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IBM Proof of Technology Probeer de Mogelijkheden van Datamining zelf uit 30-10-2014 Amsterdam, IBM Client Center Presentatie van Laila Fettah & Robin van Tilburg
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© 2014 IBM Corporation
An IBM Proof of Technology
IBM SPSS Data Mining Workshop
Laila Fettah– Technical Sales Specialist Advanced Analytics
Robin van Tilburg – Business analytics Specialty Architect
30 oktober 2014
© 2014 IBM Corporation
IBM Software
2 IBM SPSS Data Mining Workshop
Welcome to the Technical Exploration Center
Introductions
Access restrictions
Restrooms
Emergency Exits
Smoking Policy
Breakfast/Lunch/Snacks – location and times
Special meal requirements?
© 2014 IBM Corporation
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Introductions
Please introduce yourself
Name and organization
Current integration
technologies/tools in use
What do you want out of this Data Mining Workshop?
© 2014 IBM Corporation
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Agenda
10:00-10:10 Welcome and Introductions
10:10-11:00 Introduction to Predictive Analytics
11:00-11:30 Exercise: Navigating IBM SPSS Modeler
11:30-12:00 Exercise: Predictive in 20 Minutes
12:00-12:45 Lunch
12:45-13:30 Data Mining Methodology and Application
13:30-14:00 Exercise: Data Mining Techniques
14:00-14:30 Exercise: Deployment
14:30-14:45 Wrap-up
© 2014 IBM Corporation
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Objectives
Introduction to predictive analytics and data mining
Stimulate thinking about how data mining would benefit your organization
Demonstrate ease of use of powerful technology
Get experience in “doing” data mining
See examples of existing customers and their realized ROI/benefits
© 2014 IBM Corporation
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“I used to think my
job was all about
arrests. Chasing
bad guys.”
“Now, we figure out
where to send
patrols to stop crime
before it happens.”
© 2014 IBM Corporation
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Smarter Planet
The world is changing, enabling organizations to make faster,
better-informed decisions
7
Digital technologies (sensors and
other monitoring instruments) are
being embedded into every object,
system and process.
All the data generated by digital
technology is providing intelligence
to help us do things better,
improving our responsiveness
and our ability to predict and
optimize for future events.
INTELLIGENT
INSTRUMENTED
INTERCONNECTED
In the globalized, networked
world, people, systems,
objects and processes are
connected, and they
are communicating with one
another in entirely new ways.
© 2014 IBM Corporation
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With this change comes an
explosion in information …
… Yet organizations are
operating with blind spots
Inefficient Access
1 in 2 don’t have access to the
information across their organization
needed to do their jobs
Lack of Insight
1 in 3 managers frequently make
critical decisions without the
information they need
Inability to Predict
3 in 4 business leaders say more
predictive information would drive better
decisions
Variety of Information
Volume of Digital Data
Velocity of Decision Making
Source: IBM Institute for Business Value
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© 2014 IBM Corporation
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Leverage Information To Drive Smarter Business Outcomes
Increase Revenue
Increase Productivity
Reduce Costs
Reduce Risk
9
© 2014 IBM Corporation
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“I used to think my
job was all about
arrests. Chasing
bad guys.”
“Now, we figure out
where to send
patrols to stop crime
before it happens.”
© 2014 IBM Corporation
IBM Software
Door middel van data mining kan de politie de delen van hun jurisdictie rangschikken
11
Minst waarschijnlijk
dat…
Meest waarschijnlijk
dat…
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Why is predictive analytics important to your organization?
“The median ROI for the
projects that incorporated
predictive technologies was
145%, compared with a
median ROI of 89% for those
projects that did not.” – Source: IDC, “Predictive Analytics
and ROI: Lessons from IDC’s
Financial Impact Study”
© 2014 IBM Corporation
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SPSS Customers: Business Objectives
13
Attract the
best customers
Retain
profitable
customers
Grow
customer
value
Manage
Risk
Detect and prevent
Non-Compliance “What is the
likelihood a prospect will respond?”
“What is the most likely next product for
each customer?“
“Which
customers are
likely to
leave?”
