A short presentation overviewing the range of BI capabilities and how they can be used to drive company strategy and bottom line results.

1. Business Intelligence for Competitive Advantage
Bill Cassill
President
October, 2008

2. The Future is Dark
• Some say it looks grim, indeed.
“We're going to be surprised by the severity of the recession and the
severity of the financial losses.”
Nouriel Roubini
Professor of Economics, NYU
Bloomberg Interview, October, 2008
“All signs point to an economic slump that will be nasty, brutish — and
long.”
Paul Krugman
Nobel Prize Winning Economist
Op-Ed, New York Times, October, 2008
“If you're not fearful, you're crazy.”
Jamie Dimon
CEO, JPMorgan Chase
JPMorgan Conference Call, October, 2008

3. A New Approach
• In order to remain competitive, a new approach is needed.
• Those companies who will be in the best shape to ride out
and even thrive in a slower economy will be those who make
better use of their data.
• Specifically, those companies who have a strong discipline
around data and advanced analytics will be at a competitive
advantage to quickly spot opportunities and react to changing
market conditions before their competitors.
• These companies will be the big winners.

4. Some Companies Are Already There
• A few companies are already using analytics as a competitive
advantage.
Their proprietary search engine
Utilizes analytics to identify their
technology and use of analytics
most loyal customers and keep
Predicts which movies a has made them the dominant
them coming back.
customer will like based upon player in internet search and
their ratings of other movies. advertising.
This information is then used to
make movie recommendations.
Conducts over 300 experiments
Their proprietary analytics
per day to continue refining
technology makes real time
their value proposition and
product recommendations for
targeting.
cross sell based upon a
Uses analytics to identify
customer’s current and prior
trends and opportunities
purchase history.
before their competitors can.

5. Capability Spectrum
Elementary Capability Advanced Capability
Clear Linkage
Little or No Capability BI Tools and Dashboards Cutting Edge
Between Analytics
Analytical Innovators
& Revenue
(e.g. Google and
Amazon)
Customer Data Warehouse Customer Segmentation Strong Analytical Culture
Database Queries and Reports Predictive BI Analytical Optimization and Automation
Advanced Competitive
Elementary Business Transitional
Capability
Intelligence Capability

6. With the Strategy Overlay
Fast Cycle
Strong Analytical
Analytics & Test
Strategic Culture
1 or 2 Dimensional and Learn
Analytical Processes
Opportunity Assessment
Competitor
(i.e. “the average customer view”)
Linkage Between
Analytics and Revenue
Dashboards Customer /
Market
Segmentation Analytical Optimization
OLAP & Automation
Predictive BI
Reactive Business Reporting Proactive Marketing &
Risk Management
Forecasting
Ad Hoc Reports
Tactical
Backwards View Future View
Elementary Business Transitional Advanced Competitive
Intelligence Capability Capability

7. Questions, Data, & More Questions
• What kinds of questions can you answer with traditional BI?
• The problem needs to be well structured with known (or a
few hypothesized) inputs, outputs, and linkages in between.
– e.g. “What were my sales in Maine for the last three months?”
– “How did this compare to supply chain deliveries to impact inventory
levels in that state?”
• Traditional BI applications are good at:
– Automated Reporting and Dash Boarding
– Process Monitoring
– Basic Reporting and Business Analysis

8. Where the Wheels Fall Off
• What do you do if you do not know the relevant causal factors
(or need to find out)? What if you have hundreds or even
thousands of potential factors you need to consider?
– e.g. “We’ve got a customer churn problem which is eating into
margins. What do these customers look like?”
• This is where predictive BI and other machine learning
technologies can help out.
• Predictive BI and machine learning are good at:
– Helping to place defined bounds (i.e. confidence intervals) around an
outcome
– Helping to shape a story across multidimensional data

9. What Is This New Stuff, Anyway?
• Predictive BI refers to a broad set of techniques that are used
to predict and profile future outcomes.
– The result is a mathematical representation between selected inputs
and outputs
– The outputs are usually either some kind of probability or other
continuous value
• Machine learning refers to a class of modern statistical and
other algorithmic techniques for prediction and pattern
detection. These techniques are broadly used for clustering,
prediction, and time series analysis.

