http://kmchicago.blogspot.com/2010/05/may-11-meeting-using-predictive_06.html

1. Jing Shyr
Chief Statistician, SPSS Predictive Analytics Product Development
Predictive Analytics
:Overview
Business Analytics
1 © 2010 IBM Corporation

2. 2
Business Analytics
Introducing SPSS, an IBM Company
 A leading provider of predictive analytic software, services and solutions
– Software – data collection, text and data mining, advanced statistical
analysis and deployment technologies
– Services – implementation, training, consulting, and customization
– Solutions – combine software and services to deliver high-value line-
of-business solutions; used for optimizing marketing campaigns, call
center effectiveness, identification of fraudulent activity and more
 40 years of experience and a broad customer base
– 250,000 customers: 100 countries, 50 states, 100% of top universities
Enables decision makers to predict future events and proactively act upon
that insight to drive better business outcomes
© 2010 IBM Corporation

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Business Analytics
Vision
–The world becomes smarter…
Copyright © DreamWorks SKG. 2002.
© 2010 IBM Corporation

4. Business Analytics
The Predictive Analytics Process
Predict
Analyze data to Recommend
provide insight and the most
predict the future appropriate
action to take
Predictive Analytics
Understand Improve customer retention Act
Customers Constituents Grow share of wallet
Prospects Employees Store new data Minimize risk
on customers,
Increase customer satisfaction
Students Patients events, etc. for
 Enhance market share
continuous
improvement
People Data Decision
& Enterprise Data Sources Optimization
© 2010 IBM Corporation

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Predictive analytics …
Derives maximum value from its data assets
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Understands its business by gaining deep insight
I
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c
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Leverages advanced analytics to predict outcomes
Close the “Knowledge Gap”
Turns this knowledge into action
Optimize decision making across all operations
Close the “Execution Gap”
© 2010 IBM Corporation

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How PA relates to statistics and data mining?
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 PA uses statistics and data mining to Understand and Predict
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 Both of them examine and prepare data, apply or try different algorithms for
S better prediction
P
• Stat: Regression, ANOVA, MANOVA, Logistic regression, Discriminant,
S
S
I
n Factor, K-mean Cluster, Hierarchical Cluster, generalized linear model, Arima,
…
c
.
• Data mining: Neural Network including MLP (Multi-Layer Perceptron), RBF
(Radial Basis function), Kohonen, Bayesian network, Naïve Bayes,
Association, sequence …
• Other: Support vector machine (SVM), Decision Tree, projection pursuit
regression (PPR), nonnegative factorization…
© 2010 IBM Corporation

7. 8
Business Analytics
Problems lead to decisions
Predictive Analytics Driving Decisions
 Customs & Border Protection
– Problem: I can’t search every car that crosses the border.
– Decision: Which car should I search?
 Infinity
– Problem: I can’t investigate every claim for fraud.
– Decision: Should I investigate this claim?
 Cablecom
– Problem: I can’t save every customer.
– Decision: Is it worth trying to save this customer?
© 2010 IBM Corporation

8. Business Analytics
Healthcare & Insurance Claims Management
What if you could predict fraud before it
happened?
What if you could recommend preventative
care to those who most need it?
What if you could process low risk claims
faster and with less headache?
What if you could plot the expected course
of treatments for veterans?
What if you could react differently in times
of crisis?
© 2010 IBM Corporation

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Business Analytics
Two special data sources
 Text : unstructured data
 Capture customer issues/measure preferences
expressed in survey text, call center notes, and Web
data
 Social Network data
Call Detail Record (CDR): A CDR contains all the details
pertaining to a call such as the time, duration, origin,
destination, etc.
E-mail, Facebook, …
© 2010 IBM Corporation

10. Business Analytics
Text Analytics Overview
 What does Text Analytics deliver?
–Breadth: Take into account qualitative input from all sources
–Clarity: Understand related facts, opinions, and what to do about it
–Speed: Rapid understanding of qualitative feedback
 What does Text Analytics do for people?
–Extracts and classifies unstructured data in multiple languages
–Discovers patterns in events and opinions and categorizes them
–Models customer behavior based on qualitative insights
 How does Text Analytics do it?
–Natural Language Processing
–Sentiment and Event Analysis
© 2010 IBM Corporation

11. Business Analytics
Horizontal Solution Architecture: Text Analytics
Dashboard or
Presentation Tool
File
RDBS
System
Data Concept Sentiment Concept Record Category
Access Extraction Analysis Classification Scoring Deployment Analytical
Social
Application
Media
or Tool
Dashboard or
Presentation Tool
RDBS
Data is Data is Opinions Using either Using the Categorized
accessed indexed, (positive manual or classification records or
for tokenized, and automated definitions, documents
analysis. normalized. negative) means, records or are ready for
Sometimes Concepts are concepts are documents further
data might and text associated classified. are scored. analysis or
be link with Classification for various
translated patterns persons, can be done reporting
after being are places, and on a per options.
imported. generated. things. record basis
or on a
concept
basis.
12 © 2010 IBM Corporation

12. 1
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Business Analytics
IBM SPSS 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
© 2010 IBM Corporation

