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

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Presentation on theme: "How to Increase Customer Loyalty Using Cluster Analysis and Decision Tree Analysis of customer behavior and service design."— Presentation transcript:

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? 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

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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 A B C D

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


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