1. Market Basket Analysis & Neural Networks (chaps 7 & 11) Retail Checkout Data

2. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-2 MARKET BASKET ANALYSIS INPUT: list of purchases by purchaser –do not have names Identify purchase patterns –what items tend to be purchased together obvious: steak-potatoes; beer-pretzels –what items are purchased sequentially obvious: house-furniture; car-tires –what items tend to be purchased by season

3. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-3 Market Basket Analysis Categorize customer purchase behavior Identify actionable information –purchase profiles –profitability of each purchase profile –use for marketing layout or catalogs select products for promotion space allocation, product placement

4. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-4 Market Basket Analysis Affinity Positioning –coffee, coffee makers in close proximity Cross-Selling –cold medicines, tissue, orange juice –Monday Night Football kiosks on Monday p.m.

5. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-5 Possible Market Baskets Customer 1: beer, pretzels, potato chips, aspirin Customer 2: diapers, baby lotion, grapefruit juice, baby food, milk Customer 3: soda, potato chips, milk Customer 4: soup, beer, milk, ice cream Customer 5: soda, coffee, milk, bread Customer 6: beer, potato chips

6. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-6 Co-occurrence Table BeerPot.MilkDiap.Soda Chips Beer32100 Pot. Chips23101 Milk12412 Diapers00110 Soda01202 beer & potato chips - makes sensemilk & soda - probably noise

7. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-7 Jaccard Coefficient Ratio of cases together over total cases BeerPotChipMilkDiapers PotChip0.333 Milk0.143 Diapers000.200 Soda00.2000.3330

8. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-8 Market Basket Analysis Steve Schmidt - president of ACNielsen- US Market Basket Benefits –selection of promotions, merchandising strategy sensitive to price: Italian entrees, pizza, pies, Oriental entrees, orange juice –uncover consumer spending patterns correlations: orange juice & waffles –joint promotional opportunities

9. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-9 Market Basket Analysis Retail outlets Telecommunications Banks Insurance –link analysis for fraud Medical –symptom analysis

10. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-10 Market Basket Analysis Chain Store Age Executive (1995) 1) Associate products by category 2) What % of each category was in each market basket Customers shop on personal needs, not on product groupings

11. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-11 Purchase Profiles Beauty consciousKids’ playSmoker Health consciousCasual drinkerPet lover Sports consciousNew familyGardener Men’s image consciousCasual readerHobbyist Convenience foodSentimentalIllness (OTC) Home handymanAutomotiveIllness (prescription) TV/stereo enthusiastPhotographerPersonal care Seasonal/traditionalHomemakerMen’s fashion Student/home officeHome ComfortKid’s fashion Fashion footwearWomen’s fashion

12. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-12 Purchase Profiles Beauty conscious –cotton balls –hair dye –cologne –nail polish

13. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-13 Purchase Profile Use Each profile has an average profit per basket Kids’ fashion$15.24 Push these Men’s fashion$13.41 Push these …. Smoker$2.88Don’t push these Student/home office$2.55Don’t push these

14. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-14 Market Basket Analysis LIMITATIONS –takes over 18 months to implement –market basket analysis only identifies hypotheses, which need to be tested neural network, regression, decision tree analyses –measurement of impact needed –difficult to identify product groupings –complexity grows exponentially

15. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-15 Market Basket Analysis BENEFITS: –simple computations –can be undirected (don’t have to have hypotheses before analysis) –different data forms can be analyzed

16. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-16 Market Basket Software Market Basket Analysis is highly unstructured Most popular data mining software doesn’t support –Clementine does Specialty software market for this specific purpose –DataSage Customer Analysis –Xaffinity

17. Neural Networks Automatic Model Building (Machine Learning) Artificial Intelligence

18. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-18 High-Growth Product Used for classifying data –target customers –bank loan approval –hiring –stock purchase –trading electricity –DATA MINING Used for prediction

19. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-19 Description Use network of connected nodes (in layers) Network connects input, output (categorical) –inputs like independent variable values in regression –outputs: {buy, don’t} {paid, didn’t} {red, green, blue, purple} {character recognition - alphabetic characters}

20. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-20 Perceptron y Basic building block y Comprised of Synaptic Weights and Neuron y Weights scale the input values y Combination of weights and transfer function F(x) transform inputs to needed output O y Trained by changing weights until desired output is achieved

21. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-21 Network InputHiddenOutput LayerLayersLayer Good Bad

22. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-22 Operation Randomly generate weights on model –based on brain neurons input electrical charge transformed by neuron passed on to another neuron –weight input values, pass on to next layer –predict which of the categorical output is true Measure fit –fine tune around best fit

23. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-23 Operation Useful for PATTERN RECOGNITION Can sometimes substitute for REGRESSION –works better than regression if relationships nonlinear –MAJOR RELATIVE ADVANTAGE OF NEURAL NETWORKS: YOU DON’T HAVE TO UNDERSTAND THE MODEL

24. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-24 Neural Network Testing Usually train on part of available data –package tries weights until it successfully categorizes a selected proportion of the training data When trained, test model on part of data –if given proportion successfully categorized, quits –if not, works some more to get better fit The “model” is internal to the package Model can be applied to new data

25. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-25 Business Application Best in classifying data mortgage underwritingasset allocation bond ratingfraud prevention commodity trading Predicting interest rate, inventory firm failurebank failure takeover vulnerabilitystock price corporate merger profitability

26. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-26 Neural Network Process 1.Collect data 2.Separate into training, test sets 3.Transform data to appropriate units Categorical works better, but not necessary 4.Select, train, & test the network Can set number of hidden layers Can set number of nodes per layer A number of algorithmic options 5.Apply (need to use system on which built)

27. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-27 Marketing Applications Direct marketing –database of prospective customers age, sex, income, occupation, education, location predict positive response to mail solicitations THIS IS HOW DATA MINING CAN BE USED IN MICROMARKETING

28. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-28 Neural Nets to Predict Bankruptcy Wilson & Sharda (1994) Monitor firm financial performance Useful to identify internal problems, investment evaluation, auditing Predict bankruptcy - multivariate discriminant analysis of financial ratios (develop formula of weights over independent variables) Neural network - inputs were 5 financial ratios - data from Moody’s Industrial Manuals (129 firms, 1975-1982; 65 went bankrupt) Tested against discriminant analysis Neural network significantly better

29. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-29 CASE: Support CRM Drew et al. (2001), Journal of Service Research Identify customers to target Customer hazard function: –Likelihood of leaving to a competitor (CHURN) Gain in Lifetime Value (GLTV) –NPV: weight EV by prob{staying} –GLTV: quantified potential financial effects of company actions to retain customers

30. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-30 Systems A great many products general NN products $59 to $2,000@BrainBrainMakerDiscover-It components DATA MINING along with megadatabasesother products specialty products construction bidding, stock trading, electricity trading

31. McGraw-Hill/Irwin©2007 The McGraw-Hill Companies, Inc. All rights reserved 11-31 Potential Value THEY BUILD THEMSELVES –humans pick the data, variables, set test limits CAN DEAL WITH FAST-MOVING SITUATIONS –stock market CAN DEAL WITH MASSIVE DATA –data mining Problem - speed unpredictable