1. Why Predictive Analytics
Should Be Part of Your 2015
BI Strategy
Joseph Brandenburg
Predictive Analytics Practice Leader · Dunn Solutions

2. Predictive Analytics / 2
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

3. • Been performing and managing
Predictive Analytic projects for 17
years
• Have helped companies realize
multiple $billions of dollars in new
revenue and helped them avoid
potential big loses
• Expertise across many industries
Financial Services, Banking, Insurance,
CPG, Retail, Higher Education, and
more
• Predictive Analytics Practice Leader
for Dunn Solutions Group
A little about Joe Brandenburg
Predictive Analytics/ 3

4. Predictive Analytics / 4
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

5. • UDIB is a simple concept for developing Predictive Analytics:
• It is the ability Understand, Determine, Implement, Benefit
• Understand what the problem areas are or what opportunities
exist
• Determine which course of action you should take to address the
questions
• Implement the techniques and models that best answer the issues
• Benefit from the implementation by measuring the results and
setting baselines
UDIB of Predictive Analytics
Predictive Analytics/ 5

6. Predictive Analytics / 6
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

7. • 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 is Predictive Analytics?
Predictive Analytics/ 7

8. Predictive Analytics is the GPS of a Business Intelligence system
because it provides the greatest business value by making
forward-looking decisions possible
How has Business Intelligence evolved?
Predictive Analytics/ 8
Source: The Data Warehouse Institute (TDWI)

9. • It helps to assess, predict, and solve business
problems using lots of data
What does Predictive Analytics provide?
Predictive Analytics/ 9
Strategic Modeling and Forecast Prediction
Market 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. Lauderdale
Strong Strong Good Good Strong 6.5 125,451 7.40 10.6% 130.93 0.65
Spend More Effort
Expanding
Kansas City
Strong Strong Good Very Good Strong 6.0 45,804 7.10 8.4% 72.17 0.36
Spend More Effort
Expanding
Power Ranking Report
Detail – Policy and Claim
Data mining for Patterns
Transactional –
opportunity and
validation
Lifetime Value
Persistency
High Value
Customers - Products
Summary, Reporting,
Forecast, Optimization

10. Traditional Reporting
• 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?
Predictive Analytics/ 10
Predictive Analytics
• Forward Looking
• Measurable and Traceable
• Millions of Observations and Criteria
• Predictive Analytics is not just Forecasting

11. How is Predictive Analytics used?
Predictive Analytics/ 11
Where is there opportunity for me to
expand?
What factors are causing my
customers to churn?
If I sell items in bundles will my
overall sales increase?
Which segments should I target?
Can I reduce advertising costs by
20% without impacting sales?
What channel is most effective to
reach certain customers?
When should I contact customers and
what message to get them to stay?
What products and combinations do
people want?

12. Predictive Analytics / 12
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics
to stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

13. • 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 $100 for every
dollar spent
• This is a $100 your competitors are making through
changes and adjustments that you are not realizing
Why is Predictive Analytics key for everyone?
Predictive Analytics/ 13

14. • Predictive Analytics has proven over and over again that it leads to
increased sales and profit.
• Marketing, sales and customer relationship management are some
of the areas where the returns from Analytics are the highest.
• Example: One online retailer was able to increase its sales by thirty
percent for a single campaign and by 3 percent for the whole
company. This added an additional $80 million in revenue from
one area alone
Why are more and more companies are using it?
Predictive Analytics/ 14

15. Predictive Analytics / 15
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

16. • Successful predictive analytics projects are a driving force, the
movement to customer‐oriented and actual‐needs focused
organizations is accelerating.
• One Forrester study concluded, "Predictive Analytics Brings Fresh
Momentum“
Why Predictive Analytics changes the game?
Predictive Analytics/ 16

