vision 2014: understanding-marketing-efficiency-through-big-data-analytics
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Understanding marketing efficiency through Big Data analytics A case study
Brian Ardinger Experian
Armando Ramos Experian
#vision2014
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Big Data for marketing efficiencies
Marketing opportunities
Big Data challenges
Technology responds to challenges
A case study
Agenda
Big Data opportunities and challenges for analytical sandboxes for understanding financial trends in the industry and consumer behavior for both customers and prospects
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Big Data challenges
Data
Linkage
Platform
Analytics
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Marketing
opportunities
Armando Ramos
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What’s my market penetration?
Understanding marketable universe
Peer benchmarking analysis
Developing new strategies
Marketing opportunities Knowing your market footprint
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What are my customers doing?
New product with competitors
Posturing for a big purchase?
Balance transfer alerts
Payment behavior changes
Marketing opportunities Trending your customer behavior
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Deploying a new strategies
Brainstorming an idea
Sizing and validating the idea
Developing the idea into a strategy
Champion / challenger testing
In the market test and learn
Deploying a new strategy
Marketing opportunities Increase your speed to market
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Big Data challenges
Armando Ramos
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Volume limitation?
Big Data challenges Vast amount of data available
Analytical
data
warehouse
Customer
information
Marketing
history
Consumer
lifestyle
Consumer
behavior
Consumer
digital info
Consumer
credit
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Can we afford it?
Storage (Gigabyte)
► 1990 $10,000
► 2000 $10
► 2010 $0.10
CPU (Moore’s Law)
► Number of transistors on integrated circuits doubles every two years since 1965
Big Data challenges Financial feasibility
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Integration under a single key
Legacy analytic repositories
360 degree view across the enterprise
Partner data (i.e., airlines)
Vendor data (i.e., credit bureaus)
Big Data challenges Linkage technology
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Who builds these environments?
Client IT priorities
Hosted environment
► Bureaus
► Agent
► Third party processors
Big Data challenges Platforms
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Dynamic marketing environment
New data elements
New portfolios
Merges and acquisitions
New markets and channels
Big Data challenges Scalability and flexibility
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Regulatory requirements
CFPB
CCAR
Basel II
Model and data governance
Big Data challenges Regulated environment
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Analytic performance
Fully integrated view of the data
Sub populations for unit testing
Large populations for performance testing
Historical populations for validations
Query response time
IT dependencies
Big Data challenges Analytics
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Technology response
to challenges
Armando Ramos
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Hosted single full integrated platform
Linkage HUB with dynamic integration (real-time requirements)
Middleware / workflow software
► Real-time requirements
Analytical tools (i.e., SAS)
Hadoop / Cloud environments
Strategy development tools (i.e., Attribute Toolbox™)
Technology responds to challenges Technology is ahead of the game
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EXP CIS/AUTO
existing
EXP Consulting
existing
Sandbox &
Storage
Data Includes (TIPT):
• Trades
• Inquires
• Public Records
• Trended Raw File
• Consumer level data (like ZIP)
• Includes Consumer and Trade level
keys “links” (PIN, TIN & PTK)
No identifying information!!
Experian Analytic SandboxTM
Annonymized 10%
Sample Bureau
Database
56TB
EXP Sciences
existing
Sandbox &
Storage
Client Private
Sandbox
Multi - Client
Demo
Sandbox
Private sandboxs can house
client’s data with PIN’s (links)
to the Experian sandbox
and/or include non structured
data (like social media) for
analysis
Hosted
Environment
Client access
via the
internet/Https.
All data is loaded or
extracted by/through
Experian. For security
reasons, no data can
be remotely loaded or
extracted by users to
servers outside of the
environment
Client access
via the
internet/Https.
The Demo Sandbox is a
multiple client box. Client
specific data cannot be stored
on server
High level schema of Experian’s Analytic SandboxTM
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Client case studies
Brian Ardinger
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Regional bank
Regional Client
portfolio
National
prospecting
1.3 M population (100%
Client footprint)
Summarized data only
Production attributes and
scores (not to exceed 500
data elements)
Added value data
elements (not to exceed
500 (i.e., Premier
AttributesSM,
Trend ViewSM, etc.)
Six time periods (quarterly
for a 18-month
performance window)
Bank performance data
PIN’ed and integrated
Built internally at the bank
5 M population
(representative portfolio
sample)
Summarized data on
private and raw data on
10% File OneSM
Production attributes and
scores
Added value data elements
(i.e., Premier AttributesSM,
Trend ViewSM, etc.)
Twenty-four time periods of
10% File OneSM
Five time periods (quarterly
for a 15-month performance
window)
Hosted at Experian (Cloud)
10 user seats
220 M population (full
marketing file)
Summarized data only
production attributes and
scores
Added value data elements
(i.e., Trend ViewSM, etc.)
Sixty time periods (monthly
for a five-year performance
window)
Hosted at Acxiom (Cloud)
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Help clients define and size the static population with the
depth and breadth of data for the analytical data repository
to meet their analytical needs Consulting
Experian’s PINing is a core competency function for the
credit file — files with name and addresses can be linked
together with a consistent and persistent key
Data
integration
Experian can provide value-added data assets for test and
learn scenarios for calculated strategy assessment
Experian
data assets
Multiple delivery options can simplify and accelerate
implementation for rapid deployment
Delivery
options
Opportunities
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National universe
Trended data
► Credit data
► Campaign history
► New data elements
Integrate client / internal data assets
Tools for analytics
Rapid response to queries
In the market testing
Top-5 credit card issuer
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Big Data challenges
Data
Linkage
Platform
Analytics
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