SlideShare a Scribd company logo
CREATING VALUE THROUGH
ANALYTICS INNOVATION
Wayne Huang, PhD
Head of Predictive Analytics, Group Insurance
Prudential Financial Inc.
1
- Predictive Analytics World, NYC, October 30, 2017
Analytics Team Challenges
1.  Find analytics ideas
2.  Assess solution impact
3.  Prioritize analytics projects
4.  Develop & deploy analytics models
5.  Post-implementation monitoring
2
From Automation To Innovation
Manual Process
Workflow Automation
Decision & Learning
Innovation
Productivity
Time
3
1. Find The Diamonds
4
Underwriting
Review
High
Risk?
Order APS/
Med Exam
Approved
Issue
Policy
Denial
Letter
Insurance
Application
Underwriting
Review
Life Insurance Underwriting Process
MIB
Check
YesNo
Yes
No
2. Leverage New Data
Old Paradigm
§  Traditional data (Application,
MIB, APS, Labs)
§  Underwriter judgement
§  Long decision time
§  Higher cost
§  Customer abandon application
§  Painful customer experience
New Paradigm
§  New predictors (Rx, MVR,
Medical, Credit,…)
§  Predictive model
§  Decision automation
§  Lower cost
§  New revenue
§  Superior customer experience
5
3. Control Uncertainty
Uncertainty is a result of having to deal with ambiguity and too many
variables. It’s a risk to business.
1.  Use analytics to reduce ambiguity
§  Use campaign response predictive model to identify customers propensity
§  Use cluster analysis for market segmentation, product positioning, and targeted
campaign
2.  Use machine learning to identify and manage variables that have
higher predictive power for better outcome
§  Use pricing predictive model to assist insurance product pricing
§  Use prospect scoring model to determine new customer handling priority
6
SOLUTION IMPACT ASSESSMENT
7
Operational Feasibility Assessment
1.  Understand the current process
2.  Identify tasks impacted by the solution
3.  Design a new process
4.  Assess the impact of solution implementation
§ Labor – case handling volume change, new skill requirement
§ Input – existing data vs. (real-time) new data
§ Time – cycle time reduction
§ Technology – system change requirement
§ Legal and compliance
8
Financial Viability Assessment
1.  What value can the analytics project bring?
§ Lower cost, shorter cycle time, more revenue, higher profit
2.  What’s the model development and implementation cost?
3.  How to quantify the value?
§ Cost Savings: unit labor cost x reduced volume by automation, plus any
other input reduction x unit cost, minus new data cost
§ New Profit: new sales x profit margin, or new customer acquisition x life
time value
4.  Calculate five year NPV of the investment
Year 1 Year 2 Year 3 Year 4 Year 5 Total
Cashflow -$1MM $500K $1MM $1MM $1MM
NPV (10% ROE) -$1MM $413K $751K $683K $621K $1.5MM
9
Recognize Indirect Value Activities
1.  Activities that generate value to customers and firm
§  Primary activities in value chain
§  Predictive underwriting can reduce cost, increase revenue and improve customer
experience
2.  Activities that preserve value
§  If neglected, a firm loses the ability to generate economic value, e.g., advertising,
customer service
§  Targeted campaign can increase new customer acquisition and reduce cost
§  Mining customer support data can identify product improvement opportunities and
increase sales
3.  Activities that enable options (deferred value)
§  Give a firm an advantage in dealing with uncertainty and change. They help exploit
value-generating opportunities, e.g., R&D, employee training
§  Predictive analytics can help speed up life science and material science discovery
§  Predicting training effectiveness can better prepare employees for new tasks
10
PROJECT PRIORITIZATION
11
Why Analytics Projects Failed
1.  Lack of alignment with strategic goals
§  Inability to integrate analytics solutions into workflows
§  Limited front-line adoption
2.  Lack of strategic alignment and direction
§  Lack of stakeholder alignment or support
§  Lack of clear roadmap
3.  Poor data quality
§  Missing or incomplete data
§  Data quality or accuracy issues
§  Data fragmentation
4.  Other
§  Missing team skills or capabilities
§  Unclear use case scope
§  Inability to articulate value
McKinsey & Company, “Raising Returns on Analytics Investments In Insurance”, 2017 12
Project Evaluation Framework
Project Value
(5 Year NPV)
Analytics
Solutions
Data
Availability
Process
Integration
Urgency Project
Ease*
1. Pricing
Predictive Model
$3M Complex Partial
Medium
Difficulty
Normal 4
2. Claims
Analytics
$2M
Medium
Complexity
All
Medium
Difficulty
Time
Critical
3
3. Customer
Journey Analytics
$1M Simple All Easy Normal 1
4. Predictive
Underwriting
$3M
Medium
Complexity
Partial
Medium
Difficulty
Normal 3
5. Prospect
Prioritization
$1M
Medium
Complexity
Partial Difficult
Time
Critical
4
6. Campaign
Response Model
$1M Simple All Easy Normal 2
13
* Project Ease ranges from 1 to 5, with 1 being the easiest and 5 being the most difficult.
Ø  The goal of the heuristic is to choose projects, which maximize the ratio of NPV to
Project Ease
Ø  Suppose the total budget is $2.5M
Project Selection Heuristic
Project Cost Value Ease Ratio Strategic* Rank
1. Pricing
Predictive Model
$500K $3M 4 .75 1.13* 2
2. Claims
Analytics
$500K $2M 3 .66 .66 4
3. Customer
Journey Analytics
$250K $1M 1 1 1 3
4. Predictive
Underwriting
$1M $3M 3 1 1.5* 1
5. Prospect
Prioritization
$500K $1M 4 .25 .38* 6
6. Campaign
Response Model
$500K $1M 2 .50 .50 5
14
Ø  Select Projects 4, 1, 3, 2 for a total cost of $2.25M
* Strategic projects receive 50% additional weight
Wayne Huang, PhD
wayne.huang@prudential.com