13. Business Analytics
Trust Network Described as A Circle Graph
© 2010 IBM Corporation

14. Business Analytics
Traditional Applications
SNA applies to a wide range of business problems, including:
 Knowledge Management and Collaboration. SNAs can help locate expertise, seed new
communities of practice, develop cross-functional knowledge-sharing, and improve strategic
decision-making across leadership teams.
 Team-building. SNAs can contribute to the creation of innovative teams and facilitate post-
merger integration. SNAs can reveal, for example, which individuals are most likely to be
exposed to new ideas.
 Human Resources. SNAs can identify and monitor the effects of workforce diversity, on-
boarding and retention, and leadership development. For instance, an SNA can reveal
whether or not mentors are creating relationships between mentees and other employees.
 Sales and Marketing. SNAs can help track the adoption of new products, technologies, and
ideas. They can also suggest communication strategies.
 Strategy. SNAs can support industry ecosystem analysis as well as partnerships and
alliances. They can pinpoint which firms are linked to critical industry players and which are
not.
© 2010 IBM Corporation

15. Business Analytics
What are the results SNA produce?
 Identify groups (communities)
 Identify leaders or influencers
 Execute viral marketing strategies
 Identify product Up-selling and Cross-selling opportunities
 Manage contagious churn
 Identify subscriber acquisition and retention opportunities
© 2010 IBM Corporation

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Business Analytics
Act: IBM SPSS Decision Management
 Framework for domain
specific applications that
combine Models, Rules, and
Optimization to solve
business problems
 Extends predictive insights to
the business user at the point
of decision
– E.g. Should a claim be ‘fast
tracked’ or evaluated more
closely based on a calculated
risk score?
Automating high volume, high value decisions
© 2010 IBM Corporation

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Business Analytics
Repeatable Approach : 7 Steps to Analytical Decision Making
1. Connect to Data
2. Define Global Selections
Identify who or what is to be included as well explicitly excluded from the decision making process
3. Define Desired Outcomes
Define the set of potential decisions that can be made (what campaigns are available, which types
of investigation can be performed etc)
4. Define Operational Decisions with Rules & Models
Define and use rules and/or predictive models that dictate or help decide on the appropriate
outcomes
5. Optimize Outcomes
Specify how the rules and models should be combined to make the most optimal decision
6. Deploy the solution
in batch or for real time decisioning
7. Report
Monitor the decisions that have been deployed through reporting
Best practices approach to decision making
based on our experience in the marketplace © 2010 IBM Corporation

18. 1
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Business Analytics
Configurability
Configurable in the field to new business problems
 Enable services / partners to deploy decisioning services to a wide range of business
problems
 Terminology is configurable to different applications
– Customer Interactions, Claims, Risk, Churn, Underwriting, Claims, Subrogation etc.
 Configurable around the 7 steps
– Which steps are required?
– Various options for working with and combining rules / models and for optimizing
the decision returned
© 2010 IBM Corporation

19. Business Analytics
Demo Business Problem – Claims Management
 A large insurance company
wants to manage claims
more effectively
–Reduce the time needed to
process a typical claim.
–Reduce the amount paid to
fraudulent claims
 The Claims Management
Application processes
incoming claims in real time,
and recommends the best
action for each claim
© 2010 IBM Corporation

20. Business Analytics
Step 1 & 2: Define Decision Scope…
(Sample Illustration: Insurance)
The decision process begins with leveraging enterprise data
and identifying the focus of the operational decision.
excluded.
The Insurance Company elects to exclude data related
to natural phenomenon's.
Application: “I don’t want to worry about Claims associated with Katrina”
© 2010 IBM Corporation

21. Business Analytics
Step 3: Defining Desired Outcomes…
(Sample Illustration: Insurance)
Typically with all decisions there is a finite set of desired
outcomes that can be achieved.
This structure can be
multidimensional
The Insurance Company identifies three possible
outcomes to the decision.
Application: “There are three things we could do: Fast track, Standard process, Investigate”
© 2010 IBM Corporation

22. Business Analytics
Steps 4: Define Operational Decisions…
(Sample Illustration: Insurance)
Business people define
rules that embody their
priorities and experiences.
Business People
leverage existing
predictive models – or
create new ones, to
support the business
problem.
Both are critical to optimize outcomes!
Application:
“I know that claims for active servicemen go through a serious evaluation before
submittal, so even if the profile is high risk, we can still process it.” © 2010 IBM Corporation

23. Business Analytics
Step 5: Optimize Outcomes using Matrix…
(Sample Illustration: Insurance)
The decision outcome is optimized and balanced between the
predictive components that provide real time insight and the
rules that govern the policy and practices of the company.
Business people run multiple simulations and identify the best approach
Application: “I wonder what would happen if we evaluated all the claims for fraud?
Hmmm the allocation would overwhelm the department”
© 2010 IBM Corporation

24. Business Analytics
Step 5: Optimize Outcomes using Formula Approach
The decision outcome can also be
determined by configuring
formulas which will automatically
determine the right action as
projected by rules and models
It’s all controlled by the business
Application: “Recommend preventative care if the risk profile is high”
© 2010 IBM Corporation

25. Business Analytics
Step 6: Deploy Decision Pattern to Enterprise
 Single button deploy alerts IT
that it’s time to move the
solution into production
–Point of Interaction Systems can
drive best practices for every real
time decision.
–Automation service can update data
records to reflect operational policy
decisions
–Model Management capabilities
allow ongoing monitoring /
improvement of the models in
production © 2010 IBM Corporation

26. Business Analytics
Step 7: Report on outcomes – and Learn!
 The Report tab allows you to monitor the
status of deployed applications
 The business can check up on results, and
adjust how things are handled – starting the
process over……..
Application: “How did our new policy impact total claim costs?” © 2010 IBM Corporation

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Business Analytics
Summary: Enabling the Business User
Optimizing Operational Decisions for Better Results
 Web based business user
interface configurable in the
field to new business problems
 Built on Convergence!
• Data Mining
• Business Intelligence
• Business Rules
• Event Processing
• Data Management
© 2010 IBM Corporation

28. Business Analytics
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© 2010 IBM Corporation