“What activities are
likely to be
fraudulent?”
“Which customers are likely to default on a
loan?”
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Enabling the Predictive Analytics Process
14
Connect & Capture Analyse & Predict Deliver & Act Data Collection delivers
an accurate view of
customer attitudes and
opinions
Predictive capabilities bring
repeatability to ongoing decision
making, and drive confidence in
your results and decisions
Unique deployment
technologies and
methodologies maximize the
impact of analytics in your
operation
© 2014 IBM Corporation
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SPSS Predictive Analytics Software -- 4 Product Families
Data Collection (surveys) Delivers accurate view of customer attitudes & opinions
• IBM SPSS Data Collection
Statistics Drives confidence in your results & decisions
• IBM SPSS Statistics
• IBM SPSS Text Analytics for Surveys (STAFS)
Modeling (data mining) Brings repeatability to ongoing decision making
• IBM SPSS Modeler
• IBM SPSS Text Analytics (TA)
Deployment (automation, scoring service,
sharing, …) Maximizes the impact of analytics in your operation
• IBM SPSS Decision Management
• IBM SPSS Collaboration & Deployment Services
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Predictive Modeling with Modeler
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Predicting Customer Behavior
Marketing activities are driven by
predicted customer behavior
Data Mining
Data on
Historic and
Present
Customer
Behavior
Predicted Customer Behavior
Enterprise
Data
Sources
Marketing
Attitudinal
Interaction
Web
Call-center
Operational
Attrition
risk
Potential
value
Cross sell
B
Cross sell
A
Credit
risk
Fraud
risk
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Definition of Data Mining
Finding patterns in your data that you can use to do your business better
Business-oriented discovery of patterns producing insight and a predictive capability which can be deployed widely
Process of autonomously retrieving useful information or knowledge (“actionable assets”) from large data stores or set
Predictive analysis helps connect data to
effective action by drawing reliable conclusions
about current conditions and future events.”
Gareth Herschel,
Research Director, Gartner Group
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Statistical vs. Data Mining Approach
Top-Down Approaches: Query, Search
Bottom-Up Approaches: Data Mining, Text Mining
A Statistical Approach can
involve a user forming a theory
about a possible relationship in
a database and converting that
to a hypothesis and testing
that hypothesis using a
statistical method. It is a
manual, user-driven, top-
down approach to data
analysis. Source DM Review
• The difference with data mining is that the interrogation of the data is done by the data mining method--rather than by the user. It is a data-driven, self-organizing, bottom-up approach to data analysis that works on large data sets.
* "Statistical Modeling: The Two Cultures," Leo Breiman, Statistical Science, 2001, Vol.16 (3), pp.199-231.
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Data Mining: a Different Approach
Top-Down Query
Search (OLAP, BI)
Bottom-Up Data Mining
Text/Web Mining
Measurement (historical) Prediction (future)
Bu
sin
ess v
alu
e
Facts Segments & Trends Predictions
Data
mining
Which customer types are at risk
and why?
Which cities were they located in?
OLAP
How many subscribers did we lose?
Query &
Reporting
What should we offer this
customer today?
Integrated
Analytical
Solutions
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IBM SPSS Modeler
High performance data mining and text analytics workbench
Used for the proactive
• Identification of revenue opportunities
• Reduction of costs
• Increase in productivity
• Forecasting
Allows analytics to be repeated and integrated within business systems
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IBM SPSS Modeler
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IBM SPSS Modeler
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Exercise: Predictive in 20 Minutes
Goal:
Identify who has cancelled their contract
Approach:
Use a data extract from a CRM
Define which fields to use
Choose the modeling technique
Automatically generate a model to identify who has cancelled
Review results
Why?
To prevent customers cancelling, by proactively identifying those likely
to cancel before they do.
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Data mining methodology
CRoss-Industry Standard
Process Model for Data Mining
Describes Components of
Complete Data Mining Project
Cycle
Shows Iterative Nature of Data
Mining
Vendor and Industry Neutral
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Data Mining Considerations – CRISP-DM
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Business Understanding
What is the goal, what are we trying to achieve?
