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An IBM Proof of Technology
IBM SPSS Predictive Analytics Workshop with IBM SPSS Modeler
© 2013 IBM Corporation
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Agenda
Welcome and Introductions
Introduction to Predictive Analytics
IBM SPSS Modeler Overview
Exercise: Predictive in 20 Minutes
IBM SPSS Modeler Basics
Break
Predictive Analytics Methodology and Applications
Exercise 1: Find Patterns and Groups
Exercise 2: Understand the Past, Predict the Future
Exercise 3: Optimize Decisions
Integrating Predictive Analytics with Planning and BI
Closing Discussion
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Purpose of the workshop
Introduction to predictive analytics
Stimulate thinking about how predictive analytics would benefit your
organization
Demonstrate ease of use of powerful technology
Get experience in “doing” predictive analytics
Explore multiple predictive analytics techniques
Understand role of predictive analytics in decision optimization
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What is predictive analytics?
Predictive Analytics helps connect data
to effective action by drawing reliable
conclusions about current conditions
and future events
Gareth Herschel, Research Director, Gartner Group
Enabling businesses to use predictive
models to exploit patterns found in
historical data to identify potential risks
and opportunities before they occur.
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Top Five Benefits of Predictive Analytics
Achieve competitive advantage
New revenue opportunities
Increased profitability
Increased customer service
Operational efficiencies
68%
55%
52%
45%
44%
Source: Ventana Research Predictive Analytics Benchmark Research
How has your organization benefited from predictive analytics:
Related Research Points:• Management (76%) has no doubts
that predictive analytics has given its business the competitive edge.
• Small (81%) businesses are especially convinced that they have achieved competitive advantage by using predictive analytics.
In the Public Sector1:• To best position governments and
citizens for desired results, four focus areas now require integrated execution and measurement: information, engagement model, digital platform and analytics competency.
• Leaders acknowledge both a perpetual information explosion and a persistent “data paradox” – the management dilemma associated with having too much data and too little insight.
1IBM Institute for Business Value, “Opening up government: How to unleash the power of information for new economic growth”
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The Future Made Real: Predictive Analytics in Action
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IBM SoftwareAreas for Predictive Analytics
Education
Public Safety
Who are our best customers?
Can we get more like that?
What/why do they buy?
Why do they leave?
Money laundering
Tax audits &
collections
Network Intrusion
Warranty Claims
Product quality assurance
Predictive maintenance to
predict equipment failure
Which students are likely to
drop out and why?
Who are the best students and
how to recruit more of them?
Which alumni are likely to
make larger donations?
Who are our best
employees?
How do we keep our best
employees from leaving?
Which prospects to recruit?
Crime analysis
Predictive policing
.... and many more
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Need: Understand which combinations of factors predict student retention in online programs
Predictive Analytics Brings Value
Benefits:
• Predict with approximately 80% certainty whether a given student is going to drop out
• Helps APUS build programs and maintain conditions for maximum student retention
Benefits:
• 60% improvement in revenue retention rates
• Realizing millions of dollars in annualized revenue protection
Need: Predict which of its small business customers were at the highest risk of churn
Need: Fraud losses accounted for 6-10% of premium costs for customers and operational inefficiencies from agents managing high- & low-risk claims
Benefits:• Improved claims processing by 70%
• Saved $2.4M in just 4 months
Need: To drive revenue, enabled call center operatives to make relevant real-time offersbased on incoming customer calls
Benefits:• Identify customers more efficiently
• Sales from the call center doubled within six months of launch
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IBM SPSS Modeler at a Glance
Comprehensive predictive analytics platform
All types of users at multiple analytical abilities
Integrated deployment, optimization and decision management capabilities
A visual interface with built-in guidance
Structured and unstructured data
Deployed on a desktop or integrated within operational systems
Bring predictive intelligence to a single user or an entire enterprise
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Model Building for Users of Multiple Analytical Abilities
One-click modeling for the business user
Visual analytical workbench to expand and extend analysis
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Exercise: Predictive in 20 Minutes
Goal:
Identify who has responded to a marketing campaign
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 responded
Review results
Why?
To save marketing cost and increase marketing response, identify
those likely to respond and focus marketing efforts on those prospects.
