Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurturing and retaining...

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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

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March 7-

9


4

BACKGROUND

Digital is Everywhere

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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

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9


Digital

Social

Mobile Cloud

Analytics

7

BACKGROUND

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9


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

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9


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

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March 7-

9


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

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March 7-

9


11

Lack of direction, not lack of time, is the problem.

We all have 24 hour days.

- Zig Ziglar

12

OPPORTUNITY

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9


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


Win-back

4x less expensive to

retain than to

acquire

Retention / Win-back is where

most initiatives start

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9


14

PRIORITIES


Win-back

Grow share of

wallet; Customers

are sticky

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Switching costs (in most industries) are high;

1-2 years of stick time is the sweet spot

15

PRIORITIES


Win-back

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Growth drives brand equity and valuation

Growth is the

priority; Customers

are sticky

16

PRIORITIES


Win-back

$100 K - $300 K in

annual subscription

fees per source

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Purchased data is (relatively) less (or

more) expensive; It is also most curated

17

FRAMEWORK

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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

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March 7-

9


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


is adhered to in

pockets


is adhered to across

projects

Governance can be

traced and reported

on

Benefits

from

Advanced

Analytics

19

FRAMEWORK

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March 7-

9


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

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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

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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

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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

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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

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March 7-

9

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

THANK YOU!

Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurturing and retaining...

Data & Analytics

Transcript of Crossing the Digital Chasm - Applying Advanced Analytics in acquiring, nurturing and retaining...