17. • Analytically Driven companies use Predictive Analytics to help their
companies just like yours make the most of their existing data to
uncover future trends, risks and opportunities that result in bigger
returns, or avoid big losses.
How Predictive Analytics can lead to better decision making
Predictive Analytics/ 17
Source: Harvard Business Review
• Seldom trusts
analysis
• Makes
decisions
unilaterally
• Applies judgment
to analysis
• Listens to others
but is willing to
dissent
• Trusts analysis
over judgment
• Values
consensus
Reliance on
institution
Reliance on
analysis
38% of employees
are informed skeptics
19% of employees
are visceral
decision makers
43% of employees
are unquestioning
empiricists

18. • Visceral Decision Makers often make decisions based on their gut
which could be contrary to what their customers want and
operations needs
• Ideally, analytically-driven companies must have predictive
analytics embedded in all their business processes, thereby
moving away from decisions based on gut feeling or intuition.
Taking advantage of ‘Real’ Information to make decisions
Predictive Analytics/ 18

19. • Management and the company become better users of
information and make the most out of it just like they do in other
areas such as managing talent, capital, and brands.
• Too many executives treat data as something the IT department
handles and often considers themselves too novice to get deeply
involved in how data is shared across the organization.
• Managers and Senior Management need to wake up to the fact
that their data investments are providing limited returns because
their organization is underinvested in understanding the
information.
By Embracing Data, BI and Predictive Analytics
Predictive Analytics/ 19

20. • There are four phases in most industries
customer life cycle
• Acquisition
• Relationship
• Retention
• Win-back
• Each phase should have several embedded analytical
models, which can enhance operations considerably
and provide a strategic advantage.
Tailoring your management and experience to the
customer
Predictive Analytics/ 20

21. Predictive Analytics / 21
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI)
strategy to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

22. • There is no better time than the present but it is best to
incorporate predictive analysis in with other implementations of
Smart Tools and BI
• However, there are some key things to think about!!!
When to implement Predictive Analysis?
Predictive Analytics/ 22

23. • Predictive analytics is made up of three major components:
• Data
• Statistics
• Assumptions
• It is important to be prepared to make some changes to data and
the arrangement in a data warehouse or in extracts
• Many analytics softwares today have the ability to do this
automatically
Predictive Analytics Requirements and Components
Predictive Analytics/ 23

24. • Data
• Predictive Analytics needs the right kind of data and information to help
answer the main questions you are trying to predict or forecast.
• It is best practice to collect as much relevant data as possible in relation to
what you are trying to predict. This means tracking past data, customers,
demographics, and more.
• Statistics
• Most Predictive Analysis starts with curiosity about a business problem or
customers
• Assumptions
• Because predictive analytics focuses on the future, assumptions that the past
will mirror the future are imperative.
• Need to understand what you are trying to solve
• However, with modern techniques, there are options to do exploratory
analysis and identify problems or opportunities
Data, Statistics, Assumptions
Predictive Analytics/ 24

25. • Having and implementing “Big Data” does not help you make
better decisions!!!
• The process of taking big data and developing analytics and
especially predictive analytics drives better decision making
• Big Data provides a ton of great data but predictive analytics
synthesizes that data into actionable information – if done
correctly
Big Data and Predictive Analytics
Predictive Analytics/ 25

26. • 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/ 26

27. • Earlier, up-selling through campaigns used to be carried out
through trial and error.
• Nowadays privacy norms forbid operators from reaching out to
customers repeatedly.
• Hence, all instances of customer contact become significant. In
such a scenario, analytics can help develop a ‘sharp-shooting'
method to run campaigns.
• Based on historical data and customer profiles, it is possible to
classify customers according to their likelihood of buying a
product or a service through a campaign.
• Thus, every campaign can target the set of customers with better
purchasing potential for that service/product.
• While these statistics-driven campaigns yield higher ROI, they also
reduce the irritation caused by non-relevant communication,
thereby indirectly reducing customer dissonance.
• Campaign analytics is most beneficial to product management
teams new methods exclude unprofitable customers.
Campaign Analytics
Predictive Analytics/ 27