10. An Example Dashboard
What do I do about this? Or this?

11. A Predictive BI Wireless Telecom
Customer Churn Example
Slightly lower value
subscribers who have
significantly decreased
their minutes of use
during the most recent
month. They also have
higher than average
roaming calls and
overage minutes.
Higher risk subscribers
typically have older,
lower priced handsets.
These subscribers are
also somewhat younger
with better than average
credit risk.

12. Automated Decision Making
• In addition to added insight, another step in the evolution of
business intelligence is automated decision making.
• The goal is to reduce the amount of human involvement in
mundane, repetitive activities and decision making to free
them for more higher value roles. This also acts as a force
multiplier in terms of human productivity.
• This occurs through a combination of predictive algorithms
and predetermined business rules.

13. Automated Decision Making (cont.)
• Currently, these systems are already in widespread use even
though you may not even be aware of it.
• Some examples include:
– Terrorism risk assessment when you buy an airline ticket
– Your banking deposit activity (anti-money laundering algorithms)
– Fraud detection algorithms for credit card usage
– Fraud detection when you buy something online
– Automated credit scoring criteria when you apply for a card, loan, or
line of credit
– Product cross sell recommendations when you visit your local bank or
online retailer

14. Telecom Product Lifecycle Example
• One wireless telecom once had batteries of predictive cross
sell algorithms to target various stages of the product lifecycle.
Illustrative Example
Conversion Usage MRC Churn
of Non- Stimulation (Monthly (Decrease
Users of Current Plans) Usage or
Users Stop)
SMS x x x x
Int’l Dial x x x x
Int’l Roam x x x x
Wireless
x x x x
Internet
Ringtone x x x x
MMS x x x
411 x x x
*Each “x” represents a single model to predict those likely to perform the designated action in the near future.

15. Financial Services Optimization
Example
• One financial services company used predictive algorithms
plus business rules to generate product recommendations for
use by front line associates for cross sell efforts.
Illustrative Example
Product X-Sell Models
Customer # Recommended Product
Business Checking
1 Bus. Checking
Savings
2 Card, Savings
Credit Card
3 Line of Credit
Line of Credit
Optimization 4 Bus. Checking
Analysis Checking Logic
5 Fixed Lending
Fixed Lending
6 Savings, Analysis Checking
Merchant Services

16. The Fast Cycle Learning Process
• In addition to automated decision making, a true analytical
competitor uses analytics to aid the investigative process to
rapidly conduct root cause analysis and to continuously adjust
the goals and direction of the business.
• This requires getting use to the idea of the feedback loop
where fears, assumptions, and even egos may get challenged.
Identify
Investigation Decision Action
Opportunities
Assessment

17. Fast Cycle Learning (cont.)
• Ideally, the process involves a short cycle, iterative process for
ongoing organizational learning and adaptation. This short
cycle process means that the organization becomes more
agile in its ability to anticipate and react to changing
circumstances and opportunities.
Iterate Iterate
Identify Identify Identify
Decide Decide Decide
Investigate Investigate Investigate
Act Act Act
Assess Assess Assess

18. Applicable Areas
• The short cycle learning approach is suitable to a variety of
applications:
– Ongoing process refinement and reengineering
– Waste and cost reductions
– Competitive intelligence
– Pricing decisions
– Marketing and sales initiatives
– Risk management
– Customer intelligence and management
– Product development

19. Parting Thoughts
• Some organizations will ride out the current economic
conditions better than others.
• Those that will be the most competitive will have leaders who
continuously challenge the status quo, are adaptive, and use
data driven decision making.
• This leads to the concept of the “Agile” or “Learning”
organization: those that can adapt to changing circumstances
and react to new opportunities faster than the competition.

20. Parting Thoughts (cont.)
• Leaders who are unable to put reality ahead of ego will be the
ones who eventually fail.
• Successful data driven decisions require vigorous debate, a
strong investigative process, good data, and the right tools
and talent.
• It also requires a vision of what is possible and an ability to
see the future for what it might be with a little bit of creativity
and hard work.

21. More on Numerical Alchemy, Inc.
• Numerical Alchemy is a Seattle based data mining consultancy
that helps companies make better decisions using data and
analytics. With over 12 years of experience, Bill Cassill has
worked for and consulted with companies in financial
services, wireless telecom, energy, retail, and online firms.
For more information on our capabilities and services, contact Bill Cassill at:
425.996.8732 Office
425.591.5505 Wireless
bill.cassill@numericalalchemy.com
www.numericalalchemy.com
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