• Share
  • Email
  • Embed
  • Like
  • Private Content
BRIDGEi2i Case Study - Proactive employee retention strategy
 

BRIDGEi2i Case Study - Proactive employee retention strategy

on

  • 1,559 views

BRIDGEi2i helps the shared services arm of a Fortune 100 bank predict the possibility of attrition among employees and also assess its drivers.

BRIDGEi2i helps the shared services arm of a Fortune 100 bank predict the possibility of attrition among employees and also assess its drivers.

Statistics

Views

Total Views
1,559
Views on SlideShare
1,520
Embed Views
39

Actions

Likes
0
Downloads
4
Comments
0

1 Embed 39

https://twitter.com 39

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    BRIDGEi2i Case Study - Proactive employee retention strategy BRIDGEi2i Case Study - Proactive employee retention strategy Document Transcript

    • Customer Case Study BRIDGEi2i helps shared services arm of a Fortune 100 bank to predict possibility of attrition among employees and also assess its drivers. Business Challenge A fortune 100 global bank runs its critical operations through large shared services centers in India. Retention of this very large and diversified workforce is an on-going challenge while timely backfilling of critical positions are even more. The management gets a periodic high level view of the attrition numbers but very little insight into the underlying causes and hence act mostly in a reactive manner. A granular forecast of attrition is also a key requirement to help the staffing team plan for timely backfills. BRIDGEi2i Solution BRIDGEi2i engaged in a consulting mode with the client to draw insights from their employee data base as well as exit interview logs. Attrition data was overlaid on a set of explanatory variables like performance, compensation, tenure, promotion, work-stream and manager influence, location to develop a comprehensive and granular understanding of what drives attrition. The attrition trend and pattern was observed across granular segments to develop a predictive forecast to help prepare for “ahead of the curve” hiring and staffing plan. Attrition Drivers The quantitative driver analysis reveals the interplay between organizational factors that impact attrition. This enables a contextual and granular understanding of levers and helped developing a targeted and customized retention strategy Text mining of exit interview comments helped identify primary causes of variation in attrition across different teams. It helped identify the common causes impacting large number of attritions. Attrition Forecast A time series model was deployed to generate granular forecasts across segments which enabled a reliable outlook towards volume of proactive hiring requirement and minimized “downtime” of critical roles Business Impact The management is able to contextualize attrition and distinguish its occurrence across various segments, driven by their unique drivers, rather than looking at it as a massive amorphous problem. This enables development of data driven effective, high impact retention initiatives. About BRIDGEi2i BRIDGEi2i is an analytics solutions company partnering with businesses globally, helping them achieve accelerated outcome harnessing the power of data. BRIDGEi2i helps companies to BRIDGE the gap between INFORMATION, INSIGHT and IMPACT in their journey to institutionalize data driven decisions across the enterprise.For more details contact us: enquiries@bridgei2i.com Information Insight Impact© BRIDGEi2i Analytics Solutions