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Introduction to Data Mining

Introduction to Data Mining. Chapter 1. Definition. DATA MINING : exploration & analysis by automatic means of large quantities of data to discover actionable patterns & rules Data mining is a way to use massive quantities of data that businesses generate

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Introduction to Data Mining

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  1. Introduction to Data Mining Chapter 1

  2. Definition • DATA MINING: exploration & analysis • by automatic means • of large quantities of data • to discover actionable patterns & rules • Data mining is a way to use massive quantities of data that businesses generate • GOAL - improve marketing, sales, customer support through better understanding of customers

  3. Retail Outlets • Bar coding & scanning generate masses of data • customer service • inventory control • MICROMARKETING • CUSTOMER PROFITABILITY ANALYSIS • MARKET-BASKET ANALYSIS

  4. Political Data Mining • Grossman et al., 10/18/2004, Time, 38 • 2004 Election • Republicans: VoterVault • From Mid-1990s • About 165 million voters • Massive get-out-the-vote drive for those expected to vote Republican • Democrats: Demzilla • Also about 165 million voters • Names typically have 200 to 400 information items

  5. Medical Diagnosis • J. Morris, Health Management Technology Nov 2004, 20, 22-24 • Electronic Medical Records • Associated Cardiovascular Consultants • 31 physicians • 40,000 patients per year, southern New Jersey • Data mined to identify efficient medical practice • Enhance patient outcomes • Reduced medical liability insurance

  6. Mayo Clinic • Swartz, Information Management Journal Nov/Dec 2004, 8 • IBM developed EMR program • Complete records on almost 4.4 million patients • Doctors can ask for how last 100 Mayo patients with same gender, age, medical history responded to particular treatments

  7. Business Uses of Data Mining • Customer profiling • Identify profitability of customers • Targeting – used to manage customer churn • Determine characteristics of most profitable customers • 3. Market-Basket Analysis • Determine correlation of purchases by profile • Part of Customer Relationship Management

  8. Reasons why Data Mining is now effective • Data are there • Data are warehoused (computerized) • Walmart: 35 thousand queries per week • Computing economically available • Competitive pressure • Commercial products available

  9. Trends • Every business is service • hotel chains record your preferences • car rental companies the same • service versus price • credit card companies • long distance providers • airlines • computer retailers

  10. Trends • Mass Customization • produce tailored products from standardized components • Levi-Strauss - custom fit jeans • The Custom Foot • Andersen Windows • Individual, Inc. • electronic clipping • customer profiles of interests • send custom newsletter

  11. Trends • Information as Product • Custom Clothing Technology Corporation • fit jeans, other clothing • Lands End • J. Crew • INFORMATION BROKERING • IMS - collects prescription data from pharmacies, sells to drug firms • AC Nielsen - TV

  12. Trends • Commercial Software Available • using statistical, artificial intelligence tools that have been developed • Enterprise Miner SAS • Intelligent Miner IBM • Clementine SPSS • PolyAnalyst Megaputer • Specialty products

  13. How Data Mining Is Being Used • U.S. Government • track down Oklahoma City bombers, Unabomber, many others • Treasury department - international funds transfers, money laundering • Internal Revenue Service

  14. How Data Mining Is Used • Safeway • offer Safeway Savings Club card • users given discounts • users must give personal information • every use, collect data • identify aggregate patterns (what sells well together; what should be sold together) • sell names for 5.5 cents per name to suppliers

  15. How Data Mining Is Used • Firefly • asks members to rate music and movies • subscribers clustered • clusters get custom-designed recommendations

  16. Cross-selling • USAA • insurance • doubled number of products held by average customer due to data mining • detailed records on customers • predict products they might need • Fidelity Investments • regression - what makes customer loyal

  17. Warranty Claims Routing • Diesel engine manufacturer • stream of warranty claims • examine each by expert • determine whether charges are reasonable & appropriate • think of expert system to automate claims processing

  18. Retaining Good Customers • Customer loss: • Banks - Attrition • Cellular Phone Companies - Churn • study who might leave, why • Southern California Gas • customer usage, credit information • direct mail contact - most likely best billing plan • who is price sensitive • Who should get incentives, whom to keep

  19. Fairbank & Morris • Credit card company’s most valuable asset: • INFORMATION ABOUT CUSTOMERS • Signet Banking Corporation • obtained behavioral data from many sources • built predictive models • aggressively marketed balance transfer card • First Union • who will move soon - improve retention

  20. In-class activity • Exercises 3-7, p. 15

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