We are at a point of inflection of embedding Advanced Analytics everywhere. If you are interested in learning about: 1) Why should we cross the Cigital Chasm 2) Which of the areas should one focus 3) Which of the problems should one focus on 4) What are the opportunities / challenges / mitigations then this is for you.

1. www.globalbigdataconference.com
Twitter: @bigdataconf
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2. 2
Crossing the Digital Chasm
Applying Advanced Analytics to
Acquire, Nurture & Retain Customers
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara

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 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
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara

4. 4
BACKGROUND
Digital is Everywhere
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics

5. 5
In God we trust.
All others – please bring me data
W. Edwards Deming

6. 6
BACKGROUND
The focus of this discussion is
All Things Analytics
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics

7. 7
BACKGROUND
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics
Analytics powers the
remaining
Digital components

8. 8
BACKGROUND
Digital Chasm
Digital Chasm is the gap between
early adopters and early majority
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
2.5%
Innovators
Early
Adaptors
13.5%
Early
Majority
34%
Late
Majority
34%
Laggards
16%
Digital Adoption Life Cycle

9. 9
NEED
2001 – 2013 CAGR Revenue
(Firm | Industry)
Source: 2001 – 2013 Revenue figures from Capital IQ
3%
3%
3%
1%
5%
7%
7%
8%
10%
12%
Digital Chasm translates to
money left on the table;
Crossing it is necessary
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital Chasm
2.5%
Innovators
Early
Adaptors
13.5%
Early
Majority
34%
Late
Majority
34%
Laggards
16%

10. 10
OPPORTUNITY
Prospect Acquire Nurture
Retain /
Win-back
Opportunity exists across the entire customer value
chain; Determining focus area is important
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara

11. 11
Lack of direction, not lack of time, is the problem.
We all have 24 hour days.
- Zig Ziglar

12. 12
OPPORTUNITY
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
All roads lead to improving
Customer Life Time Value
Customer Life Time Value (Simplified)
𝑀𝑎𝑥𝑖𝑚𝑖𝑧𝑒 𝐶𝐿𝑇𝑉𝑗 =
(𝑅𝑒𝑣 − 𝐶𝑜𝑠𝑡𝑠)
(1 + 𝑑)𝑖
− 𝐴𝑐𝑞. 𝐶𝑜𝑠𝑡
𝑛
𝑖=1
𝑚
𝑗=1
i = year index
j = customer index
d = discount rate
Value
Operating Costs
m, number of customers
Acquisition Costs
n, longevity
Rev, spend / share of wallet

13. 13
PRIORITIES
Prospect Acquire Nurture
Retain /
Win-back
4x less expensive to
retain than to
acquire
Retention / Win-back is where
most initiatives start
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara

14. 14
PRIORITIES
Prospect Acquire Nurture
Retain /
Win-back
Grow share of
wallet; Customers
are sticky
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Switching costs (in most industries) are high;
1-2 years of stick time is the sweet spot

15. 15
PRIORITIES
Prospect Acquire Nurture
Retain /
Win-back
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Growth drives brand equity and valuation
Growth is the
priority; Customers
are sticky

16. 16
PRIORITIES
Prospect Acquire Nurture
Retain /
Win-back
$100 K - $300 K in
annual subscription
fees per source
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Purchased data is (relatively) less (or
more) expensive; It is also most curated

17. 17
FRAMEWORK
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Advanced Analytics (AA) can help in several ways
Prospect Acquire Nurture Retain / Win-back
Advisor / Agent
 Segmentation & Profiling
 Likelihood to recommend
 Referral
 Performance
 Product
Recommendations
 Personalization
 Leads
 Social Network analysis
and influence scores
 Geo-spatial analysis
 Coverage analysis
Customer
 Segmentation & Profiling
 Likelihood to buy
 Likelihood to qualify
 Like customers
 Product
recommendations
 Personalization
 Social Network Analysis
and Influence analysis
 Likelihood to recommend
Marketing
 Mix Optimization
 X-Sell & Up-Sell
 Social listening
Advisor / Agent
 Integration (with industry
standard applications)
 Industry standard scores
and pricing
Customer
 Triage prediction
 Risk class determination
 Risk class prediction
 Likelihood to misrepresent
 Misrepresentation
detection
 Likelihood to smoke
 Likelihood to get declined
 Morbidity analysis
 Co-morbidity analysis
 Mortality analysis
 Post-issue analysis
 Protective Value analysis
New Data Sources
Integration
 EMR
 EHR
 Telematics
Product
 Next best offer
 Simplified issue
 Price elasticity
 Engagement
 Assumption development
/ Experience studies
Customer
 X-Sell
Social
 Social listening
 Influencers and
Advocates
Claims
 Fraud detection
 Likelihood to commit soft
fraud
 Claim severity
 Likelihood to litigate
 Expedited adjudication
Telematics
 Simplified issue product
development
 Preferred Pricing and
Discounts
Advisor / Agent
 Propensity to recommend
 Propensity to refer
 Performance
Customer
 Likelihood to lapse
 Likelihood to win-back
Business Integration

