5 Data-Driven Ways HR Can Optimize Costs

5 Data-Driven Ways HR Can Optimize Costs
September 10, 2025 17 mins

5 Data-Driven Ways HR Can Optimize Costs

5 Data-Driven Ways HR Can Optimize Costs

Data and analytics can unlock value for HR professionals in a variety of ways. From a unified global benefits perspective to personalizing total rewards, gathering and analyzing the right types of data help companies optimize what can be their biggest expense.

Key Takeaways
  1. Retention of high-performing employees is the most important priority for HR professionals to optimize costs.
  2. Combining claims, absence and other types of data with predictive analytics can help employers identify high-cost claimants and other potential risks.
  3. How HR manages AI, both as an internal skills issue and across their vendor ecosystem, will be key.

  1. Introduction

  2. Using Global Benefit Data to Unlock Cost Savings

  3. Reduce Voluntary Attrition to Drive Workforce Performance

  4. Improving Costs Through Workforce Health and Productivity Tools

  5. Enhancing Performance Through AI Preparedness and Deployment

  6. Maximizing People Spend With Total Rewards Benchmarking Tools

HR leaders continue to take on a greater role in overall business strategy. Part of their increasing mandate is to control costs while also improving employee productivity — and in the process, improve the financial performance of the company. These goals, however, often conflict on the bottom line. That’s why data and analytics are an increasingly vital part of the toolkit.

One metric to judge how well HR is optimizing spend is revenue per total rewards dollar spent. Given that total rewards is often a company’s biggest or second biggest cost, optimizing people spend can have a significant impact.

How can data help HR leaders get the most out of their total rewards spend? We explore five key ways.

#1 Using Global Benefit Data to Unlock Cost Savings

Benefits teams in multinational organizations have typically worked in silos, isolated either by geography or job function. That isolation can contribute to inefficiencies, missed opportunities for cost savings and an inconsistent employee experience. How can companies break down these silos and work together to build a global benefits identity? It starts with data.

The Advantages of Global Health Datasets

Global benefits teams have a lot of data, but that data is often locked in multiple different systems across dozens of countries. The result is that companies are unable to see the bigger picture and end up duplicating efforts. “We have seen clients doing the same thing 60 times over in 60 different countries,” says Kathryn Davis, Vice President of Global Benefits in Aon’s Health Solutions practice in North America. The first step in unlocking the power of data is to gather and standardize datasets across systems and countries and consolidate into a global data platform.

Once the data is housed in a single repository, the next step is determining what insights the data can unlock. Here are a few examples:

  • Plan Design: The basic details about benefit offerings in various countries can show whether they are compliant, competitive and equitable across locations.
  • Health Risks: Beyond basic information about what types of medical claims are driving costs, medical claims data can start to point out what conditions are affecting employee health in different countries with potential for breakdowns by age bands or membership category.
  • Financing Strategy: Companies with a captive or multinational pool in place will have more detailed financial information about the global benefits portfolio that can help drill down on costs and identify plans that are underperforming.
  • Benefit Optimization: Benefit administration systems can provide data on which benefits — from life insurance and wellbeing allowances to pet insurance — are being chosen by employees.

The data available will vary with the type of benefits offered in a given country. The key is to gather and analyze accessible data to uncover patterns and inform global initiatives. For example, a company may find that musculoskeletal issues are prevalent across multiple regions, spurring the implementation of a global wellbeing program to combat them.

Global patterns are evident in the surge in GLP-1 medications. These medications are especially expensive in the U.S., with about half of companies covering them for weight loss, according to Aon’s 2025 U.S. Health Survey. But even in countries where the medications are less expensive or covered by public health systems, the knock-on effects of their widespread use will be important to analyze. Employees who see positive medical results may be less likely to experience other ailments associated with obesity — thereby reducing absenteeism, improving productivity and reducing and easing costs for private or public healthcare systems. On the other hand, if these medications aren’t included in the public health system but could benefit employees, companies need to decide if covering the cost is worth it in the long term — a question U.S. employers are already asking.

Gaining a Global Identity Through Global Standards and Local Flexibility

A global benefits identity relates back to the company’s values and people strategy. When employers have the right data to analyze and visualize, they can implement targeted global initiatives that bring the economies of scale needed to optimize costs while delivering on the company’s promise.

