what is predictive analytics and how is it used today
Post on 16-Jul-2015
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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
Predictive Analytics/ 2
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
Predictive Analytics/ 3
Agenda
• A Little About Joe Brandenburg
• Basics of Predictive Analytics
• Samples of Usage
• Common Tools
Predictive Analytics/ 4
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
Predictive Analytics/ 7
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
Predictive Analytics/ 12
Agenda
• A Little About Joe Brandenburg
• Basics of Predictive Analytics
• Samples of Usage
• Common Tools
Predictive Analytics/ 13
Case Study: Collections Predictive
Predictive Analytics/ 14
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
Predictive Analytics/ 15
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
Predictive Analytics/ 18
• Predictive Analysis (SAP)
• Infinite Insight (KXEN) now SAP
• SAS
• SPSS
• R
Most Common Tools Used in Predictive Analytics
Predictive Analytics/ 19
Thank You
Predictive Analytics/ 20
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