The purpose of this article is to discuss the learning organization’s role in the process of creating an exceptionally talented workforce and present some practical approaches to valuing the human capital effects of a talented workforce.
Identify Programs That Support Improvement in Human Capital
A key exercise is to identify the key learning initiatives that support talent management and the value of human capital. For example, at PeopleSoft University (PSU), the internal training arm of the software company, the major areas of emphasis identified by senior management had to do with new-hire orientation, product and business skills training, and technical training. Therefore, PSU focused on these areas. PSU has and will continue to focus on management and leadership programs to advance talent around performance management, people management and team management.
There are tangible and intangible benefits of focusing on the right programs. For organizations like PSU, better assimilation of people into the workforce culture is critical. Higher job performance and productivity can be seen; the organization is able to recruit, develop and retain individuals; and ultimately the greatest testimonial to a focus on talent is that the organization is profitable and has a solid position relative to competitors in the marketplace for its products and services.
Valuing Human Capital in Theory
Now that we have looked briefly at talent management, we need to understand how to measure and value the outcomes of increasing talent relative to human capital.
Let’s use an analogy as an example and compare it to human capital. If an organization buys a computer for $3,000, the expectation is that the company will get $3,000 of value out of the computer. The computer may help a salesperson increase sales or help a plant floor operator increase quality, but the goal is to improve the user’s job performance through the technology. The expectation is that at least $3,000 will be gained in exchange for paying $3,000 to acquire the computer.
Compare this analysis to a person (i.e., human capital). If the fully loaded salary (wages, benefits and overtime) of a newly hired employee is $50,000, the organization paying that expense expects $50,000 of value from the employee. This value could come from the employee’s contributions in one or more key business objectives such as sales, quality, productivity, cycle time, customer satisfaction, etc. But in general, the organization expects a return of at least $50,000 from the employee.
Going back to the computer example, let’s say the IT department added a $500 upgrade to the computer. The upgrade is intended to make the machine faster, more resistant to bugs and more accurate in its processing computations. The business result is more productive employees, higher quality and reduced cycle time for a user of the computer. The expectation is that the $500 spent on the upgrade will result in at least $500 returned in various benefits.
Compare this analysis with training. We use training to create a more talented workforce just as we add components to a computer to upgrade technology. Training and organizational development are proven tools to add knowledge and skills to our workforce. So if an employee goes to a $1,000 training event over a weeklong period, the goal is that the employee will leverage the training to help achieve various business results back on the job. Such results include increased sales, quality, customer satisfaction, productivity, etc. The expectation is that the $1,000 spent on the training will result in at least $1,000 returned in various benefits.
Looking at this analogy, we can see that the baseline value of human capital is inherent in the fully loaded salaries of the workforce. To the extent that training can be used as a strategic tool to enhance the talent of the workforce, it also enhances the value of the human capital.
Measuring Human Capital in Practice
So now that we have seen how human capital can be tagged with a monetary value, the next step is to actually measure how talent-driven training programs can be linked to the increase in human capital.
First, it goes without saying that linkage to business results is critical. (See Figure 1 for a macro list of business results.) If a sales program exists, understanding how training helped increase sales would be logical. To understand the business result, isolating the change in the metrics to training (as opposed to other factors; see Figure 2 for a macro list of root cause analysis variables) is vital. Finally, adjusting your isolated result for any bias, error, confidence or conservatism is always important, especially in building credibility with stakeholders relying on the metrics for decision-making purposes.
These are some of the guiding principles in the works of Jack Phillips, Ph.D., and his ROI process, an analytical tool to measure the ROI on training relative to specific business results. Phillips’ guiding principles include elements of what he refers to as estimation, isolation and adjustment. These are the cornerstones to convert a benefit of training into a monetary figure for valuation and ROI purposes.
Estimation is a process commonly used in business today. Salespeople estimate their future sales; accounting people estimate the cost of a warranty or claim that is expected in the future. Training personnel can also analyze the job-performance impact that a training program will have. Participant estimation, as it is commonly called, does not estimate performance solely related to training, but asks participants to estimate job performance changes in general, including among other factors, training. Refer again to Figure 2 for those other factors.
