Most organizations would agree that unplanned employee turnover can be extremely costly and disruptive.
Luckily, predictive analytics offers a powerful solution - enabling companies to identify flight risk employees before they leave and take targeted retention actions.
In this post, we'll break down what defines a flight risk employee, look at the typical analytics-based risk assessment framework, and explore data-driven strategies to curb turnover likelihood for high risk segments of your workforce.
Introduction to Predictive Analytics for Employee Retention
Retaining top talent is critical for organizational success, yet high employee turnover continues to challenge many companies. Identifying flight risk employees - those likely to leave within a specific timeframe - is key to developing proactive retention strategies.
Predictive analytics leverages data to forecast potential turnover issues. By analyzing metrics around performance, engagement, compensation, and more, models can detect employees exhibiting warning signs. This allows HR teams to intervene with targeted initiatives to improve satisfaction and encourage retention among high-value staff.
Understanding High Flight Risk Employee Meaning
Flight risk employees are those with a high probability of voluntarily resigning within a defined upcoming period, often the next 3, 6, or 12 months. Data points indicating potential turnover include:
- Low engagement scores
- Poor performance over time
- Minimal pay increases
- Lack of recent promotions
- Reduced workload
- Low utilization rates
When top performers disengage and leave, significant costs arise from loss of productivity, knowledge, and continuity. There are also expenses with hiring and training replacements.
Quantifying the Costs of Employee Turnover
The true cost of replacing an employee often equals 1-2x their salary when accounting for:
- Recruiting fees
- Hiring manager time
- Onboarding/training
- Lost productivity
Additional cultural impacts of turnover include:
- Declines in team morale
- Loss of organizational knowledge
- Lags meeting customer needs
Proactively curbing turnover is exponentially more affordable than managing high volumes of unplanned departures.
Who are the people who are flight risks?
Employees who are considered flight risks typically exhibit some common characteristics:
-
Low engagement - They are disengaged from their work and avoid participating in team activities or company events. These employees may only do the bare minimum required.
-
Dissatisfaction - They complain about various aspects of the workplace like company culture, leadership, compensation etc. They are generally unhappy with their current role.
-
Poor performance - Their productivity and quality of work declines over time. They miss deadlines or make mistakes frequently.
-
Lots of absences - They take more sick leaves or time-off than usual. They may call in sick frequently on Mondays/Fridays.
-
Sudden change in behavior - Previously hardworking employees lose motivation seemingly for no reason. They become withdrawn and start isolating themselves.
-
Passive job search - They inquire about job openings in other companies or subtly put feelers out via their network.
-
Policy violations - Some disgruntled employees may steal data or badmouth the company publicly. They violate company policies intentionally.
Using workforce analytics, HR can identify these flight risk patterns like absences, policy violations etc. They can then develop targeted strategies for employee retention.
What is the employee flight risk model?
The employee flight risk model is a data-driven approach used by human resources professionals to identify employees who are most likely to voluntarily leave the company. This predictive model analyzes various factors that influence an employee's propensity to quit, also known as "flight risk".
By examining key variables such as compensation, tenure, performance ratings, engagement survey responses, and career development opportunities, HR can build a flight risk matrix to determine each employee's risk score. Those with high scores may warrant proactive retention strategies.
Some examples of data points HR might incorporate into a flight risk model include:
- Compensation ratios compared to market rates
- Number of years at the company
- Recent performance review ratings
- Scores on engagement or culture surveys
- Completion of training programs
- Internal promotions vs. external hires
The outputs of the model enable HR to segment the workforce and target interventions toward high flight risk employees. Examples may include increased compensation, career development planning, cross-training, or succession planning.
Prioritizing these talent management initiatives based on flight risk analysis allows HR to optimize budgets while improving retention. It shifts efforts from reactive to proactive retention that aligns to strategic business goals. With thoughtful application, the employee flight risk model provides data-backed insights to inform management decisions.
What is identified as a flight risk?
A "flight risk" employee refers to someone who has a higher likelihood of voluntarily leaving their job in the near future. There are several indicators that can help identify employees who may be at risk of resigning:
- Low engagement or satisfaction scores in employee surveys
- Few social connections with colleagues
- Lack of career development opportunities
- Feeling undervalued or overlooked for promotions
- Disengagement behaviors like tardiness or absenteeism
- Openly browsing job boards or networking
HR professionals can leverage data analytics to uncover these signals and predict flight risk. Useful data sources include:
- Engagement survey results over time
- Participation in social events and office activities
- Performance management records
- Learning management system tracking
- Recruitment and exit data
Advanced analytics techniques like machine learning algorithms can detect complex patterns across multiple data points to classify employees by flight risk level automatically.
HR can then develop targeted retention strategies for high flight risk employees, such as:
- Clearly mapping promotion opportunities
- Creating mentorship and sponsorship initiatives
- Providing tailored learning and development plans
- Improving manager relationships
- Offering more flexibility
Proactively identifying and nurturing disengaged employees can lead to better talent retention outcomes.
