Introduction to Predictive Analytics

Joseph Brandenburg· Predictive Analytics Practice Leader · Dunn Solutions Group

Agenda

• A Little About Joe Brandenburg

• Basics of Predictive Analytics

• Samples of Stories

• Common Tools

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Brief Overview

Been performing and managing Predictive Analytic projects for 17 years

Expertise Across many industries

Financial Services, Banking, Insurance, CPG, Retail, Construction, Higher Education, and more

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Agenda

• A Little About Joe Brandenburg

• Basics of Predictive Analytics

• Samples of Usage

• Common Tools

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How has Business Intelligence Evolved?

Source: The Data Warehouse Institute (TDWI)

LOW

HIGH

HIGHBUSINESS VALUE

CO

MP

LEX

ITY

REPORTINGWhat happened?

ANALYSISWhy did it happen?

MONITORINGWhat’s happening now?

PREDICTIONWhat might happen?

Predictive analytics

Dashboards, scorecards

OLAP and visualization tools

Query, reporting and search tools

BUSINESS INTELLIGENCE TECHNOLOGIES

Predictive Analytics is the GPS of a

Business Intelligence system because it

provides the greatest business value by

making forward-looking decisions

possible

What is Predictive Analytics?

• Predictive Analytics examines patterns

found in internal and external data to

identify future risks and opportunities.

• It is projecting where the

Best and Sweetest Nuts will

be in the forest hidden

amongst all the trees and

other nuts.

• Based on:

• Where you have been before

• What you know about the situation

• What others have told you

What does Predictive Analytics Provide?

Power Ranking ReportMarket Blended

Econometric

Factors

(Home

Owners)

Broker and

Distribution

(Home Owners)

Claims

Experience

(Home

Owners)

Legal /

Regulatory

Environment

(Home

Owners)

Our

Competitive

Landscape

Compared to

the Industry

(Home

Owners)

Wealthy

Home

Owners

Buyer

Density

Wealthy

Home

Owners

Market Size

Market

Power

Ranking

Report

(Home

Owners)

Market

Penetration

(Home

Owners)

Category

Development

Index /

Sales

Strength

(Home

Owners)

Category

Development

Index p

(Home

Owners)

Market Outlook

(Home Owners)

Ft. LauderdaleStrong Strong Good Good Strong 6.5 125,451 7.40 10.6% 130.93 0.65

Spend More Effort

Expanding

Kansas CityStrong Strong Good Very Good Strong 6.0 45,804 7.10 8.4% 72.17 0.36

Spend More Effort

Expanding

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It helps to assess, predict, and solve business

problems using lots of dataStrategic Modeling and Forecast Prediction

• Works great for viewing current data

• You can use data warehouses to show historical trends

• An average person looks at 3 maybe 4 variables and get a “feel” of the behavior

• People are only good at identifying big trends and relationships

What Predictive Analytics Brings to the Table Beyond Traditional Reporting?

Traditional Reporting Predictive Analytics• Forward Looking

• Measurable and Traceable

• Millions of Observations and Criteria

• Predictive Analytics is not just Forecasting

How Predictive Analytics is Used?

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Which market segments should I target?

Can I reduce advertising costs by

20% without impacting sales?

What factors are causing my customers

to churn?

If I sell items in bundles will my overall

sales increase?

• A recent study from the Economist Intelligence Unit (EIU) revealed that:

Business leaders rate creating a data strategy for marketing and predictive analytics as one Marketing's most important priorities.

• A recent study by the Nucleus Research says:

• Analytics pays back $10.66 for every dollar spent

• Dunn Solutions projects have been more like $4,000 for every dollar spent.

Why Predictive Analytics is key for Everyone?

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Common IndustriesInitial Industries using Predictive Analytics

• Financial Services

• Banking

• Insurance

• Retail

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• Collection Analytics

• Marketing Activities• Cross-sell• Customer retention• Direct marketing• Portfolio, product or economy-level prediction• Analytical customer relationship management (CRM)

• Risk Management• Fraud detection• Underwriting• Pricing

• Disease Prevention and management• Clinical decision support systems

Most Common Uses of Predictive Analytics

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Agenda

• A Little About Joe Brandenburg

• Basics of Predictive Analytics

• Samples of Usage

• Common Tools

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Case Study: Collections Predictive

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Solution• Designed a predictive analytics modeling engine and dashboard

reporting environment that tracked all consumer lending and

mortgages

• Tracked them through models helped determine when and what

action be fit the situation

• All in five months

Results• Generated models on defaulting loans which justified an

additional $2.2 billion in TARP money

• Staffing model and Capacity Planning for Collections and

Default

• Use automated dialers to call customers

• Using targeted messages to better

• Handle the collections and default

• Saving them $9 million each month in extra phone

capacity

• Increased collections and curing by 25%

• Better Customer Risk Targeting $40 Million

• Which collections and foreclose first ?

• Which properties and customers were best to

restructure and make other offers?

• Prioritize properties, loans and targeted outcomes

Case Study: Marketing Analytics Predictive

ProblemHealth insurance servicer in 29 states, was:

• Facing specific new competition

• Mispriced products (underpriced for claims experience)

• Larger competitors were catching up to their value proposition

Solution• Performed Segmentation of Customers

• Developed Forecasting Simulation Tool to predict product performance

• Developed Marketing Mix and Market Potential

• Market based assessments to better target customers

Results• Turned Health Insurance Servicer into the

fastest growing Individual Health Insurance company in the nation at a time when most in the industry were shrinking…

• Doubling the portfolio with targeted high value customers in just 18 months to over 350,000 customers and $500 million in new annual revenue

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New Sales by Day

Kelly, 56, Graphic Artist,

Minneapolis, MN

My Media• Internet savvy

• More likely to buy items via Socially Responsible websites

• Listens to classic rock radio stations; subscribes to digital cable; reads

Gardening magazines

My Lifestyle• College graduate with a professional job

• Hobbies include gardening, entertainment and

travel

• Older ( > 45)

• Mostly home ownership

• Prefer quick, impersonal ways of handling finances

• Spends on Average $45 at a time on at my favorite

store but makes more frequent purchases than

average

• Often Looks Up Gardening Tips online

Who are my best customers? Customer Segmentation

• Identify which store most resemble each other and price target

• Which Zones require a lower price and more deals or on sale

• Retailers use this information to determine where a product should be placed and at what price.

• It also helps to better predict margin

Retail:Store Clustering and Price Zoning Analysis

Agenda

• A Little About Joe Brandenburg

• Basics of Predictive Analytics

• Samples of Usage

• Common Tools

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• Predictive Analysis (SAP)

• Infinite Insight (KXEN) now SAP

• SAS

• SPSS

• R

Most Common Tools Used in Predictive Analytics

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Thank You

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