투고일_2018.04.10 심사기간_2018.05.01-16 게재확정일_2018.05.28
A study on developing a data-based new and returning user persona model for web services
웹서비스를 위한 데이터기반 신규 및 재방문 퍼소나 모델 개발에 관한 연구
Lee, Ji Hyun_Seoul Women’s University / Tomimatsu, Kiyoshi_Kyushu University, [email protected] 이지현, 서울여자대학교 미래산업융합대학 산업디자인학과교수 / 키요시 토미마츠, 규슈대학 디자인대학원
차례 1. Introduction
1.1. Research backgrounds and objectives 1.2. Research methods and areas
2. Persona and new and returning user persona based on web usage data 2.1. Persona and data-driven persona
2.2. Log data utilization for data-driven persona of web service
2.3. Utilizing visual in-page analytics software for data-driven persona of web service
3. Case study for developing data-driven new and returning user persona 3.1. Extracting data for new and returning user persona from Google Analytics 3.2. Extracting data for new and returning user persona from Beusable analytics 3.3. Developing and evaluating new and returning data-driven user persona
4. Conclusion
References
A study on developing a data-based new and returning user persona model for web services
웹서비스를 위한 데이터기반 신규 및 재방문 퍼소나 모델 개발에 관한 연구
Lee, Ji Hyun_Seoul Women’s University / Tomimatsu, Kiyoshi_Kyushu University, [email protected] 이지현, 서울여자대학교 미래산업융합대학 산업디자인학과교수 / 키요시 토미마츠, 규슈대학 디자인대학원
ABSTRACT Data-driven design based on actual usage data is becoming more important in the field of user experience design. The goal-oriented design process based on a persona is a typical process in UX design. Persona has traditionally focused on generating rich user backgrounds, contexts, and scenarios based on qualitative data from individual user interviews. However, traditional qualitative research-based personas are costly, time consuming, and lack reliable data, so there is a growing demand for data complements with log analysis. This paper proposed a persona modeling method based on data-analysis software and a case study presented the value and application of the data–
based persona with expert evaluation. Specifically, this study used data analysis tools such as Google Analytics, which is a representative data analysis tool, and Beusable, which is newly developed data visualization analysis software in the page, to define the data types used in personas. The Beusable homepage was selected as a case study subject and new visitor and return visitor data-based personas were modeled. In addition, data-based personas were evaluated by experts. Data-based personas for new and returning visitors were found to be useful in identifying user behavior patterns, confusing factors, and design problems for the website. The perceived value of a two-page persona was higher than the perceived value of a one-page persona model.
최근 사용자 경험 디자인 분야에 실제 사용 데이터를 기반으로 한 데이터 기반 디자인의 중요도가 커지고 있다. 퍼 소나를 기반으로 한 목표 지향 디자인 프로세스는 UX 디자인의 대표적인 프로세스이며, 퍼소나는 전통적으로 개별 사용자 인터뷰를 통한 정성 데이터에 기반을 두고 풍부한 사용자 배경과 컨텍스트, 시나리오 도출에 중점을 두고 있 었다. 하지만, 전통적인 정성리서치 기반 퍼소나가 비용, 시간이 과다하게 소요되고 신뢰도 있는 데이터의 부족되는 문제가 있어 로그 분석을 통한데이터를 통한 보완에 대한 요구가 커지고 있다. 이에 본 연구에서는 데이터 기반 퍼 소나에 관한 전반적인 연구와 퍼소나 구축을 할 수 있는 데이터 분석 소프트웨어 도구 연구, 실제 사례를 통한 데이 터 기반 퍼소나 구현과 활용 가능성에 대한 전문가 평가를 통해 데이터 기반 퍼소나의 가치와 활용방안을 제시하였 다. 구체적으로 본 연구에서는 데이터 기반 퍼소나를 모델링하기 위해 대표적인 데이터 분석 도구인 구글 애널리틱 스와 새롭게 개발하고 있는 페이지내 데이터 시각화 분석 소프트웨어인 뷰저블을 활용하여 퍼소나에 활용되는 데이 터유형을 정의하고, 뷰저블 홈페이지를 사례 연구 대상으로 선정하고 신규 방문자 및 재방문자 데이터 기반 퍼소나 를 모델링하였다. 아울러, 전문가 평가를 통해 데이터 기반 퍼소나의 활용방안과 퍼소나 모델링 구현 방안을 비교 평 가하였다. 전문가 평가 결과 신규 방문자, 재방문자에 대한 데이터 기반 퍼소나가 웹사이트의 사용자 행동 패턴 파악 과 혼란 요인, 디자인 문제점을 도출하는 데 유용한 것으로 나타났으며 2페이지 형태로 데이터와 행동, 혼란과 인사 이트를 자세히 기술한 퍼소나가 1페이지로 간략하게 요약한 퍼소나보다 활용가치가 더 높은 것으로 나타났다.
