With costs continuing to rise amid ongoing economic headwinds, organizations are struggling to keep compensation and benefit programs affordable. For example, the cost of healthcare is rising globally, and many employers are dealing with higher compensation costs from paying above average new-hire salaries during a tight labor market. This creates a delicate balance: Companies want to continue to attract and retain talent through robust total rewards while maximizing their investment and making programs cost effective.
Through data and analytics, organizations can optimize their spending to achieve the best possible outcomes. But finding the right data, contextualizing it properly and using it to make better decisions about spending is easier said than done.
There are several types of data that can be used to drive decision making.
When deployed thoughtfully, data and analytics can contribute to better decisions about large-scale structural issues like overall talent strategy, location strategy, reward and benefit programs, organizational design, and diversity, equity and inclusion (DE&I) initiatives. Here are some ways organizations can use internal and external data to gather insights and make more informed decisions that generate significant return on investment.
Before making any major changes to your total rewards, assess current and future workforce needs to optimize spend. Protecting critical capabilities should be the most important goal. Benchmarking in areas like headcount, location strategy, compensation and benefits can identify where costs are higher than your peers and if you’re achieving target return on investment for the spend.
Benchmarking across multiple data points is critical to making informed decisions with optimal impact. We worked with a technology firm on location strategy for skilled technology specialists. In determining the client’s optimal site to hire and retain talent, we assessed several of their active working locations. By scoring five different markets (two different cities in the U.S., two in the UK and Tokyo, Japan) on six different categories, including labor supply, labor cost, expected employee growth and others, the recommendations were narrowed down. The resulting scores showed that Tokyo was the clear choice. This underscores the need to look at multiple data points. Focusing only on the general cost of living may have resulted in the incorrect conclusion that Tokyo would be a less attractive option.
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Workforce agility is a major component of building a sustainable working life for employees. Part of that agility is knowing what skills employees have or need to do the job you want them to — both now and in the future. While many companies use talent assessments to hire candidates, also using them to inform your decision-making process adds great value. The data from assessments can be reviewed to understand key insights, such as:
For example, based on an analysis of 15 million assessment participants taken by Aon clients annually, we found that individual resilience increases with people’s age over the past four years. Those aged 60 years and above are the most resilient; the under-20 group is the least resilient. Senior managers and customer service jobs tend to have the most resilient employees, with apprentices and recent graduates the least. Taken together, this data suggests that age is an important factor in building resilience. Data like this provides insights for employers to assess existing HR programs and target them to certain employee groups. It can also be used to measure the effectiveness of building resilience over time.
Companies that are assessing employees for agility, adaptability and digital skills.
Source: Aon Global HR Pulse Survey, 2021
An investment bank in the U.S. assessed its skill base by using market data to better understand what new skills it needed to execute technology products and which jobs were at risk of automation. Rather than mass layoffs followed by hiring new workers with the desired skills, the bank created a program to engage, understand and reskill more than 1,000 workers over a few months. Based on organizational data, we found that the cost of hiring new employees would be 10 percent higher than industry estimates due to recruiting costs, severance packages and the detrimental impact on staff morale.
People data was used to quantify the opportunity, redeploy affected employees into new roles and determine how many would be prepared to successfully reskill. The resulting savings were significant, estimated at $150 million. Additionally, the bank was able to preserve its company culture, values and reputation by keeping employees’ jobs.
Supporting your existing employees by optimizing total rewards and benefits is valuable for a few reasons. Cost savings are an obvious benefit, but there’s more: A resilient workforce means employees’ needs are being met by the benefits offered.
When looking at health benefits, data can play a huge role in program optimization. And much of that data is readily available. Insurance claims data, the number of days off employees take and workplace safety data are just a few things companies can look at to see which programs are working and which can be improved. These are all cost optimization tools that can also support talent development. For example, utilization data may show that an expensive benefit program is not providing the impact it should, allowing you to replace it with something more appropriate. Or, if a program meets identified employee needs but is still underutilized, the company can improve its communications around the program.
Benefits are most effective when the design and messaging are aligned with employee priorities. This involves a degree of personalization since not all employees value or want the same things. Collecting data on employee preferences and needs and then leveraging that data for customization purposes can help maximize the employee value proposition (EVP).
Traditional approaches to benefit customization typically group employees by generation (e.g., people in their 20s value gym memberships, while people in their 30s and 40s value childcare). But while there may be some variations by age, there is far more knowledge to be gained by surveying employees’ preferences and categorizing lifestyles.
The result is data-led personas. These guide organizations by standardizing and categorizing employee lifestyles so that the company can tailor its benefits and perhaps just as importantly, its benefit communications.
Another area where data and analytics can play a role in optimizing spend is around health, equity and affordability. Health outcomes and costs differ depending on where employees live. These social determinants of health (SDOH) can affect a population’s health, ranging from access to primary care services and pharmacies to the availability of transportation and healthy food options.
With a few key data points from a company’s census, predictive analytics can be used to create insights related to SDOH, which can pinpoint the greatest health disparities leading to higher costs. This allows an employer to target preventative and other lower-cost solutions toward the portions of their workforce who need it the most. Doing so can not only help optimize healthcare spend, but it can also help organizations meet their DE&I goals (read more about DE&I trends in Aon’s 2022 Global DE&I Survey).
Data has become a major factor in wellbeing strategies. We know that allocating funding to wellbeing programs, once seen as a perk, directly affects company performance. Improving your employees’ sustainable working life has the potential to increase company performance by between 11 and 55 percent, according to Aon’s 2022-2023 Global Wellbeing.
Aon’s Wellbeing and Business Performance Dashboard allows companies to quantify their progress in five key areas of organizational resilience. The dashboard tracks from the organizational level down to the individual level. It tracks progress across key metrics and uses predictive analytics to show how those metrics will affect costs and performance. HR leaders can see at a glance the resilience of their organization and what can be done to improve it.
By using data to take an all-encompassing perspective on performance and wellbeing, companies can offer a richer colleague and client experience. They will also be able to design better ways for their workforces to bounce back, innovate and sustain themselves over time.
In practice this may mean committing to new ways of measuring wellbeing, such as the Human Sustainability Index, and educating and empowering people to make meaningful changes for themselves, their teams and ultimately their organizations.
As we saw during the pandemic, workforce resilience is key to how a company performs during times of volatility. The same is true during uncertain economic conditions, where resilience remains critical. Only about half of companies rate their employees’ resilience as “excellent” or “very good.”1 But that numb