Consider the following scenario that may be all too familiar: HR proposes a change to drive employee productivity but then struggles to build a solid business case for it. Finance then computes the cost of this new program, leaving little or no data to show the return on that investment. Perhaps HR tries to make a case by loosely referencing employee retention or employee engagement, but in the age of big data this case is becoming less and less compelling without some real evidence. In the end, the approval of that program depends on whether the executive team believes there is value while HR continues to be seen as a cost center and pure advocate of employee interest.
It doesn’t have to be that way. While it will always be difﬁcult to prove the business impact of a new program or investment, increasingly it can be done with robust data, sound judgment, quantitative models and explicit assumptions. It’s critical for HR to use these tools to make a strong case for the value of their programs and be viewed as a trusted business partner within the organization. The good news is HR potentially has access to more data (e.g., HRIS, stafﬁng systems, spot rewards, ﬂexible work arrangements, surveys, etc.) than anyone else in the organization.
All HR programs can beneﬁt from applying analytics, and companies will miss out if they fail to adopt analytics-based methodologies and tools. Our research ﬁnds many companies still focus their efforts on processing data in ways that yield insight only into how things are today (descriptive analytics) as opposed to looking at ways to change things (prescriptive analytics) or what might happen in the future (predictive analytics). In this article, we highlight three different use cases for analytics that can make a difference today: pay equity, workforce planning, and health and wellness programs.
Ensuring your pay systems are fair is quickly becoming an imperative of doing good business. Not doing so risks violating a growing number of pay equity laws and represents a real reputational risk. As part of addressing this reputational risk, organizations need to dig below the surface to determine whether — and, more importantly, why — they have any unexplained pay equity gaps.
This is where people analytics plays an important role. A simple compensation assessment by gender won’t tell you what you really need to know. To get at the truth, you need to run a more sophisticated multivariable regression analysis to understand what really drives pay outcomes. Then you can start to ﬁx the speciﬁc policies, practices and behaviors that can lead to bad outcomes.
Once companies actually start to dig deeper through their data in a rigorous way, we tend to ﬁnd that the following issues are the most signiﬁcant drivers of gender pay gaps:
- Workforce composition
- Employee leveling and salary structures
- Starting salaries
- Connecting performance and pay
- Leave of absence policies
- Manager bias
A pay equity analysis that uses a multiple regression model (the approach we recommend) also gives insight into broader diversity topics and the driving forces behind inequities in your organization.
While most mid- to large-sized employers already have processes in place to establish and monitor pay fairness, the question is whether those existing processes are robust enough to withstand a new level of scrutiny in compensation. Radford research reveals that more than 75% of our clients either don’t do a multiple regression pay equity analysis or use it in an overly simplistic way that does not identify the root causes of pay gaps. Simplistic analyses do not advance your understanding of your pay system and what it really is you are paying for (e.g., tenure, experience, performance, education, etc.). This is a problem because today employees can quickly fact-check whether the story they have been told — for example, pay is solely based on performance — is actually true.
There are four key steps companies need to take to get their house in order; using analytics is integral to each step.
- Analyze: Review and update your job architecture and conduct an internal multiple regression based pay equity analysis.
- Strategize: Identify the skills and behaviors that should be rewarded as they align with your strategy, then use your pay equity analysis results to determine if those skills and behaviors are actually being rewarded currently.
- Implement and communicate: Strategically adjust pay for employees to align your intent (what you want to pay for) with reality (what you are actually paying for). Then, communicate what it is you are rewarding to employees (i.e., performance, education, skills, etc.).
- Maintain: Provide ongoing tools and training to equip managers and ensure pay systems are working as planned. We also recommend an annual pay equity analysis.
Doing this right will advance several organizational objectives: compliance, pay fairness for everyone and strategic alignment of rewards and business goals.
Many companies still focus on processing data in ways that yield insight into how things are today as opposed to what might happen in the future.
Strategic Workforce Planning
In the competitive job market, the long-term futility of organizations simply hiring top talent from each other has become apparent. Data and analytics is now critical in the race to gain an edge. There are four primary ways that analytics assists with workforce planning. Analytics can:
- Lead to new fact-based approaches for sourcing, attracting and retaining top talent;
- Help leaders quantify the future workforce gap;
- Allow for better strategic planning and a chance to address talent shortfalls earlier than ever; and
- Help you ﬁnd new labor markets and new talent pools that are underutilized.
If your organization wants to assess and explore new labor markets, you need a lot of data to determine and measure the labor supply, local labor demand, the desired structure of your workforce now and in the future, and current and future pay levels.
A search for new talent does not necessarily have to happen in a new geographic region; it can include talent with somewhat different skill sets that nevertheless can do the work. Sometimes a change in business strategy drives a change in hiring strategy. For example, we worked with a client that was shifting its business strategy toward technology and digitization, thus requiring new skills and a pivot in corporate culture. The CHRO was tasked with building a strategic workforce planning program that was sustainable, while also keeping the core culture of the organization intact. This was no simple task.
