Market Basket Analysis & Neural Networks (chaps 7 & 11)

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

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. 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. 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. 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. Co-occurrence Table Beer Pot. Milk Diap. Soda Chips Beer 3 2 1 0 0 Pot. Chips 2 3 1 0 1 Milk 1 2 4 1 2 Diapers 0 0 1 1 0 Soda 0 1 2 0 2 beer & potato chips - makes sense milk & soda - probably noise

7. Jaccard CoefficientRatio of cases together over total cases

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. Market Basket Analysis • Retail outlets • Telecommunications • Banks • Insurance • link analysis for fraud • Medical • symptom analysis

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. Purchase Profiles

12. Purchase Profiles • Beauty conscious • cotton balls • hair dye • cologne • nail polish

13. Purchase Profile Use • Each profile has an average profit per basket

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. Market Basket Analysis • BENEFITS: • simple computations • can be undirected (don’t have to have hypotheses before analysis) • different data forms can be analyzed

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. High-Growth Product • Used for classifying data • target customers • bank loan approval • hiring • stock purchase • trading electricity • DATA MINING • Used for prediction

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. Perceptron • Basic building block • Comprised of Synaptic Weights and Neuron • Weights scale the input values • Combination of weights and transfer function F(x) transform inputs to needed output O • Trained by changing weights until desired output is achieved

21. Network Input Hidden Output Layer Layers Layer Good Bad

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. 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. 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. Business Application • Best in classifying data mortgage underwriting asset allocation bond rating fraud prevention commodity trading • Predicting interest rate, inventory firm failure bank failure takeover vulnerability stock price corporate merger profitability

26. Neural Network Process • Collect data • Separate into training, test sets • Transform data to appropriate units • Categorical works better, but not necessary • Select, train, & test the network • Can set number of hidden layers • Can set number of nodes per layer • A number of algorithmic options • Apply (need to use system on which built)

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. 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. CASE: Support CRMDrew 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. Systems A great many products • general NN products $59 to $2,000 @Brain BrainMaker Discover-It • components DATA MINING along with megadatabases other products • specialty products construction bidding, stock trading, electricity trading

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