Aside from economies of scale, another benefit of having global standards is consistent employee experience. Employee mobility is important for multinationals. Having consistency, where possible, helps smooth transitions for employees.

Universal efficiency with local execution defines a global benefits strategy.

Case Study

Universal Efficiency with Local Execution Defines a Global Benefits Strategy

A large fintech company was looking to implement a truly global employee benefits strategy, with effective regional execution.

By developing a customized dashboard based on integrated data, Aon provided the client with the data-driven information needed to create a consistent and effective benefits strategy based on insights from the performance of their benefit programs.

The key? An integrated approach that brought together real-time data access with benchmarking tools to deliver measurable value.

A global approach helps companies standardize where possible and determine where local customization is needed. A global standard allows employers to pursue their own benefits path without always following other companies.

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By breaking down silos to share data effectively, companies can establish a global perspective on benefits that will help ensure compliance and optimize costs. It’s just one way that data and technology can bring a global benefits identity to life.

Kathryn Davis
Vice President, Global Benefits

#2 Reduce Voluntary Attrition to Drive Workforce Performance

Measuring the effectiveness of total rewards seems tricky, but it doesn’t have to be. Revenue per employee can give broad outlines of performance, but it is possible to overpay for talent. Eventually, the incremental revenue generated is outpaced by the increased cost. A better metric might be revenue per dollar of total rewards spent. That allows companies to show performance and efficiency.

Using revenue per dollar of total rewards, research that spans our health, wealth and talent data finds the biggest correlation to success is the average length of employee tenure. The longer employees stay, the better the company performs.

Aon’s research found that increasing average employee tenure by one year led to an increase of $1.26 in revenue per dollar of total rewards spent.

There are a few reasons for this:

  1. People have institutional knowledge acquired over the course of their tenure. More simply, they get better at their jobs the longer they are there. Tasks that take days for a beginner take hours for experienced employees.
  2. Recruiting, hiring and training new talent is expensive. New talent also takes time to be onboarded. If a company loses someone who has been in the job for five years, it could take years for their replacement to reach the same level of productivity in that same role, and they’ll likely be doing it at a higher price.
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By this metric, the best way to improve employee productivity is to improve employee retention, especially among high-performing employees, who provide a kind of “double boost” to productivity.

Muir Macpherson
PhD, Leader of Talent Analytics, Talent Solutions, North America
Using Data to Retain Employees

As organizations face increasing pressure to retain top talent and drive business performance, total rewards strategies must evolve. Retention is no longer just an HR metric — it’s a business imperative. By aligning rewards with employee preferences and understanding tradeoffs, employers can optimize programs that not only engage but also retain high-performing employees.

Modern analytics provide several tools HR departments can use to determine how to best retain employees. It’s easy to ask employees what they want, but it’s better to figure out what they value. Benefits are about tradeoffs — asking, for example, if employees would rather have additional paid leave or enhanced wellbeing benefits. Utilization data can also show where employees’ needs lie. Tools like machine learning, conjoint analysis and discrete choice analysis can uncover insights about where to focus total rewards.

Roadmap for Retaining High-Performing Employees
  1. Identify high-performing employees using performance reviews and feedback across job functions.
  2. Match these employees with their preferences through structured surveys and tradeoff analysis.
  3. Compare preferences with current offerings to identify gaps and opportunities.
  4. Assess differences across employee types and labor markets to tailor strategies.
  5. Follow up to assess retention outcomes — track how many high performers remain after program changes.
How Aon’s Human Capital Data is Helping Clients

Case Study

How Aon’s Human Capital Data is Helping Clients

An advisor that brings together data from a vast number of employers over a long period of time can provide benchmarks that show what different groups value, allowing employers to effectively target talent.

Aon partnered with a company that wanted to increase retention. Benchmarking around health, wealth and talent data revealed its disadvantage was that as a not-for-profit, they couldn’t offer equity to high-performing employees like its competitors. While there was no direct fix, the organization was able to pivot their total rewards strategy to offer best-in-class health and retirement benefits. By doing so, they could align their benefits to their values and attract employees who shared those values.

Retention is a strategic lever for business success. By using modern analytics to understand employee preferences and model tradeoffs, total rewards teams can design programs that are not only competitive but also personalized and impactful.

