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Early Warning System –Attrition Model using SVM

September 6, 2020

Talent Analytics

Problem Statement

  • Leading IT services provider, with a global workforce base of over 150k employees during that time
  • High attrition across its mid-level ranks and risks losing its trained talent pool to other players in the industry
  • An eclectic base of associates with varied educational backgrounds & skillsets
  • Employees are spread across various business units & divergent roles
  • Different business units have different rates and reasons for attrition
  •  

Analytics Led Approach

  • Data cleansing and missing value imputation

  • Check previous (two year back) model validity

  • Logistic, random forest and SVM techniques were used to  choose the best prediction model to score each associate’s attrition risk


Business Impact

  • Development of monthly score cards using the model where we classify employees into buckets of Red, Amber and Green Risk attrition probabilities.

  • Those in red with favorable ratings are intervened with by talent management


Critical Success Factors

  • Identify customers likely to attrite early and control unwanted talent churn
  • Based on model results HR team could target 30% of the employees which covered 70% probable attrition cases
  • Operational effectiveness by providing actionable insights and predictive modeling

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