More Related Content

What's hot

1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop
Rising Media, Inc.
 
The Business Analytics Value Proposition
The Business Analytics Value PropositionThe Business Analytics Value Proposition
The Business Analytics Value Proposition
Eric Stephens
 
1000 track2 boire
1000 track2 boire1000 track2 boire
1000 track2 boire
Rising Media, Inc.
 
1120 track1 grossman
1120 track1 grossman1120 track1 grossman
1120 track1 grossman
Rising Media, Inc.
 
Think better using “Descriptive-Prescriptive” Approach
Think better using “Descriptive-Prescriptive” ApproachThink better using “Descriptive-Prescriptive” Approach
Think better using “Descriptive-Prescriptive” Approach
STAG Software Private Limited
 
Data Mining Technique - SEMMA
Data Mining Technique - SEMMAData Mining Technique - SEMMA
Data Mining Technique - SEMMA
Ashish Chandra Jha
 
1440 track2 roberts
1440 track2 roberts1440 track2 roberts
1440 track2 roberts
Rising Media, Inc.
 
Predictive Model
Predictive ModelPredictive Model
Predictive Model
ModakAnalytics
 
Causal Inference, Reinforcement Learning, and Continuous Optimization
Causal Inference, Reinforcement Learning, and Continuous OptimizationCausal Inference, Reinforcement Learning, and Continuous Optimization
Causal Inference, Reinforcement Learning, and Continuous Optimization
ScientificRevenue
 
Introduction to Business Analytics Part 1
Introduction to Business Analytics Part 1Introduction to Business Analytics Part 1
Introduction to Business Analytics Part 1
Beamsync
 
1030 track1 heiler
1030 track1 heiler1030 track1 heiler
1030 track1 heiler
Rising Media, Inc.
 
The Value of Predictive Analytics and Decision Modeling
The Value of Predictive Analytics and Decision ModelingThe Value of Predictive Analytics and Decision Modeling
The Value of Predictive Analytics and Decision Modeling
Decision Management Solutions
 
Predictive Modelling
Predictive ModellingPredictive Modelling
Predictive Modelling
Rajib Kumar De
 
1120 track1 taylor
1120 track1 taylor1120 track1 taylor
1120 track1 taylor
Rising Media, Inc.
 
1055 track3 soules
1055 track3 soules1055 track3 soules
1055 track3 soules
Rising Media, Inc.
 
