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

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

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


Big Data challenges

Data

Linkage

Platform

Analytics


Marketing

opportunities

Armando Ramos


What’s my market penetration?

Understanding marketable universe

Peer benchmarking analysis

Developing new strategies

Marketing opportunities Knowing your market footprint


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


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


Big Data challenges

Armando Ramos


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


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


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


Who builds these environments?

Client IT priorities

Hosted environment

► Bureaus

► Agent

► Third party processors

Big Data challenges Platforms


Dynamic marketing environment

New data elements

New portfolios

Merges and acquisitions

New markets and channels

Big Data challenges Scalability and flexibility


Regulatory requirements

CFPB

CCAR

Basel II

Model and data governance

Big Data challenges Regulated environment


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


Technology response

to challenges

Armando Ramos


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


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


Client case studies

Brian Ardinger


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)


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


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


Big Data challenges

Data

Linkage

Platform

Analytics


For additional information, please contact:

[email protected]

[email protected]

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