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Organizational growth largely depends on staff retention. Losing employees frequently impacts the morale of the organization and hiring new employees is more expensive than retaining existing ones. Good news is that organizations can increase employee retention using data-driven intervention strategies. This course focuses on data acquisition from multiple HR sources, exploring and deriving new features, building and validating a logistic regression model, and finally, show how to calculate ROI for a potential retention strategy.
This chapter begins with a general introduction to employee churn/turnover and reasons for turnover as shared by employees. You will learn how to calculate turnover rate and explore turnover rate across different dimensions. You will also identify talent segments for your analysis and bring together relevant data from multiple HR data sources to derive more useful insights.What is turnover?50 xpObjectives of employee turnover prediction50 xpImporting headcount and turnover data100 xpExploring the data50 xpWhat proportion of employees have left?100 xpWhich levels have high turnover rate?100 xpIs turnover rate different across locations?100 xpHR data architecture50 xpFiltering the dataset100 xpCombining HR datasets (I)100 xpCombining HR datasets (II)100 xpMaster data overview100 xp
In this chapter, you will create new variables from existing data to explain employee turnover. You will analyze compensation data and create compa-ratio to measure pay equity of all employees. To identify the most important variables influencing turnover, you will use the concept of Information Value (IV).Feature engineering50 xpDerive age difference100 xpDerive job hop index100 xpDerive employee tenure100 xpCompensation50 xpExploring compensation100 xpPay Gap50 xpDeriving Compa-ratio100 xpDeriving Compa-level100 xpInformation value50 xpCalculating Information Value100 xpWhich variables are important?50 xp
In this chapter, you will build a logistic regression model to predict turnover by taking into account multicollinearity among variables.Data splitting50 xpSplit the data100 xpCorroborate the splits100 xpIntroduction to logistic regression50 xpBuild your first logistic regression model100 xpBuild a multiple logistic regression model100 xpInterpreting significance levels50 xpMulticollinearity50 xpDetecting multicollinearity100 xpDealing with multicollinearity100 xpBuilding your final model50 xpBuilding final logistic regression model100 xpUnderstanding the model predictions100 xpInterpret the results50 xp
Model Validation, HR Interventions, and ROI
In this chapter, you will calculate the accuracy of your model and categorize employees into specific risk buckets. You will then formulate an intervention strategy and calculate the ROI for this strategy.Validating logistic regression results50 xpCreate a confusion matrix100 xpAccuracy of your model100 xpDesigning retention strategy50 xpCalculate turnover risk probability100 xpCreating turnover risk buckets100 xpWhat would you do?50 xpReturn on investment50 xpCreate salary hike range100 xpCalculate turnover rate across salary hike range100 xpCalculate ROI100 xpWrap-up50 xp
People Analytics Practitioner
Anurag holds post graduate degree in Human Resources Management from XLRI, Jamshedpur. He has several years of experience in setting up People Analytics function for global MNCs and believes that for HR to get a seat at the C-suite table, they should incorporate data driven decisions across the employee lifecycle.
People Analytics Practitioner
Abhishek is an SHRM Certified Professional, a CAP® (Certified Analytics Professional from INFORMS), and a Six Sigma Black Belt. Has worked and held various roles in fortune 500 companies including setting up HR Analytics and Digital HR COE. He has been a visiting faculty to Indian Institute of Management and is a speaker on HR Analytics.