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Break - please return in 15 minutes
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Modeling Techniques in IBM SPSS Modeler
Technique Usage Algorithms
Classification
(or prediction)
• Used to predict group membership
(e.g., will this employee leave?) or a
number (e.g., how many widgets
will I sell?)
• Auto Classifiers,
Decision Trees,
Logistic, SVM, Time
Series, etc.
Segmentation • Used to classify data points into
groups that are internally
homogenous and externally
heterogeneous.
• Identify cases that are unusual
• Auto Clustering, K-
means, etc.
• Anomoly detection
Association • Used to find events that occur
together or in a sequence (e.g.,
market basket)
• APRIORI, Carma,
Sequence
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Extend Capabilities through R
R Integration
R Build/Score, Process and Output node support
Scale R execution by leveraging database vendor provided R engines
Custom Dialog Builder for R
Provides the ability to create new Modeler Algorithm nodes and dialogs that run R processes
Makes R usable for non-programmers
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Uncovering Patterns in Unstructured Data
• Text Analytics• Natural Language
• Sentiment Analysis
• Entity Analytics• Disambiguate identity
• People, places, things
• Social Network
Analysis• Uncover relationships
• Find leaders and
followers
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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 Analytics
Uses natural language processing
heuristic rules and statistical
techniques to reveal conceptual
meaning in text
Extracts concepts from text and
categorizes them
Makes unstructured qualitative data
more quantifiable, enabling the
discovery of key insights from
sources such as survey responses,
documents, emails, call center notes,
web pages, blogs, forums and more
Brings repeatability to ongoing decision making
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IBM SoftwareEntity Analytics OverviewIdentify Matching Entities
Analysts Struggle to Match Entities,
Especially From Diverse Sources
– Natural variability (Bob vs. Robert)
– Errors (transposed month and day)
– Fabrications (fake identities)
Enhances Model Accuracy – Model
Against a Complete, Accurate Entity
Multiple Applications
– Business: is this the same order the
customer submitted?
– Fraud: is this the same person who already
defaulted on a loan?
– Government: is this the same vehicle that
was carrying illegal content last time?
– Policing: Is this the same person who called
us before?
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Social Network Analysis
Processes CDR (Call Data Record) data
companies to produce social analysis
Focuses around identifying groups,
leaders and probabilities that others will
churn based on influence
Enhances existing churn predictions of
Modeler
Expressed as two new nodes in the
Sources Palette
– Group Analysis – what are the groups
in my data and who are the leaders
– Diffusion Analysis – uses existing
churn information to determine who
else that churner is likely to influence
to leave
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All Data
Optimized Decisions
Business Rules
PredictiveAnalyticsOptimization+ +
Optimize Actions Within Resource Constraints,
Aligning Execution With Strategy
Empower Real-time and Adaptive Decisions
Accommodating Changing Conditions
Provide Front-line Employees and Systems With
Recommended Actions
Optimize your operational decision with decision management
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Optimize Predictive Results & Business Rules vs Constraints
Determine outcome using models and rules
Optimize against constraints
Model and compare scenarios
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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
Data mining is a key discipline for applying predictive analytics
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IBM SPSS Modeler Workbench Basics
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Exercises
Classify customers into groups based on
underlying characteristics
Find Patterns and Groups
Understand the Past, Predict the Future
Optimize Decisions
Model response to marketing offers using historical data, apply to
new customers and deploy results
Evaluate marketing campaign optimization scenarios built using predicted outcomes and business rules
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Exercises
Classify customers into groups based on
underlying characteristics
Find Patterns and Groups
Understand the Past, Predict the Future
Optimize Decisions
Model response to marketing offers using historical data, apply to
new customers and deploy results
Evaluate marketing campaign optimization scenarios built using predicted outcomes and business rules
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Find Patterns and Groups
Goal:
–Create segments of customers
Approach:
–Customer data from a database or CRM
–Define which fields to use
–Automatically generate a model to group customers
Why?
–Better customer understanding (demographics, socio-economic etc)
–Tailored messages for each group/segment
–Personal and more relevant for consumers
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Exercises
Classify customers into groups based on
underlying characteristics
Find Patterns and Groups
Understand the Past, Predict the Future
Optimize Decisions
Model response to marketing offers using historical data, apply to
new customers and deploy results
Evaluate marketing campaign optimization scenarios built using predicted outcomes and business rules
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Understand the Past, Predict the Future
Goal:
– Identify who is likely to respond to a marketing offer
Approach:
– Use a data extract from a CRM
– Extract concepts from the open ended comments in a customer survey
– Define which fields to use
– Choose the modeling technique
– Automatically generate a model to identify who is likely to respond
– Review results
– Apply to new customers
– Deploy results for use by marketing team
Why?
