Reducing customer effort can feel like a monumental task, especially for big companies with legacy business processes, policies, systems, or companies in regulated industries. But these first three steps will get CX and customer care leaders started quickly and effectively.
The most frequent question I get after presenting the research that went into The Effortless Experience is, “where do we start?”
Admittedly, listening to the Effortless presentation or reading the book can feel a bit overwhelming. When I present the research, I talk about the four “pillars” of being a low-effort company. And those who’ve read the book know that there are, literally, dozens of best practices and tactics we profile from low-effort organizations.
So, deciding where to start can be a little daunting.
Having been asked this question for years now, I’ve gotten some practice in answering it. I’ve also gained some valuable perspective from companies who’ve gone down the effort-reduction path themselves.
Here are the three things I recommend companies do first. And for each, I’ve offered some DIY advice as well as some perspective on how Tethr can help:
- Understand where your customers’ effort is coming from
- Find out what’s making the job hard for your employees
- Immediately start training and coaching customer service reps on experience engineering techniques
1. Understand where your customers’ effort is coming from
While this may seem pretty obvious, it’s surprising how often companies skip it or don’t consider measurement a critical step in getting started. As the old business adage goes, “you can’t manage what you can’t measure,” and the same is true of effort.
Without some objective way to understand where effort is coming from, you’ll be at a loss of how to fix it. Without an objective way to understand where effort is coming from, you’ll be at a loss of how to fix it. What ends up happening without measurement is that leaders focus on “squeaky wheel” issues, which may not happen often enough to warrant focused energy and investment. Meanwhile, the squeaky wheels are causing high effort for customers.
So how do we measure customer effort?
When we first wrote about the idea of customer effort in 2010 in the HBR article “Stop Trying to Delight Your Customers,” we introduced the Customer Effort Score. CES was a simple question designed for use in a post-call survey. We found this question was a great way to understand the level of effort a customer experienced in the service channel.
Later, in the book, we released an updated version of the question, which we dubbed Customer Effort Score 2.0. To this day, many organizations around the world rely on CES as a “disloyalty detector.” CES enables companies to spot high-effort moments in the experience, at-risk customers and size effort-reduction opportunities.
In recent years, we’ve seen post-call surveys’ effectiveness start to wane as customers don’t complete them with the frequency they once did. We hear in the field that survey response rates for the typical company begin to dip into the 10-15% range on a good day.
So, there’s a sample-size issue (which raises doubt whether the respondents’ sample is representative). Post-call surveys are notorious for the so-called “extreme response bias,” where responses tend to come from customers whose experiences are extremely good or extremely bad).
Accounting for a lack of actionable insight in your VoC data and your customer effort scoring
But, what’s more, there’s a real lack of actionability in the survey data companies do collect. If only 10-15% of customers fill out the post-call survey, what percentage actually take the time to say why they gave the scores they did? Leaders are left wringing their hands about whether the survey data is representative… and scratching their heads trying to make sense of it.
At Tethr, we’ve developed a machine-learning-based effort score called the Tethr Effort Index (TEI). TEI takes unstructured customer conversational data (recorded phone calls, text message exchanges, customer support case emails, voice of customer data in case management systems, for example) and scores it for its level-of-effort based on more than 250 variables.
Think of it as a machine predicting the score a customer would have given on a post-call survey, but on every single customer service interaction and without your customers having to fill out a survey. So, no more small samples or bias. And, best of all, the score is tied to the actual customer conversational data itself, so no more wondering why a customer gave you the score they did.
2. Find out what’s making the job hard for your employees
I’m always quick to point out to leaders that it’s hard to be an easy company to do business with for your customers if you’re a hard company to work for.
Put differently, being a low-effort company starts at home. Start by uncovering the things that are getting in your agents’ way of providing a low-effort experience to your customers. There are many ways you could be getting in the way of your own effortlessness, like your company’s outdated policies, broken processes, antiquated systems or tools, misaligned incentives, flawed QA scorecards or managers who don’t know how to coach effectively.
In the book, we profiled Ameriprise’s “Capture the No’s” campaign. They offered companies a simple approach: find out what’s getting in the way of your reps’ delivery of a low-effort customer experience. We asked agents to jot down every time they had to say “no” to a customer — and why. Management then rolled up those results to identify systemic issues that were getting in their reps’ way.
At Tethr, we analyze both sides of the conversation (the rep and the customer). That makes us uniquely positioned to help leaders identify these obstacles preventing service reps from delivering a low-effort customer experience.
For instance, our “powerless to help” machine-learning category captures how reps hide behind company policy — effectively providing an automated version of the Ameriprise “capture the no’s” practice. And where we pick up customer care agent confusion or uncertainty, those are prime opportunities for investing in training and coaching or perhaps beefing-up knowledge resources for agents.
3. Immediately start training and coaching customer service reps on experience engineering techniques
One of the most surprising findings in the research we did at CEB (now Gartner), was that in our customers’ eyes — effort is only one-third a function of the steps the customer needs to take. For example, calling your company back multiple times, switching channels, enduring transfers, having to tell their stories over and over again. But effort is two-thirds a function of how the customer feels about what they had to do. Put another way, effort is ⅓ “do” and ⅔ “feel.”
What does this mean for service leaders? Simply put, it means that our frontline staff has a tremendous opportunity to influence the way customers perceive the experience.
During our research, we tested several language techniques like advocacy, positive language and anchoring. We found that specific language techniques can dramatically reduce the level-of-effort customers report feeling in their service experiences. These language techniques positively impact the point of resolution or what the customer got at the end of the interaction.
In the book, we profile several companies (like LoyaltyOne) that have figured out that language can be a true difference-maker in providing an effortless customer experience. Those companies invested time and energy in training and coaching frontline employees on these language techniques that can reduce effort for customers and equip reps to take control of even the most difficult customer conversations.
This sort of behavior change can be a time-consuming and resource-intensive process. And what makes behavior change particularly challenging is that organizations don’t have a good handle on which customer service reps are using language that reduces effort (like advocacy) versus using language that increases effort (like powerless-to-help language).
The reason they don’t have a good sense of this is that most companies still rely on manual QA processes that, at best, only assess one percent of call volume.
We’ve trained our machine-learning platform to identify when and where contact center agents are using experience engineering techniques. And Tethr’s trained to spot when and where customer care reps are using language that increases the customer service interaction’s effort.
Unlike traditional QA, we’re able to do this at scale, across 100% of customer interactions. We’ve recently unveiled our Agent Impact Score. AIS IDs all agent behaviors that directly impact customer effort and presents it as a single score on any individual customer interaction. This single metric allows managers to see whether customer service reps are making things better or worse for the customer (from an effort standpoint) by the way they’re handling issues.
Reducing effort can feel like a monumental task, especially for big companies with legacy processes, policies, systems, or companies in regulated industries. But these first three steps will get CX and customer care leaders headed down the right path, quickly and effectively.
Want to learn more about how Tethr can help your organization identify and reduce high-effort conversations? Request a demo here.