INTRODUCTION TO BUSINESS ANALYTICS
INTRODUCTION TO BUSINESS ANALYTICSINTRODUCTION TO BUSINESS ANALYTICS
INTRODUCTION TO BUSINESS ANALYTICS
AninditaGogoi5
 
1555 track 2 ning_using our laptop
1555 track 2 ning_using our laptop1555 track 2 ning_using our laptop
1555 track 2 ning_using our laptop
Rising Media, Inc.
 
Analytics Overview #Predictive Analytics
Analytics Overview #Predictive AnalyticsAnalytics Overview #Predictive Analytics
Analytics Overview #Predictive Analytics
Durga Palakurthy
 
How to crack down big data?
How to crack down big data? How to crack down big data?
How to crack down big data?
Ta-Wei (David) Huang
 
Five Pitfalls when Operationalizing Data Science and a Strategy for Success
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessFive Pitfalls when Operationalizing Data Science and a Strategy for Success
Five Pitfalls when Operationalizing Data Science and a Strategy for Success
VMware Tanzu
 

What's hot (20)

1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop1440 track 2 boire_using our laptop
1440 track 2 boire_using our laptop
 
The Business Analytics Value Proposition
The Business Analytics Value PropositionThe Business Analytics Value Proposition
The Business Analytics Value Proposition
 
1000 track2 boire
1000 track2 boire1000 track2 boire
1000 track2 boire
 
1120 track1 grossman
1120 track1 grossman1120 track1 grossman
1120 track1 grossman
 
Think better using “Descriptive-Prescriptive” Approach
Think better using “Descriptive-Prescriptive” ApproachThink better using “Descriptive-Prescriptive” Approach
Think better using “Descriptive-Prescriptive” Approach
 
Data Mining Technique - SEMMA
Data Mining Technique - SEMMAData Mining Technique - SEMMA
Data Mining Technique - SEMMA
 
1440 track2 roberts
1440 track2 roberts1440 track2 roberts
1440 track2 roberts
 
Predictive Model
Predictive ModelPredictive Model
Predictive Model
 
Causal Inference, Reinforcement Learning, and Continuous Optimization
Causal Inference, Reinforcement Learning, and Continuous OptimizationCausal Inference, Reinforcement Learning, and Continuous Optimization
Causal Inference, Reinforcement Learning, and Continuous Optimization
 
Introduction to Business Analytics Part 1
Introduction to Business Analytics Part 1Introduction to Business Analytics Part 1
Introduction to Business Analytics Part 1
 
1030 track1 heiler
1030 track1 heiler1030 track1 heiler
1030 track1 heiler
 
The Value of Predictive Analytics and Decision Modeling
The Value of Predictive Analytics and Decision ModelingThe Value of Predictive Analytics and Decision Modeling
The Value of Predictive Analytics and Decision Modeling
 
Predictive Modelling
Predictive ModellingPredictive Modelling
Predictive Modelling
 
1120 track1 taylor
1120 track1 taylor1120 track1 taylor
1120 track1 taylor
 
1055 track3 soules
1055 track3 soules1055 track3 soules
1055 track3 soules
 
INTRODUCTION TO BUSINESS ANALYTICS
INTRODUCTION TO BUSINESS ANALYTICSINTRODUCTION TO BUSINESS ANALYTICS
INTRODUCTION TO BUSINESS ANALYTICS
 
1555 track 2 ning_using our laptop
1555 track 2 ning_using our laptop1555 track 2 ning_using our laptop
1555 track 2 ning_using our laptop
 
Analytics Overview #Predictive Analytics
Analytics Overview #Predictive AnalyticsAnalytics Overview #Predictive Analytics
Analytics Overview #Predictive Analytics
 
How to crack down big data?
How to crack down big data? How to crack down big data?
How to crack down big data?
 
Five Pitfalls when Operationalizing Data Science and a Strategy for Success
Five Pitfalls when Operationalizing Data Science and a Strategy for SuccessFive Pitfalls when Operationalizing Data Science and a Strategy for Success
Five Pitfalls when Operationalizing Data Science and a Strategy for Success
 

Similar to 1555 track 1 huang_using his mac

Game Changing Quality Strategies that Drive Organizational Excellence
Game Changing Quality Strategies that Drive Organizational ExcellenceGame Changing Quality Strategies that Drive Organizational Excellence
Game Changing Quality Strategies that Drive Organizational Excellence
kushshah
 