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FRAMEWORK
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
An AA Maturity Model helps with visioning
Area Inception Emerging Developing Mature Best In Class
People
Thought leaders build
on the business case
to integrating AA
Leaders learn and
understand through
experimentation
Leaders build strategic
partnerships for
success
Leaders assemble /
build an in-house
teams
Advanced Analytics
enables and drives
strategic initiatives
Processes
Leaders recognize the
need for a consistent
process
Experimentation is
both limited and
controlled
Increased tolerance
for experimentation
and for failure
Emphasis is on
experiment design as
opposed to on
execution
Rapid
experimentation, fail
fast and improvise
Technology
Leaders recognize
technology is a key
enabler
Environments are
scattered
Single environment
enabling a few
selected projects
Single environment
with multi-tenancy
(projects, resources)
Multiple environments
with SDLC-like maturity
Data
Leaders identify
broadly the data
sources required
Data sources are
integrated in an ad-
hoc basis
Data is integrated in
an ad-hoc basis
Data sources are
integrated enabling
hypothesis testing
Data is central to all
decision making
Governance
Leaders recognize the
need for governance
and complexity
A governance process
is laid out
A governance process
is adhered to in
pockets
A governance process
is adhered to across
projects
Governance can be
traced and reported
on
Benefits
from
Advanced
Analytics

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FRAMEWORK
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
AA Process Maturity is THE differentiator
Data Inputs Advanced Analytics Data and Insight
Consumers
Structured
Semi-Structured
Un-Structured
ODS
Business Users /
Data Scientists
Executives
Operational
Users
Problem
Definition
Model
Strategy
Data
Engineering
Model
Build
Implementation
&
Governance
1
2
3
4
5
Define business problem, objectives and
engagement model
Translate Business Problem into a Modeling and a
Scoring Problem
Prepare, normalize and curate raw data into a
modeling ready form
Build and validate Predictive Models using
Champion Challenger Process
Use scoring equations from Champion model and
deploy the model into production environment

20. 20
PROSPECTING
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Prospecting is a conglomeration of
several small(er) problems
 Awareness (Aided and un-aided
recall)
 Drivers (Price elasticity, Value)
 Trigger points (Life stage
indicator models)
What
1. Too complex to understand (Simplify message,
not product)
2. High price sensitivity (Volume vs. Margin)
3. Un-timely identification of trigger points (Omni-
Channel – e.g., Live Ramp vs. PA Model build)
 Channel (Awareness, Response,
Conversion)
 Prospective population
identification
 Touchpoint repetition
How
1. Too high error rates in response modeling (Unit
of analysis = individual)
2. Exhausted target population (Sub-Prime
sampling with caution – cost / benefit analysis)
3. Insufficient response rates (Ad-stock models)
 Profiling (Population & Sample(s))
 Indexes
 Clone(s) | Look alike(s)
Who
1. Too Big to Profile (Stratified Sample)
2. Too many unknowns (Just focus on signal)
3. Too many / insufficient clone attributes
(Actionable (to build Persona) attributes)
4. Low match rate (Commercial vs. in-house)
Business
Problem
Sample Analytical
Problems
Sample Challenges
and Mitigation

21. Rules Based Model
 Several insignificant but
important patterns
 Supervised and Un-
supervised Learning methods
including
 Clustering &
Segmentation
 Market Basket
 PCA
Predictive Model
 Few significant and
important patterns
 Supervised Learning
methods including
 GLMs
 Non-linear (Neural Nets)
 State Space
 Genetic Algorithms
21
ACQUISITION
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Acquisition is a trade-off problem; Commercial
solution involves finding the optimal mix
In-Person
Survey / Form
3rd Party
Aggregated
PredictivePower(Lift)
Data Acquisition Costs
3rd Party
Collected
Data Sources Model Forms
Commercial
Solution

22. Data Collection
 Identify Unit of
analysis
 Curate (Collect,
De-identify,
Cleanse)
 Merge
 Repeat each
time period
22
NURTURE & RETENTION
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Nurture & Retention are BIG data collection
& engineering (maturity) problems
Internal Data (80%)
External Data (20%)
Data Engineering (Create Longitudinal View) Predictive Models
 Profiles on
variety of
dimensions
 Engagement
Index
 Product Affinity
 Next Best Offer
 Likely to Lapse
 Likely to Refer /
Recommend
Customer
Product
Point in Time
Snapshot
What data should I keep?
1Q Look back
2Q Look back
3Q Look back
4Q Look back

23. 23
Discipline
is the bridge between
goals and accomplishment
- Jim Rohn

24. Relevant Data Set
24
AA Journey
Core Inputs
(Model Build)
Historical Data
Raw
Data
Additional Inputs
(Test)
Modeling Data Set
Core Inputs
(Model Build)
Additional Inputs
(Test)
V
a
l
i
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
Getting to the finish line involves careful
planning and execution

25. 25
CLOSING
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
 Data mining is for real and not “entirely” hype
 Prioritize Process over immediate Purpose
 A structured process is critical
 There is no pixie dust
 QC every step along the way

26. 26Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
THANK YOU!