Our analysis identiﬁed two critical job types needed to support the growth of the organization that were anticipated to experience large future labor gaps. A pilot workforce plan was built for one large business unit. The participatory process — along with a rigorous analytical approach — ensured executives supported the plan to boost external hiring of junior talent and training to address future labor gaps early.
Data and technology are key ingredients to make this process work. Using our internal tools and survey data, we can visualize labor markets around the globe to help organizations implement and execute a sustainable workforce planning system.
Building the Business Case for Health and Wellness
Historically, employers have struggled to effectively manage their health-care costs. They have focused on design and contribution strategies, carrier and network management strategies, care management initiatives and efforts to improve overall employee health. Unfortunately, employers have experienced mixed results, particularly with their attempt to control line-item medical expenses by directly addressing population health. As a result, they have begun exploring the value a healthier population can bring to overall business results (via productivity and output) instead of the direct impact on medical spend. This new employer perspective, however, is not without its challenges.
While much has been written about the impact of wellness on health, most of that evidence is quite nascent. The bigger question in our mind is whether active and healthy employees actually perform better compared to their colleagues and how this translates into business value. We recently worked with a large U.S.-based manufacturing company that wanted to uncover the answer to that question.
Harnessing data to yield insights that make rewards programs more effective will ultimately make your HR function a more strategic business partner.
Our client was not under any pressure to demonstrate the business value of their wellness programs. In fact, the organization was doing quite well ﬁnancially and offered wellness programs and health beneﬁts as integral parts of their rewards system without questioning the “why.” Still, with rising health-care expenses, the cost of these programs was questioned by business leaders. Our client felt that an understanding of the performance beneﬁts of health and wellness would open the eyes of leaders that were skeptical of their beneﬁts in relation to the cost and redirect the focus from managing cost to increasing value.
There were hundreds of metrics to track these complex concepts and we had to deploy analytics and critical thinking to limit this to a reasonable and meaningful set of broad measures. Leveraging a methodology called exploratory factor analysis, we compressed the information into four measures and deﬁned corresponding hypotheses as to how these should be related to employee performance:
- Lifestyle risk: A composite score of stress risk, physical activity risk, nutrition risk, tobacco risk, life satisfaction risk, blood pressure risk and body mass index risk. We expect lifestyle risk to drive lower employee performance levels.
- Current medical payments: Medical Net Paid, Medical Allow Amount and Medical Out of Pocket Amount. We expect Medical Payments to indicate current health status that drives lower employee performance levels.
- DxCG risk score: A score that measures future health risk. We expect DxCG to indicate current and future health status that drives lower performance levels.
- Wellness participation levels. We expect wellness to drive “energy” and “health” which leads to higher employee performance levels.
We used two metrics to measure employee performance: individual performance rating and bonus payments relative to target. We linked health and wellness metrics to performance using multiple regression analysis, which allowed us to go beyond mere correlations and correct for confounding factors such as age, location and absenteeism that could inﬂuence performance and obscure the impact of health and wellness.
In looking at the four factors we highlighted above, we examined the relationship between these factors and the impact on employees’ performance. We found overwhelming support for the relationships we hoped to ﬁnd. Seven of the eight anticipated relationships were in the expected direction with six of those being at least marginally signiﬁcant from a statistical viewpoint as seen in Figure 1. Wellness participation only impacted one of the two measures we used to track performance: actual bonus received relative to target.
Using standard assumptions related to the impact of employee performance on business performance (high performers’ business contribution relative to average performers is equivalent to 40% of their annual salary), we estimated that moderate improvements in health and wellness result in a productivity gain of 5%, or about $3,500 per employee (roughly half of this impact results from wellness and managing health risks). This compares favorably to the typical annual wellness expense of about $500 per person.
Since we were able to correct for a few confounding factors, we were able to address some initial concerns. For example, the relationships were not because of employee age or absenteeism. Interestingly, we also found that while body mass index is the most obvious and visible signal of ﬁtness (or lack thereof), it was not a key lifestyle indicator impacting performance. For this client, stress and nutrition had far bigger inﬂuences on performance.
We should note that the results represent empirical relationships we found for this manufacturing organization. We are currently working on similar analyses for different clients to see if the results replicate and generalize to other organizations and employees.
Getting Started with Analytics
One reason why there is so little progress in designing more effective and productive rewards and beneﬁts programs is that we know so little about the impact of our interventions on positive outcomes. But it doesn’t have to be this way — advanced analytics can pave the way toward more informed decision making on HR programs.
Harnessing data to yield insights that make rewards programs more effective will ultimately make your HR function more strategic and a better business partner within the entire organization. Furthermore, aligning rewards programs with your business strategy will allow for additional conversations and programs targeted to leveraging the power of human capital to grow top line revenue. Getting there starts with understanding where you stand today, and showing other business leaders the clear value of investing in people analytics to address major business risks in the important areas of pay equity, workforce planning, health care and beyond.
Stefan Gaertner is a partner at Aon and co-leads the global people analytics practice.
Chris Rogers is a senior vice president at Aon.
Ada Guan is a consultant in the global people analytics practice.