Radford Perspectives

People Analytics

Discover the biggest people analytics trends, issues and challenges facing technology and life sciences companies today.

Data and analytics have evolved, but many companies remain stuck in the descriptive age; here's how to elevate your analytics capabilities.

Every facet of talent management can benefit by applying analytics, and companies will miss out if they fail to adopt analytics-based methodologies and tools. You might expect that technology and life sciences firms would be more advanced in how they use people analytics relative to other industries, but our research finds many 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).

Figure A below shows the large number of technology and life sciences companies that use descriptive analytics as opposed to more sophisticated and actionable approaches.

 

Figure A
The State of People Analytics

 

  •   Not Used
  •   Descriptive
  •   Prescriptive
  •   Predictive

Source: 2018 Radford Talent Pulse Survey

 

Surprisingly, one-in-four technology companies we surveyed and nearly half of life sciences companies, including both commercial and pre-commercial firms, don’t use people analytics at all. These figures suggest there is a significant opportunity for companies to get better at solving HR problems with data.

Now is the time to embrace the potential of people analytics, because if you’re not, your closest talent competitors will have that much more of a head start. Below, we outline different ways people analytics can be applied to reimagine the talent and rewards landscape, including in the areas of pay equity, pay transparency, productivity gains and workforce planning.

Rethinking Rewards to Achieve Greater Levels of Transparency and Fairness

The drive to make compensation fairer and more transparent is everywhere today—from the campaign trail to Hollywood to corporate boardrooms. As a result, 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, 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 multi-variable 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.

Our View

Every company should initiate pay equity audits now to determine where pay gaps exist by gender, ethnicity or other demographic characteristics, and whether those gaps can be explained through relevant business drivers (e.g., employee skill sets, experience, tenure, location, etc.) or not.

Importantly, pay equity audits are not a “one-and-done” exercise. Once a company addresses pay equity issues, they can easily creep up again— for example, when a company makes an acquisition they can absorb problematic pay practices of the acquired company.

 

Once companies actually start to dig deeper through their data in a rigorous way, we tend to find the following issues are the most significant drivers of both real and perceived gender pay gaps:

Workforce composition
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In 2017, Radford began to collect data on gender as part of our standard survey submission process. The collection of this data remains a work in progress and is optional for all companies, meaning our data is still preliminary in nature and not necessarily a complete picture of market trends. However, early patterns in our technology sector data highlight that workforce composition is a key factor to consider. HR jobs, for example, are paid relatively less than core technology jobs and are heavily skewed toward women; 73% of HR incumbents reported are women. Other functions are more balanced, like finance and marketing. Finally, some functions, like product development, are paid very well and are heavily skewed toward men.

Naturally, if functions with a larger male population pay more than other functions, this can have a dramatic effect on overall appearance of gender pay equity across an organization. Importantly, this does not mean internal systems for setting pay are necessarily unfair. It only highlights the need to examine data across multiple fronts. Looking at workforce composition is usually the best place to start a gender pay equity analysis, but it should only be the beginning. Ultimately, if a comprehensive analysis of your compensation and talent management programs suggests your approach to setting pay is fair, yet perceived pay gaps at your company are large and driven heavily by workforce composition, a different set of questions must be raised.

  • Are there specific recruiting practices or behaviors that lead certain functions and teams to be male dominated?
  • Are there certain functions where women and men are hired at fairly equal rates, but turnover among women is much higher?
  • And what can you do to increase female employment in underrepresented functions and roles over time?

 

Employee Leveling & Salary Structures
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Like workforce composition, seeing where employees sit within your employee leveling and salary structures can go a long way toward explaining perceived pay gaps. Often, aggregate gender pay gaps of 20% or more across an entire organization drop well below 5% once you begin to compare women and men in the same job families and at the same pay grades. Still, as we noted before, even slight variations in pay can be very troubling, and poorly defined policies around how employees are slotted into job families and pay grades in the first place can create significant legal issues.

