Market Basket

Analysis & Neural

Networks(chaps 7 & 11)

Retail Checkout

Data

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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

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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

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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.

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11-5

Possible Market BasketsCustomer 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

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11-6

Co-occurrence TableBeer 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 2beer & potato chips - makes sense milk & soda - probably noise

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11-7

Jaccard CoefficientRatio of cases together over total cases

Beer PotChip Milk Diapers

PotChip 0.333

Milk 0.143 0.143

Diapers 0 0 0.200

Soda 0 0.200 0.333 0

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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

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11-9

Market Basket Analysis• Retail outlets

• Telecommunications

• Banks

• Insurance– link analysis for fraud

• Medical– symptom analysis

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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

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11-11

Purchase ProfilesBeauty conscious Kids’ play Smoker

Health conscious Casual drinker Pet lover

Sports conscious New family Gardener

Men’s image conscious Casual reader Hobbyist

Convenience food Sentimental Illness (OTC)

Home handyman Automotive Illness (prescription)

TV/stereo enthusiast Photographer Personal care

Seasonal/traditional Homemaker Men’s fashion

Student/home office Home Comfort Kid’s fashion

Fashion footwear Women’s fashion

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11-12

Purchase Profiles

• Beauty conscious– cotton balls– hair dye– cologne– nail polish

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11-13

Purchase Profile Use• Each profile has an average profit per

basketKids’ fashion $15.24 Push these

Men’s fashion $13.41 Push these

….

Smoker $2.88 Don’t push these

Student/home office $2.55 Don’t push these

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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

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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

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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

Neural Networks

Automatic Model Building

(Machine Learning)

Artificial Intelligence

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11-18

High-Growth Product• Used for classifying data

– target customers– bank loan approval– hiring– stock purchase– trading electricity– DATA MINING

• Used for prediction

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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}

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11-20

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

F(x)

Bias

I1

I2

In

I3 XO

W1

W2

W3

Wn

Inputs SynapticWeights Neuron

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11-21

Network

Input Hidden Output

Layer Layers Layer

Good

Bad

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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

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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

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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

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11-25

Business Application• Best in classifying data

mortgage underwriting asset allocation

bond rating fraud prevention

commodity trading

• Predicting interest rate, inventoryfirm failure bank failure

takeover vulnerability stock price

corporate merger profitability

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11-26

Neural Network Process1. 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)

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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

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11-28

Neural Nets to Predict Bankruptcy

Wilson & Sharda (1994)

Monitor firm financial performanceUseful to identify internal problems, investment evaluation, auditingPredict 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 analysisNeural network significantly better

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11-29

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

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11-30

SystemsA great many products

• general NN products$59 to $2,000 @Brain BrainMaker Discover-It

• componentsDATA MINING along with megadatabases other products

• specialty productsconstruction bidding, stock trading, electricity trading

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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