DATA MINING

Data Mining David L. Olson James & H.K. Stuart Professor in MIS University of Nebraska Lincoln Korea Telecom: KM1 Data Mining David L. Olson Definition • DATA MINING: exploration & analysis – by automatic means – of large quantities of data – to discover actionable patterns & rules • Data mining a way to utilize massive quantities of data that businesses generate Korea Telecom: KM1 Data Mining David L. Olson 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 Korea Telecom: KM1 Data Mining David L. Olson 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 NJ – Data mined to identify efficient medical practice – Enhance patient outcomes – Reduced medical liability insurance Korea Telecom: KM1 Data Mining David L. Olson 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 Korea Telecom: KM1 Data Mining David L. Olson Retail Outlets • Bar coding & Scanning generate masses of data – – – – – customer service inventory control MICROMARKETING CUSTOMER PROFITABILITY ANALYSIS MARKET BASKET ANALYSIS David L. Olson Korea Telecom: KM1 Data Mining FINGERHUT • Founded 1948 – – – – today sends out 130 different catalogs to over 65 million customers 6 terabyte data warehouse 3000 variables of 12 million most active customers – over 300 predictive models • Focused marketing Korea Telecom: KM1 Data Mining David L. Olson Fingerhut • Purchased by Federated Department Stores for $1.7 billion in 1999 (for database) • Fingerhut had $1.6 to $2 billion business per year, targeted at lower-income households • Can mail 400,000 packages per day • Each product line has its own catalog Korea Telecom: KM1 Data Mining David L. Olson Fingerhut • Uses segmentation, decision tree, regression, neural network tools from SAS and SPSS • Segmentation - combines order & demographic data with product offerings – can target mailings to greatest payoff • customers who recently had moved tripled their purchasing 12 weeks after the move • send furniture, telephone, decoration catalogs Korea Telecom: KM1 Data Mining David L. Olson Data for SEGMENTATION cluster subj 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 age 53 48 32 26 51 59 43 38 35 27 income 80000 120000 90000 40000 90000 150000 120000 160000 70000 50000 indices marital grocery wife 180 husband 120 single 30 wife 80 wife 110 wife 160 husband 140 wife 80 single 40 wife 130 David L. Olson dine out 90 110 160 40 90 120 110 130 170 80 savings 30000 20000 5000 0 20000 30000 10000 15000 5000 0 Korea Telecom: KM1 Data Mining Initial Look at Data • Want to know features of those who spend a lot dining out • INCLUDE AS MANY ACTIONABLE VARIABLES AS POSSIBLE – things you can identify • Manipulate data – sort on most likely indicator (dine out) Korea Telecom: KM1 Data Mining David L. Olson Sorted by Dine Out cluster subject 1004 1010 1001 1005 1002 1007 1006 1008 1003 1009 age 26 27 53 51 48 43 59 38 32 35 income 40000 50000 80000 90000 120000 120000 150000 160000 90000 70000 indices marital grocery wife 80 wife 130 wife 180 wife 110 husband 120 husband 140 wife 160 wife 80 single 30 single 40 David L. Olson dine out 40 80 90 90 110 110 120 130 160 170 savings 0 0 30000 20000 20000 10000 30000 15000 5000 5000 Korea Telecom: KM1 Data Mining Analysis • Best indicators – marital status – groceries • Available – marital status might be easier to get Korea Telecom: KM1 Data Mining David L. Olson Fingerhut • Mailstream optimization – which customers most likely to respond to existing catalog mailings – save near $3 million per year – reversed trend of catalog sales industry in 1998 – reduced mailings by 20% while increasing net earnings to over $37 million Korea Telecom: KM1 Data Mining David L. Olson Banking • Among first users of data mining • Used to find out what motivates their customers (reduce churn) • Loan applications • Target marketing • Norwest: 3% of customers provided 44% profits • Bank of America: program cultivating top 10% of customers Korea Telecom: KM1 Data Mining David L. Olson CREDIT SCORING Bank Loan Applications Age 24 20 20 33 30 55 28 20 20 39 Income 55557 17152 85104 40921 76183 80149 26169 34843 52623 59006 Assets Debts Want 27040 48191 1500 11090 20455 400 0 14361 4500 91111 90076 2900 101162 114601 1000 511937 21923 1000 47355 49341 3100 0 21031 2100 0 23054 15900 195759 161750 600 David L. Olson On-time 1 1 1 1 1 1 0 1 0 1 Korea Telecom: KM1 Data Mining Characteristics of Not On-time Age 28 20 Income Assets Debts Want 26169 47355 49341 3100 52623 0 23054 15900 On-time 0 0 Here, Debts exceed Assets Age Young Income Low BETTER: Base on statistics, large sample supplement data with other relevant variables Korea Telecom: KM1 Data Mining David L. Olson