During the past four years, customer service professionals have begun looking closely at the concept of customer effort. Starting in 2010, a series of articles in The Harvard Business Review discussed the idea of—and need for—a customer effort score. At heart, the research showed that customers cared far less about being constantly delighted than they did about companies getting the basics right. If companies could provide accurate information and resolve problems quickly, efficiently, and with little friction, then customers would feel they had received good customer service. They would also provide all the "warm and fuzzy" benefits that companies desire, such as increased customer loyalty.
Since that research hit, many customer service managers have built survey functionality to capture this metric. They typically base scores on the answer to a single question. Although the wording of the question varies from company to company, it typically focuses on how much effort the customer personally had to expend to get his or her request resolved.
But simply asking customers about the effort they expended does not give companies actionable information on the specific sources of friction that cause higher effort. The same principle broadly applies to customer satisfaction. Companies need to know what drives or hinders it. If you want to get a real understanding of customer effort and customer satisfaction levels and their sources, you need to combine explicit and inferred metrics.
Start with the explicit metrics. Surveying customers immediately following your interactions with them captures the their mood and allows you to follow up with unhappy customers. For example, you can create an outbound communication program that targets customers who gave low ratings for their satisfaction; the program could attempt to discern the cause of the dissatisfaction or the friction at the point of service.
But don't ignore inferred metrics. Customer service execs can also infer customer effort by looking at system-provided data. You can see how often customers were transferred from one agent to another, whether the customer had to escalate from one channel to another, and whether the customer needed to repeat information he had already provided. This inferred method allows brands to focus their improvement efforts on these base causes of higher effort and lower satisfaction. Then you can use these metrics to develop hypotheses about what causes friction and test those hypotheses by comparing predicted outcomes with the data from the customer surveys. When those hypotheses prove true, fix the identified problems. When those hypotheses do not pan out, adjust the hypotheses and compare the next batch of interactions to the explicit metrics.
It is only by combining these explicit and inferred metrics that you'll get both a picture of your customers' perceptions of effort as well a headstart on identifying the specific causes of the friction customers feel. Then, you'll actually have a chance to start smoothing that friction and move your company toward being easy to do business with.
Don't neglect the human factor. Good customer service is the result of good technology, good customer service processes, and, most important, a well-managed organization that values its employees. Pay attention to the human factors, such as training, compensation, authority to make decisions, and career planning, to ensure that your agents deliver to expectations. After all, your agents are the key to your success. If, for example, your agents feel like they are fighting their tools instead of having the tools empower them to do their job, then your customers are likely going to feel that they too must fight to get their issues resolved. High agent effort will drive high customer effort.
Ian Jacobs is a senior analyst at Forrester Research.