Prescriptive HR: Turning Predictions into Actions

Andras Rusznyak

artificial intelligence expert

Ha magyarul szeretnéd olvasni a cikket, kattints ide

In our last article we talked about the power of predictions in preparing for the potential future. Predictive analytics whispers, “Here’s what might happen.” Prescriptive analytics shouts, “Here’s exactly what you should do.” Instead of simply forecasting turnover or hiring needs, prescriptive analytics recommends concrete actions—like adjusting compensation, designing retention plays, or targeting learning paths—that optimize outcomes. In HR, this means moving from foresight to guided, data-driven decision-making.

IMPORTANT NOTE:

We utilized generative AI in the making of this article.

Prescriptive analytics builds on insights from descriptive, diagnostic, and predictive work. By combining AI, optimization algorithms, and “what-if” scenario simulations, it identifies the most effective actions to reach HR goals. It's “What should we do?”—not just "what will happen?"

What Does Prescriptive Analytics Actually Do?

Common Use Cases in HR

What Makes Prescriptive Analytics Valuable

Here are common, high-impact applications:

• Strategic Workforce Planning
  Recommends hiring quotas, reskilling, or retention incentives to meet future demand.

• Talent Acquisition
  Advises on job posting channels, screening criteria, or offer tactics to improve hiring effectiveness.

• Learning & Development
  Tailors training and development opportunities to identified skill gaps with the highest ROI.

• Retention Actions
  Suggests specific interventions like promotions, pay raises, or coaching for high-risk employee groups.

In this section, we'll walk through a hands-on, detailed workflow showing how an HR team can turn predictive insights into meaningful action.

Scenario

You already have a set of sales employees identified as "high turnover risk" through predictive analytics. Now, the core question becomes: What specific interventions should we implement to keep them engaged and retained?

Step 1: Extract Insight from Predictive Risk Scores

• What to do: Review the predictive model’s outputs—each high-risk employee’s probability score and their underlying risk factors (e.g., tenure, recent performance dip, engagement).

• What to focus on: Use the latest turnover predictions and the same model version that generated them.

• Why it matters: This gives clarity around why employees are flagged and ensures interventions are tailored to real risks.

• Data/tools/skills needed:

  • Export risk scores and feature contributions from your predictive tool (Excel or BI dashboard).

  • Ability to interpret which variables (e.g., low engagement, recent manager change) drive the risk.

Step 2: Define Effective, Testable Intervention Options

• What to do: List and define potential actions that could be applied to high-risk staff. For each, articulate the expected benefit and required investment.

• What to focus on: Use the most influential underlying risk factors for each high-risk employee to define interventions. It's important that these are measured directly and used as inputs in the turnover prediction model. This is going to ensure that you can use the same model for scenario analysis that predicted the high-risk factor in the first place. If you define actions that have never been used in the organization, it's very difficult, if not impossible to accurately predict their effect.

• Examples of solid interventions:

  • 5% salary adjustment – financially direct, measurable.

  • Personalized mentorship – pairing with high-performing peers.

  • Leader coaching – focused management support.

  • Internal role reassignment – new responsibilities or departments.

  • Flexible work arrangements – improved work-life balance.

• Why it matters: Well-defined options make actionable scenarios testable and measurable.

• Data/tools/skills needed:

  • Internal HR operational data (budget, available mentors, training programs).

  • HR collaboration to ensure interventions are realistic and available.

Step 3: Build “What-if” Simulation Models

• What to do: Simulate how each intervention (or combination) could impact retention outcomes—e.g., salary raise vs mentorship vs both.

• How to simulate effectively:

  • Update the inputs of the turnover prediction model with the planned interventions and generate risk predictions.

  • You can use a BI tool or RanSim plugin in Excel to model effect-size assumptions (e.g., a 10% retention lift for a 5% raise).

  • Or use platforms like KNIME or RapidMiner to build interactive what-if pipelines without coding.

  • Calculate projected retention rates and cost per retained employee for each scenario.

  • Apply one intervention per employee and calculate the cost of the intervention as well as the predicted risk score. This way you can compare interventions on a cost-benefit basis. You can calculate the benefit from the attrition probability decrease and the cost of attrition for that employee.

• Why it matters: Simulations help weigh costs vs benefits before committing resources.

• Data/tools/skills needed:

  • Defined effect estimates (e.g., based on past pilot data or literature).

  • Modeling tools (BI with what-if capabilities, KNIME, RapidMiner, or advanced Excel add-ins).

  • Analytical skills to compare scenarios on retention lift vs budget impact.

Step 4: Choose Actions and Set Up Control Groups

• What to do: Decide which interventions to implement based on cost-efficiency and predicted impact—for example, a 5% salary boost for the top 20% highest-risk employees and mentorship for others.

• Control group setup:

  • Identify a similar cohort of high-risk employees not receiving the intervention (e.g., next 10%) to measure effectiveness against.

• Why it matters: Helps distinguish between true intervention impact and natural retention patterns.

• Data/tools/skills needed:

  • HRIS to flag intervention vs control groups.

  • Communication and change management to implement consistently.

  • Randomization principles to ensure fair control groups.

Step 5: Measure Outcomes with Clear Metrics

• What to do: After 3–6 months, evaluate outcomes across intervention and control groups using retention rate, engagement, and performance data.

• Metrics to track:

  • Retention rate difference (e.g., 15% retention lift vs control).

  • Cost per avoided termination.

  • Engagement score changes or performance improvements.

• Why it matters: Provides evidence of intervention effectiveness and ROI.

• Data/tools/skills needed:

  • Dashboard or Excel tracking for retention, engagement, performance.

  • Statistical comparison to assess significance.

  • Cohort analysis and simple ROI computation.

Scaling Prescriptive Capability

Build momentum:

• Automate alerting of recommended actions in dashboards

• Embed suggestions into workflow systems and manager tools

• Train HR and leadership to interpret and act on prescriptive insights strategically

Summary & Takeaways

• Prescriptive analytics answers: What should HR do?

• It bridges prediction and action—crucial for proactive talent management.

• A structured "what-if" approach reveals which interventions work best—for prevention and efficiency.

• Care, clarity, and fairness are critical to ensure prescriptive systems empower—not mislead—HR decisions.

The Final Frontier: Why Prescriptive Analytics Matters

Why This Approach Works

• It builds a feedback loop: from prediction to intervention to validation.

• Helps HR make data-backed decisions with quantifiable ROI.

• Enables scalable, ethical, and fair use of prescriptive power.

Prescriptive analytics elevates HR by:

• Offering precise, data-based recommendations for action.

• Simulating outcomes of different scenarios for informed decision-making.

• Reducing guesswork and enabling proactive, strategic HR interventions.

Practical Example: Predicting Turnover Risk in Sales

Pitfalls & Ethical Considerations

Up next: We'll explore ways to measure the impact of your analytics practice—covering ROI, continuous improvement, and key HR metrics.

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