This document summarizes how a company has evolved its use of predictive analytics in talent management over time. It started with basic reporting using Excel and PowerPoint. It then shifted to data visualization and business intelligence dashboards. In 2016, the company developed its first predictive retention model using machine learning techniques to classify flight risk. Analysis from this model guided strategies to improve retention. The company is now exploring how to further leverage predictive analytics to understand high performers, internal networks, and pave the way for future talent management.

1. Health Care │ Education
Using Predictive Analytics to
Drive Talent Management
Predictive Analytics World 2017

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2
~20
DISTINCT BUSINESS UNITS
Firm Snapshot
3,800
EMPLOYEES
12
OFFICE LOCATIONS
WORLDWIDE
HRIS
ATS
Business
Intelligence
Machine
Learning
Data
Visualization
A LOT OF
Excel

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Where WFA is connected in the Business?
WFA
HRBPs Recruiting
Finance
Learning & DevelopmentPerformance
Management
Front Line
Managers /
Executives
HR Structure
CTO
Employee
Effectiveness
/ Analytics
HR
Operations
► Communication
► L&D
► Employee
Engagement /
Perf. Mgmt
► D&I
► WFA
► Corporate
Responsibility
Compensation
► HRBP
► Recruiting
► HR Systems
► Employee Relations
Benefits

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How Are We Impacting Talent Management Today?
HIRE ONBOARD DEVELOP PROMOTE EXIT
 Provide Recruiting
KPIs to monitor
capacity demand
 Coordinate with
Finance to ensure
proper position
management
 Understand drivers /
predictors of future
success to inform
recruiting strategy
 Engage in
workforce modeling
 Inform org design
decisions to
improve efficiency
and effectiveness
 Partner with
business leaders on
strategic workforce
planning
 Analyze internal
networks
 Understand
competencies and
behaviors that lead
to high performance
in key roles / jobs
 Analyze
performance review
data to inform
learning
investments
anchored around
competencies
 Understanding
impact of promotion
timelines on org
hierarchy and
EBITDA growth
 Understanding
future talent pipeline
 Cultivate predictive
retention model
 Isolate factors
causing staff to be
identified as “HIGH”
departure risk
 Complete in-depth
analysis to
understand
departure reasons

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How did we get here?
HINDSIGHT FORESIGHTINSIGHT
DATA & BASIC
REPORTING
CONSOLIDATED
REPORTING
BASELINE
ANALYTICS
PREDICTIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
TRANSACTIONALSTRATEGIC
• Aggregated lists of
information
• Collection of different
sources of
information
• Ability to merge
information from
multiple sources
• Ability to extract
specific information
that can be used to
provide business
insight
• Collection of key
performance indicators
• Benchmark KPIs
against similar
organizations
• Ability to forecast
future KPIs based on
existing parameters
• Model future
workforce and
expected trends
• Predict future
behavior using
experiential evidence
• Determination of
causes and effects
• Synthesize big data
to suggest multiple
decision options and
its’ varying impacts
on the organization
• Evaluate impacts of
scenario modeling
and determine the
optimal solution
CURRENT
STATE
DESIRED FUTURE
STATE
WHERE WE
STARTED

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In the beginning …
When we first started…
 Basic Workday Reports
 Presenting in Excel charts & PowerPoint
Name Division Management Level
Ann Li Design Director
Adrienne Hu Product Senior Analyst
Michelle Johns Commercial Associate
Argun Gupta Technology Coordinator
Nia Washington NPD Executive
0
2
4
6
8
10
Health Care Education Firm Services
Headcount

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1
2
3
4
Focus on insights vs summary statistics
Proactively provide data to HRBPs and business leaders
Centralized all HR metrics into a Factbook
Introduced Business Intelligence Dashboard
HR FACTBOOK
Business Intelligence Tools
Ridiculously Good Looking Presentations
We shifted focus to Data Visualization

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Time to Get Smart with Data
2014
Basic
Reporting
2015
Data
Visualization
May 2016
PAW
Conference
Sep 2016
Predictive
Retention Model
Today
Using Predictive
Analytics to drive Talent
Management
MACHINE LEARNING
RANDOM FOREST MODELING
APPROACH OUTCOMES
Studied workforce data
dating back to end of year
2013 and included 38
workforce factors relating
to:
• Financial drivers
• Career drivers
• Performance drivers
• Personal drivers
Developed a statistical
model powered by 28
workforce factors to
classify employees into
three flight-risk
categories:
• High Risk
• Medium Risk
• Low Risk
MODEL PRECISION
High Risk 91%
Medium Risk 68%
Low Risk 60%
Predictive Retention Model

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Using Predictive Analytics to Drive Talent Management
Analysis reinforced the need to focus on employee engagement within first 2 years
Guided HRBPs to build playbooks to address retention, including strategies to proactively
target higher risk employees
Strengthened HR partnership with business leaders
Placed strong emphasis on management training investments given the important role
managers play in employee retention
Uncovered business units with higher turnover and completed in-depth
studies to isolate root cause

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Understanding
competencies, experiences,
and behaviors that lead to
high performance
Understanding
characteristics of HIPO
talent to inform
candidate selection
Understanding the role
of internal networks on
employee engagement
& performance?
Paving the road ahead
Picture Source: http://www.tssco.com/

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Questions
Kevin Zhan
Email: zhank@advisory.com
Questions