COVID-19 & Corporate Wellness

Fight the Great Resignation with Non-Traditional Data

Grant Gordon
Artemis Health

More than 4.5 million Americans quit their jobs during the pandemic as part of the Great Resignation. Now, as companies pull out all the stops to stanch the bleeding, many are redesigning benefits packages to attract and retain top talent—and they are relying on employee input for help.

According to an Artemis Health survey of over 300 companies across all major sectors of the U.S. economy, 75% of companies now base their benefits decisions on employee feedback, up by more than 30% since 2019.

Yet just half of benefits managers surveyed describe themselves as “very successful” in using employee feedback and other non-traditional data to make benefits decisions. Furthermore, 50% of benefits managers say they lack the tools or support to access this information effectively for decision-making.

These are signs that leaders need better access to holistic data sources and should reassess their tools to analyze data to craft competitive benefits packages.

What’s Holding Employers Back

It’s true that employee opinions are a key piece of the puzzle when crafting attractive benefits packages. When paired with objective data sources, employee feedback can provide great insight into the types of benefits most likely to attract new talent and engage existing employees in managing their health. At a time when benefits leaders are ramping up their focus on fighting attrition, the ability to listen to employees and apply their feedback is a competitive differentiator in today’s market.

However, employee feedback is just a jumping-off point for successful benefits design. Employers must also access and mine their benefits and healthcare data to determine what’s working, delivering better outcomes, and which programs offer a strong return on investment.

For example, a successful digital mental health program would take into account not just whether or not employees feel positively about the service but also hard metrics, like:

●     Utilization rates among employees/dependents with mental health diagnoses

●     The cost of the program compared to traditional mental health services

●     Health outcomes for those using the program vs those using traditional mental health services

●     The change in medical and pharmacy costs as a result of the program

And many more.

While employee feedback is a vital indicator of what members most want, metrics such as these shine a light on the benefits they most need. This data-driven approach helps improve outcomes, reduce costs, and increase member satisfaction.

Leaders should also consider point solution data, health risk assessments, and diversity, equity and inclusion statistics as they assess ways to strengthen the quality of their benefits packages.

Investing in tools that make connections between data points is also critical. When benefits managers struggle to draw conclusions from their data—or, worse, when they don’t trust the accuracy of their data—this thwarts efforts to use the data for better benefit design. As a result, these leaders face great difficulty fostering improved employee health and well-being, increased job satisfaction, and higher staff retention rates.

Keys to Unlocking Data Intelligence

So how can benefits leaders unlock accurate, trustworthy data and choose the point solutions that will best help boost employee satisfaction, improve employee health, and build attractive benefits for recruiting? Four steps are critically important to the benefits lifecycle: gathering the right data, conducting meaningful analyses, taking action based on data insights, and tapping analytics experts.      

StepOne: Gather the Data

A critical first step is getting access to a wide variety of data to set a baseline for how the current benefits package is performing. Medical claims, prescription fulfillment, health risk assessments and other objective data all play a role in that endeavor.

However, the value of non-traditional data—from employee feedback to absenteeism rates, short- and long-term disability claims, program enrollment and engagement, and employee assistance program data—should not be overlooked. This data can provide trustworthy clarity on benefits program performance. It can also pinpoint opportunities for new benefits that address an organization’s unique needs. Indeed, about 30% of benefits leaders who describe their healthcare benefits as “ahead of the curve” report leveraging non-traditional data to design and measure program performances.

StepTwo: Analyze the Data

Analyzing  data can be a formidable task: just half of benefits managers surveyed describe themselves as “very successful” in using employee feedback and other non-traditional data to make benefits decisions. Furthermore, 50% of benefits managers say they lack the tools or support to effectively access and analyze nontraditional data for decision-making.

Data partnerships, whether through a consultant or with a direct benefits analytics vendor, can be tremendously helpful. A data analytics partnership will enable employers to access benchmarks, risk scores, and other industry-standard data analytics techniques and provide benefits-specific tools to help them compare members, look across data feeds, and tell stories with their data.

Step Three: Take Action    

Next, employers can take action based on their data, whether adding a new program, incentivizing participation, or changing their coverage or plan. By using key performance indicators (KPIs) specific to the organization, benefits leaders can more easily trust the implementation of their benefits strategy. This doesn’t come without its challenges, as 27% of benefits leaders cite identifying new programs as their biggest challenge, followed by managing and measuring program performance (23%). But action based on utilization and employee engagement—particularly with leadership support—can make great headwinds for both attracting and retaining talent.

StepFour: Lean into Analytics Expertise

Successfully navigating the benefits life cycle can be tough for any company. Leading organizations will recognize where and when they need assistance, particularly with respect to analyzing the volumes of data required to design competitive benefits packages and continuously evaluate KPIs in real-time.

In fact, leaders in the Artemis Health survey who described themselves as “ahead of the curve” on benefits design say they rely on multiple resources—in-house benefits teams, benefits data consultants or brokers, for example—to guide their approach. They tap software solutions for data analytics, population health tools, consulting partners and data experts to help them uncover the most pressing challenges for their members.

The right benefits analytics partner will help leaders set goals, determine key metrics and regularly monitor indicators of program success, from member engagement to care utilization to financial impact and outcomes.

The best partners also offer a library of point solutions that make it easy to gather and mine data for answers. They help track the progress of a benefits program over time with best-in-class data visualizations and storytelling that can be shared with key stakeholders.

Crafting Better Benefits Packages During the Great Resignation and Beyond

The Great Resignation won’t last forever. How long it impacts any given company, however, may be influenced in part by the quality of its benefits package. By looking beyond traditional data sources and reaching out for analytics help, leaders can strengthen their ability to develop competitive benefits that attract and retain the best and brightest talent in 2022 and beyond.

Grant Gordon is CEO and co-founder of Artemis Health, a health data analytics company. To download the full survey and report, visit

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