Predictive Analytics in HR: Forecasting Turnover, Recruitment & Demand

Andras Rusznyak

artificial intelligence expert

Ha magyarul szeretnéd olvasni a cikket, kattints ide

In our last article we introduced descriptive and diagostic analysis. While these levels help us understand the past and its causes, predictive analytics elevates HR to proactive territory. It answers “What might happen?” — enabling HR leaders to anticipate turnover risks, optimize recruitment, and plan workforce needs before they become urgent.

This shift is critical: studies show replacement costs can reach up to 200% of an employee’s annual salary (1). At a bare minimum, the direct replacement costs, i.e. recruitment or agency costs amount to two months of salary. Onboarding, reduced productivity, business interruptions, client dissatisfaction and the potential higher compensation of the replacement all add to that. Predictive models help avoid reactive firefighting by identifying issues early and allowing for timely, strategic responses.

(1) Cascio, W.F. 2006. Managing Human Resources: Productivity, Quality of Work Life, Profits (7th ed.). Burr Ridge, IL: Irwin/McGraw-Hill. Mitchell, T.R., Holtom, B.C., & Lee, T.W. 2001. How to keep your best employees

IMPORTANT NOTE:

We utilized generative AI in the making of this article.

Predictive analytics uses historical data—via statistical or machine learning models—to forecast future outcomes like turnover, successful hires, or workforce gaps. It provides a “risk score” or probability for individuals or groups, informing decisions and optimizing resource allocation.

This approach transforms HR from hindsight-focused reporting to foresight-driven strategy.

What Is Predictive Analytics in HR?

Key Use Cases in Predictive HR Analytics

Why It Delivers Value

Here are common, high-impact applications:

• Turnover prediction
  Using variables such as tenure, engagement, performance, and compensation to estimate departure risk and target retention efforts.

• Recruitment success forecasting
  Forecasting candidate acceptance or success probability based on offer timing, feedback, and historical hiring trends.

• Workforce demand forecasting
  Estimating future hiring needs by analyzing growth trends, project demand, and attrition rates.

• Engagement and performance trends
  Projecting engagement or productivity dips ahead of time to launch timely interventions.

Scenario

A company with a 500-person Sales department is experiencing 18% annual turnover, significantly above the industry average. Leadership is concerned about the costs of recruiting and onboarding replacements, as well as declining performance due to constant team reshuffling. HR wants to anticipate who is likely to leave and take preventative action.

Step 1: Define Your Goal & Scope

What you do:
Start with a clearly defined question:
“Can we predict which salespeople are most at risk of voluntary turnover in the next 6 months?”

Why it matters:
This ensures your project stays focused and actionable. Instead of trying to model general turnover trends, you're zooming in on predictive risk at the individual level, which enables intervention.

Tools & skills:

• Problem framing

• Stakeholder alignment

• Scoping the time horizon (e.g., “next 6 months” vs. “next 12 months”)

Step 2: Collect and Prepare the Data

What you do:
Gather a dataset of past and current sales employees, including those who stayed and those who left, with variables that might influence attrition.

Data required:

• From HRIS:

  • Tenure

  • Employment type (e.g. inside vs. field sales)

  • Age

  • Compensation and changes

  • Manager assignment

  • Exit date and type (voluntary vs involuntary)

• From Engagement surveys:

  • Job satisfaction

  • Perceived manager support

  • Career development satisfaction

• From Performance Management:

  • Quarterly performance ratings

  • Sales target achievement

  • Promotion history

• Optional:

  • Time-off usage

  • Peer feedback

  • Commute distance or remote/hybrid work status

Tools & skills:

• SQL or HRIS report builder to export data

• Excel or Python/Pandas for data cleaning

• Understanding of missing data handling and variable encoding (e.g. convert ratings to numeric scores)

Step 3: Build the Predictive Model

What you do:
Use historical data to train a predictive model that estimates the probability of an employee leaving in the next 6 months.

Basic approach:

• Model type: Logistic regression (interpretable), or decision tree/random forest (non-linear but intuitive)

• Target variable: Binary outcome (1 = voluntarily left, 0 = stayed)

• Features (inputs): Tenure, engagement score, performance rating, manager changes, etc.