Data Understanding/Preparation Available data (structured/unstructured)
Relevant factors
Subject matter expertise
Modeling Supervised vs. Unsupervised
Different types of models (NN vs. Rules)
Combining models (Meta modeling)
Deployment Batch vs. Real-time
Production Automation Scheduling
Champion – Challenger
Multi-step jobs, conditional logic
Governance Version control
Security and auditing
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Business Understanding
Business Problem
Telco Company has seen an increase in Customer Churn.
Problems with the Current Process
Based on Analysis it is not clear what the factors drive churn. The
business is in reactive mode vs. proactive.
Business Need
The executives have asked the marketing department to identify the
customers that are likely to churn and create an action plan to
address the problem.
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Data Understanding
Do we have historical data that describes our customer behavior?
– Yes, the data is available in the Enterprise Data Warehouse
Do we have historical data of the customers that have churned?
– Yes, we keep that historical data in the EDW as well.
What data do we need? Where is it located?
– Billing data, call data, payment data and demographics
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Data Preparation
Aggregate the data so that we have one row for each account
Get the relevant attributes and calculate them if necessary
Demographic data
Call behavioral data
Churn flag
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Modeling
In this phase, various modeling techniques are selected and applied,
and their parameters are calibrated to optimal
values. Typically, there are several techniques for the same data
mining problem type. Some techniques have specific
requirements on the form of data.
Therefore, going back to the data preparation phase is often necessary.
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Evaluation
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Questions Customer Ask That Modeler Helps Answer
Segment – I know my customers aren’t all the same, but how?
Acquire –What customer should I be going after? –Where should I put my new store?
Grow – I’ve got dozens of products to offer– how do I know the best mix to offer? – I’m blanketing my customer base with offers, but my returns seem to be
diminishing. What am I doing wrong?
Retain – I wish I knew which customers were most likely to leave me for a competitor. – I wish I knew which customers were the most profitable
Fraud/Risk – I am spending a lot of time reviewing each claim,
I wish there was a way of identifying which claims I should focus on.
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“After a thorough
investigation of the analytical
solutions in the market, we
selected IBM SPSS for its
ease of use for the business
users and the extensive
insight it provides into
customer behavior and
profitability. The software
generates results rapidly.”
— Paul Groenland
Project manager, database
marketing Rabobank
Business challenge
Rabobank aims to strengthen its position as a market leader in financial
services by further developing and expanding its relationship with its private
and corporate customers.
Solution
Rabobank uses predictive analytics software from IBM SPSS to create and
execute targeted direct marketing and lead generation campaigns. The
quality of the leads is higher, so marketing campaigns are much more cost-
efficient and effective
Benefits
Completion time for marketing campaigns has decreased, on average,
by two to four weeks
The quality of the leads is higher, so marketing campaigns are much
more cost-efficient and effective
Highly targeted support for local banks and advisors. By providing timely
and targeted leads, they can quickly respond to changes and to
individual customers’ wishes.
Rabobank
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Zorg en Zekerheid Uses business analytics to target fraudulent insurance claims
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The need:
Processing millions of healthcare records requires surgical precision. For this
Netherlands health insurer, this level of efficiency was missing from the process of
analyzing claims and invoices to catch fraudulent activity. Manually selecting the
data on the basis of predefined risk indicators had proven to be both time-
consuming and unreliable in catching those abusing the system.
The solution:
Zorg en Zekerheid deployed a predictive analytics software solution capable of
analyzing larger quantities of data, discovering patterns automatically,and catching
anomalies in the process with a sharper level of accuracy and efficiency. The
software provides a simple, graphical interface to deliver robust data mining,
advanced analytics and interactive visualization for business users.
What makes it smarter:
Propels the fraud investigation process to action within days, instead of multiple
weeks, using predictive analytics. Enables lost money to be recovered.
Captures all relevant data, including hard-copy invoices, which the system scans
and archives.
Aggregates millions of digitally submitted records from multiple data sources and
media formats into a central database, so data can be cross-functionally
structured and automatically analyzed.
