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

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

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

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

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

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

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

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

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

Loyalty versus inertia

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

Factors that affect customer loyalty (Intimacy)

Attitudinal and behavioral components of loyalty

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

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

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

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

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

Cluster Analysis of Customers

Cluster Distribution ClusterRatio(%)Count Intimacy Level A B C D

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

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

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