Keyword
Persona
Data-driven Persona Visual in-page analytics
요약 중심어
퍼소나
데이터 기반 퍼소나 페이지내 데이터 시각화 도구
이 논문은 2018년도 서울여자 대학교 특별학술연구비의 지원 을 받았음.
1. Introduction
1.1. Research backgrounds and objective
The goal-directed design process has accepted the universal methodology used in the field of user experience design and the persona is already widely used as a modeling method to embody user types with similar goals and behavior patterns. However, there have been various objections to and opinions on the applicability of persona modeling.
Typical disadvantages of traditional personas include the cost of user research required for modeling, time problems, reliance on qualitative data such as over-observation and interviewing, and possibility of excessive intervention by the researcher in constructing a scenario. Companies that accept lean UX processes have also attempted to introduce a simple persona such as a proto-persona that quickly fills in a specific template based on existing assumptions rather than vast amounts of data. On the other hand, as the convenience of data utilization increases with the development of data analysis software, studies on the introduction of data-driven personas are also increasing. A data-driven persona has the advantage of being able to obtain reliable quantitative data within a short period of time, thus facilitating quick and objective decision-making at the point where decisions are made.
Therefore, this study proposed a method of data-based persona modeling based on a detailed case study and use of traditional log analysis software and newly developed in-page visual analytics that can extract data efficiently. First, this study looked at recent research papers on data-based personas and the persona system using Google Analytics, a representative software that can define and extract data based on underlying data types. Then it defined the data types related to the persona that can be explored and extracted for the recently developed data in-page visual analytics software, Beusable. Based on these defined data structures, this study modeled the new and returning user persona and developed one-page and two-page type personas. Each model was evaluated by a UX expert group, and a comparative expert evaluation of applicability and feasibility was carried out.
1.2. Research methods and areas
The research methods of this study are based on a literature survey, building a software-based persona data framework, comparative study with case studies, and comparative evaluation of persona modeling results with expert evaluation. First, this study investigated the features and limitations of persona that are traditionally used in the UX design field for the purpose of literature review. Then, it analyzed the existing research on the possibility, application range, and modeling method of a data–based persona.
Then, this study looked into the data analysis tool for extracting data from web services and analyzed the data types of Google Analytics, which are the most widely used in the Internet business market. On the other hand, this study analyzed the data types that can be obtained with the newly developed in-page visual analytics
software, Beusable, and studied the possibility of new perspectives and modeling. This study also analyzed differences in data types based on Google Analytics and Beusable.
Based on the analysis of software, case study subjects were selected and data-based persona modeling was conducted. The case study subjects were selected as they
viewed the Beusable homepage, which is an object that can analyze the services of an IT service provider. Based on new and returning visitor user data among the data types from Google Analytics and Beusable, this study conducted a data-based survey and modeling of new and returning personas. Persona modeling is designed to select data that can show the difference between the characteristics of a new visitor and returning persona.
