How to use Tethr to improve your Customer Effort Score
So your organization set a goal around improving your Customer Effort Score, now what? Without knowing where or why effort is occurring in customer interactions, where do you start? How do you know what to change to produce a better outcome?
At Tethr, we help customers address this problem every day, so we wanted to outline the common steps customers take when using Tethr to get started reducing customer effort and improving Customer Effort Scores.
Step #1 – Use the Tethr Effort Library to identify instances of effort in customer interactions
The Tethr Effort Library was built based on the research from The Effortless Experience (authored by Tethr’s Chief Product and Research Officer Matt Dixon) to automatically identify where effort occurs in customer phone calls and chats. With the Effort Library, the Tethr platform is able to look at thousands of different ways customers can express various high-effort experiences like frustration, recontact, or channel switching, and categorize those occurrences into structured data points (At Tethr we refer to these structured data points as categories). The Effort Library also enables the Tethr platform to identify agent behaviors that are known to either contribute to or mitigate customer effort.
Once the Effort Library has been applied to a significant set of your recorded customer interactions, you will have a good sense of where effort is occurring today and will be able to get started on making improvements. For instance, you may find that there’s a significant number of calls where customers express that they had to contact customer support after trying (and failing) to solve a problem on their own on your company’s website. To address this, you may decide to partner with the team that manages your online knowledge base and see what improvements could be made to your online self-service experience to prevent those situations from happening in the future.
By applying the Effort Library to your company’s customer interactions, you gain visibility into where and how customer effort is occurring. But more importantly, you establish a foundation upon which you can run more sophisticated diagnostics to discover why customer effort is occurring.
Step #2 – Train Tethr to find and categorize other relevant customer interaction data points
Categorizing instances of customer effort is a great start. But to really have the context you need to address your customer effort score, you need to build a broader foundation of categories that represent details about interactions specific to your business. Tethr has pre-built libraries for certain types of businesses that you can apply to automatically categorize common interaction events like a customer asking to check their policy when contacting an insurance company, requesting to transfer funds when contacting a financial company, or requesting to cancel a service.
Once you’ve utilized all of Tethr’s pre-built libraries that are relevant to your business, you’ll want to use Tethr’s AI-enabled category creation capability to create custom categories. These can be anything but usually include mentions of things like product names, specific marketing offers or campaigns, competitor mentions, and any specific agent behavior your organization coaches.
With Tethr enabled to categorize all these different interaction events, you now have the foundation you need to build sophisticated reports that provide actionable insights.
Step #3 – Build reports to understand the context surrounding instances of customer effort
After applying the Effort Library and building your own set of additional categories, you will have identified where certain agent behaviors, topic mentions, instances of customer effort and more occur in an interaction. But unfortunately, you still won’t have a great sense of how those things relate to one another. With Tethr’s reporting interface, you can easily start building reports that incorporate multiple categories and other pieces of metadata to uncover key trends and relationships.
Here are some examples of questions you may try to answer when building these reports:
“In calls with where a customer expresses frustration, what’s the most common reason for call?”
“In calls where the agent expresses uncertainty, which product is mentioned most often?”
“Are agents responding to high-effort customer statements with the effort-mitigating strategies we coach?”
“Which agent behaviors are immediately followed by customer frustration?”
“Which agents are expressing uncertainty most often?”
“In calls where a customer mentions they tried to solve their issue online before calling, what’s the most common reason for call?”
When you start analyzing Effort categories in relation to each other and other categories, you start to see very clear areas for improvement that you can act on immediately to impact your Customer Effort Score.
Step #4 – Relate interaction data to Customer Effort Score survey data
Finally, it’s time to tie in the actual Customer Effort Scores from the surveys customers complete after interacting with customer service agents. We integrate this data into the Tethr platform as a “post-call outcome” (see this post for details), and it becomes available to report on just like any other piece of metadata you may have used to build reports in step three. With this final piece of the puzzle, you can start to narrow down exactly what about an interaction leads to high or low survey responses.
You may decide during this process (like many of our customers do), that measuring Customer Effort Score by surveying customers isn’t the most effective way to track and address Effort in your broader customer experience strategy. For one thing, when you ask a customer to complete a survey, you’re asking them to exert more effort (which is a little counterproductive). You also generally end up with a fairly tiny percentage of customers that actually complete the survey, which leads to skewed data that isn’t a great representation of what’s really happening.
Consequently, many companies we work with decide to use Tethr reports based on categories from the Effort Library to track quantifiable metrics around customer effort rather than use the traditional, survey-based Customer Effort Score going forward. Other companies decide to report on both survey results and interaction data to get a sense of perceived effort (what customers submit in surveys) vs actual effort (what occurs in calls and chats).
These steps represent just a small taste of what it’s like to work with Tethr to reduce your customer effort score. Our customer success team is constantly working with customers to perform deeper analyses that go even further in shining a light on the context surrounding high and low effort customer experiences.
Ready to learn more about working with Tethr to reduce customer effort? Click here to schedule a call with us.