When it comes to both assessing and addressing gender pay equity issues, building effective and consistent employee leveling and salary structures is critically important. There is a strong legal basis for thinking in this manner. Most new state laws related to pay equity essentially require companies to compare employees across jobs that are “similar” in terms of skills requirements, responsibility, working conditions, and other factors. If grades and job families are defined too broadly, employers put themselves at risk because pay equity analyses based on large groupings may point to problems that don't actually exist. Conversely, if grades and jobs are too granular, employers are at risk because pay equity analyses may not be able to identify real pay gaps when they do exist. A good place to start is leveraging established job classification systems that are based on employee categories, levels, functions and job families.

At Radford, we begin every pay equity project by reviewing how employees at the client company are matched to our globally consistent job leveling and job family system. Getting this step right ensures your analysis has the best chance of identifying real issues accurately and sets the table for long-term consistency in applying go-forward pay decisions.

 

Starting salaries
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The notion that the process of setting starting pay can drive gender pay gaps is gaining real momentum. After all, we know what it feels like when a recruiter asks us what we currently make. Some may be tempted to add a little something on top, but fundamentally it's an uneasy exercise. On the one hand, we fear we might be asking for too much, and on the other hand, we risk pegging ourselves to a salary rate below what the company might otherwise offer. It is the latter case that some states, by virtue of making it more difficult for companies to ask for salary history, are attempting to correct.

In theory, these laws force companies to determine the fair market value of jobs with less input from candidates, which in turn should drive companies to make more consistent offers to all candidates. In our work with clients, we find the thinking behind these laws is generally sound. Starting salaries, for both women and men, go a long way toward explaining long-term pay outcomes within organizations, and in some extreme cases, explain as much as half of the difference in pay between women and men.

In our view, companies who embrace the opportunity to create stronger mechanisms for determining fair starting salaries for all employees, with less input from job candidates, will enjoy a competitive and legal edge by better aligning pay offers to both internal and external labor markets.

Connecting Performance & Pay
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In our experience, it's rare to find cases where poorly implemented or executed performance management systems explain most of, or the entire pay gap, between genders or ethnic groups. This is the good news. However, when we do find situations like this, companies have a real problem on their hands. How do you rationally explain why one demographic group might systematically underperform another? As such, a consistent performance rating gap that drives pay gaps is a real red flag. Although performance management should be more straight-forward for teams with objective performance measures, such as sales quotas or customer satisfaction scores, issues can still occur. In theory, things like commissions payments shouldn't be an issue if there is a strong formulaic link between sales wins and pay outcomes. Yet, companies increasingly face questions from attorneys and government agencies when women and minorities systematically generate lower win rates. The key question then becomes, are these groups put in an unfair position by virtue of being assigned less-favorable or smaller sales territories, which in turn creates less-favorable pay outcomes even if people perform well?

 

Leave of Absence Policies
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In some cases, we find that leave of absence policies have an outsized impact on gender pay gaps. While employees on leave are legally protected from discrimination, this does not mean compensation, performance and promotion decisions made during or after a leave of absence are always fair. The simple fact is, a lot of managers struggle with this issue, often due to a lack of coherent and consistent policies on how to fairly assess the impact a leave of absence should or should not have on pay decisions. Companies need to ask themselves, when should bonuses be prorated or paid in full, when should promotions be on or off the table after a leave, how should a leave impact annual merit increases, if at all, and when should exceptions be made?

Given the potential for leave of absence policies to drive gender pay gaps, we recommend clients pay close attention to this issue when analyzing gender pay equity. If employees, both women and men, with extended leaves of absences in their work history have noticeably slower pay progression, this could be a sign that their leave has become a drag on pay that may be hard to justify to employees and regulators.