What does it mean when you say someone is a flight risk?
When referring to an employee as a "flight risk", it means that they are considered likely to voluntarily leave their job in the near future. Specifically, a flight risk employee is someone that an organization identifies as having a high probability of turnover.
There are a few common indicators that can help identify if an employee may be a flight risk:
- They have been with the company for a short period of time, less than 1-2 years
- They have few connections to other employees or engagement with the company culture
- Their skills are very in-demand in the current job market
- They have expressed dissatisfaction with their job, manager, pay, etc.
- External factors like family relocation or continuing education
- They have already been searching and applying to other jobs
Identifying flight risk employees proactively is important because turnover, especially of top talent, can be very costly and disruptive for an organization. HR professionals analyze various workforce metrics, survey data, and performance records to create flight risk assessment models. This allows them to pinpoint vulnerable segments of the employee population who may be planning to leave.
Intervening before an employee departs allows the company to address pain points and potentially retain them. It also provides more time to prepare for a smooth transition. Overall, identifying flight risks is a data-driven way to get ahead of attrition issues.
sbb-itb-34a8e9f
The Employee Flight Risk Assessment Matrix Explained
Crafting a Data-Driven Flight Risk Framework
To build an effective flight risk assessment framework, HR professionals can leverage existing HR data on key factors that correlate with turnover, such as:
-
Performance ratings: Employees receiving low performance scores or negative feedback may be more likely to leave. Monitoring scores over time can identify trends.
-
Compensation history: Pay that significantly lags behind market rates or peers can drive employees to other opportunities. Regularly benchmarking compensation can highlight flight risks.
-
Tenure patterns: Longer-tenured employees tend to be more engaged and less likely to leave. Tracking tenure by department/role allows customized retention initiatives.
By compiling this data into a flight risk matrix, HR can assign risk scores to employees based on these factors. Additional predictive variables like engagement survey results or skills demand can also be incorporated.
Incorporating Survey Data for Engagement and Satisfaction
Surveying employees directly provides attitudinal signals to complement HR metrics on flight risk. Areas to gather feedback through pulse surveys or engagement assessments include:
-
Job satisfaction with role, manager, work-life balance, growth opportunities etc. Dissatisfaction predicts turnover.
-
Organizational commitment regarding aligning values, feeling valued and involved. Low commitment employees tend to disengage.
-
Growth outlook regarding career development support. Employees who feel their career is stagnating often leave.
Feeding these survey results into the flight risk model provides a rich view of which employees may be more likely to quit based on their attitudes and perceptions.
Designing and Utilizing an Employee Flight Risk Assessment Template
A flight risk matrix compiles key variables shown to predict turnover, with customized weighting and scoring mechanisms. An example template may include:
| Variable | Weight | Risk Score |
|---|---|---|
| Tenure | 30% | 0-2 years = 5 points |
| Performance Rating | 20% | 1 (lowest) = 5 points |
| Pay vs. Market | 15% | >15% below = 5 points |
| Engagement Survey | 20% | 1 (disengaged) = 5 points |
| Manager Assessment | 15% | 1 (poor) = 5 points |
| Total Risk Score | 100% | Sum of Weighted Scores |
Based on total scores, HR can divide employees into low, moderate and high flight risk categories for targeted retention initiatives per segment.
This framework powered by HR analytics enables data-driven assessment of employee flight risk.
Employing an Employee Flight Risk Assessment Tool
Identifying flight risk employees through data analysis enables organizations to develop targeted retention strategies. HR professionals can leverage analytics to uncover macro turnover trends, pinpoint higher risk segments, and model the likelihood of attrition on an individual level.
Segmentation Analysis for Risk Identification
Segmenting the workforce into categories allows HR to compare turnover rates across different groups. Useful segments include:
- Location
- Department
- Tenure
- Performance rating
- Age range
Discovering that one office has significantly higher attrition than others prompts an investigation into local management issues or unfavorable policies. Finding millennials quit at 2x the rate of older workers indicates a need for more personalized career development and advancement opportunities.
Predictive Modeling for Turnover Likelihood
Advanced analytics like regression accurately forecast who is most likely to leave. HR provides historical workforce data on who quit previously. The algorithm identifies the strongest indicators of flight risk such as:
- Low engagement scores
- Minimal pay raises
- Few promotions
It then calculates a flight risk score for each employee. HR managers follow up with high risk individuals to address pain points proactively.
Visualizing Turnover Trends and Metrics
Data visualization through dashboards allows executives to monitor turnover KPIs. Key metrics to track include:
- Voluntary/involuntary turnover rate
- Retention rate
- Exit interview sentiment
Spotting an uptick in volatility early on facilitates rapid response to mitigate issues before further attrition occurs. This enables strategic management decisions grounded in real-time workforce analytics.