Finally, expert evaluation was conducted by eight UX experts of Internet services in order to grasp the utilization plan and the advantages and disadvantages of a
data-based persona. To secure the objectivity of the evaluation, this study conducted reviews and evaluations of each expert of the UX expert group of the general online company. From the two types of persona modeling results, a model with high effect was derived, along with the method of application of the persona and future research method.
2. Persona and new and returning user persona based on web usage data 2.1. Persona and data-driven persona
The concept of persona was introduced by Alan Cooper, the inventor of Visual Basic, as a user-centered design tool in his book “The Inmates Are Running the Asylum.”1) Cooper pointed out problems such as poor usability and development practices that focus on convenience when development is conducted from the perspective of a developer, not a user, and had a philosophy that development should be carried out with the user's features, goals, and actions in mind. He presented the concept of persona, but with the efforts of UX experts working in his company, Cooper, and the UX professional association User Experience Professional Association(UXPA), persona became one of the representative methods of UX design.
According to “About Face 4,” persona is defined as “the most central fictional user character for a narrative scenario-based design process."2) "On the other hand, according to the Nielsen Norman Group, persona is defined as "a user model expressed as a fictional character that shares a specific goal."3) Also, according to Reidwell et al., "Persona is a fictional character that uses certain sites, products, and brands with similar patterns."4) The advantages of a persona are that they enable team members to share specific user goals, help designers determine how well they fit into persona needs, and prioritize decisions. Above all, a persona has the advantage of helping team members to empathize with customers by adding vitality photographs and explanations for customers.5)
Despite these advantages, personas are subject to the following disadvantages and disputes. The controversy over persona can be summarized by the controversies about the logic of modeling and practical application, and the disadvantage that there are no provable results. The controversy about the logic for modeling is that it cannot scientifically prove a clear causal relationship between user research data and persona
1) Cooper, Alan, The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore Sanity, Sams - Pearson Education, 1999, p. 123
2) Cooper, Alan, Reimann, Robert and Cronin, David, About Face 4: The Essentials of Interaction Design, Wiley, 2014, p. 26
3) Harley, Aurora, Segment Analytics Data Using Personas, https:// www.nngroup. com/articles /analytics- persona – segment/
4) Lidwell, William, Holden, Kritina, Butler, Jill, Universal Principles of Design, Rockport Publishers, 2010, p. 182 5) Cooper, Alan, The Inmates are Running the Asylum, SAMS, 1999
modeling results.6) The controversy about practical application is that a persona focuses on a virtual character rather than being immersed in actual world stories and the actions of real users, thereby distancing them from their actual needs. In addition, the disadvantage of a persona is that it cannot be proved with scientific research methods.7)
In order to overcome these drawbacks, many studies have been carried out to establish logic and a system based on qualitative research data. However, studies have been conducted to secure the reliability and objectivity of the persona based on quantitative data. This way, a persona can be acquired relatively quickly and easily, rather than the original features that help to make decisions. The quantitative data-based persona can be classified into the following three types. First, there are statistical data types with questionnaires. Second, a network-based log analysis engine is used to acquire actual usage data such as clicks and touch data. Third, there are various measurement sensors such as cameras and microphones, and sensing data is used for modeling personas.
2.2. Log data utilization for data-driven persona of web service
The quantitative data for a web service, which is the subject of this study, can be said to be the clickstream data collected with a log analyzing tool presented in the second quantitative data type in the front. The touch data collected when a finger touches a smartphone can also be referred to as the same type of data. The clickstream is a set of click data that can be extracted from one connection and login session. As shown in
<Figure 1>, the access data and the clicked action data are recorded in chronological order.8)
To analyze clickstream data in web services, companies use their own developed solutions or select commercially available solutions. Among these, the ranking of commercially available solutions shows that Google Analytics has a dominant share of Fortune 500 companies. Google Analytics has a 69% market share,9) followed by Adobe Analytics (28%), Webtrends (10%) and IBM Core Matrix (Coremetrics) (4%).10) According to Google Analytics, the company "not only measures sales and conversions, but it also provides an up-to-date analysis of how visitors interact with your site's activity, your site's traffic, and your customers' return."11) Google Analytics includes content analytics to analyze high-performing content pages, social analytics to analyze content social sharing activity, conversion web analytics to analyze customer conversion rates, and ad analytics to evaluate ad effectiveness.
