Presentación Leandro Ruiz, Director Preventa Regional para CLA en Teradata en el 14º Congreso Internacional de Tecnología para el Negocio Financiero. 2 y 3 de julio de 2014

1. Leandro Ruiz
Teradata
BIG DATA: FROM HYPE TO
REALITY

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What Is BIG DATA?

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BIG DATABIG DATA
WEBWEB
PetabytesPetabytes
CRMCRM
TerabytesTerabytes
GigabytesGigabytes
ERPERP
ExabytesExabytes
INCREASING Data Variety and ComplexityINCREASING Data Variety and Complexity
User Generated
Content
Mobile Web
SMS/MMS
Sentiment
External
Demographics
HD Video
Speech to Text
Product/
Service Logs
Social Network
Business Data
Feeds
User Click Stream
Web Logs
Offer History A/B Testing
Dynamic Pricing
Affiliate Networks
Search Marketing
Behavioral
Targeting
Dynamic Funnels
Payment
Record Support Contacts
Customer Touches
Purchase
Detail
Purchase
Record
Offer Details
Segmentation
DECREASING Value Density in the DataDECREASING Value Density in the Data
Big Data: From Transactions to Interactions
Behavioral Analytics
ALL DATA

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New Data Structures Across Industries
Finance:

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Flow DATA -> INSIGHTS -> ACTIONS
Big Data is an Evolution not a Revolution
Flow BIG DATA -> INSIGHTS -> ACTIONS
Predictions
Events
Patterns
Hypothesis
Testing
Strategic
Actions
Operational
Actions
Is the Ultimate USE of Big Data Different? No.

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Analysts Recommend:
Shift from a Single Platform to an Ecosystem
“We will abandon the old models
based on the desire to implement for
high-value analytic applications.”
"Logical" Data Warehouse

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Discovery Platform
Data Warehouse/
Business Intelligence
Advanced
Analytics
The Problem
Proliferation of advanced analytics
environments has resulted in
fragmented data, higher costs,
expensive skills, longer time to insight

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Discovery Platform
The Problem
Integrated
Discovery
Platform
(IDP)
Data Warehouse/
Business Intelligence
Advanced
Analytics
The Solution
Proliferation of advanced analytics
environments has resulted in
fragmented data, higher costs,
expensive skills, longer time to insight
Integrated discovery analytics provides
deeper insight, integrated access, ease of
use, lower costs, better insight
SQL Framework Access Layer
Pre-Built Analytics Functions

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Discovery Platform Requirements
1
2
3
4
All Data
Multiple Analytic Methods
Diverse Enterprise Analysts
Rapid Exploration

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What do you
want to
discover?Recommend
Analysis
Influence
Analysis
Website
Analysis
Satisfaction
Metrics
BusinessValue
TX
Data
IX
Logs
Review
Text
Social
Graph
Events
Time
Emails
Text
All Data: Web Analytics

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Predictive
Analytics
Influencer
Analysis (6X)
Percentile
Analysis
Churn
Analysis
Behavioral
Analysis
(+25%)
BusinessValue
Statistics
Path
and Time Text SQL Graph Text
Better Predictive Results
Multiple Analytic Methods: Attrition

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Diverse Enterprise Analysts
Business Analysts
Apps
Data Scientists
and Analysts
SQL, BI Tools
Developers
Workbench, IDE, Library

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Rapid “Iterative” Exploration
Data Scientist
Business User
Rapid Exploration
Discovery More and Fail Fast
? Data
Acquisition
1
Data
Preparation
2
Visualization
4
Analysis
3

14. USE CASES

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Customer Interactions Across Multiple Channels
Teller Withdrawal
Teller ComplaintATM Deposit
Online Transfer
Cancel accountEmail Complaint
Call Center Inquiry

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What if you had a 360
degree view of all
interactions you are
having with the customer
and could proactively
identify high value
customers at risk of leaving
in the next 5 days?

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• Customerize – Identify the customer in the data
• Sessionize – Identify the session occurrence in time
• Sojournize – Stich together sessions to recreate cross-channel journey
07:05:32 09:20:23 09:25:32 11:05:48 1:05:06 1:35:12 1:42:58 1:45:14 3:05:58 4:15:22
Omni-channel Customer Journey

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Events Preceding Account Closure

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Finding Signal in the Noise
SELECT * FROM npath (
ON (
SELECT …
WHERE u.event_description IN (
SELECT aper.event FROM attrition_paths_event_rank aper
ORDER BY aper.count DESC LIMIT 10)
)
…
PATTERN ('(OTHER|EVENT){1,20}$')
SYMBOLS (…) RESULT (…)
)
) n;
Interactive Analytics
Reducing the “Noise” to
find the “Signal”

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Finding Signal in the Noise
SELECT *
FROM nPath (
ON (…)
PARTITION BY sba_id
ORDER BY datestamp
MODE (NONOVERLAPPING)
PATTERN ('(OTHER_EVENT|FEE_EVENT)+')
SYMBOLS (
event LIKE '%REVERSE FEE%' AS FEE_EVENT,
event NOT LIKE '%REVERSE FEE%' AS OTHER_EVENT)
RESULT (…)
) n;
Reducing the “Noise” to
find the “Signal”
Fee Reversal seems to
be a “Signal”

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Delivering Outstanding Customer Experiences
What if I knew that this customer was likely to leave?
One could…
• Apologize
• Offer an explanation
• Reverse the $5 fee
Jan 5: Reverse Fee
Request Jan 10: Request Made Again
Jan 15: Request Made AgainJan 7: Request Made Again
Jan 20: Account Closed

22. GRAPH ANALYSIS
Enhancing Churn Prediction

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Social Networks & Graph
May Leave
Likely to Leave
Happy Customer
Happy
Customer
Happy Customer
Power of Social Networks
• People interact with each other’s
behavior; influence each other
• People make decisions in a social
network context
• Ignoring social network context means
you’re missing a major influencer on
your customers’ choices
Graph models relationships
between objects like people
products and processes
Why Graph?

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Advanced Churn Analysis Today
Statistical
Analysis + Multi Channel
Behavioral
Path Analysis
Churn
Potential
Statistical
Model
Behavioral
Model
=
Best in Class
Churn Analytics
WHAT IF I COULD GRAPH THESE CUSTOMERS?
+ Sentiment
Analysis
Sentiment
Score
+ =

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Graph Churn Social Network
Nodes
High Churn
Risk
Low Churn
Risk

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Enhanced Churn with Social Graph Analysis
Best in Class Churn Scores without Social
Graph analysis
Social Graph visualization can help visualize
associations and areas to investigate
Apply graph analytics, such as “Closeness” and
“LocalClusteringCoefficient” to calculate and provide
new insight on strong relationships!

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Better Churn Scores on All Your Customers
+ = + =+
Churn
“Social Graph Visualized”
Churn
“Social Graph Analyzed”

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Single SQL-MR/GR Statement in Aster
+ =+
ASTER DISCOVERY PLATFORM
TERADATA ASTER DATABASE
Conduct behavioral and
social network churn
analysis with prebuilt
functions
Generate
enhanced churn
ranking scores
Graph Analytics at Scale
on All Customers

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Questions
and Answers
Questions
and Answers
Thank You!