Download A case on Cluster and Intimacy

Dynamic Web Personalizing Using Intimacy Theory
(A case on divide and conquer strategy)
Introduction
A major difference between on-line service and off-line service is the
quality of the service. On-line services lack of dynamic interface
between customer and employee or service provider. This is one of the
major reasons why some customers are still in favor of off-line service,
where they can have differentiated individual services face to face.
Traditional studies on web personalizing also do not consider the
intimacy level of a customer to a web service. This study suggests a web
personalizing method to satisfy customers in on-line with improved
services by personalizing web services dynamically.
There are desirable physical distances depending on level of intimacy
among persons: Intimate Distance, Personal Distance, Social Distance,
and Public Distance. A set of data on customer’s information is collected.
The data is clustered into four major groups using selected variables on
customer information such as membership period, number of visits each
week, amount of total purchase. An analysis shows a significant
difference in intimacy level among these clustered groups. Information is
inducted from these groups using a data mining tool, SEE5. This
information is used to describe the differences among these groups and
is used for personalizing customer services.
The method suggested in this study supports more intimate and
dynamic interface to customers. If a customer’s status such as
membership period or number of visits to the site changes, the method
dynamically changes his/her classification. The service is automatically
differentiated and dynamically adapted to the changes in customer
information.
Intimacy Theories
Researches have been done on personalization of web services.
(Mobasher and et al 2002; Kuo and et al 2001; Chen and et al 2004).
Personalization or customization is considered as one of the key success
factor in web services. This study suggests more dynamic and intimate
interface between web users and web services.
Researches have been developed on measuring inter-personal
distances between persons based on their intimacy levels (Ickinger 2001).
People surround themselves with a “bubble” of personal space (Edward
Hall 1966). Intimate distance is 0 to 1.5 feet. Personal distance is 1.5 to 4
feet. Social distance is 4 to 12 feet. Public distance is more than 12 feet.
Whenever people meet, they set up their own comfortable distances they
feel based on their intimacy level.
Suggested Methodology
For a greater customer satisfaction, this study suggests a web
personalization paradigm using intimacy theory, cluster analysis, and data
mining skills. Below shows research processes of this study.
Step
Step
Step
Step
1:
2:
3:
4:
Select variables affecting intimacy level of web users.
Cluster customer data using variables in step 1.
Measure intimacy level of each clustered group in step 2.
Find characteristics of each group in step 3 using data mining
tool.
A set of data on customer age, gender, occupation, a number of visits
to a site in a month, membership period, customer satisfaction to the site,
site reputation evaluated by user, total amount a customer spent, degree
of preference to this site, a personal information the site has for a
customer, and intimacy level was collected. An internet shopping mall
selling computer and computer related products was selected for the test.
A total of 230 data samples were collected. Below shows result from
clustering analysis.
Figure 1: Result from Cluster Analysis using Kohonen Networks
Using cluster analysis, the data is divided into 4 groups as in the
Figure 1 above. Table 1 below shows average intimacy level of each of
these 4 groups.
Test and Analysis
Cluster
Rate
Average
Frequency
(%)
Intimacy 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
Table 1: Average Intimacy Level of Each of 4 Groups
The table above shows the difference in average intimacy level of
each clusterd group. Clusterd group A shows minimum intimacy level and
cluster C shows maximum intimacy level.
-----------Read below after decision tree -------------------Using a rule induction system, SEE5, we derived rules from each of
these 4 clusterd groups to find characterists differentiating each group.
The result from data mining shows that reputation, number of visits to a
site per month, and membership period are three most important
variables differentiating these 4 groups.
Rules from decision tree in
Figure 2 below describe characteristics of each group.
Figure 2: Rules (Characteristics) for Each Group
Conclusions
With the information from Table 1, the intimacy level of each group
can be used to design personalized web for each user. Interface for a
group of users with low level of intimacy can be designed for users such
as new customers. The information this site provides can be more public.
A group of users with high level of intimacy can be loyal customers.
Personalized web services for these loyal customers can be more
customized with their private information such as their preference in
prices and product styles.
The information from data mining shows characteristics of each group.
This information can also be used to strategic decision making on
designing webs. If a customer’s reputation for a company is very low, the
chance of selling expensive good is low. More general goods with low
price are suitable for this customer. If a customer visits this site daily,
daily update for him/her site is desirable. A customer is dynamically
classified with his/her features. If a customer visits the site more
frequently than before or if a customer’s membership period reaches a
certain period, the system re-classifies him/her into another group. This
enables dynamic adjustment of classes for users with changes in their
status.