Good customer service is an essential driver for increasing brand loyalty. Brand loyalty is when consumers remain customers, even when there are other less expensive, more convenient, or higher quality alternatives available. Organizations commonly focus on exceeding expectations or “delighting” the customer and measure their performance through CSAT surveys.
However, research from the Harvard Business Review (HBR) finds that reducing customer effort, rather than delighting customers builds loyalty. How easy is it for your customers to work with you to get a question answered or resolve an issue?
Low customer effort correlated with 94% intent to repurchase, and 88% intent to increase their spending.
In contrast, high customer effort correlated with 81% intention to spread negative word-of-mouth.
We know this intuitively. People are far more likely to talk negatively about a brand than positively. Negative word-of-mouth has more impact than positive.
The critical solution, then, is for companies and brands is to avoid a bad experience. To do this, you must ensure that the experience is above a certain threshold.
Here is data on what constitutes high perceived customer effort:
Let’s look at this from the perspective of the customer named Carla. She recently purchased a home appliance from a DTC company online.
Within a month of purchase, she faces an issue while changing the device setting, so she sends an email to the customer support team. The first agent asks her a few questions and then transfers her to another team member. The second agent responds that he needs more information and requests a call.
Carla is already dissatisfied by now but sets up a time to discuss the issue. On the call, the second agent asks Carla to explain the problem all over again. Carla explains the issue, and the agent notes down the points and says that he will connect her with the engineering team and get back.
A few days go by, and there’s no response yet. Carla has to send another follow-up email. The second agent then responds with the steps to fix the issue. Carla follows the instructions, and this resolves her problem.
A successful resolution, yes, but due to the level of effort Carla had to exert, she wasn’t satisfied with the experience.
Let’s go back to Carla’s example and see how to improve the interaction and measure the improvement.
If the first agent assigned to the conversation could deal with Carla’s issue, that would solve one problem. Resolving the entire issue over the first communication channel, which was email, would be another good step. And if there were no delays in responding, that would again reduce customer effort. Instead of relying on surveys to measure CES, here are some specific metrics to track that can have a significant cumulative impact on customer satisfaction. In our study, the net change in satisfaction was more than 10% when agents improved the following criteria.
Customers may be patient, but they are less likely to feel satisfied with their experience the longer they have to wait for a response. From our research, we have seen a measurable delta in the CSAT score if the time to respond was within 1 to 4 hours versus >2 days. There was an even larger gap in CSAT scores when subsequent follow-ups were within 24 hours compared to interactions that extended beyond a day.
We noticed that if the number of back and forth interactions was fewer than 6, it led to an average CSAT improvement of 5% compared to conversations with more than ten interactions. Interestingly, we did not see any correlation between CSAT and FCR (first customer resolution). Proper troubleshooting and a successful resolution were more predictive of a satisfied customer than an immediate, error-prone response.
How long does it take to resolve customer issues? We saw that if the resolution time was more than a week, customers were more likely to have lower CSAT Scores.
These above metrics can be used in combination to provide a new automated score to measure effort, instead of conducting another survey.
In addition to reducing the customer’s effort, it’s still important to make the customer feel like they’re not interacting with a bot and appreciated.
What gets measured gets managed. — Peter Drucker
We have previously talked about the benefits of 100% unbiased coverage for analyzing CS performance. At Emtropy, we use powerful machine learning models to provide scalable and timely coverage of tickets and enable more accurate, valuable insights to agents.