How to Identify Retention Risk Before You Lose Your Best People

Most companies discover retention risk the same way: someone resigns. By then, the cost is already locked in. This guide walks through how to identify compensation-driven retention risk across your workforce before it shows up as a resignation letter on your desk.

What Is Retention Risk?

Retention risk is the likelihood that an employee will leave your organization within a defined period. While many factors influence turnover (management quality, career growth, culture), compensation remains one of the most measurable and actionable drivers. Unlike engagement survey data or manager intuition, pay gaps relative to the market can be quantified and tracked over time.

From a compensation analytics perspective, retention risk answers a few core questions:

• Which employees are paid meaningfully below market for their role and location?
• How long has it been since each employee’s last salary adjustment?
• Are there clusters of underpayment within specific departments or job families?

Why Retention Risk Matters

The average cost of replacing an employee ranges from 50% to 200% of their annual salary, depending on the role’s seniority and specialization. For a mid-level professional earning $75,000, that’s $37,500 to $150,000 in recruiting, onboarding, lost productivity, and institutional knowledge walking out the door.

Pay transparency legislation in 15 states has also shifted employee expectations. Workers are increasingly aware of market ranges for their roles, which means a compensation gap that might have gone unnoticed five years ago is now visible. If your employees can look up salary ranges on a state-mandated job posting, they already have a reference point. The question is whether you have one too.

For HR professionals, retention risk analysis is also a credibility tool. Presenting data-backed retention flags to leadership is a fundamentally different conversation than reporting anecdotal turnover concerns.

Step 1: Calculate Comp Ratios for Every Employee

Comp ratio measures an individual’s salary as a percentage of the market reference point (typically the median). The formula is straightforward: Comp Ratio = Current Salary ÷ Market Median. A comp ratio of 1.00 means the employee is paid exactly at market median. Below 0.85, most compensation professionals consider the gap significant enough to flag.

The most common error at this step is using national data when state-level data is available. BLS wage data is published at both levels, and the difference can be substantial. A registered nurse’s median in California is not the same as in Arkansas. Using the wrong geographic reference inflates or deflates your comp ratios across the board.

Step 2: Layer in Time Since Last Salary Change

A comp ratio tells you where someone stands today. Time since last adjustment tells you the trajectory. An employee at a 0.92 comp ratio who received an increase six months ago is in a different position than someone at 0.92 who hasn’t had an adjustment in two years.

Markets move. BLS data is updated annually, and in high-demand occupations, wages can shift several percentage points in a single cycle. An employee whose pay was competitive 18 months ago may now be trailing the market without any change in performance or scope.

Flag any employee who has gone 18 or more months without a salary change and whose comp ratio is below 0.90. This combination is the most reliable early indicator of compensation-driven attrition.

Step 3: Segment by Department and Job Family

Retention risk is rarely distributed evenly. It tends to cluster in specific departments, job families, or locations where market movement has outpaced internal pay adjustments. Looking at averages across the entire organization masks these clusters.

Run your comp ratio analysis at the department level. If your engineering team’s average comp ratio is 0.88 while your operations team is at 0.97, you have a targeted problem, not a systemic one. This distinction matters when presenting findings to leadership because the remediation plan and budget ask will be different.

Step 4: Build a Retention Risk Matrix

Combine comp ratio and time since last change into a simple matrix. Employees who fall into the low comp ratio and long time since adjustment quadrant are your highest-risk individuals. This isn’t a prediction model. It’s a triage tool that tells you where to focus limited budget and attention first.

Sort the results by department and present them alongside the estimated replacement cost for each role. When leadership can see that five flagged employees in the same department represent $400,000 in potential replacement costs, the conversation shifts from “should we give raises” to “where do we allocate retention dollars most effectively.”

Step 5: Establish a Review Cadence

A one-time retention risk analysis is useful. A quarterly review is transformational. Markets shift, new hires come in at different rates, and internal promotions change the landscape. Set a cadence (quarterly is ideal, biannually at minimum) to recalculate comp ratios and update your risk flags.

Document each cycle’s results. Over time, you build a record that shows whether your compensation strategy is keeping pace with the market or falling behind. That longitudinal data is far more persuasive to a CFO than a single snapshot.

How What It Pays™ Supports Retention Risk Analysis

What It Pays™ uses government-verified BLS wage data as its foundation, covering over 800 occupations across every U.S. state. The platform calculates comp ratios automatically, flags employees whose comp ratio falls below your set threshold, and tracks time since last salary change to surface retention risk indicators without manual spreadsheet work. As the platform grows, it will layer in anonymized, real-time company salary data on top of the BLS foundation to sharpen benchmarking further.

Employers on the Essential plan and above can upload their workforce via CSV, run benchmarking across all employees at once, and export the results. Explore the platform at whatitpays.com.

Frequently Asked Questions

What is a retention risk indicator?

A retention risk indicator is a data-driven flag that suggests an employee may be at elevated risk of leaving. In compensation analytics, the two primary indicators are a comp ratio below a defined threshold (typically 0.85) and an extended period without a salary adjustment (typically 18 or more months). These are lagging signals of market misalignment, not predictions of behavior.

What comp ratio signals high retention risk?

Most compensation professionals flag employees with a comp ratio below 0.85 as high risk. This means the employee earns 15% or more below the market median for their role and location. A comp ratio between 0.85 and 0.95 may warrant monitoring depending on the role’s market demand.

How often should I run a retention risk analysis?

Quarterly is the recommended cadence for organizations with 50 or more employees. At minimum, run the analysis biannually. BLS data updates annually, but internal changes (new hires, promotions, departures) shift your risk profile more frequently.

Does What It Pays™ use self-reported salary data?

No. What It Pays™ uses government-verified BLS (Bureau of Labor Statistics) wage data as its foundation. BLS data is collected from employer payroll records through mandatory surveys, not self-reported by individuals. As the platform scales, it will also layer in anonymized, real-time company salary data to supplement the BLS baseline. This approach eliminates the self-selection and inflation bias common in crowdsourced salary platforms.

What is the average cost of replacing an employee?

Replacement costs typically range from 50% to 200% of the departing employee’s annual salary, depending on the role’s seniority, specialization, and the local labor market. For a mid-level professional earning $75,000, that translates to $37,500 to $150,000 in direct and indirect costs.

Can retention risk analysis prevent all turnover?

No. Compensation is one factor among many. Retention risk analysis focused on pay data helps identify and address market-driven attrition risk, but it does not capture factors like management quality, career development, or organizational culture. It is most effective as one component of a broader retention strategy.

Dr. Bruce Brown is the founder of CompRatio LLC and the creator of What It Pays™. He holds a PhD in Human Resources and the SHRM-SCP certification, and works as a practicing HR professional.

Ready to identify retention risk in your workforce? Explore the platform at whatitpays.com.

Disclaimer: This article is intended for educational and informational purposes only and does not constitute legal advice. Compensation practices vary by organization, jurisdiction, and circumstance. Nothing in this article should be relied upon as a substitute for consultation with a qualified HR professional or employment attorney regarding your specific situation. What It Pays™ and CompRatio LLC are not law firms and do not provide legal services.