Nowa strona 1 » How to Build a Predictive Model for Flight Risk Using Workforce Data

Anticipating turnover with data-informed precision

High-performing organizations don’t just react to resignations—they anticipate them. Predictive analytics in HR, particularly flight risk modeling, has emerged as one of the most powerful tools in strategic talent management. By identifying who might leave before they disengage, HR can take targeted, proactive action to retain critical talent segments. This guide walks through how to develop such a model—starting with smart data collection and ending with actionable dashboards—without needing to be a data scientist.

I. Building the Foundation: Data Collection and Feature Selection

Creating a reliable flight risk model begins not with algorithms, but with understanding your people and the context behind their decisions. The foundation is built through structured workforce data that reflects behaviors, milestones, and attitudes that commonly precede voluntary exits.

1. Define Your Objective

Be clear on what you're solving. For example:

Are you trying to predict resignation in the next 3–6 months?
Are you focused on a specific talent segment (e.g., top performers, critical roles, Gen Z)?
Will the model support strategic planning or manager-level interventions?

Having a specific objective will guide the data scope and the required granularity.

2. Identify and Collect the Right Data Sources

Pull from systems you already have access to (HRIS, LMS, ATS, survey platforms, time tracking, etc.). The goal is to gather structured and time-bound data that offers signals of disengagement or context for turnover.

Key data categories to collect:

Demographic & Role Data: age, tenure, job level, function, location, internal mobility history
Behavioral Data: absenteeism patterns, performance rating changes, promotion/raise history
Engagement Signals: survey scores (especially “intent to stay,” recognition, trust in leadership)
Manager & Team Dynamics: span of control, manager turnover, team attrition rate
Career Progression: time since last development move, lateral transfers, stalled career velocity

Example: A software developer who’s had no promotion or skill development for 2 years, had a manager change twice, and recently reported low trust in leadership may exhibit high flight risk—even with solid performance.

II. Feature Engineering: Turn Data into Predictive Inputs

"Feature selection" means choosing which variables to feed into the model. Think in terms of predictive power—what correlates most strongly with past resignations?

Steps to follow:

Review attrition trends from the last 12–24 months to identify recurring patterns.
Create calculated features (e.g., time since last raise, performance delta, percent change in engagement).
Normalize your data (e.g., scale tenure, encode categorical variables like job level).

Pro Tip: Don't overfit the model with too many features. Start with 8–15 that are most interpretable and available at scale.

III. Build the Predictive Model: From Concept to Algorithm

Now it’s time to apply statistical modeling to test and validate your flight risk hypothesis.

Recommended Approaches:

Logistic Regression: A good baseline model to assess binary outcomes—stay or leave.
Random Forest / Decision Tree Classifiers: Handle nonlinear relationships and provide better interpretability.
Gradient Boosted Machines: High-performance algorithms for larger datasets with many features.

Train your model using historical attrition data, split into training/test datasets (e.g., 80/20). Evaluate accuracy using:

Confusion matrix: How many true positives (correctly predicted leavers)?
Precision/Recall: Especially important in identifying rare but critical leavers.
ROC-AUC score: The higher the score (>0.7), the better the model differentiates between stayers and leavers.

IV. Translate the Output: Dashboards and Actionable Insights

1. Create Flight Risk Dashboards by Segment

Design dashboards not just for data scientists but for HR business partners and line managers.

Structure them around:

Heatmaps of risk levels by department, role, or region
Individual risk scores with top contributing factors
Alerts for high-risk individuals in business-critical roles
Trends over time to show whether risk is increasing or decreasing

Example: A dashboard shows that mid-level engineers in the Warsaw office have a 40% higher risk score when engagement scores dip below 60 and tenure exceeds 18 months without internal mobility.

2. Embed Results into Talent Review and Planning

Use the data during succession planning, performance calibration, or compensation cycles.
Share only interpretable and actionable results with managers—not black-box scores.
Design "what-if" scenarios (e.g., what happens to risk if we increase career mobility or improve leadership trust).

V. Model Maintenance and Ethical Considerations

Predictive models are not set-and-forget tools—they require continuous tuning.

Best Practices:

Refresh data quarterly to keep signals current.
Review features annually as culture, policies, and employee expectations evolve.
Ensure transparency and fairness—avoid using protected characteristics (e.g., gender, ethnicity) and monitor for unintended bias.

Ethical example: A model might predict higher risk for single parents if attendance is used as a proxy—this must be flagged and adjusted to prevent systemic bias.

Closing Advice for HR Leaders

Building a flight risk model isn’t just a data project—it’s a cultural one. It demands cross-functional collaboration, strong data governance, and an ethical lens. But done right, it empowers HR to elevate its strategic advisory role by:

Catching early signals of disengagement
Informing retention investments with precision
Reducing reliance on reactive exit interviews

Remember: The true value of the model lies not in the algorithm, but in how it drives smarter, more human-centered decisions at scale.