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© 2010 IBM Corporation

Business Analytics

Predictive Analytics:Overview

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Jing ShyrChief Statistician, SPSS Predictive Analytics Product Development


Business Analytics

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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


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–The world becomes smarter…

Copyright © DreamWorks SKG. 2002.

Vision

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The Predictive Analytics Process

Decision Optimization

Recommend

the most

appropriate

action to take

People Data

& Enterprise Data Sources

Store new data

on customers,

events, etc. for

continuous

improvement

Predictive Analytics

Analyze data to

provide insight and

predict the future

Understand

Predict

ActImprove customer retention

Grow share of wallet

Minimize risk

Increase customer satisfaction

Enhance market share

Prospects

Customers Constituents

Employees

Students Patients


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Close the “Execution Gap”

Close the “Knowledge Gap”

Predictive analytics …

Derives maximum value from its data assets

Understands its business by gaining deep insight

Leverages advanced analytics to predict outcomes

Turns this knowledge into action

Optimize decision making across all operations


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How PA relates to statistics and data mining?

PA uses statistics and data mining to Understand and Predict

Both of them examine and prepare data, apply or try different algorithms for

better prediction

• Stat: Regression, ANOVA, MANOVA, Logistic regression, Discriminant,

Factor, K-mean Cluster, Hierarchical Cluster, generalized linear model, Arima,

…

• 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…


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Problems lead to decisionsPredictive 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?

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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?


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Two special data sources

Text : unstructured data

Capture customer issues/measure preferences

expressed in survey text, call center notes, and Web

data

Social Network dataCall Detail Record (CDR): A CDR contains all the details

pertaining to a call such as the time, duration, origin, destination, etc.E-mail, Facebook, …

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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


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Horizontal Solution Architecture: Text Analytics

DataAccess

ConceptExtraction

ConceptClassification

RecordScoring

Category Deployment

FileSystem

RDBS

SocialMedia

RDBS

AnalyticalApplication or Tool

Data is

accessed

for

analysis.

Sometimes

data might

be

translated

after being

imported.

Using the

classification

definitions,

records or

documents

are scored.

Using either

manual or

automated

means,

concepts are

classified.

Classification

can be done

on a per

record basis

or on a

concept

basis.

Data is

indexed,

tokenized,

normalized.

Concepts

and text

link

patterns

are

generated.

Categorized

records or

documents

are ready for

further

analysis or

for various

reporting

options.

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Sentiment Analysis

Dashboard or Presentation Tool

Opinions

(positive

and

negative)

are

associated

with

persons,

places, and

things.

Dashboard or Presentation Tool


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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


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Trust Network Described as A Circle Graph


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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.


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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


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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


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1. Connect to Data

2. Define Global SelectionsIdentify 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 OutcomesSpecify how the rules and models should be combined to make the most optimal decision

6. Deploy the solutionin batch or for real time decisioning

7. ReportMonitor the decisions that have been deployed through reporting

Best practices approach to decision making

based on our experience in the marketplace

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Repeatable Approach : 7 Steps to Analytical Decision Making


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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

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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


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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”


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Step 3: Defining Desired Outcomes…

(Sample Illustration: Insurance)

Typically with all decisions there is a finite set of desired

outcomes that can be achieved.

The Insurance Company identifies three possible

outcomes to the decision.

Application: “There are three things we could do: Fast track, Standard process, Investigate”

This structure can be

multidimensional


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Steps 4: Define Operational Decisions…(Sample Illustration: Insurance)

Both are critical to optimize outcomes!

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.

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.”


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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”


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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”


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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


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The Report tab allows you to monitor the

status of deployed applications

Application: “How did our new policy impact total claim costs?”

The business can check up on results, and

adjust how things are handled – starting the

process over……..

Step 7: Report on outcomes – and Learn!


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Summary: Enabling the Business UserOptimizing 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

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