Validation:

• Train/test split (e.g. 70% train, 30% test)

• Assess model performance using accuracy, precision, recall, or AUC

Tools:

• Excel (with Data Analysis ToolPak or Analyse-it for regression)

• Python (e.g. scikit-learn, with Jupyter Notebooks)

• R (e.g. caret package or GUI like Rattle)

• AutoML platforms like Google AutoML, Azure ML, or KNIME for non-coders

Skills:

• Regression logic

• Interpreting feature importance

• Reading confusion matrices and ROC curves

• Understanding overfitting and model robustness

Step 4: Generate Risk Scores & Segment Employees

What you do:
Use the model to assign a “flight risk score” to each active employee in Sales—essentially, their predicted probability of leaving.

Then categorize employees into risk tiers:

• High risk (>70%)

• Moderate risk (40–70%)

• Low risk (<40%)

Why it matters:
This makes the output actionable and helps prioritize interventions. You’re not treating everyone equally—you’re focusing energy where it counts.

Tools & skills:

• Excel dashboards or BI tools (e.g. Power BI, Tableau, Looker)

• Conditional formatting or filtering by risk level

• Communicating results visually to stakeholders

Step 5: Design Targeted Interventions

What you do:
Create tailored retention plans for high- and moderate-risk groups. For example:

• High risk:

  • Initiate stay interviews

  • Review compensation competitiveness

  • Offer fast-track development programs

  • Reassign to stronger-performing managers (if relevant)

• Moderate risk:

  • Check in with their managers

  • Offer optional mentorship

  • Monitor pulse survey responses monthly

Data used to guide action:

• Risk score

• Performance trajectory

• Manager stability

• Career progression opportunities

Tools:

• HRIS or performance system for tagging risk level

• Internal HR workflows or ticketing systems to track follow-ups

• Pulse survey platforms (e.g. CultureAmp, Glint, Officevibe)

Skills:

• Change management

• People-first communication

• Program design with measurable outcomes

Step 6: Monitor & Refine

What you do:
Assess impact after 3–6 months. Have turnover rates dropped among those who received targeted action? Were scores accurate?

Measure:

• Attrition in high-risk vs. low-risk cohorts

• Intervention effectiveness by manager or region

• False positives and false negatives (were some flagged incorrectly?)

Tools & skills:

• KPI tracking dashboards

• Cohort analysis

• Feedback loops to improve data quality and model logic

Scaling and Embedding Predictive HR

Start small—focus on turnover. Then:

• Automate alerts (e.g., flagging “high risk” populations on dashboards)

• Embed predictive insights into performance reviews or talent planning

• Train team members on model interpretation and action strategies

Summary & Key Takeaways

• Predictive analytics lets HR shift from reacting to anticipating and shaping outcomes.

• Use cases—turnover, recruitment success, workforce need—are accessible and impactful.

• Even basic models deliver insight; more complex methods (e.g., ML, LLMs) amplify specificity.

• Ethical use, data quality, and integrating human judgment are essential.

Why Predictive Analytics Matters

Why This Approach Works

• Actionable: Outputs can be immediately tied to practical HR processes.

• Low barrier to entry: Models can be built in Excel or simple BI tools.

• Scalable: Once the process is established, it can be applied to other roles or departments.

• Data-driven: Prioritization is no longer gut-based but rooted in risk probability.

• Transparent: Stakeholders can see what factors matter—and why the model flagged someone.

Predictive models allow:

• Early intervention before a flight risk becomes a turnover, reducing replacement costs.

• Smarter recruitment by prioritizing candidates with higher success likelihood.

• Strategic workforce planning, reducing staffing surprises.

• Better resource alignment, by focusing HR investment where it prevents worst-case outcomes.

Practical Example: Predicting Turnover Risk in Sales

Pitfalls & Ethical Considerations

Up next: We’ll explore Prescriptive Analytics—turning predictions into recommendations and actions.

Have you read our other articles? Go to Motioo Insights

Do you have any questions or comments? Contact us