Leading retirement services provider
- A leading retirement services wanted to predict the attrition of its company for defined business units and defined bands of its employees
- The company was facing high attrition across its mid-level ranks and risks losing its key trained talent pool to other players in the industry
- Key employee attrition was a pain point and expectation was to build predictive models to understand attrition at different bands based on the internal and external factors
Analytics Led Approach
- Data cleansing and missing value imputation
- Time series data set created for modeling (quarterly
- Research on external variables influencing attrition
- Some of factors evaluated are like performance ratings, salary attitudes, designation, state wage index, resident status, department/BU etc.
- Explore various classification statistical techniques for an appropriate prediction model. (Decision trees, logistic and Support Vector Machine)
exploratory analysis on the data, where key factors impacting attrition were identified.
- A model was developed and delivered to predict the attrition over the next few quarters.
- A sensitivity (what if analysis) model was delivered for the consumption of the HR team to predict / forecast the attrition rate based on different internal (performance rating, bonus payouts, etc.) and external environmental factors (unemployment rate, stock index etc.)
Critical Success Factors
Close involvement of stake holders during model development and validation phase
HR team uses the model to predict the attrition rate of its key employees based on the different internal and environmental factors
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