“The analytics solution has
doubled our financial results
each year since 2007.”
— Andor de Vries, Fraud Analyst,
Zorg and Zekerheid
Solution component:
IBM® SPSS Modeler
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Data Mining Methods
Unsupervised Learning – Input and outputs are unknown, finds useful patterns
Supervised Learning – Modeler specifies what to predict
Clustering Associations / Sequences
Regression
• Exploratory data analysis • Reveals natural groups within a data set • Distance Measure: No prior knowledge about
groups or characteristics • Not always an end in itself
• Finds things that occur together • Associations can exist between any of the
attributes • Discovers association rules in time-oriented data • Find the sequence or order of the events
Customer Segmentation Market Basket Analysis, Next logical purchase
Classification
• Predicts an fixed outcome based on a set of inputs.
• Modelers pre-defines input and outputs
Fraudulent insurance claim prediction
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Unsupervised Learning - Cluster and Associate
Clustering
– An exploratory data analysis technique
– Reveals natural groups within a data set
– Distance Measure:
No prior knowledge about groups or characteristics
– Not always an end in itself
Associations
– Finds things that occur together – ex: events in a crime incident
– Associations can exist between any of the attributes
(no single outcome like Decision Trees)
Sequential Associations
– Discovers association rules in time-oriented data
– Find the sequence or order of the events
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Supervised Learning - Classification
Neural Networks
– A technique for predicting outcomes based on inputs
where the inputs are weighted on hidden layers
– Behaves similar to the neurons in your brain
– Powerful general function estimators
– Require minimal statistical or mathematical knowledge
Decision Trees and Rule Induction
– Classification systems that predict or classify
– Technique that shows the ‘reasoning’
– contrast with Neural Network
– Builds sets of easy to understand ‘If – Then’ Rules
– Eliminates factors that are unimportant
Cat. % n
Bad 52.01 168
Good 47.99 155
Total (100.00) 323
Credit ranking (1=default)
Cat. % n
Bad 86.67 143
Good 13.33 22
Total (51.08) 165
Paid Weekly/Monthly
P-value=0.0000, Chi-square=179.6665, df=1
Weekly pay
Cat. % n
Bad 15.82 25
Good 84.18 133
Total (48.92) 158
Monthly salary
Cat. % n
Bad 90.51 143
Good 9.49 15
Total (48.92) 158
Age Categorical
P-value=0.0000, Chi-square=30.1113, df=1
Young (< 25);Middle (25-35)
Cat. % n
Bad 0.00 0
Good 100.00 7
Total (2.17) 7
Old ( > 35)
Cat. % n
Bad 48.98 24
Good 51.02 25
Total (15.17) 49
Age Categorical
P-value=0.0000, Chi-square=58.7255, df=1
Young (< 25)
Cat. % n
Bad 0.92 1
Good 99.08 108
Total (33.75) 109
Middle (25-35);Old ( > 35)
Cat. % n
Bad 0.00 0
Good 100.00 8
Total (2.48) 8
Social Class
P-value=0.0016, Chi-square=12.0388, df=1
Management;Clerical
Cat. % n
Bad 58.54 24
Good 41.46 17
Total (12.69) 41
Professional
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Anomaly Detection
Anomalies
– Anomaly detection is an exploratory method
– Designed for quick detection of unusual cases or records that should
be candidates for further analysis
– These should be regarded as suspected anomalies, which, on closer
examination, may or may not turn out to be real
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Disclaimer: Common Sense Check
© 2014 IBM Corporation
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Richmond Police Department Curbing crime with predictive analytics
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The need: Facing a rising crime rate, the Richmond Police Department needed an efficient and cost-effective way to analyze crime data, assess public safety risks and make intelligent decisions about personnel deployment.
The solution: The Department turned to IBM SPSS, to deploy a powerful predictive analytics tool that brings data from multiple sources into one data warehouse; discovers hidden relationships in the data; and automatically generates crime forecasts.
