how to increase customer loyalty using cluster analysis and decision tree analysis of customer...
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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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What is the most important factor in CRM or servicing
customers?1.
2. Identify Needs of customers
3. Loyalty
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Questions on brand loyalty
• Why is brand royalty so important to most companies?
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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
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Organizations and their loyal customers
• Airlines• Credit card companies• Internet stores• Banks• Car dealers• Cell phone
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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
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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
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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
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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
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Loyalty versus inertia
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Inertial loyalty
• Habitual
• Latent loyalty -strong commitment -low repeat purchase [example] SONY PS2, Nintendo
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Factors that affect customer loyalty
(Intimacy)
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Attitudinal and behavioral components of loyalty
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15
Personalization of Service in the Web Using Intimacy Theory,
Cluster Analysis, and Decision Tree
: How to increase intimacy with customers
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Introduction
• Face – to – face• Object – medium - object
– Digital interaction with Internet
• Setting Interpersonal Distance– Intimacy theory– Web interface development
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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
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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
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Machine Learning Modeling
• Prediction(supervised learning)– Inputs output– Neural networks, rule induction,
regression
• Clustering(unsupervised learning)– Inputs similarity– k-means
• Association– Input output
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Cluster Analysis of Customers
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Cluster Distribution
Cluster
Ratio(%)
CountIntimacy
Level
A 20.86 34 2.41
B 25.77 42 3.02
C 24.54 40 3.85
D 28.83 47 2.87
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• 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
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• Physical distance
Analysis from Rules/Decision Tree
object object
Psycholgical distance
reputationreputation No. of visitsNo. of visits
Membership periodMembership period
X
Y
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Dynamic Web Page Personalized
Main Page
Logged/personalizing
Web Page Type IFor Cluster A
Web Page Type IIFor Cluster B
Web Page Type IIIFor Cluster C
Web Page Type IVFor Cluster D