CHURN • Customer turnover • critical to: – – – – telecommunications banks human resource management retailers Korea Telecom: KM1 Data Mining David L. Olson Identify characteristics of those who leave Age Time-job Time-town min bal checking years months months $ 27 12 12 549 x 41 18 41 3259 x 28 9 15 286 x 55 301 5 2854 x 43 18 18 1112 x 29 6 3 0 x 38 55 20 321 x 63 185 3 2175 x 26 15 15 386 x 46 13 12 1187 x 37 32 25 1865 x Korea Telecom: KM1 Data Mining David L. Olson savings card x x x x x loan x x x x x x x x x x x x Analysis • What are the characteristics of those who leave? – Correlation analysis • Which customers do you want to keep? – Customer value - net present value of customer to the firm Korea Telecom: KM1 Data Mining David L. Olson Correlation Age Age 1.0 Job Town Min-Bal Check Saving Card Loan Time Job 0.6 1.0 Time Town 0.4 0.9 1.0 min-bal check -0.4 -0.6 -0.5 1.0 0.0 0.1 -0.1 -0.2 1.0 saving 0.4 0.6 0.3 0.3 0.5 1.0 card loan 0.2 0.9 0.5 0.6 0.2 0.9 1.0 0.3 -0.2 0.4 -0.1 0.2 0.3 0.5 1.0 Korea Telecom: KM1 Data Mining David L. Olson Mortgage Market • Early 1990s - massive refinancing • need to keep customers happy to retain • contact current customers who have rates significantly higher than market – a major change in practice – data mining & telemarketing increased Crestar Mortgage’s retention rate from 8% to over 20% Korea Telecom: KM1 Data Mining David L. Olson Banking • Fleet Financial Group – $30 million data warehouse – hired 60 database marketers, statistical/quantitative analysts & DSS specialists – expect to add $100 million in profit by 2001 Korea Telecom: KM1 Data Mining David L. Olson Banking • First Union – concentrated on contact-point – previously had very focused product groups, little coordination – Developed offers for customers Korea Telecom: KM1 Data Mining David L. Olson CREDIT SCORING • Data warehouse including demand deposits, savings, loans, credit cards, insurance, annuities, retirement programs, securities underwriting, other • Statistical & mathematical models (regression) to predict repayment Korea Telecom: KM1 Data Mining David L. Olson CUSTOMER RELATIONSHIP MANAGEMENT (CRM) • understanding value customer provides to firm – Kathleen Khirallah - The Tower Group • Banks will spend $9 billion on CRM by end of 1999 – Deloitte • only 31% of senior bank executives confident that their current distribution mix anticipated customer needs Korea Telecom: KM1 Data Mining David L. Olson Customer Value Middle aged (41-55), 3-9 years on job, 3-9 years in town, savings account year annual purchases profit discounted net 1.3 rate 1 1000 200 153 153 2 1000 200 118 272 3 1000 200 91 363 4 1000 200 70 433 5 1000 200 53 487 6 1000 200 41 528 7 1000 200 31 560 8 1000 200 24 584 9 1000 200 18 603 10 1000 200 14 618 Korea Telecom: KM1 Data Mining David L. Olson Younger Customer Young (21-29), 0-2 years on job, 0-2 years in town, no savings account year annual purchases profit discounted net 1.3 1 300 60 46 46 2 360 72 43 89 3 432 86 39 128 4 518 104 36 164 5 622 124 34 198 6 746 149 31 229 7 896 179 29 257 8 1075 215 26 284 9 1290 258 24 308 10 1548 310 22 331 Korea Telecom: KM1 Data Mining David L. Olson Credit Card Management • Very profitable industry • Card surfing - pay old balance with new card • promotions typically generate 1000 responses, about 1% • in early 1990s, almost all mass-marketing • data mining improves (lift) Korea Telecom: KM1 Data Mining David L. Olson LIFT • LIFT = probability in class by sample divided by probability in class by population – if population probability is 20% and sample probability is 30%, LIFT = 0.3/0.2 = 1.5 • best lift not necessarily best need sufficient sample size as confidence increases, longer list but lower lift Korea Telecom: KM1 Data Mining David L. Olson Lift Example • Product to be promoted • Sampled over 10 identifiable segments of potential buying population – Profit $50 per item sold – Mailing cost $1 – Sorted by Estimated response rates Korea Telecom: KM1 Data Mining David L. Olson Lift Data Seg Rate 1 2 3 4 5 Rev Cost Profit $1.10 $0.75 $0.25 Seg Rate 6 7 8 Rev Cost Profit -$0.35 -$0.55 -$0.75 -$0.80 -$0.95 0.042 $2.10 $1 0.035 $1.75 $1 0.025 $1.25 $1 0.017 $0.85 $1 0.015 $0.75 $1 0.013 $0.65 $1 0.009 $0.45 $1 0.005 $0.25 $1 0.004 $0.20 $1 0.001 $0.05 $1 -$0.15 9 -$0.25 10 Korea Telecom: KM1 Data Mining David L. Olson Lift Chart LIFT 1.2 1 0.8 0.6 0.4 0.2 0 0 1 2 3 4 5 6 7 8 9 10 Segment Cum Response Random Korea Telecom: KM1 Data Mining Cumulative Proportion David L. Olson Profit Impact PROFIT 12 10 8 6 4 2 0 -2 -4 Segment 0 1 2 3 4 5 6 7 8 9 10 Cum Revenue Cum Cost Cum Profit Korea Telecom: KM1 Data Mining Dollars David