P 01 advanced_people_analytics_2016_04_03_v11

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HR Science
Vishwa Kolla
Head of Advanced Analytics | John Hancock Insurance
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
meets

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In God we trust.
All others – please bring me data
- W. Edwards Deming

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Vishwa Kolla
Head of Advanced Analytics
John Hancock Insurance, Boston
MBA Carnegie Mellon University
MS University of Denver
BS BITS Pilani, India
 Advanced Analytics
CoE, Maturity Model
 Customer /
Workforce Analytics
(entire value chain)
 Machine Learning
 Scoring Engine
 Optimization
 Simulations
 Forecasting & Time
Series
• 15+ Years
• John Hancock Insurance
• Deloitte Consulting (Industries –Insurance,
Retail, Financial, Technology, Telecom,
Healthcare, Data)
• IBM
• Sun Microsystems
Business
Analytical
(Math, Stats)
Technical
(Programming)
Expertise
Experience

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What are your firm’s biggest assets?

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Finance
Finance will probably say Product

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Marketers
Marketers will probably say Customers

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Finance Marketers
In reality, it is your employees

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To win in the marketplace, first win in workplace
IQ (1x)
EQ (2x)
RQ (5x)
Products
Sales
Customer
Experience
Productivity

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Lack of direction, not lack of time, is the problem.
We all have 24 hour days
- Zig Ziglar

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Where should we focus?
Acquire Nurture Retain

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Retention is where most initiatives start
2x - 6x less
expensive to retain
than to hire

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Acquisition kick starts the journey
Cost of a bad hire is
3x that of a good
hire (read cultural
damage)

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Nurture is often the less investigated of all areas
Productivity gains
are highly
correlated to
engagement

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Often, the best solution to a
management any problem is
the right person
- Edwin Booz

Home Run
Total Potential
• Height
• Weight
• High School / College Home Runs
• Home Park Layout
• History Of Home Runs
Running Back
Draft Potential
• 40-Yard Dash Time
• Total Rushing Yards in College
• Total Touchdowns in College
• # of Heisman Trophies /
Championships University Earned
Historically
Shots Blocking
Potential
• Height
• Arm length
• Hand size
• Position
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Applying AA in acquisition is natural in sports
Target
Variable
Predictors
 Performance
Measurement
is intrinsic
 Large sample
sizes
 Demonstrated
Value

Predictive Model Build Process
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Applying AA in acquisition can get expensive
9 – 36 mos.; 4-5 ppl.; $1-2 M; Repeat for BU / Function
Challenges
Time consuming
Expensive
Limited
Measurability
Amount of
customization
Cumbersome
Abandon
Collect Data Score Interview
Predictive Models Nudges
Verify

Build a
6 Person
AA team
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Applying A can be practical (for a small shop)
Life
Insurance
Advanced
Analytics
(AA)
Experience
Levels
Insights from Work History
 Insightful Summary
 Succinct Project description
 Use of quantification
 Consistent structure
 Buzz : Real work ratio
 Job change frequency
 Time at any position
 Progression history
 Proximity to Workplace
 Capability index
 Tier 1 / 2 / 3 Schools
Insights from Work Product (Resume)
 Spacing between sections
 Appeal of layout
 # Punctuation issues
 # Grammatical errors
 # Positive words
 # Theme repetitions
Insights from Open Ended Questions
 # Impactful Initiatives
 Fit in Analytical spectrum
 Listening index
 Coach-ability index
Work Ethic
Role –
Leader /
Talker /
Thinker /
Doer
Personality

Engagement Campaigns
• Star Performer Appreciation
• Star Group Activity
• Quarterly Outing
• Annual Holiday Party
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Engagement improvement needs re-thinking
12345
Performance 
NumberofPeople
Improved
Engagement
Challenges
• Not enough lift
in scores
• Not timely
enough
• Not relevant
enough

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It is all about who we interact with & how much
Management
Consulting
Hours
on
Project
Individual, Peers
 How much, how long
 Time of day, month, year,
entry / exit from project
 Where (home | away)
 Performance history
 Time-off(s)
 Life stage
 Personality
Project
 Size, Budget, Duration,
Location, Timing, # of
functions, expenses
Synthetics
 Definition and comparison
to peers
 Senior / Executive
leadership to Staff ratio
etc.
Non
Professional
Service
Industries

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Network analysis gets to the heart of the issue
 Actors
 Interaction(s)
 Number
 Speaking time
 Average speech segment length
 Variation in speech energy
 Variation in movement
 Self perceived dominance
 Actors
 Interaction(s)
 Number
 Time of interactions
 Length of message
 Attachments
 Subject categorization
 Number of conversations

Data Collection
 Identify Unit of
analysis
 Curate (Collect,
De-identify,
Cleanse)
 Merge
 Repeat each
time period
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Nurture & Retention are big data problems
Internal Data (90%)
External Data (10%)
Data Engineering (Create Longitudinal View) Predictive Models
 Profiles on
variety of
dimensions
 Engagement
Index
 Likely to get
promoted
 Likely to attrite
Customer
Project
Point in Time
Snapshot
What data should I keep?
1Q Look back
2Q Look back
3Q Look back
4Q Look back

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Insights from use case were eye opening
 Data collection timeframe defines action ability
period
 Life / career stages make recommendations counter
intuitive – e.g., travel
 High burn projects were good (for younger
population), and with time-off
 Network effect (individuals consistently on projects
comprised of more stars had higher risk of attrition)
 Blogging is good
 Some voluntary attrition is good

Relevant Data Set
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Getting to the finish line involves careful planning
and execution
Core Inputs
(Model Build)
Historical Data
Raw
Data
Additional Inputs
(Test)
Modeling Data Set
Core Inputs
(Model Build)
Additional Inputs
(Test)
V
a
li
d
a
t
e
TestTrainRelevant Data Noise
DataPartitioning
DataExtraction
DataEngineering
ApplyFilterRules
DataAggregation
Predictive Model Build Scoring Engine Development Live Scoring Engine
Evaluate
FinalModelEquations
RollouttoProduction
Data
Integration
Model
Integration
Systems
Integration
Real – time Scoring Engine Development
Service Layer Development
UI Engine QC Engine
Business Objective – Any Predictive Model
1
2
Uni-VariateAnalysis
Bi-VariateAnalysis
3
4 5
Problem
Definition
Model
Strategy
Data
Engineering
Model
Build
Model
Implementation
& Governance
1 2 3 4 5
01/## Current

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Closing
 Advanced People Analytics is for real and not
“entirely” hype
 Work closely with Business
 Prioritize Process over immediate Purpose
 A structured process is critical
 There is no pixie dust
 QC every step along the way

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THANK YOU!

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Credits
The Noun Project
Humanyze
MIT Media Lab

P 01 advanced_people_analytics_2016_04_03_v11

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