Case Illustration: MoneyCo

Let’s examine some outputs from the ILM models and see how the results from ILM analysis come together to shed light on an organization’s internal labor market. We’ll use the case of an organization we call MoneyCo, a financial services organization with operations in several regions of the United States. Its core business serves a specialized segment of the financial market. Many of its competitors offer almost identical products and services.

MoneyCo competes on service quality and to some extent on price. Financial results indicated that MoneyCo was not faring well on either dimension. However, upon its acquisition by a larger regional bank, MoneyCo sought to expand its range of services and exploit linkages with its parent company, including opportunities for cross-selling. This represented a significant shift in its business strategy.

Difficult times had forced MoneyCo to go through several rounds of layoffs, a shakeup in the senior management team, and reorganization. Those dislocations had produced an unstable workforce and weakened its management system. The chief executive officer (CEO) recognized these problems and knew that MoneyCo had to tend to its human capital if it hoped to succeed.

The executive team agreed that certain human capital requirements were paramount. To avail themselves of cross-selling opportunities, they would need a workforce that could play more of an advisory than a purely selling role. That workforce had to be customer-focused and equipped with excellent relationship-building skills. In addition, customer-facing employees would need expertise in a full range of products and services, both those of their own company and those of their parent, and have the perceptiveness and discipline to match them to customer needs. This combination of capabilities could neither be created overnight nor “bought.” It represented firm-specific human capital that could only be built from within. Finally, the company would need to expand its workforce’s ability to support a broadening of its product portfolio.

An ILM analysis was conducted to determine whether MoneyCo’s internal labor market as it was managed currently would produce the workforce needed to achieve the company’s objectives. Some key results of our analysis of rewards at MoneyCo are summarized in Figure 5-3.

Figure 5-3: MoneyCo’s Rewards 2003, Mercer Human Resource Consulting LLC

The grid shown in the figure—a key output of ILM Analysis—represents the combined results of statistical modeling of the drivers of (1) promotion, (2) year-to-year pay growth, and (3) pay levels at MoneyCo. It encapsulates what the organization actually rewards. It identifies the factors (individual, organizational, and environmental) associated with individual success in a particular company. At the individual level it is a success profile. Viewed from the organizational perspective, it is something of a culture map, representing the characteristics that the organization most values in its employees as evidenced by actual reward patterns. Yes, there is more to organizational culture than rewards, but the grid provides insight into aspects of an organization that strongly influence its culture.

Here is how to read this rewards grid. Promotion likelihood is on the horizontal axis; annual pay growth is on the vertical axis. Italicized factors are associated with higher pay levels. The center of the grid, where the lines cross, is the origin. Factors near the origin add nothing to the probability that an employee will be promoted in the next year and do not influence pay increases.

Consider these examples. In the upper-right-hand corner one sees “higher performance ratings” in italics. This indicates that all else being equal, an individual with a higher performance rating is more likely to be promoted, is experiencing larger pay growth, and tends to be more highly paid overall. In other words, when one compares like people in like jobs and like locations, those rated higher tend to do better across all the reward dimensions than do their lower-rated counterparts. Thus, MoneyCo’s performance management and rewards systems clearly differentiated employees according to individual performance. Because of the way work was structured at MoneyCo, that seemed to be a good thing. It would encourage high performers without obstructing the cooperation required for cross-selling and referral activity. And it would encourage high performers to stay.

Now let’s look at the elements in the center of the grid. Note that “education” is located there, in italics. This means that education contributed positively to pay levels. That finding is not surprising. Employers typically recognize the increase in human capital that arises from an increase in education and reward it with higher pay, and MoneyCo was no exception. To hire someone with a higher degree, it had to pay more. Note, however, that education contributed nothing to the other components of rewards. All else being equal, annual pay growth was not higher for the more educated employees, and neither was the likelihood of promotion. In other words, once employed, those with higher degrees were not doing any better than were their less-educated counterparts.

There are two possible interpretations of this finding. The first suggests inappropriate matching of workforce capabilities to company needs. Perhaps formal education did not contribute incremental value to the firm even though it increased the market value of the individual employee. Because of the nature of this business, other factors not associated with educational attainment may have outweighed education: people skills, experience, selling skills, even street smarts. We’ve encountered this phenomenon many times before. The second explanation is that the current rewards and performance management systems were failing to recognize the real value attributable to education. Either because of the way more educated employees were utilized or because of the failure of supervisors to evaluate performance properly, the more educated people were not getting their due. How could the company expect to keep those people if it failed to value them?

The CEO of MoneyCo didn’t care which of these explanations was accurate. He wanted a more educated workforce. He and his team were convinced that the ability of the workforce to take on an advisory role, match products and customers, and build productive relationships with the parent company was enhanced by education. “Yes, perhaps it was true in the past that an individual’s performance had little to do with what degree that person had,” he said. “But that won’t be the case under our new business model. We really need more educated employees. I want to see education take its place alongside individual performance as something we value in this company. Let’s make this happen!”

A final observation concerns MoneyCo’s rewards grid. Note that length of service (tenure) is in the lower left corner. Simply put, it had a negative impact on all the dimensions of rewards we measured. A negative relationship with pay growth and promotion came as no surprise. It is known from labor economics that although pay typically grows over an employee’s work life, its rate of growth begins to decline on average when the individual reaches his or her late thirties. Tenure is not the same as age, and depending on the organization’s human capital strategy and the way it structures rewards, the observed relationship between pay growth and tenure can vary. In most cases, however, it follows a trajectory similar to that of the pay-age relationship. That is what we have observed in the vast majority of organizations for which we have done this kind of work.