Richard Marshall EARL 2019.pptx
Richard Marshall EARL 2019.pptxRichard Marshall EARL 2019.pptx
Richard Marshall EARL 2019.pptx
Dean Maitland
 
Changing the way companies achieve results
Changing the way companies achieve resultsChanging the way companies achieve results
Changing the way companies achieve results
tkclarke7
 
Clewed overview 1 15 2015
Clewed overview 1 15 2015Clewed overview 1 15 2015
Clewed overview 1 15 2015
tkclarke7
 
Introducing data driven practices into sales environments
Introducing data driven practices into sales environmentsIntroducing data driven practices into sales environments
Introducing data driven practices into sales environments
Barry Magee
 
1340 keynote minkowski_using our laptop
1340 keynote minkowski_using our laptop1340 keynote minkowski_using our laptop
1340 keynote minkowski_using our laptop
Rising Media, Inc.
 
Healthcare Project Prioritization 102708
Healthcare Project Prioritization 102708Healthcare Project Prioritization 102708
Healthcare Project Prioritization 102708
Carolyn Reid
 
Summer Shorts: Using Predictive Analytics For Data-Driven Decisions
Summer Shorts: Using Predictive Analytics For Data-Driven DecisionsSummer Shorts: Using Predictive Analytics For Data-Driven Decisions
Summer Shorts: Using Predictive Analytics For Data-Driven Decisions
ibi
 
ROI-Institute-Brochure1
ROI-Institute-Brochure1ROI-Institute-Brochure1
ROI-Institute-Brochure1
ROI Institute Inc.
 
Using Cost of Delay to de-scale your organisation through decentralised decis...
Using Cost of Delay to de-scale your organisation through decentralised decis...Using Cost of Delay to de-scale your organisation through decentralised decis...
Using Cost of Delay to de-scale your organisation through decentralised decis...
Michael Fagan
 
Presentation on 'Why cant people estimate' event, 23rd June 2016
Presentation on 'Why cant people estimate' event, 23rd June 2016 Presentation on 'Why cant people estimate' event, 23rd June 2016
Presentation on 'Why cant people estimate' event, 23rd June 2016
Association for Project Management
 
201406 IASA: Analytics Maturity - Unlocking The Business Impact
201406 IASA: Analytics Maturity - Unlocking The Business Impact201406 IASA: Analytics Maturity - Unlocking The Business Impact
201406 IASA: Analytics Maturity - Unlocking The Business Impact
Steven Callahan
 
Nancy's webinar
Nancy's webinarNancy's webinar
Nancy's webinar
VWO
 
The Agile Manager: Empowerment and Alignment
The Agile Manager: Empowerment and AlignmentThe Agile Manager: Empowerment and Alignment
The Agile Manager: Empowerment and Alignment
Software Guru
 
201306 IASA Conference-Session 602: Operational Efficiency
201306 IASA Conference-Session 602: Operational Efficiency201306 IASA Conference-Session 602: Operational Efficiency
201306 IASA Conference-Session 602: Operational Efficiency
Steven Callahan
 
Implementing portfolio managment tools, Ed Couch, Astra Zeneca
Implementing portfolio managment tools, Ed Couch, Astra ZenecaImplementing portfolio managment tools, Ed Couch, Astra Zeneca
Implementing portfolio managment tools, Ed Couch, Astra Zeneca
Association for Project Management
 
What is analytics oct 16
What is analytics oct 16What is analytics oct 16
What is analytics oct 16
seamsltd
 
MonetizingStatistics
MonetizingStatisticsMonetizingStatistics
MonetizingStatistics
Aaron Sankey
 
1000 track1 gland_sims
1000 track1 gland_sims1000 track1 gland_sims
1000 track1 gland_sims
Rising Media, Inc.
 
