How Much Employee Turnover Will Cost Your Organization?

Employee turnover drains budgets in two ways: direct expenses you can see (recruiting, onboarding, training) and hidden hits you feel (lost productivity, lower morale, diminished knowledge). When you add both buckets together, it’s clear why unchecked churn is one of the most avoidable—and most expensive—line items in your operation.

Key Takeaways

• High Financial Impact: Reducing turnover by just 1% can save a 5,000-person organization over $1.5 million annually.
• Variable Replacement Costs: Replacing an employee typically costs 40%–60% of their salary, but can exceed 200% for specialized or critical roles.
• Predictive Capability: Modern HCM data and predictive analytics can identify “flight risk” employees up to six months in advance.
• Data-Driven Retention: Effective retention plans use “what-if” scenarios to forecast the ROI of specific interventions before they are implemented.

Decreasing employee turnover by one percentage point can save a 5,000-person organization over $1,500,000 annually. This makes for a great strategic HR goal, especially considering recent data on the massive financial drain of avoidable turnover. While that figure is significant for a single company, the macro-impact is staggering. This makes for a primary strategic HR goal, especially when considering the massive financial drain of avoidable turnover on the global economy.

The $1 Trillion Reality

According to Gallup, voluntary turnover is a $1 trillion problem for U.S. businesses every year. Most of this cost is unnecessary; research suggests that 52% of voluntarily exiting employees say their manager or organization could have done something to prevent them from leaving. By leveraging workforce planning software, organizations can identify these “avoidable” exits before they happen. When you align human capital management with predictive modeling, you aren’t just tracking a metric, you are recouping a portion of that trillion-dollar loss and reinvesting it into growth.

The Impact of Reducing Turnover

Before you can deliver and measure this outcome, you’ll need to complete several key steps:

1. Understand the actual cost to replace an employee
2. Calculate cost of undesirable employee turnover over the last X months
3. Predict 6-month employee exit risk and calculate the cost
4. Forecast outcomes of various retention scenarios
5. Select and implement one or more scenarios
6. Measure the results

Understand the Cost of Replacing an Employee

The first challenge is to estimate the cost to replace the employees who have exited the organization. There are many aspects of replacing an employee, each of which has a price. According to the Society for Human Resource Management (SHRM), replacement costs vary significantly by role:

• Standard Roles: 40%–60% of annual salary
• Critical or Specialized Roles: Up to 200% of annual salary
• Top Performers: Approximately 130% of annual salary due to massive productivity loss

The final component of calculating replacement cost is employee performance. The impact of losing a top performer is far more significant than losing an average employee. Studies have shown that a top performer does 400% of the work of an average employee.

Predict Exit Risk and Forecast Replacement Cost

Modern HCM systems contain vast amounts of historical data that reveal patterns invisible to the naked eye. Predictive analytics utilizes this data to:

• Identify hidden patterns in employee behavior.
• Process complex historical “employee stories.”
• Predict the likelihood of an individual exiting within the next six months.

A robust People analytics platform will handle most of this work for you. Understanding how it works helps you interpret and act on the results. The next section explains how we predict exit risk. Note: The following section covers the technical details of predictive modeling. Skip ahead if you prefer to focus on application rather than methodology.

Tech talk – how predictive exit risk works

Leveraging Historical Employee Data

You forecast employee exit risk by examining completed employee stories (employees who have quit) over the past few years. You know a lot of attributes of your past employees and can examine them before employees quit or didn’t quit. Here are a few:

1. Tenure
2. Time in current position
3. Performance score
4. Manager performance score
5. Manager team size
6. Distance to the office
7. Drive time to the office
8. Rate of recent exits on team
9. Length of time at previous employer
10. Location
11. Benefit enrollment
12. Ratio of comp to team
13. Ratio of comp to job grade

Identifying Precursors to Exit

Beyond static attributes, predictive models also examine precursors – things that people did before exiting the organization. Precursors look at mostly the same fields as the attributes. The difference is that we’re looking for changes in the attributes in the time before resignation.

• Decrease or increase in performance score
• Significant change in manager’s team size
• Moved home address farther from office
• Finished an advanced degree
• Change in marital status
• Change in benefits enrollment
• Change in comp
• Recent bonus
• Recent promotion or transfer

Defining the Statistical Variables

There are hundreds of factors and precursors that you can consider. In statistical analysis, these are called independent variables. The big question “did they quit within six months,” is the dependent variable.

Uncovering Correlations with Regression Analysis

The goal is to determine which independent variables influence the crucial dependent variable. More accurately, the task is to remove the factors that don’t correlate or are redundant. There can also be conflicting factors to mitigate. A regression analysis will tell us how much each factor and precursor correlates with exits. It also tells us which factors to ignore.

Fitting the Model to Your Data

Evaluating these correlations is known as “fitting the model.” This requires precision to avoid two common pitfalls:

• Overfitting: Where the model overemphasizes a specific attribute or outlier, making it too rigid to predict future trends.
• Underfitting: Where the model fails to recognize existing correlations, leading to vague results.

Training the Predictive Engine

Now that you have your model fit to the data, you can learn from it. You’ll run about 80% of the finished employee stories through another regression analysis using our final set of attributes. It trains the model. In this case, “machine learning” refers to a matrix of correlation figures. This trained model can accurately identify the factors that correlate most strongly with employee exits.

Validating Accuracy: Predicting the Past

To ensure reliability, you’ll then use the other 20% of employee stories to validate the model and predict whether past employees exited after six months. In other words, we ask the model to predict the past. The goal is to predict historical exits accurately, maybe 80% – 90%.

Actionable Exit Risk Predictions

If the model perfectly predicts the past, it’s probably been over fitted to the data and will be a poor predictor of the future. If the model is terrible at predicting the past, it’s likely under fitted. It will also be terrible at predicting the future. Fitting the model to your data requires expertise and iteration—this is where a purpose-built workforce analytics platform delivers significant value. With the model trained and validated, you can now predict the exit risk of current employees. This forecast should be about as accurate as our predictions of the past. For every employee, we know the likelihood of exiting in the next six months.

End of tech talk

SplashBI People Analytics handles all this complexity for you—the predictive models are pre-built, continuously refined, and ready to use within days of connecting to your existing HR systems. You get the insights without needing data science expertise or a lengthy implementation. You now know who is likely to leave and how much it will cost to replace them. You can forecast turnover costs over the next six months and segment these forecasts by location, department, manager, or any other dimension.

Create retention plans and forecast outcomes

You already know who is likely to leave—and more importantly, why. The predictive model highlights the specific factors behind each at-risk employee, giving you clear levers to pull in your retention plan. These are the exit drivers we want to address in retention plans. SplashBI’s predictive analytics can do more than forecast flight risk—they can run what-if scenarios and forecast the outcomes of changes. The platform makes it easy to create retention plans that:

• Identify high performing employees
• Identify those who are at risk of exit
• Identify exit drivers specific to this group of people
• Forecast the impact of changes to these drivers

Before initiating a plan, you’ll know the expected return. It’s much easier to fund and implement retention initiatives when you know the expected return.

Measure the results

Once your employee retention plan is underway, you’ll measure the results. You’ll use the same dashboards that identified the issue to track outcomes. The outcomes are all shown in the KPIs. Your workforce analytics platform makes it easy to view results by specific groups, locations, departments, and other dimensions. Since you know the cost to replace each employee, you can easily quantify outcomes in dollars.