolved the issue, but because of a disconnect between the rep and the customer, the customer ends up calling back anyway. The fix here is clear: the company needs to offer more targeted coaching on experience engineering skills. Namely, supervisors need to help those reps who are using language that indicates they are powerless to help or uncertain to replace those behaviors with advocacy and guidance.
What’s interesting is that there is a massive skew toward a certain handful of agents who are in most need of this targeted coaching intervention. The bulk of reps at the companies in our study only use powerless to help language 5-15% of the time—and top performers use it less than 5% of the time. But, we still found hundreds of reps who use it between 15-25% of the time and a small group of low performers who use this language on more than 25% of their calls.
You see a similar trend with the use of uncertainty language among reps in the study. The vast majority of reps rarely, if ever, express uncertainty on calls—most doing it less than 15% of the time and the majority of those doing it even less frequently than that (closer to 5-10% frequency). But we still found hundreds who use this sort of language on 15-25% of calls and a small group of outliers who use it on more than 25% of calls.
When you cross-reference these reps with callback rates, we find that the worst offenders generate disproportionately more callbacks than their peers. In fact, the worst-performing reps each generate greater than 17% one-day callback rates (compared to an average of 12%). Clearly, there’s a huge opportunity to coach to behaviors that can generate a dramatic reduction in repeat contact rates.
Finally, we have downstream issues. When we looked at each company’s data, we saw that there were a handful of issue types that each drove a significant chunk of the company’s total callback volume. As you can imagine, the actual issues varied across companies, but most of them pertained to either (1) fairly complex products that the companies sell to customers or (2) account maintenance and billing issues. Payment issues seemed to be a particularly vexing call type. Across companies, a significant percentage of customers who call in to make a payment ended up calling back to change the date of the payment or, in some cases, to ask why the payment hadn’t yet come out of their bank account.
These downstream issues are also examples of implicit issue failure. For instance, in many cases, one rep takes the payment and marks the issue as resolved—but because the first rep failed to tell the customer that the amount wouldn’t be debited from their bank account for 48 hours, some of those customers end up calling back 24 hours after the first call to ask when the charge would actually appear. The fix in these situations is to give reps a cheat sheet—for certain issues that drive a lot of downstream callbacks—that reminds them not only to solve the issue the customer called in about but to forward resolve the issue the customer might call back about.
For several of the companies in our study, looking at just customer recontacts that occur within a seven-day window, a conservative estimate would suggest a more than $3 million cost-savings opportunity (assuming $8 per contact which is about average for B2C companies). And, as previously discussed, that’s a significant number of customers experiencing the highest of effort drivers—customers who leave those situations less likely to renew, less likely to spend more and highly likely to spread negative word of mouth about the company to friends, family members, colleagues, etc.
Interested in understanding what’s causing customer recontacts? Contact us and let us take a look at your call data and propose an action plan for eliminating this effort driver from your customer experience.
The Effortless Experience team at CEB that I mentioned earlier is now part of a standalone company named Challenger. This team is a great resource for anyone looking to learn more about the Effortless Experience research—and how to develop effort-reduction skills at the frontline. They work with organizations around the world on this sort of stuff every day and have a wealth of experience and insight to share.
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