Rapid Assessment for Professional Services
Rapid Assessment for Professional ServicesRapid Assessment for Professional Services
Rapid Assessment for Professional Services
Arpin Consulting
 

Similar to 1555 track 1 huang_using his mac (20)

Game Changing Quality Strategies that Drive Organizational Excellence
Game Changing Quality Strategies that Drive Organizational ExcellenceGame Changing Quality Strategies that Drive Organizational Excellence
Game Changing Quality Strategies that Drive Organizational Excellence
 
Richard Marshall EARL 2019.pptx
Richard Marshall EARL 2019.pptxRichard Marshall EARL 2019.pptx
Richard Marshall EARL 2019.pptx
 
Changing the way companies achieve results
Changing the way companies achieve resultsChanging the way companies achieve results
Changing the way companies achieve results
 
Clewed overview 1 15 2015
Clewed overview 1 15 2015Clewed overview 1 15 2015
Clewed overview 1 15 2015
 
Introducing data driven practices into sales environments
Introducing data driven practices into sales environmentsIntroducing data driven practices into sales environments
Introducing data driven practices into sales environments
 
1340 keynote minkowski_using our laptop
1340 keynote minkowski_using our laptop1340 keynote minkowski_using our laptop
1340 keynote minkowski_using our laptop
 
Healthcare Project Prioritization 102708
Healthcare Project Prioritization 102708Healthcare Project Prioritization 102708
Healthcare Project Prioritization 102708
 
Summer Shorts: Using Predictive Analytics For Data-Driven Decisions
Summer Shorts: Using Predictive Analytics For Data-Driven DecisionsSummer Shorts: Using Predictive Analytics For Data-Driven Decisions
Summer Shorts: Using Predictive Analytics For Data-Driven Decisions
 
ROI-Institute-Brochure1
ROI-Institute-Brochure1ROI-Institute-Brochure1
ROI-Institute-Brochure1
 
Using Cost of Delay to de-scale your organisation through decentralised decis...
Using Cost of Delay to de-scale your organisation through decentralised decis...Using Cost of Delay to de-scale your organisation through decentralised decis...
Using Cost of Delay to de-scale your organisation through decentralised decis...
 
Presentation on 'Why cant people estimate' event, 23rd June 2016
Presentation on 'Why cant people estimate' event, 23rd June 2016 Presentation on 'Why cant people estimate' event, 23rd June 2016
Presentation on 'Why cant people estimate' event, 23rd June 2016
 
201406 IASA: Analytics Maturity - Unlocking The Business Impact
201406 IASA: Analytics Maturity - Unlocking The Business Impact201406 IASA: Analytics Maturity - Unlocking The Business Impact
201406 IASA: Analytics Maturity - Unlocking The Business Impact
 
Nancy's webinar
Nancy's webinarNancy's webinar
Nancy's webinar
 
The Agile Manager: Empowerment and Alignment
The Agile Manager: Empowerment and AlignmentThe Agile Manager: Empowerment and Alignment
The Agile Manager: Empowerment and Alignment
 
201306 IASA Conference-Session 602: Operational Efficiency
201306 IASA Conference-Session 602: Operational Efficiency201306 IASA Conference-Session 602: Operational Efficiency
201306 IASA Conference-Session 602: Operational Efficiency
 
Implementing portfolio managment tools, Ed Couch, Astra Zeneca
Implementing portfolio managment tools, Ed Couch, Astra ZenecaImplementing portfolio managment tools, Ed Couch, Astra Zeneca
Implementing portfolio managment tools, Ed Couch, Astra Zeneca
 
What is analytics oct 16
What is analytics oct 16What is analytics oct 16
What is analytics oct 16
 
MonetizingStatistics
MonetizingStatisticsMonetizingStatistics
MonetizingStatistics
 
1000 track1 gland_sims
1000 track1 gland_sims1000 track1 gland_sims
1000 track1 gland_sims
 
Rapid Assessment for Professional Services
Rapid Assessment for Professional ServicesRapid Assessment for Professional Services
Rapid Assessment for Professional Services
 

More from Rising Media, Inc.

1415 track 1 wu_using his laptop
1415 track 1 wu_using his laptop1415 track 1 wu_using his laptop
1415 track 1 wu_using his laptop
Rising Media, Inc.
 
Matt gershoff
Matt gershoffMatt gershoff
Matt gershoff
Rising Media, Inc.
 
Keynote adam greco
Keynote adam grecoKeynote adam greco
Keynote adam greco
Rising Media, Inc.
 
1620 keynote olson_using our laptop
1620 keynote olson_using our laptop1620 keynote olson_using our laptop
1620 keynote olson_using our laptop
Rising Media, Inc.
 
1530 track 2 stuart_using our laptop
1530 track 2 stuart_using our laptop1530 track 2 stuart_using our laptop
1530 track 2 stuart_using our laptop
Rising Media, Inc.
 
