Early Warning System –Attrition Model using SVM

Share on twitter
Share on facebook
Share on linkedin
Share on whatsapp
Share on email

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

    XTechalpha: Launching a “next-in class“ platform of exponential technologies: AI, Blockchain, Cloud, Cybersecurity, IoT & emerging segments: GCCs, Startups and Academic Institutions & Edtech accentuated with an enviable line up of proven and seasoned thought leaders bringing together and ensemble, curated content, networking interventions and learning enhancement. A must visit primer for GIC heads, Technology leaders, Exponential technologies enthusiasts, Academic deans and Entrepreneurs.

    Watch the Launch video:

    XTechalpha: reimagine your THINKING