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Transcript of Business Intelligence Symposium Presentation
Business Intelligence for Competitive Advantage
Bill CassillPresident
October, 2008
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, 2008Bloomberg Interview, October, 2008
“If you're not fearful, you're crazy.”
Jamie Dimon
CEO, JPMorgan Chase
JPMorgan Conference Call, 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
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.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.
Some Companies Are Already There
• A few companies are already using analytics as a competitive
advantage.
Utilizes analytics to identify their Their proprietary search engine Utilizes analytics to identify their
most loyal customers and keep
them coming back.
Their proprietary analytics
technology makes real time
product recommendations for
cross sell based upon a
customer’s current and prior
purchase history.
Predicts which movies a
customer will like based upon
their ratings of other movies.
This information is then used to
make movie recommendations.
Conducts over 300 experiments
per day to continue refining
their value proposition and
targeting.
Their proprietary search engine
technology and use of analytics
has made them the dominant
player in internet search and
advertising.
Uses analytics to identify
trends and opportunities
before their competitors can.
Capability Spectrum
Elementary CapabilityElementary Capability Advanced CapabilityAdvanced Capability
Little or No Capability
Customer Data Warehouse
Database Queries and Reports
BI Tools and Dashboards
Customer Segmentation
Predictive BI
Elementary Business
Intelligence
Analytical Optimization and Automation
Strong Analytical Culture
Cutting Edge
Analytical Innovators
(e.g. Google and
Amazon)
Transitional
Capability
Advanced Competitive
Capability
Clear Linkage
Between Analytics
& Revenue
With the Strategy Overlay
Strategic
DashboardsDashboards Customer / Customer /
Linkage BetweenLinkage Between
Analytics and RevenueAnalytics and Revenue
Strong Analytical Strong Analytical
CultureCulture
Analytical
Competitor
1 or 2 Dimensional
Opportunity Assessment(i.e. “the average customer view”)
Fast Cycle Fast Cycle
Analytics & Test Analytics & Test
and Learn and Learn
ProcessesProcesses
Elementary Business
Intelligence
Transitional
Capability
Advanced Competitive
Capability
Tactical
Future ViewBackwards View
Ad Hoc ReportsAd Hoc Reports
OLAPOLAP
Customer / Customer /
Market Market
SegmentationSegmentation
Predictive BIPredictive BI
ForecastingForecasting
Analytical OptimizationAnalytical Optimization
& Automation& Automation
Reactive Business Reporting Proactive Marketing &
Risk Management
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?”– 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
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
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 valuecontinuous 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.
An Example Dashboard
What do I do about this? Or this?
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 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.
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 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.
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– 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
Telecom Product Lifecycle Example
• One wireless telecom once had batteries of predictive cross
sell algorithms to target various stages of the product lifecycle.
Conversion
of Non-
Users
Usage
Stimulation
of Current
MRC
(Monthly
Plans)
Churn
(Decrease
Usage or
Illustrative ExampleIllustrative Example
Users of Current
Users
Plans) Usage or
Stop)
SMS x x x x
Int’l Dial x x x x
Int’l Roam x x x x
Wireless
Internetx x x x
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.
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 ExampleIllustrative Example
Product X-Sell Models
Business Checking
Savings
Credit Card
Line of Credit
Analysis Checking
Fixed Lending
Merchant Services
Customer # Recommended Product
1 Bus. Checking
2 Card, Savings
3 Line of Credit
4 Bus. Checking
5 Fixed Lending
6 Savings, Analysis Checking
Optimization
Logic
Illustrative ExampleIllustrative Example
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 • This requires getting use to the idea of the feedback loop
where fears, assumptions, and even egos may get challenged.
Investigation Decision ActionIdentify
Opportunities
Assessment
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.
Investigate
Identify
Decide
ActAssessAssess
Investigate
Identify
Decide
ActAssessAssess
Investigate
Identify
Decide
ActAssessAssess
IterateIterate IterateIterate
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
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 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.
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 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.
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
www.numericalalchemy.com
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