Reducing customer effort has become a major focus for customer experience professionals in recent years after “The Effortless Experience” (co-authored by our own Chief Product & Research Officer, Matt Dixon) was published in 2013. The ideas and research shared in the book assert that the most effective way to drive customer loyalty and satisfaction is by creating an experience for customers that requires minimal effort. Since that research was published, many organizations have implemented training programs to better enable their contact center agents to deliver low effort experiences to the customers they interact with.
Survey dependency is counterproductive
When we talk to companies that have prioritized reducing customer effort in their contact center, they tell us that they love the book and believe in the approach, but also that they have trouble proving that their strategies are working. They can send out surveys that ask customers to rate their level of effort and track that aggregate metric over time, but we argue that this data is biased and incomplete. Not to mention that it’s a little counterproductive to ask a customer to exert effort to fill out a survey in the name of reducing customer effort.
Today, most organizations implementing strategies to reduce customer effort simply coach the behaviors that research suggests reduce effort, and wait to see if their effort survey results change after the coaching. This method leaves these customer care teams with very little insight into how this coaching is being understood and implemented by agents. Additionally, it ignores the context surrounding instances of high or low effort and fails to directly correlate particular coached behaviors to the desired outcome.
Effort can be recognized in real-time
At Tethr, we believe that in order to build a successful strategy for reducing customer effort, you need to have access to real-time, micro-level data on effort-related occurrences in customer interactions. In order to provide customer experience teams with this level of granularity, we developed a research-backed Effort Library that has taught our AI-powered voice analytics platform how to listen for effort-related utterances that occur in calls. These include things like when a customer mentions they already tried to solve their problem through another channel, when a customer expresses frustration and when agents convey uncertainty in how to resolve the customer’s issue.
A major North American insurance company is working with Tethr to measure how their effort reducing strategies are being adopted by contact center agents and how those strategies are affecting key metrics. In one coaching sprint, they were training agents to do two things:
- Increase Advocacy language: Agents taking ownership of the customer’s problem and using proactive language to assure the customer that their problem will be solved
- Reduce Powerless to Help language: Agents indicating that they don’t have the power to resolve the customer’s issue
With Tethr’s Effort Library, this company was able to quickly and easily set up reports that tracked every time these agent behaviors occurred in customer calls. With this data, they were able to pinpoint which agents were struggling to adopt these new techniques and provide them with extra coaching, as well as measure the change in behavior before and after the coaching sprint. This insight alone went a long way in improving the efficacy of this company’s coaching program, but it provides them many more opportunities as well.
Solid insights yield real results
This team can now do further analyses to better understand the context surrounding these behaviors. For instance, they can look at a report of all the calls where agents use Powerless to Help language broken down by the reason for call. They may find a broader issue that needs addressing or identify holes in their coaching program.
With Tethr and the Effort Library, this team can now also correlate instances of Effort-related behaviors to KPIs to understand exactly how each effort-reducing tactic employed by agents impacts key metrics. In particular, this insurance company correlated use of advocacy language to two proprietary KPIs that measure how well agents are reducing customer effort and resolving issues to show that agents with high use of advocacy language deliver scores in both metrics that are more than 10 percent better than those of the average agent. Additionally, they were able to report an improvement of more than 160 percent in their Effort KPI in 2018 as they rolled out this and other effort reducing strategies.
This company has gone on to use Tethr to successfully launch many more programs for reducing customer effort that have made a significant impact to their overall customer experience. If you want to learn more about what they’ve been able to accomplish, contact us!