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

bill.cassill@numericalalchemy.com

www.numericalalchemy.com

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