28. • Churn is expensive, costing companies millions of dollars every
year. Probably the most widely used analytics models are churn
modeling to help reduce the impact of voluntary churn to the
bottomline.
• Typically, the model measures the ‘Churn Propensity' of a
customer, and profitable customers are wooed through a set of
personalized actions, like offering freebees.
• The value of the offers can be tailored to suit customer
profitability. Churn modeling is an easy analytics option, with
relatively low complexity, wide acceptance among functions and
immediate results.
• The marketing department usually initiates these activities, and
most companies use their customer service and sales teams to
execute these plans.
Churn Modeling
Predictive Analytics/ 28

29. • A very real challenge in most industries is how to increase yield
from the current subscribers/customers, and how to improve
Average Revenue Per User (ARPU).
• Cross-selling and up-selling activities can now be supported by
predictive analytics based on transaction histories and like
customer behavior.
• Analytically driven cross-selling and up-selling campaigns provide
remarkably higher returns. Most of our projects see a 40% lift in
Cross-sell and Upsell and by moving beyond financials, they also
increase retention and reduce the number of contacts required for
cross-selling and up-selling.
• These models may be integrated with real-time decision engines
and developed as real time cross-selling and up-selling systems.
• The campaigns are usually planned by the marketing department
and executed through in-bound customer facing teams, mainly
call centers and web transactions
Cross-Selling and Up-Selling
Predictive Analytics/ 29

30. • Not all customers are the same. Although most organizations follow this
credo at one level, it is important to assign a dollar value to each
customer, in order to prioritize various sets of customers.
• The Customer Lifetime Value (CLV) model provides the predicted yield
from each customer over the customer life cycle.
• High priority customers can be given loyalty bonuses, preferential
treatment through personalized service, better credit norms for contract
subscribers etc.
• These analytic models may be utilized across all the functions like
marketing, credit risk, customer service and so on.
Customer Lifetime Value Analytics
Predictive Analytics/ 30

31. • This has been one of the main analytical models in the retail,
financial services, CPG and Service industries.
• Customers are segmented both at the prospect and purchase
phases.
• At the first stage, segmentation helps reach out to prospects with
higher predicted conversion rates, thereby increasing the
campaign success rate as well as the ROI.
• During campaigns, prospects are divided into segments to which
specific campaigns are targeted.
• This approach helps eliminate meaningless segments that
unnecessarily clutter the thinking and execution of the campaign.
Customer Segmentation
Predictive Analytics/ 31

32. • Fraud is a key root cause of lost revenue in the many industries.
• Efficient fraud detection systems can help companies save a
significant amount of money.
• Fraud detection systems depend on data mining algorithms to
identify and alert the company to fraudulent customers and
suspicious behavior.
• Remember, there are several methods of fraud, requiring other
analytic models to aid in detection.
• Risk management teams are the largest users of fraud
management systems.
Fraud Analytics
Predictive Analytics/ 32

33. • Companies spend heavily on mass media.
• Spends cover television, newspapers, radio, magazines, email
and the internet.
• The marketing spends are often apportioned on the basis of
instinct rather than hard facts.
• A Marketing Spend Optimization or better know as marketing mix
model helps marketing managers and product managers take
decisions based on what works and what does not.
• This analytics model has been of considerable benefit to the
marketing function, and is hence widely used to improve
marketing Return on Investment (ROI).
Marketing Spend Optimization – Marketing Mix
Predictive Analytics/ 33

34. • Price optimization contributes significantly to revenue
development and profitability and is especially important in the
corporate sales segment, where awareness of the impact of the
various pricing options offered is critical.
• Simulated scenarios can help evaluate the revenues at various
price points.
• These models are widely used by product managers and finance
teams.
Price Optimization and Elasticity
Predictive Analytics/ 34

35. • Traditionally, customers have been measured in terms of the
revenue they bring to the company.
• In this age of social media, how can a company measure the value
of a customer who is socially influential' among his/her peer
groups?
• It is well-known that early adopters in families and offices influence a
large number of followers.
• Such ‘influencers’ require a special, differential treatment, even though
their billing is often low and customer lifetime value is not very high.
• This kind of differentiation is possible through Social Network
Analytics.
• This model is of great help in the areas of churn prevention of
profitable customers, cross-selling of new products and so on.
• There have also been instances where social network analytics
have been used to test advertisements.
Social Network Analysis
Predictive Analytics/ 35

