Business Intelligence Technologies – Data Mining

Lecture 1 Introduction

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Agenda

Course Description

Course Logistics

Case discussion

Introduction to Data Mining

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What is covered in this course Theories/Methods

Data mining cycle/process/methodology, evaluation Association rules, decision trees, clustering, nearest

neighbor, neural networks, link analysis, Web mining etc.

Applications Market basket analysis, customer segmentation,

CRM, personalization, Financial analysis etc. Business Cases

Hands-on Experience SAS – Enterprise Miner

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Course Objectives

Understand data mining theories Learn popular data mining methods Enable you to solve special business

applications Master a data mining package

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Agenda

Course Description

Course Logistics

Case discussion

Introduction to Data Mining

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Course Logistics Qing Li

kooliqing@gmail.com

TA Jia Wang xiaojiajia198796@gmail.com

Office hours: Walk-in By appointment Before and after class Call me

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Class Resources

Class homepage: http://liqing.cai.swufe.edu.cn/ post slides,

announcements, downloads

Text Book + Cases + Handouts

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Text Book

Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition

Michael Berry and Gordon Linoff, 2004,  Wiley, ISBN 0471-470643

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Class Schedule

Topic

1 Course Overview, Intro to Data Mining

2 Market Basket Analysis & Association Rules, CRM

3 Market Segmentation & Clustering, Prepare data

4 Prediction & Classification – Decision Tree

5 Personalization & Nearest Neighbor

6 Financial Forecasting & Neural Networks

7 Link Analysis & Web mining

8 Misc. Topics

9 Guest Speaker

10 Term project presentations

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Group Term Project Group of 2-3 (3 is better).

Due one week from now Identify a company to study

Focus: Data and Business Intelligence Current practiceYour recommendations

Two phasesPhase 1: Identify the company and brief

description (Due 3 weeks from now)Phase 2: Final report + class presentation

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Software

SAS – Enterprise Miner Used for homework assignments

Need Windows XP Professional or Mac OS9 I’ll demo SAS in most classes. Tutorial available on course website Every student is recommended to have a copy in

order to follow class demo. Alternative for Vista users - WEKA

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Grading 15% Participation

3: Excellent 2: Good 1: OK 0: Absent with good reason and advance notification -3: Absent with no reason

50% Homework 2 big assignments Problem solving, data analysis and/or case discussion. 25% each

35% Term Project Phase 1 report --- 5% Final report --- 20% Class presentation --- 5% Peer evaluation --- 5%

(No Curve)

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Misc. Issues Slides are available before class

Download or print them before class

Lectures may be different from the text book Some materials in the lectures may not be in the

book, so please focus in class The book is a great reference book, not a bible

Finish assigned case readings before each class

Attendance is required

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Survey

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Agenda

Course Description

Course Logistics

Case discussion

Introduction to Data Mining

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Case 1: Bank of America

Discussion Questions:1. What is BoA trying to achieve?2. What are the alternative solutions? Pros and

cons of each?3. What are the stages of data mining?

Describe each.4. What are the data mining techniques used,

and what are the findings from each technique?

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Case 2: A Wireless Company

Discussion Questions:1. What is the company trying to achieve?2. How can data mining help?3. Where did data come from and How are data

processed?4. How is the data mining approach evaluated?

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Case 3: SUV

Discussion Questions:1. What is the company trying to achieve?2. How can data mining help?3. What data files are used? What information

are contained in these files?4. How is the two data mining technique

combined and why is it more powerful to combine?

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Agenda

Course Description

Course Logistics

Case discussion

Introduction to Data Mining

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What is data mining? Informal definition: Finding patterns in data

More formal definition: Non-trivial process of identifying valid, novel, potentially useful, and understandable patterns in data

Business Intelligence: a process for increasing the competitive advantage of a business by intelligent use of available data in decision making. (one definition)

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What is a pattern? Informal definition: Any structure that can

be found in the data. e.g.People with good credit ratings have fewer

accidentsRisk = 0.93*prior_default + 0.23*num_cards

–1.3* employed On Friday nights male customers who buy

diapers also tend to buy beer Not every pattern is desirable

People with high income buy expensive cars

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Marketing Which customers are likely to respond to this campaign? What other products or services should be offered to a customer? (cross-selling) What types of customers are loyal?