1530 track 1 fader_using our laptop
1530 track 1 fader_using our laptop1530 track 1 fader_using our laptop
1530 track 1 fader_using our laptop
Rising Media, Inc.
 
1415 track 2 richardson
1415 track 2 richardson1415 track 2 richardson
1415 track 2 richardson
Rising Media, Inc.
 
1215 daa lunch owusu_using our laptop
1215 daa lunch owusu_using our laptop1215 daa lunch owusu_using our laptop
1215 daa lunch owusu_using our laptop
Rising Media, Inc.
 
1215 daa lunch a bos intro slides_using our laptop
1215 daa lunch a bos intro slides_using our laptop1215 daa lunch a bos intro slides_using our laptop
1215 daa lunch a bos intro slides_using our laptop
Rising Media, Inc.
 
915 e metrics_claudia perlich
915 e metrics_claudia perlich915 e metrics_claudia perlich
915 e metrics_claudia perlich
Rising Media, Inc.
 
855 sponsor movassate_using our laptop
855 sponsor movassate_using our laptop855 sponsor movassate_using our laptop
855 sponsor movassate_using our laptop
Rising Media, Inc.
 
1615 plack using our laptop
1615 plack using our laptop1615 plack using our laptop
1615 plack using our laptop
Rising Media, Inc.
 
1530 rimmele do not share
1530 rimmele do not share1530 rimmele do not share
1530 rimmele do not share
Rising Media, Inc.
 
1325 keynote yale_pdf shareable
1325 keynote yale_pdf shareable1325 keynote yale_pdf shareable
1325 keynote yale_pdf shareable
Rising Media, Inc.
 
1115 fiztgerald schuchardt
1115 fiztgerald schuchardt1115 fiztgerald schuchardt
1115 fiztgerald schuchardt
Rising Media, Inc.
 
1000 kondic do not share
1000 kondic do not share1000 kondic do not share
1000 kondic do not share
Rising Media, Inc.
 
905 keynote peele_using our laptop
905 keynote peele_using our laptop905 keynote peele_using our laptop
905 keynote peele_using our laptop
Rising Media, Inc.
 
Stephen morse sharable
Stephen morse sharableStephen morse sharable
Stephen morse sharable
Rising Media, Inc.
 
Elder shareable
Elder shareableElder shareable
Elder shareable
Rising Media, Inc.
 
1115 ramirez using our laptop
1115 ramirez using our laptop1115 ramirez using our laptop
1115 ramirez using our laptop
Rising Media, Inc.
 

More from Rising Media, Inc. (20)

1415 track 1 wu_using his laptop
1415 track 1 wu_using his laptop1415 track 1 wu_using his laptop
1415 track 1 wu_using his laptop
 
Matt gershoff
Matt gershoffMatt gershoff
Matt gershoff
 
Keynote adam greco
Keynote adam grecoKeynote adam greco
Keynote adam greco
 
1620 keynote olson_using our laptop
1620 keynote olson_using our laptop1620 keynote olson_using our laptop
1620 keynote olson_using our laptop
 
1530 track 2 stuart_using our laptop
1530 track 2 stuart_using our laptop1530 track 2 stuart_using our laptop
1530 track 2 stuart_using our laptop
 
1530 track 1 fader_using our laptop
1530 track 1 fader_using our laptop1530 track 1 fader_using our laptop
1530 track 1 fader_using our laptop
 
1415 track 2 richardson
1415 track 2 richardson1415 track 2 richardson
1415 track 2 richardson
 
1215 daa lunch owusu_using our laptop
1215 daa lunch owusu_using our laptop1215 daa lunch owusu_using our laptop
1215 daa lunch owusu_using our laptop
 
1215 daa lunch a bos intro slides_using our laptop
1215 daa lunch a bos intro slides_using our laptop1215 daa lunch a bos intro slides_using our laptop
1215 daa lunch a bos intro slides_using our laptop
 
915 e metrics_claudia perlich
915 e metrics_claudia perlich915 e metrics_claudia perlich
915 e metrics_claudia perlich
 
855 sponsor movassate_using our laptop
855 sponsor movassate_using our laptop855 sponsor movassate_using our laptop
855 sponsor movassate_using our laptop
 
