Client Challenge

A large professional services organization relied on a legacy turnover prediction model to inform hiring, retention, and succession planning decisions. While widely used by executives and HR business partners, the model had become increasingly misaligned with the complexity of the organization’s workforce and operating environment.

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The existing approach was built on a limited statistical model using fewer than ten variables—primarily age, tenure, and location—and delivered results through static Excel files. As a result, the model provided limited explanatory power, struggled to accurately identify true drivers of turnover, and was difficult for leaders to interrogate or apply in practice. Leadership recognized that without a more robust, interpretable, and actionable solution, turnover risk insights would remain reactive rather than strategic.

Approach

From Data to Action was engaged to redesign the turnover prediction capability end-to-end, transforming it into a modern decision-support tool that could meaningfully inform workforce planning, retention strategy, and leadership decisions.

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The engagement began with a critical assessment of the existing model’s performance, assumptions, and limitations, including goodness-of-fit, predictive accuracy, and interpretability. From Data to Action then led a multi-phase redesign focused on improving accuracy, expanding analytical depth, and translating insights into business action.

Model and Data Enhancement

Key elements of the redesign included:

1. Advanced Modeling Techniques
   The legacy statistical model was replaced with an ensemble machine learning approach designed to improve predictive accuracy while better capturing complex, non-linear relationships between employee attributes, work context, and turnover outcomes. This shift significantly enhanced model performance and the reliability of risk signals.

2. Expanded Feature Set
   The number of variables included in the model increased from fewer than 10 to over 100, creating a more comprehensive view of turnover drivers. Variables spanned employee demographics, performance history, role characteristics, progression patterns, engagement signals, and organizational context—allowing the model to move beyond surface-level predictors.

3. Enhanced Segmentation and Filtering
   To improve usability and relevance for leaders, From Data to Action expanded filtering capabilities beyond location to include rank, performance review status, service line, and gender. This enabled more targeted insights and supported differentiated retention strategies across the organization.

4. Financial Impact Integration
   The redesigned tool incorporated the cost of turnover as a core metric, translating risk insights into financial terms. From Data to Action calculated the estimated business impact of turnover and quantified how much the organization could save by reducing turnover by as little as 1%, enabling leaders to prioritize interventions based on return on investment.

Visualization and Decision Enablement

To ensure adoption and practical impact, From Data to Action replaced static Excel outputs with an interactive dashboard. The dashboard allowed users to:

• View the proportion and number of employees at risk of leaving across selected segments

• Identify the top drivers contributing to turnover risk for each population

• Distinguish between “good” turnover and “bad” turnover based on role criticality and performance

• Model potential cost savings associated with improved retention outcomes

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By surfacing both risk levels and underlying drivers, the tool shifted conversations from prediction to action.

Impact

The redesigned turnover prediction platform enabled the organization to:

• Improve confidence in turnover risk signals and driver identification

• Move from reactive reporting to proactive workforce planning

• Equip executives and HRBPs with actionable, segment-specific insights

• Align retention investments with financial impact and business priorities

• Strengthen succession planning by identifying risk in critical roles early

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This engagement transformed turnover analytics from a static reporting exercise into a strategic capability—empowering leaders to anticipate risk, target interventions, and make more informed talent decisions.