Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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What are your firm’s biggest assets?
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Finance
Finance will probably say Product
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Marketers
Marketers will probably say Customers
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Finance Marketers
In reality, it is your employees
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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To win in the marketplace, first win in workplace
IQ (1x)
EQ (2x)
RQ (5x)
Products
Sales
Customer
Experience
Productivity
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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Lack of direction, not lack of time, is the problem.
We all have 24 hour days
- Zig Ziglar
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Where should we focus?
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Retention is where most initiatives start
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
2x - 6x less
expensive to retain
than to hire
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Acquisition kick starts the journey
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Cost of a bad hire is
3x that of a good
hire (read cultural
damage)
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Nurture is often the less investigated of all areas
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Applying AA in acquisition is natural in sports
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Target
Variable Predictors
Performance
Measurement
is intrinsic
Large sample
sizes
Demonstrated
Value
Predictive Model Build Process
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Applying AA in acquisition can get expensive
9 – 36 mos.; 4-5 ppl.; $1-2 M; Repeat for BU / Function
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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 Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Applying A can be practical (for a small shop)
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Engagement improvement needs re-thinking
1 2 3 4 5
Performance
Nu
mb
er
of
Pe
op
le
Improved
Engagement Challenges
• Not enough lift
in scores
• Not timely
enough
• Not relevant
enough
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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It is all about who we interact with & how much
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Network analysis gets to the heart of the issue
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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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
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Insights from use case were eye opening
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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)
Valida
te
Test Train Relevant Data Noise
Da
ta P
art
itio
nin
g
Da
ta E
xtr
ac
tio
n
Da
ta E
ng
ine
erin
g
Ap
ply
Filt
er
Ru
les
Da
ta A
gg
reg
atio
n
Predictive Model Build Scoring Engine Development Live Scoring Engine
Ev
alu
ate
Fin
al M
od
el E
qu
atio
ns
Ro
ll o
ut
to P
rod
uc
tio
n
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
Un
i -V
aria
te A
na
lysi
s
Bi-V
aria
te A
na
lysi
s
3
4 5
Problem
Definition
Model
Strategy
Data
Engineering
Model
Build
Model
Implementation
& Governance
1 2 3 4 5
01/## Current
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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Closing
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
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
Predictive Analytics World for Workforce | April 4-6 2016 | San Francisco
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THANK YOU! Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco