The right way to measure customer experience

Originally published with the Process Excellence Network, in this article author and customer experience speciallist Andrew Spanyi discusses the best way to measure customer experience. 

If you want to capture competitive advantage, conventional wisdom advises that customer experience (CX) is key: there is a strong correlation between a positive customer experience and customer loyalty. Happy, loyal customers buy more. Equally important is that happy employees make for happy customers. So what's the problem?

Many organizations still struggle when it comes to measuring the return on investment (ROI) on work to improve customer experience. Focusing on metrics - such as cross-sell, upsell, or cost of sale or campaign response rate — does very little to measure customer benefit. Some organizations begin by measuring customer satisfaction and churn. While that may be a good start, it provides little direct correlation to ROI. Measuring Net Promoter Score (NPS) is becoming increasingly popular. For example, the Temkin Group conducted research to measure the NPS of more than 340 companies based on a survey of 10,000 U.S. consumers. While NPS can provide insight (and a baseline) by itself, it is not sufficient to estimate the ROI on CX.

It’s important to understand that customer experience revolves around the quality of all a customer’s encounters with a company’s products, services, and even the brand. That begins with the quality of the customer’s experience in accessing information on the Web, followed by the ordering experience, and includes the payment experience, the delivery experience, the warranty experience (if applicable) and the inquiry/complaints experience. An effective effort at measuring the ROI on CX therefore has to encompass each of these key touchpoints or moments of truth.

As with many measurement systems, it’s important to begin with a set of baseline metrics such as displayed in the table below.

These baseline metrics facilitate alignment around a common set of CX metrics – and in turn can help build a clear, consistent vocabulary. If properly communicated, the set of baseline metrics and the discussion of progress on each can facilitate discussion of CX by management and employees alike.

While typical measurement examples of 'overall customer satisfaction' and 'likelihood to repurchase' can be useful, note that the blend of survey- and activity-oriented metrics on the table above provide much more tactical guidance and drive day-to-day business decisions, such as when faced with decisions on where to deploy project resources, for example. Would it be better to focus on improving perfect order delivery (on time, complete, defect-free), improving the ease of use and navigation of the company Web site, or improving first time right responses to customer complaints? This is when the baseline metrics can be leveraged through predictive analytics and qualitative input from customer-facing employees.

That’s because quantitative analysis starts with recognizing a problem or decision and beginning to solve it – often called framing. Once the business objective is framed and well understood, the baseline data can help clearly define a specific prediction objective such as; 'Which current customers with a history of at least one year of purchasing will likely increase their purchase volume if we improve our perfect order delivery performance by 10 per cent?'

A solid set of CX baseline data can help avoid common mistakes in using predictive analytics such as relying on buzzwords, focusing too much on software, or number crunching.

Data silos and departmental silos stand in the way of effective CX measurement. So, an increasing number of companies are using small, cross-functional, agile teams to improve measurement and manage performance at key customer touchpoints. Such teams typically recognize that the key to significant improvements in CX relies on the improvement of customer touching core processes. These teams can roll out small experiments that rapidly produce measurable outcomes. An experiment that doesn’t promptly generate customer enthusiasm can be discarded, while those with the most potential can be harvested.

So how do you know if all this attention to baseline metrics, business process and predictive analytics technology are working and paying off? The answer is to be found in the business results that are generated by improving the quality of a customer’s encounters with a company’s products and services.