L. Olson INSURANCE • Marketing, as retailing & banking • Special: – Farmers Insurance Group - underwriting system generating $ millions in higher revenues, lower claims • 7 databases, 35 million records – better understanding of market niches • lower rates on sports cars, increasing business Korea Telecom: KM1 Data Mining David L. Olson Insurance Fraud • Specialist criminals - multiple personas • InfoGlide specializes in fraud detection products – similarity search engine • link names, telephone numbers, streets, birthdays, variations • identify 7 times more fraud than exact-match systems Korea Telecom: KM1 Data Mining David L. Olson Insurance Fraud - Link Analysis claim type amount physician back 50000 Welby neck 80000 Frank arm 40000 Barnard neck 80000 Frank leg 30000 Schmidt multiple 120000 Heinrich neck 80000 Frank back 60000 Schwartz arm 30000 Templer internal 180000 Weiss Korea Telecom: KM1 Data Mining attorney McBeal Jones Fraser Jones Mason Feiffer Jones Nixon White Richards David L. Olson Insurance Fraud • Analytics’ NetMap for Claims – uses industry-wide database – creates data mart of internal, external data – unusual activity for specific chiropractors, attorneys • HNC Insurance Solutions – workers compensation fraud • VeriComp - predictive software (neural nets) Korea Telecom: KM1 Data Mining David L. Olson – saved Utah over $2 million TELECOMMUNICATIONS • Deregulation - widespread competition – churn • 1/3rd poor call quality, 1/2 poor equipment – wireless performance monitor tracking • reduced churn about 61%, $580,000/year – cellular fraud prevention – spot problems when cell phones begin to go bad Korea Telecom: KM1 Data Mining David L. Olson Telecommunications • Metapath’s Communications Enterprise Operating System – help identify telephone customer problems • dropped calls, mobility patterns, demographics • to target specific customers – reduce subscription fraud • $1.1 billion – reduce cloning fraud • cost $650 million in 1996 Korea Telecom: KM1 Data Mining David L. Olson Telecommunications • Churn Prophet, ChurnAlert – data mining to predict subscribers who cancel • Arbor/Mobile – set of products, including churn analysis Korea Telecom: KM1 Data Mining David L. Olson TELEMARKETING • MCI uses data marts to extract data on prospective customers – typically a 2 month program – 20% improvement in sales leads – multimillion investment in data marts & hardware – staff of 45 – trend spotting (which approaches specific customers like) David L. Olson Korea Telecom: KM1 Data Mining Telemarketing • Australian Tourist Commission – maintained database since 1992 • responses to travel inquiries on tours, hotels, airlines, travel agents, consumers • data mine to identify travel agents & consumers responding to various media • sales closure rate at 10% and up • lead lists faxed weekly to productive travel agents Korea Telecom: KM1 Data Mining David L. Olson Telemarketing • Segmentation – which customers respond to new promotions, to discounts, to new product offers – Determine who • to offer new service to • those most likely to commit fraud Korea Telecom: KM1 Data Mining David L. Olson Human Resource Management • Identify individuals liable to leave company without additional compensation or benefits • Firm may already know 20% use 80% of offered services – don’t know which 20% – data mining (business intelligence) can identify • Use most talented people in highest priority(or most profitable) business units Korea Telecom: KM1 Data Mining David L. Olson Human Resource Management • Downsizing – identify right people, treat them well – track key performance indicators – data on talents, company needs, competitor requirements • State of Mississippi’s MERLIN network – 30 databases (finance, payroll, personnel, capital projects) Korea – Cognos Impromptu system - 230 users Telecom: KM1 David L. Olson Data Mining CASINOS • Casino gaming one of richest data sets known • Harrah’s - incentive programs – about 8 million customers hold Total Gold cards, used whenever the customer spends money in the casino – comprehensive data collection • Trump’s Taj Card similar Korea Telecom: KM1 Data Mining David L. Olson Casinos • Bellagio & Mandelay Bay – strategy of luxury visits – child entertainment – change from old strategy - cheap food • Identify high rollers - cultivate – identify those to discourage from play – estimate lifetime value of players Korea Telecom: KM1 Data Mining David L. Olson ARTS • computerized box offices leads to high volumes of data • Identify potential consumers for shows • software to manage shows – similar to airline seating chart software Korea Telecom: KM1 Data Mining David L. Olson