Digital Transformation

Making Employee-Centric Decisions via Holistic Benefits Analysis

Organizational leaders are rethinking their approach to benefits, creating strategies to address holistic employee needs. They need good data pulled from multiple systems and converted into actionable analytics.
— By Esther Burt

Employers spend billions on a variety of employee benefits, ranging from health insurance to wellness programs to training and development opportunities. Despite the commitment of resources, business leaders continue to find themselves wondering if the mix of benefits is right and what could be done to improve employee engagement and satisfaction.

Though employee surveys provide feedback, surveys can create critical data gaps and deliver shallow information – some employees never get around to completing them or they do not make thoughtful responses while in a rush. Employers need information that only good benefits data can provide.

Good benefits data can help decision-makers discover employee expectations and the path to balancing a wide range of needs to increase employee satisfaction and engagement. Leveraging data and analytics that help with human capital management is crucial to ensuring resources are placed in the right buckets.

Moving Toward People-Centric Analytics
Benefits analytics and people analytics are intricately tied together because “benefits” include items like health insurance and retirement plans, but also training and development opportunities, flex time, stress management programs, and financial health counseling, to name a few options.

People analytics and benefits analytics should be intricately tied together to drive decisions that add holistic value to the employee experience, turning the analytics into people-centric analytics. People-centric analytics are analytics that provide insights on the talent management process that increase employee satisfaction through individualization and while also improving business outcomes.

AIHR Analytics uses employee training as an example of the difference. The simplest process is human resources ensures managers or supervisors train employees to do their job and analytics are collected to measure changes in factors like productivity and engagement. In a people management system, the company decides on the training courses it can offer within a budget.

Analytics look at factors like workforce participation and productivity changes. Employers are still not getting good information through data analytics because the training options were pre-determined, people are assessed on course completion and not on their needs or expectation, and business outcomes are not addressed. People analytics will assist employers with determining which training method has a positive effect on customer satisfaction, but people–centric analytics will help the organization develop tailor-made training courses for employees to help them provide better customer service.

People-centric analytics point to the benefits that help people achieve their personal and work goals while meeting the needs of the organization. It is frustrating for employers to not have the data analytics that can help them address holistic employee needs.

The analytics produced should help with decisions at every step of the employee experience. Data analytics can tell decision-makers how to change the onboarding process to help new employees reach productivity sooner and feel included from day one. They can help with recruiting by helping target the best people for the organization and how to engage them, discover financial or well-being needs, and link work performance to benefits offerings.

Holistic Approach to Benefits
Much of the data made available to organizational leaders assesses a point in time, especially in the benefits arena. Most companies have detailed data on compensation and benefits, leave days, turnover, training days, and headcounts of diverse people. What they often do not have is good data for better performance management, employee engagement, career expectations, mobility, and potential leadership qualities. Employee health and well-being is another area where data analytics gaps exist. Yet each one of these employee-related areas have a direct influence on business outcomes.

To get to the data, first decide on the human capital information desired within context of the needs of employees. In MetLife’s 17th annual study on employee benefit trends titled “Thriving in the New Work-Life World,” researchers found the theme that employers need to think holistically about the lives and needs of employees.

People-centric analytics point to the benefits that help people achieve their personal and work goals while meeting the needs of the organization.
Benefits influence many aspects of employee lives. For example, they impact the financial condition of employees, a huge source of stress, and stress is a major factor in employee well-being and satisfaction. MetLife found that approximately five-out-of-10 employees say that benefits are crucial to thriving. Other major stressors included the stress of the work, personal or family and health, managing personal commitment.

Similar to the research of AIHR Analytics research, MetLife found that employees want to be viewed holistically. Analytics should dive deeper into helping employers understand their employees for better benefits design. Deeper means assessing attitudes, values, motivators and goals to develop employee profiles. The profiles are used for benefits, program development and personalized communications.

Offering a comprehensive benefits program is one element of a strategy to elevate employee satisfaction. Other offerings include recognition systems, competitive compensation, employee feedback, training opportunities, meaningful work, professional development, timely promotions, and so on.

MetLife found gaps between what employees think and what employers think concerning what employers are doing to make employees feel appreciated and valued. In the area of benefits, MetLife’s survey found that only 35 percent of employees believe the employer was offering a comprehensive benefits program that made them feel valued, but 44 percent of employers believed they did.

There are many unmet needs among employees, but without data analytics, it is difficult to identify the gaps. Does an organization know if employees are interested in long-term leave to attend a structured program, more work flexibility, student debt assistance, more help with achieving financial wellness, more remote work, or assistance with developing healthy behaviors?

Identifying the buckets of benefits helps data analysts produce the specific analytics that decision-makers need. The first bucket is traditional benefits. Some of the analytics must come from health insurance companies (without violating privacy rights), while some data is available internally, like the effectiveness of wellness programs. Other data is available from claims processes.

With the data, algorithms can identify facts like the prevalent health conditions of the workforce. Benefits-related metrics can inform about health conditions most likely to impact the workforce.

Insights into the ‘Why’
Employee feedback from surveys remains important, but there is a wealth of information found in other talent management systems and employee performance records.

It is not enough to know an employee has taken a large number of sick days. The analytics should help identify the “why” and guide decision-makers in addressing the issues as much as possible.

The strategy is to create individual profiles and meet each employee’s needs in a holistic manner through analytics.