1. Business Intelligence Technologies – Data Mining Lecture 1 Introduction 1

2. Agenda Course Description Course Logistics Case discussion Introduction to Data Mining 2

3. 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 3

4. Course Objectives Understand data mining theories Learn popular data mining methods Enable you to solve special business applications Master a data mining package 4

5. Agenda Course Description Course Logistics Case discussion Introduction to Data Mining 5

6. Course Logistics Qing Li  kooliqing@gmail.com kooliqing@gmail.com TA  Jia Wang  xiaojiajia198796@gmail.com xiaojiajia198796@gmail.com Office hours:  Walk-in  By appointment  Before and after class  Call me 6

7. Class Resources Class homepage: http://liqing.cai.swufe.edu.cn/http://liqing.cai.swufe.edu.cn/ post slides, announcements, downloads Text Book + Cases + Handouts 7

8. Text Book Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management, Second Edition Michael Berry and Gordon Linoff, 2004, Wiley, ISBN 0471-470643 8

9. Class Schedule Topic 1Course Overview, Intro to Data Mining 2Market Basket Analysis & Association Rules, CRM 3Market Segmentation & Clustering, Prepare data 4Prediction & Classification – Decision Tree 5Personalization & Nearest Neighbor 6Financial Forecasting & Neural Networks 7Link Analysis & Web mining 8Misc. Topics 9Guest Speaker 10Term project presentations 9

10. 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 practice  Your recommendations Two phases  Phase 1: Identify the company and brief description (Due 3 weeks from now)  Phase 2: Final report + class presentation 10

11. 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 11

12. 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) 12

13. 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 13

14. Survey 14

15. Agenda Course Description Course Logistics Case discussion Introduction to Data Mining 15

16. 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? 16

17. 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? 17

18. 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? 18

19. Agenda Course Description Course Logistics Case discussion Introduction to Data Mining 19

20. 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) 20

21. What is a pattern? Informal definition: Any structure that can be found in the data. e.g.  People with good credit ratings have fewer accidents  Risk = 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 21

22. 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 22

23. 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

24. Why Data Mining Now? Better and cheaper Computing Power Mature data mining technology Improved Data Collection & Storage DM Plus: Data is being produced at a tremendous speed. Competitive pressures are enormous 24

25. 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 25

26. 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 http://www.kdnuggets.com 26

27. 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 27

28. 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. 28

29. 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 29

30. 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 month 30

31. 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… 31

32. 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 32

33. Detailed Data Mining Process – 11 Steps 1, 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

34. 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 34

35. 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 35

36. Step 7-9: Model Building Choice 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 data 36

37. 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 37

38. 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 38

39. Simpson’s Paradox 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. 39

40. 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 40