What makes it smarter:
Analyzes extremely large datasets and predicts crime patterns, giving the
Department intelligence it needs to curb crime
Enables the Department to be efficient about how, where and when to deploy
patrol and tactical units
Demonstrates ability to reduce violent-crime rates (homicide rates dropped 32 %
from 2006-2007 and an additional 40 % from 2007-2008)
“The big performance boost
has been for my new guys
on the streets. IBM SPSS
essentially does the work
that is gained only from
experience.”
— Stephen Hollifield
Head of Technology
Richmond Police Department
Solution components: IBM SPSS Statistics
IBM SPSS Modeler
IBM Business Partner
Information Builders
IBM Business Partner RTI
International
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Association Classification Segmentation
Exercises
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Association Classification Segmentation
Exercises
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Association model
Goal:
Identify what products are being sold together
Approach:
Use a data extract from a transactional system
Define which fields to use
Visualize relationship between products
Generate association model
Review results
Why?
Identify next likely purchase
Create bundles to increase $ value
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Association Classification Segmentation
Exercises
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Segmentation model
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Association Classification Segmentation
Hands on sessions
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The importance of text
Because people communicate with
words, not numbers, it has become
critical to be able to mine text for its
meaning and to sort, analyse, and
understand it in the same way that data
has been tamed. In fact, the two basic
types of information complement each
other, with data supplying the “what”
and text supplying the “why”.
Source IDC: “Text Analytics: Software’s Missing Piece?”
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Text data and text analytics
Around 80% of data held within a company is in the form of unstructured text
documents or records:
– Insurance claim notes
– Emails
– Call center logs,
– Reports
– Surveys
– Web pages
– Blogs
– …
Text Analytics connects unstructured text data to effective action by drawing
reliable conclusions about current conditions and future events
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IBM SPSS Text Analytics
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Bring repeatability to ongoing decision making
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Sentiment Analysis
Hundreds of customers reviews at a glance…
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Text Mining
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Free form notes entries
Linguistic Text Mining: 1. Language analysis
2. Concept extraction
3. Process types,
frequencies, & patterns
Integrated structured and unstructured data ready for Predictive Text Analytics
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Use Text Analytics results to Improve Predictive Models
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RTL Nederland / InSites Consulting - Analyzing social media buzz to increase TV viewer involvement
The need:
RTL Nederland aimed to evaluate its television programs in the Dutch market and
increase viewer satisfaction making use of online conversations. Therefore, RTL
Nederland needed a way to analyze, interpret and successfully respond to
audience feedback from social media sources.
The solution:
RTL Nederland worked with InSites Consulting to capture viewer opinions from
user-generated comments on social media and other online buzz by using IBM
predictive analytics software. This helps RTL Nederland to better understand
audience needs and preferences, and hence increase viewer satisfaction and
involvement. The obtained insight on viewer likes and dislikes allows RTL
Nederland to optimize its product offering.
What makes it smarter:
Analyzed the sentiment of over 71,000 online conversations about ‘X FACTOR’,
providing RTL Nederland with a powerful tool to measure attitudes indirectly and
quickly adapt the program accordingly
Captures unstructured data automatically from the web with sophisticated text
analytics technology
Approaching the final episodes of the reality competition shows, online buzz on
the program even increased by about 400 percent, which provided a very rich
source of information about viewer opinions
“Collecting and analyzing
feedback from social media is
of great importance to RTL
Nederland in order to offer
programmes that are fully
aligned with the target
audience.”
— Emilie van den Berge, senior
Research & Intelligence project
leader, RTL Nederland
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Classification model
Goal:
Identify who is likely to cancel their contract
Approach:
Use a data extract from a CRM
Use open ended comments from call center
Extract concepts from the text
Define which fields to use
Choose the modeling technique
Automatically generate a model to identify who has cancelled
Review results
Why?
Identify customers at risk before they churn
Unstructured data can provide insight into customers actions and
improve model accuracy
© 2014 IBM Corporation
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Association Classification Segmentation
Exercises
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Deployment
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Deployment
Goal:
Deploy a predictive model
Approach:
Use the stream generated in the earlier session
Pass new data through the stream and ‘score’ the data
Identify those likely to cancel
Export an .xls file with 50 most likely to cancel
Why?
