Data mining
Download
1 / 49

Data Mining - PowerPoint PPT Presentation


  • 147 Views
  • Updated On :

Data Mining. David L. Olson James & H.K. Stuart Professor in MIS University of Nebraska Lincoln. Definition. DATA MINING : exploration & analysis by automatic means of large quantities of data to discover actionable patterns & rules

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Data Mining' - jacob


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Data mining l.jpg

Data Mining

David L. Olson

James & H.K. Stuart Professor in MIS

University of Nebraska Lincoln

David L. Olson


Definition l.jpg
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

David L. Olson


Political data mining l.jpg
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

David L. Olson


Medical diagnosis l.jpg
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

David L. Olson


Mayo clinic l.jpg
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

David L. Olson


Retail outlets l.jpg
Retail Outlets

  • Bar coding & Scanning generate masses of data

    • customer service

    • inventory control

    • MICROMARKETING

    • CUSTOMER PROFITABILITY ANALYSIS

    • MARKET BASKET ANALYSIS

David L. Olson


Fingerhut l.jpg
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

David L. Olson


Fingerhut8 l.jpg
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

David L. Olson


Fingerhut9 l.jpg
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

David L. Olson


Data for segmentation l.jpg
Data for SEGMENTATION

cluster indices

subj age income marital grocery dine out savings

1001 53 80000 wife 180 90 30000

1002 48 120000 husband 120 110 20000

1003 32 90000 single 30 160 5000

1004 26 40000 wife 80 40 0

1005 51 90000 wife 110 90 20000

1006 59 150000 wife 160 120 30000

1007 43 120000 husband 140 110 10000

1008 38 160000 wife 80 130 15000

1009 35 70000 single 40 170 5000

1010 27 50000 wife 130 80 0

David L. Olson


Initial look at data l.jpg
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)

David L. Olson


Sorted by dine out l.jpg
Sorted by Dine Out

cluster indices

subject age income marital grocery dine out savings

1004 26 40000 wife 80 40 0

1010 27 50000 wife 130 80 0

1001 53 80000 wife 180 90 30000

1005 51 90000 wife 110 90 20000

1002 48 120000 husband 120 110 20000

1007 43 120000 husband 140 110 10000

1006 59 150000 wife 160 120 30000

1008 38 160000 wife 80 130 15000

1003 32 90000 single30 160 5000

1009 35 70000 single40 170 5000

David L. Olson


Analysis l.jpg
Analysis

  • Best indicators

    • marital status

    • groceries

  • Available

    • marital status might be easier to get

David L. Olson


Fingerhut14 l.jpg
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

David L. Olson


Banking l.jpg
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

David L. Olson


Credit scoring l.jpg
CREDIT SCORING

Bank Loan Applications

Age Income Assets Debts Want On-time

24 55557 27040 48191 1500 1

20 17152 11090 20455 400 1

20 85104 0 14361 4500 1

33 40921 91111 90076 2900 1

30 76183 101162 114601 1000 1

55 80149 511937 21923 1000 1

28 26169 47355 49341 3100 0

20 34843 0 21031 2100 1

20 52623 0 23054 15900 0

39 59006 195759 161750 600 1

David L. Olson


Characteristics of not on time l.jpg
Characteristics of Not On-time

Age Income Assets Debts Want On-time

28 26169 47355 49341 3100 0

20 52623 0 23054 15900 0

Here, DebtsexceedAssets

Age Young

IncomeLow

BETTER: Base on statistics, large sample

supplement data with other relevant variables

David L. Olson


Churn l.jpg
CHURN

  • Customer turnover

  • critical to:

    • telecommunications

    • banks

    • human resource management

    • retailers

David L. Olson


Identify characteristics of those who leave l.jpg
Identify characteristics of those who leave

Age Time-job Time-town min bal checking savings card loan

years months months $

27 12 12 549 x x

41 18 41 3259 x x x

28 9 15 286 x x

55 301 5 2854 x x x

43 18 18 1112 x x x

29 6 3 0 x

38 55 20 321 x x x

63 185 3 2175 x x x

26 15 15 386 x x

46 13 12 1187 x x x

37 32 25 1865 x x x

David L. Olson


Analysis20 l.jpg
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

David L. Olson


Correlation l.jpg
Correlation

Age Time Time min-bal check saving card loan

Job Town

Age 1.0 0.60.4-0.4 0.0 0.4 0.2 0.3

Job 1.0 0.9-0.6 0.1 0.60.9 -0.2

Town 1.0 -0.5 -0.1 0.30.50.4

Min-Bal 1.0 -0.2 0.30.6 -0.1

Check 1.0 0.5 0.2 0.2

Saving 1.0 0.90.3

Card 1.0 0.5

Loan 1.0

David L. Olson


Mortgage market l.jpg
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%

David L. Olson


Banking23 l.jpg
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

David L. Olson


Banking24 l.jpg
Banking

  • First Union

    • concentrated on contact-point

    • previously had very focused product groups, little coordination

    • Developed offers for customers

David L. Olson


Credit scoring25 l.jpg
CREDIT SCORING

  • Data warehouseincluding demand deposits, savings, loans, credit cards, insurance, annuities, retirement programs, securities underwriting, other

  • Statistical & mathematical models (regression) to predict repayment

David L. Olson


Customer relationship management crm l.jpg
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

David L. Olson


Customer value l.jpg
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

David L. Olson


Younger customer l.jpg
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

David L. Olson


Credit card management l.jpg
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)

David L. Olson


Slide30 l.jpg
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

David L. Olson


Lift example l.jpg
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

David L. Olson


Lift data l.jpg
Lift Data

David L. Olson


Lift chart l.jpg
Lift Chart

David L. Olson


Profit impact l.jpg
Profit Impact

David L. Olson


Insurance l.jpg
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

David L. Olson


Insurance fraud l.jpg
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

David L. Olson


Insurance fraud link analysis l.jpg
Insurance Fraud - Link Analysis

claim

type amount physician attorney

back 50000 Welby McBeal

neck 80000 Frank Jones

arm 40000 Barnard Fraser

neck 80000 Frank Jones

leg 30000 Schmidt Mason

multiple 120000 Heinrich Feiffer

neck 80000 Frank Jones

back 60000 Schwartz Nixon

arm 30000 Templer White

internal 180000 Weiss Richards

David L. Olson


Insurance fraud38 l.jpg
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)

    • saved Utah over $2 million

David L. Olson


Telecommunications l.jpg
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

David L. Olson


Telecommunications40 l.jpg
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

David L. Olson


Telecommunications41 l.jpg
Telecommunications

  • Churn Prophet, ChurnAlert

    • data mining to predict subscribers who cancel

  • Arbor/Mobile

    • set of products, including churn analysis

David L. Olson


Telemarketing l.jpg
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


Telemarketing43 l.jpg
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

David L. Olson


Telemarketing44 l.jpg
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

David L. Olson


Human resource management l.jpg
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

David L. Olson


Human resource management46 l.jpg
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)

    • Cognos Impromptu system - 230 users

David L. Olson


Casinos l.jpg
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

David L. Olson


Casinos48 l.jpg
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

David L. Olson


Slide49 l.jpg
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

David L. Olson


ad