Using Cluster Analysis in Persona Development

Using Cluster Analysis in Persona Development
Nan Tu
Research Center for Modern Logistics
Graduate School at Shenzhen, Tsinghua University
Shenzhen, 518055, P.R. China
dr.nan.tu@gmail.com
Xiao Dong / Pei-Luen Patrick Rau / Tao Zhang
Department of Industrial Engineering,
Tsinghua University,
Beijing, 100084, P.R.China
Abstract—Personas are user models that represent the user
characteristics. In this paper we describe a Persona creation
process which combines the quantitative method such as cluster
analysis with qualitative method such as observation and
interview to produce convincing and representative Personas.
We illustrate the Personas creation process through a case study.
We use cluster analysis to group the users by their similarities in
goals and decision-making preference.
Keywords- Personas, user profiles, cluster analysis, user
observation and interview
I. INTRODUCTION
Persona, as a concept, has its root in marketing. Copper
proposed that the Persona as an interactive design technique to
be used in product design [2]. For the past few years, Personas
have gained enthusiasm from both academic and practitioner
community.
Most of the paper and field reports describe the Personas
creation using qualitative methods such as ethnographic studies
and user observations. Grudin & Pruitt think that finding the
representative user is the key to Personas creation [3]. Sinha
shows that qualitative method can be used to identify important
underlying groups of information needs [7].
This paper illustrates the Persona creation process through a
case study. We show that the qualitative and quantitative
methods can be combined to create Personas.
II. RELATED RESEARCH
We categorize the Persona research paper into two
domains: (1) Personas definition and its creation; (2) statistical
methods: clustering analysis.
A. Personas Definition and Its Creation
Goodwin defines Personas as: “User models, or Personas,
are fictional, detailed archetypical characters that represent
distinct groups of behaviors, goals and motivations observed
and identified during the research phase.”[1] By this definition,
Personas have the following characteristics:
• A persona is a detailed user model that represents
archetypical users. In other words, a persona has the
characters of a group of similar users. A persona is
not a real person but a fictitious person.
• A Persona is defined by his or her goals. Cooper
explains that the Personas are used in goal-directed
design [2]. He further categorize the Personas goals
as personal goals, corporate goals, practical goals and
false goals. Goals are different than the tasks in that a
goal is an end condition while a task is an
intermediate process that is necessary to accomplish
goals.
• A Persona is created by analyzing the real users’
goals, behaviors and motivations. The real users’
data may come from marketing research, user study
which includes interviews, questionnaires,
observations, and ethnographic studies, etc.
Ethnography studies the users’ behaviors in a natural
setting (field observations). While the other user
study methods are used mostly in the laboratory
environment.
Sinha pointed out that the current Persona development
processes emphasize precision (building detailed descriptions),
but not accuracy (identifying representative users) [7]. Upon
reviewing the current state-of-the-art researches and practices,
we noticed that the Personas creation process is viewed as a
qualitative method by observing, interviewing and abstracting.
However, the qualitative methods have met some criticism
such as: the created Personas may not represent the typical
users, the Personas are too subjective and different creators will
have different Personas.
To address the above criticisms, we propose an approach
that combines the quantitative and qualitative methods in the
Personas creation. We will use a case study to illustrate this
approach.
B. Statistical methods: Principal Components Analysis and
Cluster Analysiss
Marketing data and ethnography studies data may consist of
large quantity of multidimensional data sets. The statistical
method Principal Components Analysis (PCA) can be used to
reduce the dimensionality of the data while Cluster Analysis
(CA) can group the data based on their similarities.
Principal Components Analysis (PCA) is used to reduce the
dimensionality of large datasets by identifying important
underlying factors. Sinha shows how it can be used in the
Personas creation process [7]. In the example, he first identified
32 dimensions of the restaurant experience and asked the
restaurant finders to rate the experience on the scale of 1 to 5 (1
= not important, 5 = very important). He then used the PCA to

