Measuring the Drivers of the Customer Experience

As we described at the beginning of this chapter, there has been no shortage of interest in establishing measurable links between human capital and customer behavior. Data is plentiful. Some of the data is subjective, dealing with the feelings, impressions, and private evaluations experienced at various moments of truth. Other data is more objective; it is the actual record of customer transactions, their frequency, and their value.

Many early attempts to link human capital data to customer data involved subjective reports from employees and customers. Benjamin Schneider and David E. Bowen reported a breakthrough study of this sort in 1985.^[9] Those researchers found a significant correlation between employees’ reports of their satisfaction and customers’ reports of their satisfaction in bank branches. The next generation of research took this a step further, as illustrated by the work of Rucci and his colleagues at Sears. In that work employee attitudes measured through surveys were found to be related to customer satisfaction, also as indicated by survey responses. The next step was to relate customers’ satisfaction to a measure of their behavior: spending. Here customer attitudes predicted store revenue. Thus, the employee-customer-profit “chain” was born. Although there was no direct link between employee attitudes and store revenue, the chain was put together by virtue of the data that linked customer satisfaction to both.

A third wave of linking human capital to customer behavior is in progress. It involves reading the objective record of workforce attributes and practices and assessing their impact on customer behavior. This, of course, involves the application of Business Impact Modeling (see Chapter 6) in the service of understanding the drivers of customer behavior and experience.

First Tennessee National Corporation engaged in this type of analysis, as we reported in Chapter 3. That case pointed out the substantial contribution of one workforce attribute—years of service—to both revenues from current customers and the acquisition of new customers. Among all bank employees, customers responded better to those who had more years of service with the bank. The way the bank managed its human capital also mattered to customers. For example, pay practices that emphasized individual performance, as evidenced by greater differences in base pay and base pay growth among employees in the same unit, were counterproductive. Those pay practices contributed to declines in revenue per employee by disrupting the delivery of effective customer service in that environment, which was team-based. Incentives that emphasized the individual over the team got in the way of effective team functioning. That effect could play out in many subtle ways, such as not sharing information with coworkers, not covering for them when they took breaks, intercepting customers, and not collaborating when necessary to meet a customer’s needs.

Business Impact Modeling is well suited to understanding the drivers of customer experiences and behavior. A fact-based, data-hungry approach, it can take advantage of the wealth of employee and customer data maintained by organizations. It combines easily with data on both employee attitudes and customer satisfaction to create more complete assessments of the causes and consequences of the customer experience. Most important, Business Impact Modeling helps an organization identify the most important human capital attributes and practices in its system and set priorities for action.

^[9]Benjamin Schneider and David E. Bowen, “New Services Design, Development, and Implementation and the Employee.” In W. R. George and C. Marshall, eds., New Services. Chicago: The American Marketing Association, 1985, 82–101.