Telecommunications Which customers will switch to competitors ? Which calls are fraudulent?

Finance and Insurance What types of customers have high credit risks / insurance risks ? What interest rate or insurance premium should be given to different customers? Which stocks are likely to perform well in the next 3 months?

Healthcare Which patients may take longer to recover ? What is the likely cause of an illness ?

Retail Which products do customers buy together (or in sequence)?

Customer Support Which customer service representative should be assigned to a task ? When a customer calls, the customer representative’s screen shows exactly where to

lead the conversation.

Why Data Mining ?

Wherever there is data, there is and should be data mining!

Because Data Mining virtually affects all data-intensive industry

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Why Data Mining ? – Some Real Examples Safeway:

Shopper cards capture point-of-sale data and personal information. Arrange products on shelves: Beer & Diaper Sell names to suppliers so that manufacturer coupons can be targeted.

Pfizer pharmaceuticals: Construct a predictive model which tells patients their cholesterol risk

score. High risk patients can request Lipitor, Pfizer’s cholesterol medication.

Fidelity: Cross selling, when a customer calls, know what other services to offer Build models to figure out what makes a loyal customer These models saved a marginally profitable bill-paying service

Amazon: Recommendations

Capital One: What terms should be offered to different customers? The lowest loan loss rates in the industry 23

Why Data Mining Now?

Better and cheaper Computing

Power

Mature data miningtechnology

Improved Data Collection & Storage

DM

Plus: Data is being produced at a tremendous speed. Competitive pressures are enormous

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Descriptive vs. Predictive Data Mining Descriptive DM is used to learn about and understand the

data. What items are purchased together? Identify and describe groups of customers with common buying

behavior

Predictive DM aims to build models in order to predict unknown values of interest. A model that given a customer’s characteristics predicts how

much the customer will spend on the next catalog order. Predicting which customers are likely to leave Which direction is Stock X going to move tomorrow? Most predictive models are also descriptive

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Data Mining Software Big Names:

IBM Intelligent Miner SPSS Clementine Microsoft SQL Server 2000 Analysis Service Oracle 9i Data Mining SAS Enterprise Miner

Smaller Companies: ANGOSS KnowledgeStudio XLMiner MegaPuter PolyAnalyst DBMiner

Free or Open Source: Weka Lots of free programs on the Internet supporting individual data mining

techniques. A good portal for data mining related stuff:

http://www.kdnuggets.com

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Virtuous Cycle of Data Mining

1, Identify the business problem

2, Mining data to transform the data into actionable information

3, Acting on the information

4, Measuring the results

Finding patterns is not enough Must respond to the patterns

by taking action Turning:

Data into Information Information into Action Action into Value

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1, Identify the Business Opportunity Many business processes are good candidates:

New product introduction Direct marketing campaign Understanding customer attrition/churn Evaluating the results of a test market

Or more specific problems What types of customers responded to our last campaign? Where do the best customers live? Are long waits in check-out lines a cause of customer attrition? What products should be promoted with our XYZ product?

TIP: When talking with business users about data mining opportunities, make sure you focus on the business problems/opportunities and not on technology and algorithms.

Another goal of this course is for you to think strategically about what business opportunities can be addressed by data mining techniques.

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2, Mining the Data to Transform it into Actionable Information Success is making business sense of the data Need to figure out the specific data mining tasks used to

address the business opportunities identified in the first step.

