Using Cluster Analysis in Persona Development

can use the standard measurement scale tool to convert the
responses into the 1 to 7 rating. The questions asked were:
1: If you are a teacher, what course do you prefer to teach?
A: Courses discuss about facts
B: Courses discuss about theory
2: Which one do you think is a better compliment?
A: You are rational
B: You are emotional
3: When making a decision, which one is more important?
A: Take all factors into consideration
B: Focus on the feelings and viewpoints of people.
etc.
4) Online Survey
We notified each of the participants by email and telephone
the online survey web address and the purpose of the survey.
The participants were asked to fill in the online questionnaire
(see Figure 1). The results of the survey were imported
directly into our database.
We used the cluster analysis to group the users into subgroups
(vertical rows). We input the data (see Figure 2) into
statistic software, and then obtained the following output (see
Figure 3).
Since the algorithm of using complete linkage clustering
and Euclidean Distance is simple and quite efficient, we choose
them as the rule of distance measurement. The steps in our
cluster analysis calculations are:
Step 0: In the analysis process, each participant is first put
in separate cluster. This means there are 24 clusters initially
and we use C_1, C_2,..., C_24 to denote these clusters. The
distance between two clusters is defined to be the distance
between two participants they contain; that is dC_iC_j=dij. Let
t=1 be an index of the iterative process.
Figure 3. The Statistic Output of the User Clustering
Figure 1. The screenshot of the online survey
form 1
Figure 2. Data matrix based on users’ goals
B. Data Analysis - Clustering
We organized the survey results into a data matrix (see
Figure 2), in which the columns are the 27 dimensions and the
rows are records of participants. Please note that the personal
demographic information such as age, income, gender, job, etc
were not used in the cluster analysis, thus did not appear in this
data matrix.
Step 1: Then find the smallest distance between any two
clusters. Denote these closest clusters C_i and C_j
Step 2: Amalgamate clusters C_i and C_j to form a new
cluster denoted C_n+t.
Step 3: Define the distance between the new cluster C_n+t
and all remaining clusters C_k as follows:
dC_n+tC_k=min{dC_iC_k, dC_jC_k}.
Step 4: Add cluster C_n+t as a new cluster and remove
clusters C_i and C_j. Let t=t+1.
Step 5: Return to step 1 and continue until only one cluster
of size 24 remains.
The result, known as Tree Diagram (see Figure 3), can
clearly indicate which observations are joined together at what
step of the analysis.
Based on the outputs and after discussing with our client,
we obtained 2 clusters: (participants 1, 12, 24, 13, 18, 20, 3, 16,
23, 15) and (participants 2, 4, 9, 11, 6, 10, 8, 21, 17, 5, 7, 14, 19,
22) (see Figure 3). The distance level between the two clusters
is around 14. This 2-cluster user classification formed the basis
from which we created two typical user profiles.
Note: Figure 1, 4 and 5 are intentionally small and hide details to preserve
proprietary information in them