 

Manager Bias
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Manager bias is always hard to quantify directly, but in some cases, it's just obvious. When above-average gender pay gaps within an organization are isolated to specific functions, teams, or even a handful of managers (e.g., male managers with female supervisees), it's reasonable to assume that manager bias or a lack of training may play a role. Truly assessing the degree to which bias, conscious or unconscious, has played a role in determining pay outcomes for women vs. men usually requires a qualitative touch that data alone cannot address. And most often, the outcomes of such studies are recommendations for greater investments in diversity and inclusion training and better leadership coaching on career development, active listening and pay planning, as opposed to disciplinary action. Still, in every pay equity analysis, this is an angle that must be considered and screened for.


A pay equity analysis that uses a multiple regression model (the approach we recommend) gives insight into broader diversity topics and the driving forces behind inequities in your organization. Using an illustrative example, Figure B shows how much the pay gap inside an organization can change when controlling for various factors.
 

Figure B
Policies or Controls that Explain the Gender Pay Gap

 

20% represents the “raw” gender pay gap or what gender pay equity looks like when we simply compare male vs. female pay without controlling for any additional variables. As you read this figure from left to right, note that it is additive: each bubble represents the gender pay gap once we have controlled for that variable and all the variables to the left of it. When we start controlling for those variables, we see the perceived 20% shrink significantly. In the end, a fair assessment of pay equity needs to take into account a variety of factors.

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But pay equity is just one important business issue within rewards programs that people analytics can help address. There is also the related issue of pay transparency.

For more than two decades, employees have had access to online sites and tools designed to help them negotiate a better paycheck. Yet, while access to crowdsourced compensation data on the internet is nothing new, how employees use these online resources, discuss pay with others and feel empowered to challenge corporate pay decisions is changing in profound ways.

Pay equity laws often get most of the credit for ushering in greater pay transparency (particularly California’s new law that requires companies to disclose pay scales to job applicants). However, there are several other important reasons why this change is happening now:
 

The number of websites offering crowdsourced compensation data has proliferated dramatically in the past three years, meaning more and more people have access to compensation information and are willing to use it.

The type of information available to employees online is expanding, going beyond basic benchmarks to include data on pay practices, benefits, perks and culture at specific companies.

Younger generations of employees are increasingly open to sharing their pay details with colleagues. Pay fairness is now a mainstream media and political issue that has filtered into people’s daily lives.

Even when companies are very well-intentioned, once employees start comparing pay with one another, they will create their own definitions of fairness and strategic value. From there, if employees believe there are serious disconnects between pay and fairness it’s easy to see the potential for serious internal side effects like poor engagement, lower productivity and higher turnover. In addition, there are significant reputational risks that are increasingly having an effect on an employer’s ability to attract new employees.

The likelihood of side effects of this nature means companies can't treat pay equity adjustments as a simple matter of compliance. They must consider the full gamut of long-term ripple effects once they publicly declare to employees that they take fairness seriously. Pay transparency will force employers to proactively identify and manage a shared understanding of fairness that transcends all groups and individuals— not just pay equity by gender and race but pay equity for everyone. This is where analytics can help.

Most mid- to large-sized employers already have processes in place to establish and monitor pay fairness externally and internally, and strategic alignment between what the company intends to reward for and actual pay outcomes. The question is whether those existing processes are robust enough to withstand a new level of scrutiny. The difference is that employees can now actually fact-check whether the company’s pay-for performance philosophy is real. Figure C below outlines the four key steps companies need to take to get their house in order and where analytics fits into the process.

