What are some of the tools or methods that you use to collect and analyze data on your benefits programs?

One of the easiest approaches is to use a post-test survey two or three months after the training took place. You would ask the participants to judge their own on-the-job performance against KSAs (Knowledge, Skills, and Attributes).

While surveys are a good way to get data on employees feedback on their employer benefit offerings, surveys do not culminate a clear grand prospective that transcends whereas it pools or buckets employees in groups based on level of understanding and other important data. Essentially, the micro details of the employee which are important to evaluate and get clear employee level data prospective. Furthermore, this type of detail would serve to be robust in useful data when making decisions about benefits plan changes. A companies total rewards objectives, initiatives & employee feedback data should be intertwined but not across disseminated data, rather cohesive data that links to Logic. Employee engagement and survey take up are not @ best

Surveys are a great way to gather feedback. However, knowing what questions to present to employees for that feedback can make or break your results. If you ask all the wrong questions, you will have zero engagement. Building relationships and asking employees during your daily chats about their perspective sets you up to have engagement on said surveys. Following up on those survey results is also paramount. Employees will continue to engage if they receive acknowledgment on their feedback.

I believe the key to benchmarking is alignment... Alignment across interfacing important to minor business data line items. Collectively you would look at key impacting and influential factors by means of social science, other technical sciences, governance initiatives & business initiatives and the interconnectivity between these things. Government & certain businesses have partnership and initiatives are driven by developments on both sides. To achieve a oneness alignment data collection across all companies globally need to be congruent. The difficulty is there are too many directions and styles from leaders in business and goverment. Intelligent consultative opinions and recommendations from experts are many...

Benchmarking gives you the bare minimum that you should be providing in your benefits offerings, for similar industries and as compared to competitors. Looking at your people analytics should give you a wider picture as to innovation potential.

Employee benefits, also known as perks or fringe benefits, are provided to employees over and above salaries and wages. These employees benefit packages may include overtime, medical insurance, vacation, profit sharing and retirement benefits, to name just a few.

Using other not common data channels as insurance claims rate. Most of the benefits foundation are based on medical needs of the employees. From there I can analyze the data and create new programs or change the project outcome. For example in a school we have young -out of college teachers and old teachers near to retirement. Most of them have kids, so from them we can create a research hypothesis for what benefits we can give them to increase fidelity. If they have insurance and they are not using it, maybe we need to change the benefits scope

Claims data analysis for gaps in care and for determining possible wellness initiatives is not championed enough. There are great analysis tools that allow for this review while still upholding employee privacy.

This is true and is key in achieving a concordant state in connection to previous comment on alignment. I believe one major issue that is difficult to comb through is the rise of AI with biased and discriminatory data and algorithms... This is relative to indirect and direct components that impact benefits design specifically at social science and matters. I believe more market analysis on a micro and macro scale in social science matters and we need to pay more attention and do more work on inferences and evaluating focal point alignment to business impact. There are many synergistic points of Interest like: risk assessment, actuarial science projections, market predictions, cost containment and management. Issue is privacy matters.

You can use various techniques and tools to do this, such as descriptive statistics, inferential statistics, regression analysis, factor analysis, or data visualization software. You should also compare your data with benchmarks, goals, or best practices, and look for gaps, strengths, and areas for improvement.

Analytics on this can reveal a lot to enhance the value of different components of the benefit program. Identifying high cost claim trends, predicting the risk of high cost claims, figuring intervention strategies, and getting continuous visibility on aggregate employee engagement / utilization are examples of such insights.

Surveys and feedback from employees are invaluable for optimizing benefits programs. They provide direct insights into employee satisfaction and preferences, helping identify which benefits are most valued and which may need improvement. This data allows employers to tailor benefits to better meet employee needs, increasing overall satisfaction and engagement. Additionally, feedback can highlight emerging trends and unmet needs, guiding future benefits planning and adjustments. By actively listening to employees, organizations can create a more supportive and attractive benefits package, enhancing retention and recruitment efforts.

1. Increased employee satisfaction 2. Happier employees 3. Higher employee well-being 4. Improved employee retention 5. More trust and empathy 6. Employees that feel aligned around your company’s mission 7. A community where everyone feels heard & valued 8. A sense of employee loyalty 9. Better performance 10. Improved employee safety

I agree... I am passionate and in love with data science, stating inferences and making recommendations. One thing I believe is key to many areas in business decisions and initiatives is social science. Evaluating the emotional state of society and the influential factors, grouping the physical state of society, categorizing the styles/types of personalities and accurate aptitude testing to employment are a few items that would be significant in moving the dial on directives, initiatives, testing, experimenting and business decisions such as benefit plan design. The grey area in connecting social science data to the equation would be a matter of privacy issues. But not in full spectrum regards. So it still can be done.

Testing might also lead to repeating the Design Thinking process if the end-user faces usability issues with using the solution provided. It provides valuable insights that allow the designer to rework the problem definition at hand and create a new solution that can better empathize with the end-user.

Excellently stated. This leads to a a collective grand data state of autonomy for algorithm programing and data science analytics.

Analytics on this can reveal a lot to enhance the value of different components of the benefit program. Identifying high cost claim trends, predicting the risk of high cost claims, figuring intervention strategies, and getting continuous visibility on aggregate employee engagement / utilization are examples of such insights.