This document describes how a company improved employee retention rates through a multi-pronged approach beyond just predictive analytics. They first built a predictive model that achieved over 93% accuracy in predicting attrition 1-3 months out but found it lacked attitudinal and historical data to be truly actionable. Further analysis uncovered root causes related to decision-making processes and a lack of coordination between teams. They addressed this by changing VP approval processes, providing weekly hiring updates, and better aligning HR and finance practices. This reduced attrition substantially within months and provided a 10x greater ROI than predictive modeling alone.

1. A Use Case:
When Predictive Analytics Isn't
Enough
How We Got 10X Greater ROI Than We Could with Predictive Models

2. TL;DR: Predictive is Only One Piece
• We did use predictive, but needed more
• Optimization, systems, ethnographic analysis, decision science, etc.

3. The Situation
• Software Engineers are expensive
• Attrition rates industry average, but we believed we could do better
• Need to build burning platform – not everyone thought like we did

4. The Predictive Solution…and Problem
• 75 Variables -> 12 Features
• 2 days -> 93+% accuracy 1, 2, & 3 months out
• Not really actionable because of lack of attitudinal data and detailed
history – can only aggregate
• Estimated value: $15-$20M (approx. $2,500/ee)

5. A Second Look
• Bi-modal distribution
• Nobody leaves when they say they should
• Very expensive revolving door
• This pattern had persisted for as long as we had data

6. Digging Down to Root Cause
• Starting with the recruiters and hiring managers, interviewing to build
hypotheses
• Use data to validate hypotheses and determine experiments and
intervention points
• E.g., Higher quality of fit (retention & performance) take longer to find and
hire

7. Two Issues Emerge
• “Nobody can make a decision around here!”
• VP’s controlled headcount approvals
• Waited until the last possible minute because, “Hey, sometimes things change
and we solve the problem without adding heads.”
• Just have to get a warm body
• “You never know when finance will call a hiring freeze.”
• HR owns number of heads, finance owns dollars
• Finance: Headcount forecast on monthly basis with straightline attrition
• Finance: Headcount approvals done on quarterly basis
• Finance: Actual headcounts measured at the end of the month

8. A Lever and a Fulcrum
• Change default VP behavior and support
their decision needs through training
and prep
• Executives supported new approach
• Weekly updates on actual hires to
executives
• No need for:
• Complex software integration or training
• Significant process changes by finance or
other teams
• Large scale data clean up or analysis

9. Measuring Results
• Monitoring the revolving door
• Substantial reduction within a few months
• Monitoring stakeholders involved in the change
• VP’s and Directors were good with the change
• Finance and Executives really liked the new, more current information

10. Doing It Again
• It takes time to interview and validate, but buy-in is easier
• Manage the expectation of how much time it will take and why
• People aren’t used to thinking this way
• Partner with organizational design professionals to help
• The Problem with “Too Easy”
• Build a bigger “burning platform”