Intelligent Decision Support Systems

Transcription

1 Intelligent Decision Support Systems (Case Study 6 Market Basket Analysis ) Anna Gatzioura, Miquel Sànchez i Marrè Course 2017/2018

2 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

3 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

4 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

5 Market Basket Analysis (4) Retail outlets Telecommunications Banks Insurance link analysis for fraud Medical symptom analysis

6 Market Basket Analysis (5) 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

7 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

8 Co-occurrence Table Beer Pot. Milk Diap. Soda Chips Beer Pot. Chips Milk Diapers Soda beer & potato chips - makes sense milk & soda - probably noise

9 Purchase Profiles (1) beauty conscious kids play convenience food health conscious pet lover women s fashion sports conscious gardener kid s fashion smoker automotive hobbyist casual drinker photographer student/home office new family tv/stereo enthusiast illness (prescription) illness over-the-counter seasonal/traditional personal care casual reader homemaker home handyman home comfort men s image conscious fashion footwear sentimental men s fashion

10 Purchase Profiles (2) Beauty conscious cotton balls hair dye cologne nail polish

11 Purchase Profiles (3) Each profile has an average profit per basket Kids fashion $15.24 push these Men s fashion $ Smoker $2.88 don t push Student/home office $2.55 these

12 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.

13 Market Basket Analysis (MBA) (7) Search for meaningful associations and relationships in customer 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, bbbbbbbbbb {bbbbbbbb}

14 Market Basket Analysis (8) A mathematic data modeling technique used for the identification of patterns and relationships between selected 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

15 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

16 Market Basket Market basket: a set of items bought together by one customer in a single visit to a store Let II = ii 1, ii 2,, ii nn be the set of available items TT = tt 1, tt 2,, tt mm is the set of recorded transactions Each transaction consists of a subset of items from I, tt kk = tt aa, tt bb,

17 Transactional Matrix MBA can be represented with a binary nn mm table, the transactional matrix rr iiii equals 1 if the j-th item is present in the i-th transaction and 0 otherwise Transactions Items i 1 i 2 i m t t t i 0 1 t n 1

18 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

19 Association Rules (ARs) Aim to discover interesting hidden patterns & frequent associations 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 cooccurrence of other items

20 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 occur together more often than expected Lift (interest) Pr XX/YY EE(Pr XX YY )

21 Association Rules Mining ARs mining refers to identifying all rules from a set with ssssssssssssss mmmmmmmmmmmm & cccccccccccccccccccc mmmmmmmmmmmmmm 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

22 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

23 Miquel Sànchez i Marrè, KEMLG, 2016 Anna Gatzioura, Miquel Sànchez i Marrè 23