In summary, analytics transforms employee flight risk assessment from reactive to proactive. Macro trends, at-risk segments, and individual scores guide personalized interventions to retain talent.
Strategic Management Decisions for Managing Flight Risks
Identifying flight risk employees through data analysis can empower organizations to make strategic management decisions to curb turnover. HR professionals can leverage predictive analytics to pinpoint vulnerabilities and retain talent through targeted initiatives.
Compensation Strategies for Employee Retention
Compensation analysis using AI algorithms helps identify inequities and retention risks based on compensation. Organizations can then correct pay gaps, ensure internal equity, and provide merit-based raises to high-potential flight risks. This evidence-based approach boosts employee satisfaction and curbs turnover by fairly rewarding top talent.
For example, data insights may reveal that high-performing employees in a certain department are paid 10-15% below market rate. Proactively addressing this pay inequity for flight risks makes them less likely to leave for better offers elsewhere.
Leadership Development as a Retention Lever
Analytics often uncover flight risk patterns tied to leadership and career growth barriers. Organizations can respond with leadership training, mentorship programs, and upskilling initiatives aimed at high-potential employees showing turnover warning signs.
Empowering flight risks with new skills, responsibilities, and career mapping helps re-engage them. For instance, a targeted development program for at-risk millennials can curb regrettable turnover by clearing pathways to advancement.
Enhancing Work Environment to Curb Turnover
Data analysis also provides visibility into workplace experience gaps causing flight risk. Organizations can introduce improvements to culture, flexibility, DEI, managerial support, and other environmental factors to directly address turnover root causes.
If analytics connect high churn risk to lack of work-life balance, management can expand remote work options. By using data insights to remove turnover friction points in the work environment, organizations see greatly reduced regrettable attrition.
Measuring and Refining Retention Strategies
Evaluating Retention Efforts Through Key Metrics
To measure the effectiveness of retention strategies, HR professionals should track key metrics over time, particularly the employee turnover rate. The turnover rate quantifies what percentage of employees voluntarily leave the organization during a certain period.
By monitoring turnover rates on a monthly or quarterly basis, HR can determine if current initiatives to mitigate flight risk are working. For example, if the 3-month rolling turnover rate declines after implementing a retention program focused on career development and growth opportunities, it indicates that program is effectively addressing a key driver of turnover.
Additional metrics to quantify include:
- Retention rate: Percentage of employees retained over a specific timeframe
- Time-to-fill for open positions: How long it takes to fill a vacant role
- Cost-per-hire: Expenses incurred filling a vacant position
Tracking these metrics provides insight into the business impact of employee churn. If time-to-fill and cost-per-hire rise due to high turnover, it further emphasizes the need for effective retention tactics.
Adapting Predictive Models with New Data
To keep predictive models accurate, new data should continually be fed back into the algorithms. This includes updated information on both current employees as well as new hires.
For existing employees, any changes over time in key variables used in the model, such as job satisfaction, manager feedback, compensation, should be incorporated. This allows the model to identify new patterns that indicate emerging flight risks.
Likewise, collecting data on new hires allows assessments of which employees are most likely to churn during the crucial first year at the company based on latest trends.
Continually refining the predictive models ensures proactive identification of flight risk employees so mitigation actions can be targeted appropriately.
Optimizing Retention Tactics Based on Data Insights
By linking retention tactics to risk scores from predictive models, HR has a framework to determine which initiatives are most impactful.
For example, offering tuition reimbursement to employees flagged as flight risks could be tested as a targeted tactic. After rolling out the pilot program, any subsequent improvement in risk scores would quantify that financial support for career development is an effective retention lever.
Likewise, predictive models could reveal manager quality as a key driver of turnover. HR could then develop manager training programs and measure subsequent changes in risk scores of employees under those managers.
This data-driven process facilitates incremental optimization of retention initiatives by pinpointing what works based on changes in measured employee flight risk.
Conclusion: Synthesizing Data Analytics for Employee Retention
Recap of Flight Risk Identification and Management
Data analytics provides HR professionals with valuable insights to proactively identify employees who may be at risk of leaving the organization. By assessing factors like engagement survey results, performance ratings, compensation history, and career development opportunities, HR can build predictive models to determine flight risk scores for each employee.
Those flagged as potential flight risks can then be targeted for retention initiatives like increased communication, career development planning, special assignments, and revised compensation packages. Over time, HR analyzes the impact of these initiatives by continuing to track flight risk scores and actual turnover rates. This enables the refinement of predictive models and retention strategies for optimal results.
The Role of Strategic Analytics in Retention Outcomes
Taking a data-driven approach transforms employee retention from a reactive to strategic function. Analytics empowers HR to move from guesswork to evidence-based, targeted initiatives that quantifiably improve retention, productivity, and costs. Rather than broadly applying blanket retention policies, HR can use predictive insights to focus resources on talent that is likely to deliver the highest ROI. With greater workforce visibility, HR evolves into a key strategic partner for optimizing human capital management across the organization.