Extend the reach of analytics in an organization
Allows analytics at the point of impact rather than being reactive
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Based on the
predictive model,
a single offer is
presented to the
customer
A call center agent
submits customer
information during
an interaction
The reaction to the offer
is tracked and used to
refine the model
Deployment – integrating with existing systems
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Customer Example Customer Growth from Inbound Contacts
“I’m calling to get my information on my download limit”
Next Best Action : Recommend Broadband Unlimited
“Certainly, Mr. Watson. I’ll just get
that for you right now… “
“Mr.Watson, you currently close to your 10GB
monthly limit however, as a valued long-term
customer, we’re able to make you an offer on
unlimited broadband”
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Deployment – integrating with Cognos BI
3) Results widely
distributed via BI for
consumption by
business Users
Cognos BI
Common
Business
Model
1) Leveraging BI,
identify problem or
situation needing
attention
2) SPSS
predictive
analytics feed
results back into
the BI layer
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Modeler’s Unique Capabilities
Easy to Learn / Intuitive Visual Interface –Visual approach - no programming –Comprehensive range of data mining
functions –Flexible deployment options
Powerful Automated modeling –Automated data preparation –Multi model creation & evaluation – Integrated analysis of text, web, & survey
data
Open and scalable architecure –Data mining within standard databases
with SQL pushback support –Maximized use of infrastructure with
multithreading, clustering and use of embedded algorithms (in database mining)
– Integration with IBM technologies such as IBM Cognos Business Intelligence, Netezza and IBM InfoSphere Warehouse
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Modeler Editions
IBM SPSS Modeler Professional
–Modeler Professional is a data mining workbench for the analysis of
structured numerical data to model outcomes and make predictions that
inform business decisions with predictive intelligence.
IBM SPSS Modeler Premium
–Modeler Premium allows organizations to tap into the predictive intelligence
held in all forms of data. Modeler Premium goes beyond the analysis of
structured numerical data alone and includes information from unstructured
data such as web activity, blog content, customer feedback, e-mails, articles,
and more to create the most accurate predictive models possible.
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IBM SPSS Modeler Deployment Options
Client (Desktop)
–Access local files
–Connect to operational databases
–Connect to Cognos BI
–Processing performed on local installation
Client/Server
–Data operations/processing on server
– In-database data mining
–SQL pushback
–Modeler Batch
–SuSE Linux Enterprise Server 10 (zLinux)
– Inclusion in Smart Analytics System for Power (AIX)
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Workshop Takeaways
Easy to use, visual interface
Short timeframe to be productive with actionable results
Does not require knowledge of programming language
Business results focused
Cost effective solution that delivers powerful results across organization
Flexible licensing and deployment options
Full range of algorithms for your business problems
End-to-end solution
Data preparation through real time interactions
Use structured, unstructured and survey data
Full suite of products, from data collection through deployment
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Workshop Takeaways
Flexible architecture
Leverages the investments already made in technology
Does not require data in a proprietary format or DB
Structured and unstructured data
Open architecture (both inputs and outputs)
SQL Pushback
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Predictive analytics customer success
“94% achieved a positive return on investment with an average
payback period of 10.7 months.”
“Returns were achieved through reduced costs, increased productivity,
increased employee and customer satisfaction, and greater visibility.”
“Flexibility, performance, and price were all key factors in purchase
decisions.”
Nucleas Research, An independent provider of Global Research and Advisory Services.
“30 Million Euro in new revenue” “100% increase in
campaign effectiveness”
“Reduced churn from 19 to 2%” “35% reduction in mailing cost,
2X response rate, 29% more
profit”
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Thank You
Laila Fettah Client Technical Professional Advanced Analytics
IBM
Johan Huizingalaan 765
1066 VH Amsterdam
Tel: +31 (0)20 513 8950
Mobile: +31 (0)6 11 87 61 55
Robin van Tilburg Client Technical Professional Advanced Analytics
IBM
Johan Huizingalaan 765
1066 VH Amsterdam
Tel: +31 (0)20 513 8371
Mobile: +31 (0)6 31 04 10 74
Contact