Predictive analytics is starting to play a key role in employee retention, an area that is a critical concern for organizations striving to keep skilled employees. Ensuring an engaged workforce is important in today’s competitive talent market and predictive analytics is helping HR professionals to take a proactive approach towards identifying flight risks and understanding factors contributing towards turnover. It also helps them implement targeted retention strategies.
What is predictive analytics for employee retention?
It is the use of predictive analytics in statistical algorithms and machine learning techniques that analyse historical data to make predictions about future trends and events. In employee retention it sorts the data on employees into demographics, engagement, performance and any other relevant factors which to help forecast turnover and identify at-risk employees.
Key concepts and methods
Using various sources HR can collect and aggregate relevant data. HRIS, engagement surveys, performance reviews and exit interviews are used to source data which is then cleaned and processed to ensure data quality and consistency.
Criteria such as tenure, performance ratings, compensation and job satisfaction are identified and analysed. Additional features maybe included through engineering techniques to understand more complex relationships and interactions.
Predictive models such as logistic regression, random forests, decision trees or neutral networks are trained on historical data to predict employee turnover. Using techniques such as cross-validation, hyperparameter running and model comparison models are revaluated and refined.
Once validated predictive models are deployed to generate real time predictions regarding employee turnover risk. HR can then interpret predictions and implement retention strategies and interventions.
Benefits of predictive analytics for employee retention
Predictive analytics helps HR to identify potential flight risk employees early and to take proactive intervention and retention measure.
It provides actional insights into the factors that drive turnover so that HR personnel can prioritise resources, target interventions and allocate budgets more effectively.
Predictive analytics enables tailored retentions strategies to suit individual employee characteristics, preferences and risk profiles to increase the likelihood of success.
It enables greater savings in the long run by reducing turnover and by retaining top talent. It reduces the costs associated with recruitments, onboarding, training and lost productivity.
Employees enjoy a more engaged work environment thanks to proactive efforts which demonstrates an organisation’s commitment towards employee wellbeing. This fosters a culture of loyalty and trust.
What are the challenges and considerations?
High quality, up to date, comprehensive data is essential for predictive models to ensure data integrity and completeness for accurate predictions.
As predictive models can be complex and opaque it can become a challenge to interpret their predictions and turnover risk factors. It is essential that model interpretability and transparency are prioritised.
Predictive models must comply with legal and ethical standards, which include privacy regulations, anti-discrimination laws and employee consent requirements. This ensures that they are fair and transparent.
Human judgement and intervention are still necessary to evaluation predictions and interventions and to make final decisions. HR must find the balance between the algorithms and human expertise for retention strategies to be successful.
Best ways to implement predictive analytics in employee retention
Predictive analytic initiatives should be aligned with clearly defined objectives and goals to guarantee required outcomes.
Always promote transparency and accountability by documenting model inputs, outputs and decision making criteria.
Invest in quality data with assessments, cleansing and enrichment efforts to ensure accuracy, completeness and relevance for predictive models.
Effective measures should be implemented to mitigate unconscious biases in predictive models with diversity awareness training data and bias detection tools.
Constant monitoring is essential with feedback from stakeholders and monitoring of the predictive model performances to ensure fairness and to improve accuracy.
Predictive analytics is a powerful solution for employee retention helping HR to predict turnover risks, understand underlying factors and to implement targeted retention strategies. Organisations that adopt predictive analytics will enjoy an enhanced organisational performance to optimise employee retention and achieve their workforce and management goals.