Deal with messy data Don’t expect clean data. Data cleaning accounts for 70% of efforts

Implementation problems: What techniques to use? How to use the techniques? Selecting the right model

Other problems Data privacy issue

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3, Take Action Taking action is the whole purpose of data mining Now with discovered patterns (from mining data),

we have better informed decisions. Examples

Contact targeted customers Prioritizing customer service

Cingular and AT&T were fined for $1.5 million on Sept. 10, 2004 for discriminating their services based on customers’ credit rating.

Adjusting inventory levels Rearrange products on the shelves Verizon sends out 40k mails to selected customers per

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4, Measuring Results

Assess the impact of the action taken Often overlooked, ignored, skipped Planning for the measurement should begin

when analyzing the business opportunity, not after it is “all over”

Assessment questions (examples): Did this campaign do what we hoped? Did some offers work better than others? Lower cost, increase profit? Tons of others…

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Data Mining General Guidelines

The DM virtuous cycle (4 steps) is iterative No steps should be skipped Common sense prevails with respect to

how rigorous each step is carried out The 4 steps of the virtuous cycle expand to

become an 11-step methodology --- more rigorous

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Detailed Data Mining Process – 11 Steps1, Translate the business problem into a data mining problem

2, Select appropriate data

3, Get to know the data

4, Create a model set

5, Fix problems with the data

6, Transform data to bring information to the surface

7, Build models

8, Assess models

9, Deploy models

10, Assess results

11, Begin again 33

Business problems can often be big and vague Data mining tasks need to be more concrete Sample business problems:

How to improve response to a direct marketing campaign?

Which ads to place on web pages in order to improve click thorough rate?

How to transform these to DM task?

Step 1: Transforming Business Problems into DM Tasks

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Step 2-6: Data Preparation

Get data Different (heterogeneous) sources Need to collect additional data? Credit card charge records, points-of sale, web log etc.

Clean/correct data Correct errors Add missing values Discard of garbage, remove outliers

Transform data if needed Derived attributes --- bring information to the surface Income Income bracket when model requires categorical data DOB Age

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Step 7-9: Model BuildingChoice of model, model building and model assessment Decide what model type to use

Descriptive or Predictive model? Which specific technique? Often can try different techniques Things to consider:

Computational issues Implementation issues Availability of relevant and amount of data Do we have the necessary expertise

Assess Models Accuracy on testing data Small is beautiful Easier to understand

Step 9 is more about scoring or ranking in the real data36

Step 10: Assess the Result

It’s not model accuracy any more It’s more about achieving the business goal It’s closely related to business decisions

E.g. if it’s more expensive to deploy a data mining model, a mass mailing may be more cost-effective than a targeted one.

But it’s often hard to isolate the effect of a solution. Indirect benefits may be hard to see. Do a market test

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Common Data Mining Mistakes Learn things that aren’t true

Patterns may not represent any underlying rule Tall candidates win presidential election True in data, but has no predictive power

The data may not reflect the relevant population The sample should not be biased Otherwise, the result can not be extended E.g. Your existing customers are not like the customers you want to acquire

Data may be at the wrong level of detail Refer to the Simpson’s paradox (next slide)

Learn things that are true, but not useful Things that are already known

Majority of rules learned are normal business rules E.g. Retired employees don’t respond to retirement plan promotion

Things that can’t be used (AT&T/Cingular example) Inability to act upon patterns because of political, legal and ethical reasons

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Simpson’s Paradox

Admit Deny TotalMale 480 (80%) 120 (20%) 600

Female 180 (90%) 20 (10%) 200

Business School Law Admit Deny Total

Male 10 (10%) 90 (90%) 100

Female 100 (33%) 200 (66%) 300

Admit Deny TotalMale 490 (70%) 210 (30%) 700Female 280 (56%) 220 (44%) 500

Business and Law Schools

Simpson’s Paradox refers to the reversal of the direction of a comparison or an association when data from several groups are combined to form a single group.

This is caused by the different percentages in admission in the two tables - they really shouldn't be combined.

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What to Do After Class

Read Chapter 1, 2, 3 Read cases for Lecture 2 Install SAS Find a group member for your term

project and start thinking about which company to select for your project

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