Cases on Association Rules

Analysis of Customer Behavior and Service

Modeling

CASE 1:

Icecream -Target Brands & Variables

1. Icecream -Target Brands & Variables1. Icecream -Target Brands & Variables

Brands

Gusttimo

Baskin Robbins

Natture

Haagen-dazs

Etc.

Brands Varaibles

Taste

Price

Mood

Distance

Brand Image

Service

Rumor

Variables

• Why do you visit there?• ①.Taste ②.Price ③.Mood ④ .Distance

⑤. Image ⑥. Service ⑦. Rumor

2. Questionnaires2. Questionnaires

• Where do you visit the most for ice cream?• ①.Gusttimo ②.Baskin Robbins ③.Natture ④.H

aagen-daz ⑤. Red Mango ⑥. Palazzo ⑦.etc

• Choose the number below according to your value level when you choose to eat ice cream

3. Questionnaire Method3. Questionnaire Method

Classification

Not important-------------Most important

Taste (1) (2) (3) (4) (5) (6) (7)Price (1) (2) (3) (4) (5) (6) (7)Mood (1) (2) (3) (4) (5) (6) (7)

Distance (1) (2) (3) (4) (5) (6) (7)Image (1) (2) (3) (4) (5) (6) (7)Service (1) (2) (3) (4) (5) (6) (7)Rumor (1) (2) (3) (4) (5) (6) (7)

4. Average value on each variables4. Average value on each variables

0

1

2

3

4

5

6

7

Average

5. Derive rules by using Magnum Opus 5. Derive rules by using Magnum Opus

Mood -> Red Mango [Coverage=0.025 (4); Support=0.019 (3); Strength=0.750; Lift=4.11; Leverage=0.0143 (2.3); p=0.0195]

Red Mango -> Mood [Coverage=0.182 (29); Support=0.019 (3); Strength=0.103; Lift=4.11; Leverage=0.0143 (2.3); p=0.0195]

Baskin -> distance [Coverage=0.390 (62); Support=0.145 (23); Strength=0.371; Lift=2.03; Leverage=0.0735 (11.7); p=1.28E-006]

distance -> Baskin [Coverage=0.182 (29); Support=0.145 (23); Strength=0.793; Lift=2.03; Leverage=0.0735 (11.7); p=1.28E-006]

Gustimo -> Taste [Coverage=0.258 (41); Support=0.233 (37); Strength=0.902; Lift=1.56; Leverage=0.0835 (13.3); p=3.18E-007]

Taste -> Gustimo [Coverage=0.579 (92); Support=0.233 (37); Strength=0.402; Lift=1.56; Leverage=0.0835 (13.3); p=3.18E-007]

CASE 2: Lotte World

Contents 1

1.Problem Definition & constraints

2.Phases

3.Alternatives 1. Using RFID

2. Exit Poll

4. Result Example

5. Effectiveness & application

Problem Definition & Phases 2

Problem Definition

- Attraction locating : avoid conflict between target segments - inefficient customer route Lower customer satisfaction

Constraints

- Attraction re-location : Impossible

1. Data Mining2. Using Model3. Finding efficient customer route & Promotion strategy4. Max (Customer satisfaction)

Phases

HOW – Association Rules 3

Association RulesIndicating & Choice of Alternatives

1. Using RFID

- Clear understanding about customer’s moving route, location, time - Technical Difficulty, and Heavy cost

2. Exit Poll

- Easy application - Light cost - Limit in data : quantity, quality

Survey : Let customers to check all facilities they rode

Make Market-Basket

Association Rules Analysis : based on Market-Basket

Survey Example 4

For Lotteworld Adventure For Magic IslandAdventure of Sinbad ( ) Atlantis ( )Spain Pirate Ship ( ) Gyro Drop ( )Frog Hooper ( ) Gyro Swing ( )Marry-go-round ( ) Bunge Drop ( )Crazy Bumper Car ( ) Comet Express ( )Kids Bumper car ( ) Marry-go-Round 3 ( )Ball Battle ( ) Sky Surfing ( )Flume Ride ( ) Bumper car ( )Giant Roop ( ) Ghost House ( )Marry-go-round 2 ( ) Castle Music Show ( )Illusion Odyssey ( ) Fantasy Dream ( )Out-Law ( ) Automoblile Racing ( )4D Movies ( ) Kingdom of Children ( )Magic Theater ( ) EureKa ( )Puppet Theater ( ) Geneve Excursion Ship ( )French Revolution ( ) Lake Boat ( )Jungle Adventure ( )World Monorail ( )Rage of Parao ( )Balloon Travel ( )dynamic Theater ( )Animal Theater ( )Garden Stage Concert ( )Street Concert ( )Parade ( )

※ Mark Attractions that you rode today

Basic information

1.gender

2.age

3.Marriage

4.Children

5.Age of Children

6.Place of

Residence

7.others

Result Example & Applications 5

1. Student (age 12~18)

- P(Gyro drop Atlantis) : 70%

2. Parents with Little children

- P(Marry-go-Round Kids Bumpercar) : 60%

We will be able to check : Who is using what kind of facilities

Finding Effective Route

Making Promotion StrategyLocate extra store at specific

customer’s route

Result Example

Applications

CASE 3:Gmart case

Data Collection

: Gmart located at Yongsan

- Customer data POS data from daily transaction

- Location Chung-pa dong, Yongsa,, Seoul

- Period 2005. 9. 1 ~ 2005. 12. 7.

- Number of data 1,334 cases

- Contents in the data POS fields names(date, time, POS manager, receipt number, product name , quantity, amount, classification)

- G mart system screen -

Data

Cookies, MilkYogurt ,Frozen foolBear, Cookies, CoffeeMilk, Water. . ..

.

Extracted Association RuleWateris associated with Cookieswith strength = 0.500coverage = 0.002: 2 cases satisfy the LHSsupport = 0.001: 1 case satisfies both the LHS and the RHSlift 500.00: the strength is 500.00 times greater than the strength if there

were no associationleverage = 0.0010: the support is 0.0010 (1.0 case) greater than if there

were no association

Wateris associated with Coffee and Newspaperwith strength = 0.500coverage = 0.002: 2 cases satisfy the LHSsupport = 0.001: 1 case satisfies both the LHS and the RHSlift 500.00: the strength is 500.00 times greater than the strength if there

were no associationleverage = 0.0010: the support is 0.0010 (1.0 case) greater than if there

were no association