divide the dimensions into 5 major components. Each
component can be regarded as an independent cluster of needs.
Cluster Analysis (CA) involves the categorization of data.
It divides a large group of observations into subsets so that
observations within each subset are relatively similar while
observations in different groups are relatively dissimilar. Two
major different types of cluster analysis are widely used:
hierarchical methods (in which the k-cluster solution is
constructed by joining together two clusters from the k+1
cluster solution) and partitioning methods (in which the
observations are separated into a given number of subsets, and
the k-cluster solution and the k+1 cluster solution are not
necessarily nested) [4]. In both methods, there is no definitive
answer regarding how many clusters should be chosen. It is up
to the analyst to determine the “best” cluster solution.
Since its objective is to address the heterogeneity in each
data subset, cluster analysis has become a common tool for
marketing researchers to develop empirical groupings of
persons, products, and usage occasions that share certain
common characteristics. While its primary use has been
focused on market segmentation, there is growing interest on
applying cluster analysis into the classification of relevant
buyer characteristics and identify homogeneous groups of
customers [6]. The results of cluster analysis can contribute to
the definition of a classification scheme, or indicate rules for
assigning new cases to classes, or provide measures of
definition, size and change of broad concepts, or find
representative users and respective classification from a large
sample, which is most important in user experience research.
III. METHOD
We worked with a company to develop the Personas for
their online travel service business. The company’s main
business is selling airline tickets, hotel bookings and tour
packages through the company websites and telephone booking
system. The company has been in business for a few years and
has enjoyed stable growth of their core business.
We were given two typical user descriptions by the
company’s marketing department. The descriptions include the
gender, age, annual income, family members, frequency using
the company’s service, etc. We were to find out and write the
Personas for their online tickets booking business.
A. Participants and Procedure
1) Recruiting participants:
The two typical user profiles given to us are based on the
marketing department recommendation. We refined the profile
by.
1: Include people who have not used the company’s online
booking system but they have similar experience on
competitors’ websites to our user base.
2: Find out the users’ goals and their decision making
process.
We decided to use an online survey to gather more user
data. We recruited a total of 24 participants from two sources.
Although more participants are appropriate for the qualitative
analysis, we are limited by the project budget and time. First,
we selected some participants from the name list given to us by
the company market department. These participants have used
the company service and were willing to participant in the
company’s future customer researches. Then, we put on
advertisement which specified the type of people that we are
looking. Using the advisement, we recruited some
participants who had not used the company website but had
similar experience with competitor’s products.
2) Defining dimensions
In the Persona Creation and Usage Toolkit [5], Olsen thinks
that Personas should include information in the following
categories:
• Persona’s Biographic Background
• Business’ Relation to Persona
• Persona’s Relation to Product/Business
• Specific Goals/ Needs/ Attitudes
• Specific Knowledge / Proficiency
• Context of Usage
• Interaction Characteristics of Usage
• Information Characteristics of Usage
• Sensory/Immersive Characteristics of Use
• Emotional Characteristics of Usage
• Accessibility Issues
He also outlines the dimensions in each of the categories.
Using the categories and dimensions outlined by Olson as
template, and after discussing with the company
representatives, we identified 45 dimensions that would be
used in our survey. Among them:
1: 18 dimensions will be used in the Persona definitions.
These dimensions, such as Persona’s Biographic Background
and these attributes will be used in the final Personas definition
but they do not contribute to the user clustering analysis.
2: 27 dimensions will be used in the clustering of the users.
These dimensions represent user goals and behaviors, such as:
• What is your spending habits in purchasing travel
products?
• How will you select a travel agent?
• What is your frequency of traveling? etc.
3) Measuring dimensions
For each of the dimensions, we asked the participants to
rate it on the scale of 1 to 7, with 1 being the lowest and 7 the
highest. For some of the dimensions that can not be easily
measured by the participants’ subjective ratings, we used
standard measurement tools. For example, on the question
regarding the participants spending habit: is he emotional or
rational, we asked 7 indirect questions. With the answers, we

