In many areas of management it is clear what to measure and what action to take when things go wrong. For example, if a product comes out of a manufacturing process too cold you need to increase the temperature. In contrast, customer satisfaction is very hard to measure. Customer service is subjective and is only one part of the consumer’s overall experience with a company. There are many variables that can affect how people perceive customer service and no single measurement of what makes it good.

Organizations need to measure customer service in order to see the effect of management actions. As the saying goes, “you can’t manage what you don’t measure” — if contact centers aren’t measuring customer satisfaction levels problems won’t be identified and will escalate. Ultimately, customers will leave.

Measure what?

There has been lots of academic and market research to try and decide how and what to measure. Enterprises attempt to measure the customer experience through focus groups, post call surveys, and quality scores.

Within the contact center environment there are many operational metrics that are measured such as first call resolution (FCR) and average handle time (AHT). But it’s very unclear how these relate to traditional customer satisfaction measures.

Net Promoter Score?

Traditionally, businesses have used broad consumer attitude measures such as the Net Promoter Score (NPS). NPS is a customer loyalty metric based on a simple question: How likely are you to recommend our company/product/service to your friends and colleagues?

To achieve a good NPS it was considered that you needed to “delight” your customer with excellent customer service. For some time, NPS was viewed as the only measure that mattered because it summarized consumers’ attitudes to a company’s brand, product and service in one metric. The trouble is NPS is not very diagnostic. If you have a poor NPS it isn’t clear what to do about it.

In 2010, Dixon, Freeman and Toman (DFT) published an influential paper in the Harvard Business Review, titled Stop Trying to Delight Your Customers. They pointed out that excellent customer service only contributes a small amount to increased customer loyalty but that poor customer service contributes disproportionately to the reasons why customers defect. As a consequence, it is much more important to solve customers’ problems than it is to exceed customers’ expectations.

Customer Effort Score?

DFT proposed a Customer Effort Score (CES) based on an intuitive question: How much effort did you personally have to put forth to handle your request on a simple five point scale? In addition, they proposed tracking the number of repeat calls in a 7-14 day window instead of tracking FCR. This shifts the measurement away from the agent’s opinion about whether the consumer’s problem was resolved to the consumer’s actual behavior. If the consumer is not satisfied they will call again. This also creates an incentive for the organization to try to predict what problems the consumer might run into and to provide additional advice in the first call to prevent repeat calls in the following 1-2 week period.

Emotional reaction?

More recent studies have supported the general concept of CES, but have pointed out that the consumer’s emotional reaction to the start and end of calls is also important, as are the absence or presence of “Oh No!” and “Wow!” moments where the agent’s response is a lot worse or much better than the customer expected. These are typically assessed by sampling call recordings after the event.

eGain provides a consistent way of capturing operational metrics such as CES, sample call assessments and post call survey results. The collection of this data allows for a systematic approach to the management of customer service from training agents, to the design of scripts for calls, to the overall resources needed to handle call volumes.

With this approach, organizations can understand where consumers are having difficulties and redesign their products, operational processes, and resources to ensure that consumers can resolve problems quickly and effortlessly. This is the foundation of good customer experience management.

Organizations can take this further. By analyzing what makes consumers happy or unhappy, enterprises can create a customer satisfaction model. This could connect the data about the individual’s experience to operational metrics and give a more direct measure of customer experience. After each customer interaction, agents could automatically be scored based on how likely the customer is to be satisfied or not.

Real-time decision making

This becomes even more powerful when decision making is put in the hands of business users. With historic and real-time event handling, eGain allows users to see what has been happening and what is happening in call queues and make changes to contact center systems there and then. This means managers can move agents from queues that are busy to those that are under capacity, as and when they are needed. They can change the hours of operation, for example, on a busy day when lots of orders are being taken. They can also make changes to IVR applications.

Using eGain, managers can decide where to route calls, how to reduce bottlenecks, when to redirect to another team and whether contact center capacity should be increased or decreased. By keeping call flows as rapid and efficient as possible, organizations can improve agent productivity as well as customer service.

eGain’s Analytics platform allows organizations to measure operational metrics, set goals and thresholds and take action to re-assign resources. This ensures contact centers are running at maximum efficiency and are equipped with a true understanding of what makes their customers’ happy, helping to improve sales, loyalty, and customer retention at the same time.