1615 plack using our laptop
1615 plack using our laptop1615 plack using our laptop
1615 plack using our laptop
 
1530 rimmele do not share
1530 rimmele do not share1530 rimmele do not share
1530 rimmele do not share
 
1325 keynote yale_pdf shareable
1325 keynote yale_pdf shareable1325 keynote yale_pdf shareable
1325 keynote yale_pdf shareable
 
1115 fiztgerald schuchardt
1115 fiztgerald schuchardt1115 fiztgerald schuchardt
1115 fiztgerald schuchardt
 
1000 kondic do not share
1000 kondic do not share1000 kondic do not share
1000 kondic do not share
 
905 keynote peele_using our laptop
905 keynote peele_using our laptop905 keynote peele_using our laptop
905 keynote peele_using our laptop
 
Stephen morse sharable
Stephen morse sharableStephen morse sharable
Stephen morse sharable
 
Elder shareable
Elder shareableElder shareable
Elder shareable
 
1115 ramirez using our laptop
1115 ramirez using our laptop1115 ramirez using our laptop
1115 ramirez using our laptop
 

Recently uploaded

The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
Social Samosa
 
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Kaxil Naik
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
Lars Albertsson
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
wyddcwye1
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
jitskeb
 
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
a9qfiubqu
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
AlessioFois2
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
nyfuhyz
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Bill641377
 
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google CloudBuild applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
Márton Kodok
 
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
xclpvhuk
 
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
taqyea
 
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
Social Samosa
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
apvysm8
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
Sachin Paul
 
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
y3i0qsdzb
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
VyNguyen709676
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Sm321
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
bopyb
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Kiwi Creative
 

Recently uploaded (20)

The Ipsos - AI - Monitor 2024 Report.pdf
The  Ipsos - AI - Monitor 2024 Report.pdfThe  Ipsos - AI - Monitor 2024 Report.pdf
The Ipsos - AI - Monitor 2024 Report.pdf
 
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
 
End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024End-to-end pipeline agility - Berlin Buzzwords 2024
End-to-end pipeline agility - Berlin Buzzwords 2024
 
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
原版一比一利兹贝克特大学毕业证(LeedsBeckett毕业证书)如何办理
 
Experts live - Improving user adoption with AI
Experts live - Improving user adoption with AIExperts live - Improving user adoption with AI
Experts live - Improving user adoption with AI
 
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
原版一比一弗林德斯大学毕业证(Flinders毕业证书)如何办理
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
 
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
一比一原版(UMN文凭证书)明尼苏达大学毕业证如何办理
 
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
 
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google CloudBuild applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
 
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
一比一原版(Unimelb毕业证书)墨尔本大学毕业证如何办理
 
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
 
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
 
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
办(uts毕业证书)悉尼科技大学毕业证学历证书原版一模一样
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
 
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
 
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more importantChallenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
 
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataPredictably Improve Your B2B Tech Company's Performance by Leveraging Data
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
 

