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CS548 Spring 2015 Association Rule Mining Showcase Showcasing work by Ting, Pan, and Chou on "Finding Ideal Menu Items Assortments: An Empirical Application.

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Presentation on theme: "CS548 Spring 2015 Association Rule Mining Showcase Showcasing work by Ting, Pan, and Chou on "Finding Ideal Menu Items Assortments: An Empirical Application."— Presentation transcript:

1 CS548 Spring 2015 Association Rule Mining Showcase Showcasing work by Ting, Pan, and Chou on "Finding Ideal Menu Items Assortments: An Empirical Application of Market Basket Analysis" by Cory Hayward and Marcus Moyses

2 Sources Ting, P., Pan, S., & Chou, S. (2010). Finding Ideal Menu Items Assortments: An Empirical Application of Market Basket Analysis. Cornell Hospitality Quarterly 51 (4), Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD International Conference on Management of Data, Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. 20th International Conference on Very Large Data Bases, Whitehorn, M. (2006). The parable of the beer and diapers. The Register, from

3 Definitions Association Rule Mining ●Method for discovering interesting relations between variables in large databases Applications ●Market Basket Analysis ●Intrusion Detection ●Web Usage Mining ●Medical Research ●Recommender Systems ●Fraud Detection

4 Market Basket Analysis Data Analysis and Data Mining technique used to discover co-occurrence relationships among activities performed by individuals or groups in a retail setting Has several marketing applications including ●promotional pricing ●product placement ●cross-selling ●up-selling

5 Classic Example: Beer and Diapers Popular cross-promotional example using Market Basket Analysis ●cross-promotional, or cross-selling, involves selling an additional product or service to an existing customer Through association analysis, a drugstore found that beer had a strong relationship with diapers Theories can then be created to understand why these relationships occur (fathers who buy diapers also buy beer for the weekend) and actions could be taken to improve product placement

6 Measurements Example Support: 2 / 5 = 40% Confidence:.4 /.4 = 100% Lift: 1 /.6 = Transaction 1: Rice, beef, bread Transaction 2: Beef, potato chips Transaction 3: Rice, bread Transaction 4: Beef, milk Transaction 5: Bread, milk Analysis for Rice and Bread: Taken from: Ting, Pan, Chou (2010)

7 Measures of Interestingness Support: probability that a randomly selected set of transactions from a database include items A and B Formula: P(A ∩ B) Confidence: probability that a randomly selected set of transactions will include B given that they include A Formula: P(B | A) = P(A ∩ B) / P(A) Lift: improvement in probability of B occurring in a transaction given that the transaction includes A Formula: P(B | A) / P(B) = P(A ∩ B) / (P(A)*P(B))

8 Showcase Application: "Finding Ideal Menu Items Assortments: An Empirical Application of Market Basket Analysis" by Ting, Pan, and Chou

9 The Data Set Taken from: Ting, Pan, Chou (2010)

10 The Problem Japanese-style restaurant in Taiwan with fixed-price meals (includes entrée + appetizer/soup + starch side dish) What are the ideal combinations of: ●entrée + appetizer/soup ●entrée + starch side dish ●entrée + drink ●entrée + dessert Why not find the ideal combination of: entrée + appetizer/soup + starch side dish + drink + dessert ?

11 Pair-Occurrence vs. Co-Occurrence Ideal association of menu items is determined by the confidence value of pairs of entrées and side dishes ●only sets of pairs are important, while individual items have less significance Certain limitations make pair-occurrence preferable for analysis: ●too many unimportant combinations with co-occurrence o in a restaurant, if all combinations were considered each would only account for a small percentage of sales ●limit on free choice for items (such as in a fixed-price restaurant) ●easier to memorize results from pair-occurrence

12 5 Step Process 1.Retrieve restaurant transaction data 2.Analyze data with Excel’s PivotTable a.calculate support and confidence for each item set 3.Identify ideal menu item assortments 4.Validate the findings on real-world transactions 5.Present conclusion and recommendations Taken from: Ting, Pan, Chou (2010)

13 Point of Sales Transactions Data set: data collected in 85 days => 3,727 transactions Combinations of 24 entrées, 7 appetizers or soups, 2 starch side dishes, 23 drinks and 17 desserts Taken from: Ting, Pan, Chou (2010)

14 Association Results Preferences Entrée and appetizer/soup: most expensive items Starch side dish: bread had a strong reputation among customers Drink: hot coffee had free refill; iced tea had the largest portion size Dessert: mango cheese was free Taken from: Ting, Pan, Chou (2010)

15 Recommendations Based on Results Overall success: 40/145 × 23.75% + 61/145 × 63.52% + 44/145 × 71.02% = 54.82% Taken from: Ting, Pan, Chou (2010)

16 Employee training: memorize ideal menu combinations for each entrée and focus on giving suggestions to undecided or customers without preference Test price demand elasticity: increase price of popular items to see if revenue also increases Suggestions for Restaurant Owner

17 Questions?


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