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www.globalbigdataconference.com
Twitter: @bigdataconf Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
1
2
Crossing the Digital Chasm
Applying Advanced Analytics to
Acquire, Nurture & Retain Customers
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
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
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
4
BACKGROUND
Digital is Everywhere
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics
5
In God we trust.
All others – please bring me data
W. Edwards Deming
6
BACKGROUND
The focus of this discussion is
All Things Analytics
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics
7
BACKGROUND
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Digital
Social
Mobile Cloud
Analytics
Analytics powers the
remaining
Digital components
8
BACKGROUND
Digital Chasm
Digital Chasm is the gap between
early adopters and early majority
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
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
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
OPPORTUNITY
Prospect Acquire Nurture Retain /
Win-back
Opportunity exists across the entire customer value
chain; Determining focus area is important
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
11
Lack of direction, not lack of time, is the problem.
We all have 24 hour days.
- Zig Ziglar
12
OPPORTUNITY
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
PRIORITIES
Prospect Acquire Nurture Retain /
Win-back
4x less expensive to
retain than to
acquire
Retention / Win-back is where
most initiatives start
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
14
PRIORITIES
Prospect Acquire Nurture Retain /
Win-back
Grow share of
wallet; Customers
are sticky
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
PRIORITIES
Prospect Acquire Nurture Retain /
Win-back
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Growth drives brand equity and valuation
Growth is the
priority; Customers
are sticky
16
PRIORITIES
Prospect Acquire Nurture Retain /
Win-back
$100 K - $300 K in
annual subscription
fees per source
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
Purchased data is (relatively) less (or
more) expensive; It is also most curated
17
FRAMEWORK
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
18
FRAMEWORK
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
19
FRAMEWORK
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
PROSPECTING
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
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
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
Pre
dic
tive
Po
we
r (
Lift
)
Data Acquisition Costs
3rd Party
Collected
Data Sources Model Forms
Commercial
Solution
Data Collection
Identify Unit of analysis
Curate (Collect, De-identify, Cleanse)
Merge
Repeat each time period
22
NURTURE & RETENTION
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
Discipline
is the bridge between
goals and accomplishment
- Jim Rohn
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
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 R
ule
s
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
Getting to the finish line involves careful
planning and execution
25
CLOSING
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
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
Global
Big Data
Conferen
ce, Santa
Clara
March 7-
9
26 Global Predictive Analytics Conference | March 7 - 9 | Santa Clara
THANK YOU!