#3 Improving Costs Through Workforce Health and Productivity Tools

Identifying Health Risks

Rising healthcare costs aren’t just a budget issue — they’re increasingly unpredictable. Five years ago, few expected GLP-1s to become a major cost driver for weight loss. It was also speculated that gene and cell therapies would be a much bigger driver of costs than they are. Meanwhile, benefit systems are evolving worldwide: Private medical insurance is growing in the UK, and high-cost claimants are exploding in the U.S., where just 5% of members can account for up to 60% of total health spend, according to Aon data. This presents both a risk and an opportunity.

Some health risks can be anticipated and managed. Forward-thinking employers have long realized that investing in targeted interventions, like smoking cessation programs, pays off in the long run. By analyzing workforce data, these companies identified high-risk groups early and implemented solutions that improved health outcomes while cutting costs. Modern tools that use predictive analytics can take this to the next level. For example, a large transportation company was struggling with significant prevalence of recurring high-cost members with multiple health conditions, along with low member engagement with navigation and care management programs. Utilizing Aon’s Health Risk Analyzer, they were able to identify future high-risk members, many of whom were not previously identified as potential high-risk. As a result, engagement increased by 20% and the company saved an estimated $1,000 per engaged member annually.

Claims and Wellbeing Data

By sifting through claims and wellbeing data, these platforms can flag employees at risk of becoming high-cost claimants. This allows employers to implement early interventions like preventative treatments, advanced screenings or personalized care management before costs spiral. By leveraging multiple sources of data like medical, pharmacy, attendance, disability records and workers compensation claims, employers can get a more accurate and holistic view of a company’s exposure to potential claims.

Absentee Data

Absentee data is just as critical. While employees who miss fewer days due to illness will likely be more productive, it’s also true that certain health conditions can increase workplace risks. For example, employees with high blood pressure or Type 1 diabetes could be at higher risk for workplace accidents, especially in safety-sensitive roles. Using health and absence data helps HR teams proactively support at-risk staff and safeguard productivity. In addition, offering programs that can improve employee wellbeing can increase both retention and health.

Climate Data

Taking the analysis a step further, certain climate-related data can be layered into health data to quantify the risk of extreme weather events like heatwaves on employee health. This type of climate data can add another dimension to employers’ understanding of employee health and offer greater insight into how to protect workers.

There are headwinds to a data-driven approach. Differing healthcare systems can affect what’s possible. High turnover rates can have an impact as well. The higher the turnover, the more likely employers will be hesitant to invest in long-term health programs. But the overall message is still clear: Wherever possible, use data to target interventions, reduce unpredictable costs and build a healthier, more productive workforce.

#4 Enhancing Performance Through AI Preparedness and Deployment

How is artificial intelligence (AI) changing the health and talent functions of HR? The pace of change is accelerating, stoking both optimism and fear that AI will become the transformative technology its advocates describe — and its rate of adoption has left many struggling to keep up.

This accelerating pace of change is a challenge for HR professionals, as they attempt to get a handle on the ethical, legal and business considerations of deploying AI. Where should it be used next? What should employers prioritize to ensure the safe, effective and transparent use of AI?

There are several potential use cases for AI in the HR space, leaving many leaders overwhelmed. That’s why it’s important to remember that AI is first and foremost a tool to help solve problems, not a solution itself. The questions that need to be answered first are basic. Here are a few:

Who is using these tools? Throughout every company’s vendor ecosystem, there are vendors touting their new AI capabilities. It’s important to understand what they offer, and what risks and opportunities their capabilities bring. This is especially true among health vendors, as AI tools are doing everything from answering member inquiries to predicting high-cost claimants.

What problems can AI solve? Optimizing spend is the ultimate goal. It’s important to therefore determine what steps AI can help with to achieve this. For example, by using AI to analyze total rewards holistically, companies can personalize benefits to improve ROI. AI can also aid in communicating total rewards more efficiently and effectively to improve choice and uptake of benefits.

Do we have the skills we need? Skills assessment can help in a few ways. By learning what skills are needed to use AI, companies can shift to a skills-based workforce. This can have positive knock-on effects on the entire organization. Additionally, employers can learn which skills AI tools can enhance or replace, freeing time for workers to focus on business objectives.