36. • Listening to the customers and understanding their needs is
crucial, especially in this era of viral communication.
• While it has been an established practice to undertake sentiment
analysis based on content from call center logs and social media
through reporting and dashboards, predictive models can be
utilized in social media analysis to listen to the written word.
• Data mining techniques add tremendous value in this area.
• Similar feedback around key discussion areas pertaining to the
company's activities can be brought together using various
methods.
Social Media Analytics
Predictive Analytics/ 36

37. • Web Analytics has traditionally been an area where only reporting
or dashboarding was implemented.
• Now organizations are utilizing predictive analytical models to
take web analytics to a new level.
• Using data mining techniques, customer profiles that help
determine which factors differentiate the behaviors of one group
from another, may be generated.
• Models can be used to identify new visitors and predict their
future behavior, for example, if they will subscribe to a particular
service or not.
Web Analytics
Predictive Analytics/ 37

38. Predictive Analytics / 38
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for
companies of all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for significant ROI
Agenda

39. • Predictive Analysis (SAP)
• Infinite Insight (KXEN) now SAP
• SAS
• SPSS
• R
*Future webinars will discuss technologies and give
demos of the top tools.
Most common tools used in Predictive Analytics
Predictive Analytics/ 39

40. Predictive Analytics / 40
A Little About Joe Brandenburg and Dunn Solutions Group
What is UDIB?
What is Predictive Analytics?
Why it is important for companies to use Predictive Analytics to
stay competitive?
How key decision makers can make better plans and improve
business decisions with Predictive Analytics
How and when departments and organization can incorporate
Predictive Analytics as part of the Business Intelligence (BI) strategy
to minimize effort and cost
How current technologies make it easier than ever for companies of
all sizes to implement Predictive Analytics
How Predictive Analytics is used in the real world for
significant ROI
Agenda

41. CASE STUDY
Top U.S. Bank
Solutions
Predictive Modeling
Engine & Dashboards
Challenge
• Top 10 U.S. Bank faced with mounting delinquencies in all banking areas
• A huge backlog of foreclosures
Solution
• Designed a predictive analytics modeling engine and dashboard reporting environment
that tracked all consumer lending and mortgages
• Tracked customers through models to help determine when and what action best fit
the situation
Result
• Generated models on defaulting loans which justified an additional $2.2 billion in TARP money
• Staffing Model and Capacity Planning for Collections and Defaults
• Use automated dialers to call customers
• Using targeted messages to better handle the collections and default, saving $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
Predictive Analytics / 41

42. CASE STUDY
Online Mass Retailer
Solutions
Market Basket Analysis
Predictive
Recommendations
Challenge
• Online Mass Retailer was missing out on the opportunity to add additional revenue by
providing differentiated customer experiences and by offering the right items in the right
order to increase cross-selling
Solution
• Identified optimal product mix and cross-sell offering
• Helped to customize the consumer experience and optimize revenue through more
revenue per customer
Result
• Recurring annual benefits of more than $80 M in additional cross-sell revenue and an
average increase of $.94 per customer purchase
Predictive Analytics / 42

43. CASE STUDY
Fortune 100 Insurance Company
Solutions
Predictive Forecasting
Simulation Tool
Marketing Mix
Modeling
Challenge
• A subsidiary of a Fortune 100 insurance company provides health insurance services
in 29 states and was facing specific new competition
• Products were underpriced for claims experience
• Larger competitors were quickly gaining market share
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
Result
• Transformed the subsidiary into the fastest growing individual health insurance company in the nation at a time when
most in the industry were shrinking
• Doubled the portfolio with targeted high-value customers in just 18 months to over 370,000 customers and $500 million
in new annual revenue
Predictive Analytics / 43

44. Thank you
Joseph Brandenburg
Predictive Analytics Practice Leader · Dunn Solutions