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[ UNRAVELING THE RETENTION ENIGMA ]

LEVERAGING ANALYTICS TO DRIVE STRATEGIC INTERVENTIONS IN A LIFE SCIENCES CONGLOMERATE'S BATTLE AGAINST HIGH ATTRITION

  1. Key inference/learning through Predictive Model basis ML.
  2. Salary hike was effected basis natural intelligence and market forces. However it did not rank in top 5 influencing factors on attrition.

For one of the Life Sciences unit of a large conglomerate, one of the key issues was
attrition (19% (236/1470) in a quarter). Bench marking for Comp & Ben done and salary
adjustments effected. Its has not had desired effect. The leadership wanted to take
actions through appropriate interventions to improve retention.

  • Determine the relationship between Attrition and impacting variables. Validate
    existing assumptions.
  • Build predictive model for managing potential attrition.
  • Define Metrics and Data source and
    Acquire Data
  • 45 variables * 1470 data points from 4
    different system
  • Data from Employee DB
  • Time management system
  • Data Payroll MS Data
  • Other Databases
  • Rotation policy crafted and implemented for job enrichment.
  • Mentor –Mentee program.
  • Soft nudges supported by OT policy (Layered approval, Transportation availability etc)