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1
HR Science
Vishwa Kolla
Head of Advanced Analytics | John Hancock Insurance
Predictive Analytics World for Workforce | A...
2
In God we trust.
All others – please bring me data
- W. Edwards Deming
3
Vishwa Kolla
Head of Advanced Analytics
John Hancock Insurance, Boston
MBA Carnegie Mellon University
MS University of D...
4
What are your firm’s biggest assets?
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
5
Finance
Finance will probably say Product
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
6
Marketers
Marketers will probably say Customers
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Franci...
7
Finance Marketers
In reality, it is your employees
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Fra...
8
To win in the marketplace, first win in workplace
IQ (1x)
EQ (2x)
RQ (5x)
Products
Sales
Customer
Experience
Productivit...
9
Lack of direction, not lack of time, is the problem.
We all have 24 hour days
- Zig Ziglar
10
Where should we focus?
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francis...
11
Retention is where most initiatives start
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6...
12
Acquisition kick starts the journey
Acquire Nurture Retain
Predictive Analytics World for Workforce | April 4 – 6 2016 ...
13
Nurture is often the less investigated of all areas
Acquire Nurture Retain
Predictive Analytics World for Workforce | A...
14
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
Run...
Predictive Model Build Process
16
Applying AA in acquisition can get expensive
9 – 36 mos.; 4-5 ppl.; $1-2 M; Repeat for B...
Build a
6 Person
AA team
17
Applying A can be practical (for a small shop)
Predictive Analytics World for Workforce | Apri...
18
The Inspired Inspire
Engagement Campaigns
• Star Performer Appreciation
• Star Group Activity
• Quarterly Outing
• Annual Holiday Party
19
Enga...
20
It is all about who we interact with & how much
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Franc...
21
Network analysis gets to the heart of the issue
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Franc...
Data Collection
 Identify Unit of
analysis
 Curate (Collect,
De-identify,
Cleanse)
 Merge
 Repeat each
time period
22
...
23
Insights from use case were eye opening
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
 D...
Relevant Data Set
24
Getting to the finish line involves careful planning
and execution
Core Inputs
(Model Build)
Historic...
25
Closing
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
 Advanced People Analytics is for ...
26
THANK YOU!
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
27
Credits
The Noun Project
Humanyze
MIT Media Lab
Predictive Analytics World for Workforce | April 4 – 6 2016 | San Franc...
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P 01 advanced_people_analytics_2016_04_03_v11

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A look into embedding AA into entire employee value chain.

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P 01 advanced_people_analytics_2016_04_03_v11

  1. 1. 1 HR Science Vishwa Kolla Head of Advanced Analytics | John Hancock Insurance Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco meets
  2. 2. 2 In God we trust. All others – please bring me data - W. Edwards Deming
  3. 3. 3 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
  4. 4. 4 What are your firm’s biggest assets? Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  5. 5. 5 Finance Finance will probably say Product Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  6. 6. 6 Marketers Marketers will probably say Customers Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  7. 7. 7 Finance Marketers In reality, it is your employees Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  8. 8. 8 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
  9. 9. 9 Lack of direction, not lack of time, is the problem. We all have 24 hour days - Zig Ziglar
  10. 10. 10 Where should we focus? Acquire Nurture Retain Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  11. 11. 11 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
  12. 12. 12 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)
  13. 13. 13 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
  14. 14. 14 Often, the best solution to a management any problem is the right person - Edwin Booz
  15. 15. 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 15 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
  16. 16. Predictive Model Build Process 16 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
  17. 17. Build a 6 Person AA team 17 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
  18. 18. 18 The Inspired Inspire
  19. 19. Engagement Campaigns • Star Performer Appreciation • Star Group Activity • Quarterly Outing • Annual Holiday Party 19 Engagement improvement needs re-thinking 12345 Performance  NumberofPeople 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
  20. 20. 20 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
  21. 21. 21 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
  22. 22. Data Collection  Identify Unit of analysis  Curate (Collect, De-identify, Cleanse)  Merge  Repeat each time period 22 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
  23. 23. 23 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
  24. 24. Relevant Data Set 24 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 Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  25. 25. 25 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
  26. 26. 26 THANK YOU! Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco
  27. 27. 27 Credits The Noun Project Humanyze MIT Media Lab Predictive Analytics World for Workforce | April 4 – 6 2016 | San Francisco

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