– Target those likely to respond to offers to increase revenue, cut costs
– Using unstructured data improves modeling accuracy and provides more insight
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Exercises
Classify customers into groups based on
underlying characteristics
Find Patterns and Groups
Understand the Past, Predict the Future
Optimize Decisions
Model response to marketing offers using historical data, apply to
new customers and deploy results
Evaluate marketing campaign optimization scenarios built using predicted outcomes and business rules
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Optimize Decisions
Goal:
Identify which optimization scenario to use
Approach:
Optimize based on revenue expectation, cost and predicted response
Modify parameters to create scenarios
–Run scenario simulations
Review optimization output
Compare scenarios on multiple dimensions
Select best scenario
Why?
Understand what will happen to revenue, costs, and other business
outcomes based on optimization
Confidently select best optimization scenario for deployment
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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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Exercises
Classify customers into groups based on
underlying characteristics
Find Patterns and Groups
Understand the Past, Predict the Future
Optimize Decisions
Model response to marketing offers using historical data, apply to
new customers and deploy results
Evaluate marketing campaign optimization scenarios built using predicted outcomes and business rules
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IBM SPSS Modeler Within the Business Analytics Ecosystem
PredictiveOperational Analytics
ManageMaintainMaximize
PredictiveThreat & Fraud Analytics
MonitorDetectControl
PredictiveCustomer Analytics
AcquireGrowRetain
Etc…
IBM Research
Collaboration and Deployment Services
Analytic Server & Catalyst
ModelerSocial Media
AnalyticsData
CollectionStatistics
Analytic Answers
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Integration with IBM Cognos Business Intelligence
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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Integration with IBM Cognos TM1
TM1 Source and Export nodes
Score and/or manipulate data and export directly to TM1
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Integration with IBM Cognos TM1
Before
After
How does predictive analytics enhance planning?• Using sophisticated forecasting
algorithms including:• ARIMA• Exponential Smoothing
• Adding external predictors such as:• Weather patterns• Inflation data• Unemployment data
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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
No proprietary data formats
Open architecture
Business results focused
–Leverages the investments already made in technology
Cost effective solution that delivers powerful results across organization
Full range of algorithms for your business problems
Big Data enabled (Hadoop, SQL Pushback)
End-to-end solution
Data preparation through real time interactions
Use structured, unstructured and semi-structured data
Integrated portfolio for business analytics
Scales from a single desktop to an enterprise deployments
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Closing Discussion
YOUR
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Suggested BooksAppendix
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IBM SPSS Modeler Cookbook
From Amazon.com
– IBM SPSS Modeler Cookbook
– by Keith McCormick, Dean Abbott, Meta S. Brown, Tom Khabaza, Scott R. Mutchler
– Paperback - 382 pages (October 2013) (also on Kindle)
– ISBN : 1849685460
Written by those who teach and have been working with IBM SPSS Modeler since its beginnings. Full of practical examples that span the full gamut of capabilities.
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Data Mining Overview
From Amazon.com
– Paperback: 512 pages
– Publisher: Wiley; 1 edition (December 28, 1999)
– Language: English
– ISBN-10: 0471331236
– ISBN-13: 978-0471331230 ;
Good introductory text on data mining for marketing from two top communicators in the field
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Handbook of Statistical Analysis and Data Mining Applications
Handbook of Statistical Analysis and
Data Mining Applications
Robert Nisbet, John Elder IV, and Gary
Miner
Academic Press (2009)
ISBN-10: 0123747651
An excellent guide to many aspects of
data mining including Text mining.
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Data Mining Algorithms
From Amazon.com
– Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
– by Eibe Frank, Ian H. Witten
– Paperback - 416 pages (October 13, 1999)
– Morgan Kaufmann Publishers;
– ISBN: 1558605525;
Best book I’ve found in between highly technical and introductory books. Good coverage of topics, especially trees and rules, but no neural networks.
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