IBM SPSS Predictive Analytics Workshop · Explore multiple predictive analytics techniques ......

© 2013 IBM Corporation

An IBM Proof of Technology

IBM SPSS Predictive Analytics Workshop with IBM SPSS Modeler


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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

IBM SPSS Predictive Analytics Workshop2


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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



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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17

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



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





Optimize Decisions






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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





Optimize Decisions






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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





Optimize Decisions






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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





Optimize Decisions






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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.


http://www.amazon.com/gp/reader/0123747651/ref=sib_dp_pt


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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.


IBM SPSS Predictive Analytics Workshop · Explore multiple predictive analytics techniques ......

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