1. How to Increase Customer Loyalty Using Cluster Analysis and Decision Tree Analysis of customer behavior and service design

2. What is the most important factor in CRM or servicing customers? 1. 2. Identify Needs of customers 3. Loyalty

3. Questions on brand loyalty Why is brand royalty so important to most companies?

4. Perspectives of Brand Loyalty Customer loyalty as customer’s commitment or attachment to a brand, store, manufacturer, service provider Or Entity based on favorable attitudes and behavioral responses, such as repeat purchases Ex) ‘Red Devil’ for national soccer team

5. Organizations and their loyal customers Airlines Credit card companies Internet stores Banks Car dealers Cell phone

6. Brand Loyalty as Behavior Rate of repurchasing [examples] Chicago Bulls, Cubs, Heinz, Crispy Cream donuts, Starbuck Proportion of purchase = the number of time the most frequently purchased brand total number of times the product category is purchased

7. 5 types of customer behaviors Undivided loyalty: A A A A A A A A A Occasional switcher: A A A B A A A C Switched loyalty: A A A A A B B B B B Divided loyalty: A A A B B B A A A B B B Indifference: A B C D A B C D A B C D

8. Churn rate Switch from one brand to other brand Customers RFM (key variables in market segmentation, also understanding loyal customer) - recency - frequency - monetary: average purchase size

9. Brand loyalty as attitude Why customer has loyalty on a brand? [example] bank, internet shop, airlines, credit cards Brand loyalty is a behavioral response to an attitude toward a brand

10. Loyalty versus inertia

11. Inertial loyalty Habitual Latent loyalty -strong commitment -low repeat purchase [example] SONY PS2, Nintendo

12. Factors that affect customer loyalty (Intimacy)

13. Attitudinal and behavioral components of loyalty

15. 15 Personalization of Service in the Web Using Intimacy Theory, Cluster Analysis, and Decision Tree : How to increase intimacy with customers

16. Introduction Face – to – face Object – medium - object –Digital interaction with Internet Setting Interpersonal Distance –Intimacy theory –Web interface development

17. Research Background Designer, Web Master based pages … –Personalization, categorization - User, customer based web pages Relations adjustment of interface by emplyee Frequent Customer Not Frequent Clerk

18. Proxemics People surround themselves with a “ bubble ” of personal space(Hall, 1966) Intimate distance: 0 ~ 1.5 feet(0.45 m) Personal distance: 1.5 ~ 4 feet(1.2 m) Social distance: 4 ~ 12 feet(3.6 m) Public distance: more than 12 feet person

19. Machine Learning Modeling Prediction(supervised learning) –Inputs  output –Neural networks, rule induction, regression Clustering(unsupervised learning) –Inputs  similarity –k-means Association –Input  output

20. Cluster Analysis of Customers

21. Cluster Distribution ClusterRatio(%)Count Intimacy Level A20.86342.41 B25.77423.02 C24.54403.85 D28.83472.87

22. Cluster A –if (Rep = good) And (period = 6 months) Or (rep = excellent) Or (Rep = good) And (visit = weekly) Rule Set for each cluster Cluster B if ( Rep = good) And (period = 1 year) Or (rep = good) And (visit = monthly) And ( period = 1year) Or ( rep = good) And ( visit = monthly) And ( period = 1month) Or ( rep = good) And ( visit = monthly) And ( period = 2years) Or ( rep = good) And ( visit = monthly) And ( period = 6months) Cluster C if ( Rep = good) And ( visit = 1 year) Or ( Rep = good) And ( visit = > 1 year Or ( Rep = good) And ( visit = monthly) And (period = > 2 years) Or ( Rep = good) And ( visit = daily) Cluster D if ( Rep = middle) And ( period = 1month) Or ( Rep = middle) And ( period = 2years) Or ( Rep = middle) And (period = >2years

23. Physical distance Analysis from Rules/Decision Tree object Psycholgical distance reputation No. of visits Membership period X Y

24. Dynamic Web Page Personalized Main Page Logged/ personalizing Web Page Type I For Cluster A Web Page Type II For Cluster B Web Page Type III For Cluster C Web Page Type IV For Cluster D