https://kemlg.upc.edu

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Intelligent Decision SupportSystems

(Case Study 5 – Market Basket Analysis )

Anna Gatzioura, Miquel Sànchez i Marrè

Course 2015/2016

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Market Basket Analysis (1)

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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Market Basket Analysis (2)

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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Market Basket Analysis (3)

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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Market Basket Analysis (4)

Retail outlets Telecommunications Banks Insurance

link analysis for fraud Medical

symptom analysis

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Market Basket Analysis (5)

Chain Store Age Executive (1995)

1) Associate products by category2) what % of each category was in each market basket?

Customers shop on personal needs, not on product groupings

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Possible Market Baskets

Customer 1: beer, pretzels, potato chips, aspirinCustomer 2: diapers, baby lotion, grapefruit juice, baby food, milkCustomer 3: soda, potato chips, milkCustomer 4: soup, beer, milk, ice creamCustomer 5: soda, coffee, milk, breadCustomer 6: beer, potato chips

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Co-occurrence Table

Beer Pot. Milk Diap. SodaChips

Beer 3 2 1 0 0Pot. Chips 2 3 1 0 1Milk 1 2 4 1 2Diapers 0 0 1 1 0Soda 0 1 2 0 2

beer & potato chips - makes sensemilk & soda - probably noise

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Purchase Profiles (1)

beauty conscious kids’ play convenience foodhealth conscious pet lover women’s fashionsports conscious gardener kid’s fashionsmoker automotive hobbyistcasual drinker photographer student/home officenew family tv/stereo enthusiast illness (prescription)illness over-the-counter seasonal/traditional personal carecasual reader homemakerhome handyman home comfortmen’s image conscious fashion footwearsentimental men’s fashion

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Purchase Profiles (2)

Beauty conscious cotton balls hair dye cologne nail polish

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Purchase Profiles (3)

Each profile has an average profit per basket Kids’ fashion $15.24 push these Men’s fashion $13.41 …. Smoker $2.88 don’t push Student/home office $2.55 these

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Market Basket Analysis (6)

Affinity Positioning coffee, coffee makers in close proximity

Cross-Selling cold medicines, kleenex, orange juice Monday Night Football kiosks on Monday p.m.

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Market Basket Analysis (MBA) (7)

Search for meaningful associations and relationships incustomer purchase data

Discover patterns behind the composition of market baskets Mainly solved by applying ARs mining: 𝑎𝑎, 𝑏𝑏, 𝑐𝑐 , a rule of 𝑎𝑎, 𝑏𝑏 → {𝑐𝑐} is interpreted as “if a customer

has bought 𝑎𝑎, 𝑏𝑏 he probably will also purchase {𝑐𝑐}” Example: 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚, 𝑏𝑏𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏 → {𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏}

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Market Basket Analysis (8)

A mathematic data modeling technique used for theidentification of patterns and relationships betweenselected items (or groups of items).

Used to Analyze customer preferences that construct market

baskets through time Observe and evaluate customer buying habits Identify the rationale behind the joint selection of items –

product groups

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Market Basket Analysis (9)

Uncover correlations between items Not only to predict whether a user will like an item or not Given a user has already placed some items in his

basket: Provide more insight into customer behavior Recommend the most appropriate items to fill this basket

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Market Basket

Market basket: a set of items bought together by onecustomer in a single visit to a store

Let 𝐼𝐼 = 𝑚𝑚1, 𝑚𝑚2, … , 𝑚𝑚𝑛𝑛 be the set of available items 𝑇𝑇 = 𝑡𝑡1,𝑡𝑡2, … , 𝑡𝑡𝑚𝑚 is the set of recorded transactions Each transaction consists of a subset of items from

I, 𝑡𝑡𝑘𝑘 = 𝑡𝑡𝑎𝑎, 𝑡𝑡𝑏𝑏, …

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Transactional Matrix

MBA can be represented with a binary 𝑛𝑛 × 𝑚𝑚 table, thetransactional matrix

𝑏𝑏𝑖𝑖𝑖𝑖 equals 1 if the j-th item is present in the i-th transactionand 0 otherwise

Transactions Items

i1 i2 im

t1 1 1

t2 0 1

ti 0 1

tn 1

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Market Basket Analysis (10)

MBA can be used as a powerful tool for: Cross-selling, market research and strategic marketing

activities Additional sales support through items’ placement in

physical stores Customer behavior analysis, decision support, credit

evaluation, privacy issues

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Association Rules (ARs)

Aim to discover interesting hidden patterns & frequentassociations from large data sets

Extract rules and relations from data “If-then” statements

X and Y where 𝑋𝑋 ∩ 𝑌𝑌 = ∅ 𝑋𝑋 → 𝑌𝑌, “if X – then probably Y”

To predict the existence of an item based on the co-occurrence of other items

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Association Rules

Measures of rule importance: How often the rule is relevant (relative number of

transactions containing both X and Y) Support, 𝑠𝑠 = Pr 𝑋𝑋,𝑌𝑌 = Pr 𝑋𝑋 ∪ 𝑌𝑌

How possible is X given that Y has occurred Confidence, c = Pr 𝑋𝑋/𝑌𝑌 = Pr 𝑋𝑋∩𝑌𝑌

Pr 𝑋𝑋

If X and Y are statistically independent, do they occurtogether more often than expected Lift (interest) Pr 𝑋𝑋/𝑌𝑌

𝐸𝐸(Pr 𝑋𝑋𝑌𝑌 )

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Association Rules Mining

ARs’ mining refers to identifying all rules from a set with 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑏𝑏𝑡𝑡 ≥ 𝑚𝑚𝑚𝑚𝑛𝑛𝑠𝑠𝑠𝑠𝑠𝑠 & 𝑐𝑐𝑠𝑠𝑛𝑛𝑐𝑐𝑚𝑚𝑏𝑏𝑏𝑏𝑛𝑛𝑐𝑐𝑏𝑏 ≥ 𝑚𝑚𝑚𝑚𝑛𝑛𝑐𝑐𝑠𝑠𝑛𝑛𝑐𝑐

A collection of k items is referred to as a k-itemset An itemset with support greater than a minimum support

threshold is referred to as frequent itemset

Rules’ discovery in two phases Frequent itemsets’ generation Rules’ extraction

Apriori algorithm

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Association Rules

Pros Simple rules Used in cases of unequal items’ distribution

Cons Computational cost Limited performance with large datasets Difficulty of evaluation Deceptive rules

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Anna Gatzioura, Miquel Sànchez i Marrè

http://kemlg.upc.edu/

© Miquel Sànchez i Marrè, KEMLG, 2016