Figure C
Four Key Steps to Addressing Pay Transparency

Review and update your job architecture

Conduct an internal pay equity analysis

Identify skills and behaviors that should be rewarded given your strategy

Use your pay equity results to compare if skills and behaviors that should be rewarded are actually rewarded

Align your rewards strategy with actual pay levels

Update pay for employees, taking into account budget constraints, wherever you find misalignments

Communicate your fact-based story about what it is you are rewarding (e.g. performance, education, job content)

Provide tools and training so managers can make pay decisions that are consistent with your rewards strategy

Annually update your internal pay equity analysis

Driving Better Business Outcomes

In order for HR leaders to get the most out of analytics, they’ll need to integrate their goals and strategy around people analytics with cross-functional business leaders. Some companies just implement analytics technologies and pass metrics along to managers in the hopes that sharing information will lead to change. But what makes analytics most effective is integrating these capabilities into every decision you make. This requires discipline and collaboration to identify problems and solutions.

As an illustrative example, we recently worked with a large US-based life sciences company to understand the link between their health and wellness programs and employee performance using state-of-the-art analytical techniques. The work allowed our client to assess the full business impact of their health and wellness programs and make modifications rooted in fact-based findings. During our work with this client, we found there are four key health “concepts” that either drive or suppress performance: lifestyle risk, current medical payments, DxCG Risk Score (a widely used risk scoring model) and participation in wellness programs. Using statistical analysis, we charted employee performance against these four health concepts to find if health and wellness actually improves employee productivity.

While controlling for job characteristics and factors such as age and absenteeism using a multiple regression analysis, we found a strong incremental link between high health/wellness and employee productivity. Using a few standard assumptions we were then able to quantify the impact, showing that moderate increases in health and wellness would improve employee productivity by 5%, driving business value up by about $25 million per year.


Figure D
Business Impact of Health and Wellness Programs

 

This figure shows the magnitude of the impact of each of the factors on two performance metrics. The numbers represent T-values. A T-value with a value of more than absolute 1.64 (larger than 1.64 or smaller than negative 1.64) is regarded as significant, with a less than a 10% chance of being a random relationship.

Read our related article:
How Health and Wellness Policies Affect Employee Performance: A Case Study
 

 

 

Location Analysis and Strategic Workforce Planning

Our clients need innovative solutions to address today’s talent gap. We all know technical talent in Silicon Valley is scarce and expensive, but the same is now true in hundreds of locations around the world. The long-term futility of organizations simply hiring top talent from each other has also become apparent. Data and analytics is now critical in the race to gain an edge. For one, analytics can lead to new fact-based approaches for sourcing, attracting and retaining top talent. Analytics can also help leaders quantify the future workforce gap, allowing for better strategic planning and a chance to address talent shortfalls earlier than ever. Finally, analytics can help you find new labor markets and new talent polls that are underutilized.

If your organization wants to assess and explore new labor markets you need a lot of data to determine and measure:

Labor supply: How many workers are currently in the market with the skillsets you desire?

Local labor demand: How many job postings are there and how much overlap is there in the skills other firms are looking for with our own?

The desired structure of local workforce now and later: What are the goals and how do competitors with similar goals structure their workforce.

Pay levels: What are people paid today and where is pay headed?

 


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 it is a change in business strategy that 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, and would be simple to implement and maintain.

No easy task! Our analysis identified two critical jobs with 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.

 

Next Steps

People analytics has evolved. When companies reach the “fourth tier” of people analytics as shown in Figure E below, their analytics capabilities become a very powerful tool, enabling business leaders to predict the performance of potential talent and rewards programs with a good degree of accuracy.

Figure E
The Evolution of People Analytics

Tier 4: Predictive analytics
If we increase pay, what would out future turnover rate be?

Tier 3: Advanced analytics
What are the causes of employee turnover?

Tier 2: Advanced reporting
What is the employee turnover rate for high performers?

Tier 1: Reporting
How many employees quit in 2017?

 

Harnessing data to yield insights that make talent and rewards programs more effective will ultimately make your HR function more strategic and a better business partner within the entire organization. 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 areas of pay equity, pay transparency, worker productivity and workforce planning.

 
 

Meet Our Authors

Stefan Gaertner, Ph.D.
Partner, Head of Workforce Analytics

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Ranjan Dutta, Ph.D.
Partner, People Analytics

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