For example, if an employee attends sales training, one might estimate an increase in job performance, but that increase could be related to other factors, such as a competitor going out of business, that increase sales performance more than training. So estimates of performance change need to take into account many factors, not just training. Those factors include process changes, people changes, marketplace changes, technology changes and, of course, training.
When estimating the increase, the analyst should think carefully about all the factors mentioned. They may want to review historic data and forecast data to reasonably factor into their overall performance change.
Logically, the training department is keenly interested in the effect training had on the performance improvement. The next step is to isolate the estimated increase in performance to just training. In this part of the process, the analyst should estimate how much the training has influenced or will influence job performance, relative to the other factors, and assign a value to it. So if the salesperson felt that training was the strongest factor that caused change or will be the driving force behind future change, it would receive a higher value.
Finally, because the data may be based on estimates, one must adjust any resulting metrics for this factor. Again, this is common in other facets of business. Using analyses such as “most likely,” “optimistic” and “pessimistic” adjusts estimates for bias by the estimator and flaws in assumptions. You’ll often see sales forecasts reported in this manner.
Taken together, the principles of estimation, isolation and adjustment form a powerful model in tabulating a systematic, replicable and comparable valuation model for linking training to business results. So how can it be applied to the actual valuation of human capital?
The key is to leverage estimation, isolation and adjustment to derive the change in overall job performance relative to a particular skill set. First, an estimate of the change is derived. Second, a root cause analysis is done to factor out other reasons for the change. And third, an adjustment factor is put in place. The resulting percentage is a monetary benefit factor that can be multiplied by the value of human capital (i.e., the fully loaded salary) to have a reasonable indicator of the increase in human capital from the talent-driven initiative relative to the base value of human capital.
Let’s drive it home with an example. Say key executives go to a leadership program. The intent is to drive various business results but also impact overall performance that encompasses many intangibles as well, such as increased communications, better delegation of tasks, more effective coaching and mentoring, etc. From a measurement perspective, the analyst should gather metric data from the participant, and possibly the participant’s manager, peers or subordinates, on job performance before versus after the training. It can be quantified in percentage terms. Second, you want to isolate the effects of training to the performance change. So you need to factor out those causes not tied to training. Referring to Figure 2, you can see the variables to include in your isolation analysis. Finally, adjust for error. The process is not an exact science, so be conservative.
The net percentage improvement in performance isolated to training, adjusted for bias and for time on the job, can be derived from this data-collection exercise. This is then multiplied by the base value of human capital as measured by the fully loaded salary for an individual. This represents an indicator for the hard and soft dollars returned to the organization as a result of leveraging training as a strategic tool to improve on-the-job performance.
Taken as a single number, it is not a meaningful or constructive way to leverage the metric as an indicator of human capital value. But if the metric is computed for each participant on each training initiative and then benchmarked internally and externally, it is a powerful tool to measure the value of the human capital. For example, PeopleSoft University would tabulate this metric (in addition to an entire scorecard of metrics) by business unit. Eaton University would tabulate this metric by learning delivery (e-learning versus instructor-led training). New Horizons would tabulate this metric by customer, course offering and center. All of these organizations not only compare their valuations using these internal benchmarks, but they also compare against a benchmark database of more than 50 million data points as an external point of reference.
If the organization leverages automation and technology, the process of valuing human capital relative to talent becomes practical, scalable and replicable. These are very important to take into account when deriving human capital metrics.
Conclusion
The quest for talented workforces is never-ending. Learning organizations should understand the role they play in helping achieve a talented workforce. Parallel to this, the learning organization should formulate a model to value and benchmark the change in human capital
Jeffrey Berk is the vice president of products and strategy for KnowledgeAdvisors, a business intelligence software company that helps organizations
measure and manage their learning investments. Jeffrey can be reached at jberk@clomedia.com.