This negative relationship between length of service and pay levels is rare. It usually occurs when companies hire aggressively in tight labor markets, something MoneyCo was doing. Those companies pay such a high premium to new entrants that they end up devaluing their incumbent employees. The “return to tenure,” as it is called, declines, sometimes even turning negative. In these cases an additional year of service in the company is worth less than a year working outside it. Apparently, employees at MoneyCo caught on to this.

Greater clarity about this problem emerged when we looked at the results from the analysis of turnover. The results, based on drivers of turnover over a five-year period, are depicted in the bar chart shown in Figure 5-4 where stronger drivers have longer bars.

Figure 5-4: Turnover Drivers At MoneyCo

One thing to note right away is that MoneyCo was extremely vulnerable to conditions in the external labor market. A one-point change in unemployment rates in its geographic areas of operation was associated with at least a four-point change in annual turnover, all else being equal. This was high by any standard, higher than what we’ve seen in most organizations for which we’ve estimated this relationship. This was the case for two reasons. One, already discussed, is that the value of employment at MoneyCo was at or below market alternatives, and so employees were quick to leave as outside opportunities increased. The other is that there was little or no “backloading” of rewards at MoneyCo, no glue to bind employees to the company for the long term. Many organizations backload rewards by tying certain benefits to length of service. In others backloading is achieved through the carrot of valuable advancement opportunities. The mere prospect of significant financial rewards—if they are credible—encourages employees to forgo other opportunities and stay with the organization. Neither of these incentives was at work within MoneyCo.

This pattern would not have been a problem if the company’s business strategy required mostly general human capital, but it didn’t. Its strategy depended on firm-specific knowledge and experience, which was undermined by MoneyCo’s exceptional vulnerability to labor market forces. That vulnerability was reinforced by what we learned about the retention effects of compensation at the company compared with longer-term career rewards.

It can be seen in the bar chart that MoneyCo’s employees were highly responsive to short-term incentive compensation. Employees who received it were about half as likely to leave the company as those who did not, all else being equal. They clearly responded to money. Employees also were responsive to promotion and the trajectory of pay.

At first blush that might suggest that MoneyCo employees had a strong career orientation after all. A deeper look, however, cast doubt on that interpretation. The effect of promotion was shown to dissipate very quickly. Only a promotion within the year reduced the likelihood of turnover, and the same thing was true of past pay increases. Employees seemed to respond only to the most recent pay actions, not to how they were faring over the longer haul. It seemed as if employees looked on promotion not as a meaningful career event but simply as MoneyCo’s mechanism for delivering more money.

The company had established a “show me the money” culture, and that had created a serious danger. Unless MoneyCo’s financial performance improved quickly, it would be unable to enhance its pay position relative to the market for the incumbent workforce. New hires, whose pay levels better reflected market rates, would continue to outpace longer-term employees, eroding the value of service with the company. If that pattern held, how could the company retain its seasoned, high-performing employees? How would it develop the firm-specific skills that its business strategy required?

The turnover drivers chart shows that those with more years of service were more likely to stay, a behavior we have observed in most companies we have analyzed. However, while directionally correct, the effect was notably small and disappeared after three years with the company.

The analysis confirmed another critical vulnerability: Employees with only a high school education were significantly more likely to stay than were similarly situated employees with a college degree. The more educated employees deemed essential to the new business strategy were walking out. This was by no means a problem unique to MoneyCo: The educated generally have more opportunities and often are more mobile. However, some organizations are able to retain them more readily than others can. The way they utilize and reward those employees is often the key. In light of what we learned about rewards at MoneyCo, was it any wonder that they were leaving at significantly higher rates?

The findings we have revealed here paint a dismal picture, but not everything in MoneyCo’s human capital system was misaligned. The rewards and performance management systems appeared to differentiate well between high performers and low performers. Voluntary turnover was much higher among low performers than among those who performed well. Employees with the industry experience needed to enhance MoneyCo’s product/service portfolio and expand its customer base were both rewarded and retained. Also, the company had avoided reward disparities for women and minorities, an outcome that supported management’s diversity goals.

Still, the ILM analysis revealed that MoneyCo’s human capital strategy was not fully aligned with its business needs and market environment. That analysis helped the CEO and his team get a handle on the company’s internal labor market, both where it was and where it was heading. Because it quantified the critical dimensions of the workforce situation, management could more easily prioritize its agenda for change. On that basis, MoneyCo developed a new human capital strategy that aimed to achieve the following:

• Reduce its vulnerability to external labor markets

• Restore a credible career structure

• Align rewards, performance management, and supervisory practices with new human capital priorities

• Adjust recruitment and selection criteria to better match the required workforce profile

• Improve retention among employees with critical experience and skills

The tactics used to advance MoneyCo’s agenda were selected on the basis of modeling results that allowed the company to prioritize actions and forecast effects. The ILM analysis also positioned the company to create a scorecard of metrics for tracking changes in key components of its internal labor market, assuring accountability for results. Those actions set the company on the road toward building the workforce it needed.