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AI isn’t going to replace jobs, but people with AI skills could replace people without them. Rather than use AI to cut costs, we can use it to innovate.

Ernest Paskey
Head of Workforce Transformation, Talent Solutions, North America

Can we use AI effectively and responsibly? From governance and transparency to data security and workforce readiness, AI can pose many risks. Ensuring appropriate tools are used responsibly will allow companies to grow their use of AI sustainably. Those who jump in without that level of preparation may find themselves scrambling to retreat and recalibrate.

“Given the number of things a company can do both by using AI itself and through vendors’ AI capabilities, prioritization will be of the utmost importance,” says Kevin Fyock, Health Solutions Innovation and Commercialization Leader for North America. “Skills assessment is important, as is applying predictive analytics to health data — another place where an advisor can help HR teams talk through what they want to accomplish to manage the transition to an AI-enabled future.”

#5 Maximizing People Spend With Total Rewards Benchmarking Tools

Increasingly, total rewards are seen not just as an expenditure, but as a way to drive business outcomes. The best way to achieve this is through total rewards programs that are personalized, relevant and aligned with the company’s values. But those rewards also need to be competitive. Employers need to know how their total rewards program stacks up, and what effect changes to that program will have. Using the company’s own data on compensation and benefits, it’s possible to not only see how competitive its total rewards are, but also how to optimize the program for maximum effect. Here’s how to get started:

  • Step 1: Collect and Benchmark the Data

    Collecting data may seem like a simple task, but that data is often stored in separate systems with multiple vendors. This is especially true for multinational corporations, who may have data across dozens of countries, all with their own systems and vendors.

    Once the company has its total rewards data in one place, it’s time to compare it to the market. It may be tempting to only look at compensation data against direct competitors, but it’s important to remember that not every employee of an organization is competing in the same job market. For example, engineers and accountants may have sought-after skills that transfer to a number of industries. The value of a total rewards package goes beyond compensation — so it stands to reason that a competitive analysis would be far less meaningful if it only incorporated pay.

    Using digital tools like benchmarking dashboards , employers can begin to see where they stand on their overall rewards program, while drilling into individual components. As an added bonus, this process will also help employers kickstart their progress toward total rewards transparency.

  • Step 2: Optimize Programs

    Total rewards is fundamentally about tradeoffs. Understanding what employees truly value can guide smarter investments. Advanced analytics, such as employee preference modeling and predictive tools, can help employers evaluate the impact of different program elements. For example, how does an additional dollar spent on wellbeing compare to transportation allowances for driving improved performance and engagement?

  • Step 3: Model the Changes and See the Impact

    Retention of high-performing employees has outsized impact on business outcomes. Modeling how different total rewards strategies affect retention across employee segments can clarify which changes will move the needle. Tools like machine learning, conjoint analysis and discrete choice modeling help uncover what employees value most — regardless of what they say they want. These insights allow employers to design programs that reflect real preferences and drive measurable results.

Learn More

Aon’s integrated approach — combining human capital data, predictive analytics and advanced modeling — helps employers on their journey to optimize total rewards spend.

Our cross-functional and industry experts deliver actionable, data-driven insights that help employers attract and retain their high performing talent.

Contact an Aon advisor to get started.

Aon’s Thought Leaders

Kathryn Davis
Vice President, Global Benefits, Asia Pacific

Carl Redondo
Leader, Global Benefits, United Kingdom

Muir Macpherson
Leader of Talent Analytics, Talent Solutions, North America

Jesse Sulfridge
Leader of Aon’s Human Capital Analyzers, Asia Pacific

Kevin Fyock
Innovation Leader, Health Solutions, North America

Ernest Paskey
Head of Workforce Transformation, Talent Solutions, North America

Stephanie DeLorm
Global Total Rewards Commercial Leader, North America

Jane Kwon
Total Rewards Strategy Leader, North America

General Disclaimer

This document is not intended to address any specific situation or to provide legal, regulatory, financial, or other advice. While care has been taken in the production of this document, Aon does not warrant, represent or guarantee the accuracy, adequacy, completeness or fitness for any purpose of the document or any part of it and can accept no liability for any loss incurred in any way by any person who may rely on it. Any recipient shall be responsible for the use to which it puts this document. This document has been compiled using information available to us up to its date of publication and is subject to any qualifications made in the document.

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