1555 track 1 huang_using his mac

  • 1. CREATING VALUE THROUGH ANALYTICS INNOVATION Wayne Huang, PhD Head of Predictive Analytics, Group Insurance Prudential Financial Inc. 1 - Predictive Analytics World, NYC, October 30, 2017
  • 2. Analytics Team Challenges 1.  Find analytics ideas 2.  Assess solution impact 3.  Prioritize analytics projects 4.  Develop & deploy analytics models 5.  Post-implementation monitoring 2
  • 3. From Automation To Innovation Manual Process Workflow Automation Decision & Learning Innovation Productivity Time 3
  • 4. 1. Find The Diamonds 4 Underwriting Review High Risk? Order APS/ Med Exam Approved Issue Policy Denial Letter Insurance Application Underwriting Review Life Insurance Underwriting Process MIB Check YesNo Yes No
  • 5. 2. Leverage New Data Old Paradigm §  Traditional data (Application, MIB, APS, Labs) §  Underwriter judgement §  Long decision time §  Higher cost §  Customer abandon application §  Painful customer experience New Paradigm §  New predictors (Rx, MVR, Medical, Credit,…) §  Predictive model §  Decision automation §  Lower cost §  New revenue §  Superior customer experience 5
  • 6. 3. Control Uncertainty Uncertainty is a result of having to deal with ambiguity and too many variables. It’s a risk to business. 1.  Use analytics to reduce ambiguity §  Use campaign response predictive model to identify customers propensity §  Use cluster analysis for market segmentation, product positioning, and targeted campaign 2.  Use machine learning to identify and manage variables that have higher predictive power for better outcome §  Use pricing predictive model to assist insurance product pricing §  Use prospect scoring model to determine new customer handling priority 6
  • 8. Operational Feasibility Assessment 1.  Understand the current process 2.  Identify tasks impacted by the solution 3.  Design a new process 4.  Assess the impact of solution implementation § Labor – case handling volume change, new skill requirement § Input – existing data vs. (real-time) new data § Time – cycle time reduction § Technology – system change requirement § Legal and compliance 8
  • 9. Financial Viability Assessment 1.  What value can the analytics project bring? § Lower cost, shorter cycle time, more revenue, higher profit 2.  What’s the model development and implementation cost? 3.  How to quantify the value? § Cost Savings: unit labor cost x reduced volume by automation, plus any other input reduction x unit cost, minus new data cost § New Profit: new sales x profit margin, or new customer acquisition x life time value 4.  Calculate five year NPV of the investment Year 1 Year 2 Year 3 Year 4 Year 5 Total Cashflow -$1MM $500K $1MM $1MM $1MM NPV (10% ROE) -$1MM $413K $751K $683K $621K $1.5MM 9
  • 10. Recognize Indirect Value Activities 1.  Activities that generate value to customers and firm §  Primary activities in value chain §  Predictive underwriting can reduce cost, increase revenue and improve customer experience 2.  Activities that preserve value §  If neglected, a firm loses the ability to generate economic value, e.g., advertising, customer service §  Targeted campaign can increase new customer acquisition and reduce cost §  Mining customer support data can identify product improvement opportunities and increase sales 3.  Activities that enable options (deferred value) §  Give a firm an advantage in dealing with uncertainty and change. They help exploit value-generating opportunities, e.g., R&D, employee training §  Predictive analytics can help speed up life science and material science discovery §  Predicting training effectiveness can better prepare employees for new tasks 10
  • 12. Why Analytics Projects Failed 1.  Lack of alignment with strategic goals §  Inability to integrate analytics solutions into workflows §  Limited front-line adoption 2.  Lack of strategic alignment and direction §  Lack of stakeholder alignment or support §  Lack of clear roadmap 3.  Poor data quality §  Missing or incomplete data §  Data quality or accuracy issues §  Data fragmentation 4.  Other §  Missing team skills or capabilities §  Unclear use case scope §  Inability to articulate value McKinsey & Company, “Raising Returns on Analytics Investments In Insurance”, 2017 12
  • 13. Project Evaluation Framework Project Value (5 Year NPV) Analytics Solutions Data Availability Process Integration Urgency Project Ease* 1. Pricing Predictive Model $3M Complex Partial Medium Difficulty Normal 4 2. Claims Analytics $2M Medium Complexity All Medium Difficulty Time Critical 3 3. Customer Journey Analytics $1M Simple All Easy Normal 1 4. Predictive Underwriting $3M Medium Complexity Partial Medium Difficulty Normal 3 5. Prospect Prioritization $1M Medium Complexity Partial Difficult Time Critical 4 6. Campaign Response Model $1M Simple All Easy Normal 2 13 * Project Ease ranges from 1 to 5, with 1 being the easiest and 5 being the most difficult.
  • 14. Ø  The goal of the heuristic is to choose projects, which maximize the ratio of NPV to Project Ease Ø  Suppose the total budget is $2.5M Project Selection Heuristic Project Cost Value Ease Ratio Strategic* Rank 1. Pricing Predictive Model $500K $3M 4 .75 1.13* 2 2. Claims Analytics $500K $2M 3 .66 .66 4 3. Customer Journey Analytics $250K $1M 1 1 1 3 4. Predictive Underwriting $1M $3M 3 1 1.5* 1 5. Prospect Prioritization $500K $1M 4 .25 .38* 6 6. Campaign Response Model $500K $1M 2 .50 .50 5 14 Ø  Select Projects 4, 1, 3, 2 for a total cost of $2.25M * Strategic projects receive 50% additional weight