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

C. Identify Typical User
In order to generate a user profile for each group, we need
to identify the participants who can represent each group. We
revised the data matrix (see Figure 2) and put participants who
were in the same group adjacent to each other. We calculated
the average scores for each of the group. We noticed that the
average score of these two groups differ in quite a few
dimensions, particularly on dimensions such as:
• Rational/Emotional
• Hotel budget
We then compared the scores of each participant to the
average scores of his or her groups.
We reviewed the data and selected two participants whose
scores are closest to the average score. We gave preference to
the dimensions that make the two groups different such as
Rational/Emotional, Hotel budget, etc. Then we selected two
real people to represent the participants of the same group.
D. User Observation and Interview
In order to develop the user profiles and the final Personas,
we conducted a user observation and interview. The objective
is to understand the users’ behavior, goals and requirements,
obtain more detailed user information and create the basis for
the Personas illustration. Various types of user information,
including name, age, gender, working experience, education
level, hobbies, internet usage, traveling experiences,
preferences, expectations, individual requirements, etc. were
collected during the observation and interview process.
The user observation and interview process was divided
into three phrases: preparation and pilot test, formal test, and
data analysis. Preparation of user testing included the scenarios
design, questionnaire, interview script writing. A scenario is a
concise description of a person using the website to achieve an
end goal, which consists of actor, background information,
context assumption, goal, task procedure and events. The
scenarios we used were designed to reflect the major user tasks
performed in the company’s online business and to reveal the
user goals and requirements. For example, typical scenarios of
using travel service website are determining destination,
selecting travel time, booking air ticket and hotel.
We recruited 9 participants from original 24 participants of
our online survey to participate in our user observation and
interview. The 2 typical users identified earlier were among the
9 participants selected for the user observation and interview.
E. User Profile and Persona Creation
Based on the online survey and user observation and
interview results, we wrote a user profile for each of the 2
typical users. The 2 user profiles contained the following
information:
• Demographic information: Name, Age, Working
experience, Income, Traveling experience, Internet and
computer experience, Traveling service website experience.
• Typical activities in scenarios, based on abstraction of
the interview records: Involvement description, User goal and
respective task procedure (planning, information searching,
decision making)
After creating the initial user profiles for these two typical
users, a few revisions were made to enrich the content with
other participants’ information. The goal is to create the user
profile that can truly represent the participants of all the group
members. This process is consistent with the idea from Cooper
that stresses “Design for just one person” [2]. Personas are the
fictitious people that best represent the very users whose
requirements we want to satisfy. The results of the revision
and enrichment were the two Personas for our project.
We separate the two Personas into primary and secondary
Personas (see Figure 4, 5) Primary Persona are the most
important user or target user that must be satisfied, because
failing to satisfy their requirements means failing to satisfy the
other Persona. The primary Persona’s requirements can not be
satisfied if the website is designed for the other Persona.
Figure 4. Primary Persona of this project 1
Figure 5. Primary Persona of this project 1

IV. CONCLUSIONS AND DISCUSSION
In this project, we recruited 24 users based on the two
typical user descriptions that were given by the marketing
department. We identified 27 dimensions that represent the
users’ goal and decision making process. We designed an
online questionnaire to gather the information about the users’
demographic information and the goal/decision making
process. We used cluster analysis method to group the
participants into two groups. From these two groups, we
identified two typical users. We then created 2 detailed users
profile for the two typical users. We interviewed and observed
9 users from 24 participants when they were interacting with
the system. We then enriched and refined the two user profile
based on the user interview and observations. We constructed
the primary and secondary Persona. The company then used
the created Personas for their website redesign project and
reported very satisfactory results. The designers are pleased
when they have a very concert user to design the website. They
can design the business flow that better serve the users
requirements. For example, the designers created a short cut
pass for the more business oriented Persona as these users
know what they are purchasing and want to buy the tickets and
leave the site. The designers also created a BBS (online
discussion) for the casual users who have no particular
objectives in mind when they visit the website. These users
simply want more information on destinations, discount ticket,
etc. The designers also reported more confidence when
designing the site as they learned more about the users during
the Persona creation process.
Through the experience gained though our own project, we
learned that Personas can be created using both quantitative
and qualitative method. The final Personas are more
representative and less ambiguous that all the team members
can agree upon.
By first using the quantitative methods to identify the
cluster of Personas, we addressed the limitations of the
qualitative only approach. Although the data from 24 users is
rather limited for real statistic analysis, we showed that a
Persona could be created in two steps: clustering (quantitative
method) and abstracting (qualitative method). The cluster of
the users is based on the similarity between the users’ goals,
behaviors and motivations. Once the users are clustered, a user
profile is created from the characteristics of this group of users.
The qualitative methods such as user observation and interview
can be used to refine and enrich the details of Persona.
Additional observation and interview will provide detailed
information which can be used in enriching and refining the
typical user profile into Personas.
In the future, we are looking for opportunities to use a
larger data set to perform the cluster analysis. We also wish to
find out how different it is to create Persona using the
quantitative methods such as cluster analysis vs. qualitative
methods such as observation, interviewing, etc.
ACKNOWLEDGMENT
We would like to thank Songyue Wang for helping us on
the user studies, Danshuo Zhuang and Yun.Wen for helping us
on report writing and data analysis
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