Google Analytics gives priority to the number of users, sessions, drop-off rate, and session duration data within the setup period. The company offers the ability to analyze the characteristics of demographic information, interests, geography, behavior,
6) Chapman, Christopher N, Milham, Russell P, The Personas' New Clothes: Methodological and Practical Arguments against a Popular Method, 2014
7) Chapman, CN; Milham, R, The personas' new clothes, Human Factors and Ergonomics Society, 2006
8) Zang, Xiang, Brow, Hans-Frederick, Shankar, Anil, Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016, P 5351
9) https://www.e-nor.com/blog/google-analytics/google-analytics-maintains-lead-with-fortune-500-in-2014
10) https://www.e-nor.com/blog/google-analytics/google-analytics-and-google-tag-manager-dominate-fortune-500-in -2015
11) https://www.google.com/intl/ko_ALL/analytics/features/index.html
<Figure 1> Concept of Clickstream Analysis Tool
technology, and mobile behavior patterns by age and gender, which can used as basic persona data. In addition, it can be used to compare the customer conversion rate and the web service goal contribution, such as acquisitions and conversions, and it is important data for modeling personas.
Google Analytics is very useful for configuring persona by comparing the above data with customer segment types with a large set of customer segment settings. The types of segments currently offered are: number of transactions, number of search traffic, number of buyers, session users, all users, mobile and tablet traffic, mobile traffic, non-exit sessions, non-converting visitors, site searchers, new user paid traffic, abandoned sessions, natural traffic, re-users, numbers, direct traffic, referral traffic, tablet and desktop traffic, and tablet traffic.
2.3. Utilizing visual in-page analytics software for data-driven persona of web service Google Analytics provides a vast amount of functionality for web service customer analysis, which is useful in persona modeling but does not provide analytic data for individual web pages or individual web UI elements. In addition, because it is an analysis tool based on page view data, it is insufficient in persona modeling as it cannot analyze user interaction or customers by interaction type. Due to these limitations, in-page data visualization analytics tools (visual in-page analytics) that help measure and visualize user interactions in the page to facilitate analysis are emerging. In sum, the difference between traditional clickstream analysis tools focused on page-to-page navigation and in-page data visualization analysis tools can be summarized as shown in Figure 2.12) The data visualization analysis tools in these pages include representative ones such as heatmaps showing the distribution and density of clicks in a page, session replay showing the average page click sequence and flow, and form analytics by UI elements. Commercial in-page data visualization
12) Mozyrko, Bartosz, Visual Analytics: uncovering the why in your data, UX Camp Europe 2016
<Figure 2> Concept of in-page data visualization analysis tools
analysis tools include hotjar, mouseflow, SessionCam, clicktale, inspectlet, fullstory, luckyorange, CrazyEgg, and Beusable.
Among them, one of the study authorsparticipated in research and development in the UX field and explored the possibility of modeling a database based on a viewable solution that can acquire actual data. Beusable includes basic functions and in-page data visualization analysis tools such as heat map, session re-view, and UI element click analysis, Comparing as Referrers, user analytics show key information of Google Analytics, (activity stream), segmenting CTA, A / B testing, and funnels. These functions are based on eight user behavior data items, as shown in <Table 1>, and these behavior data can be used as basic elements in data-based perspective modeling. In terms of behavioral data, Beusable has features that provide visual feedback on mouse hover and data on reaching by page height.
Data Types Description
Mouse Click Data Click Count: ranking of the most clicked contents
Unique Click: rank between contents by unique click counts
Mouse Movement Data Hover Count: Displays the contents rank based on the number of users’
hover
Reached Rate by the Page Height Scrolled heights graph visualizes users’ contents reach rate
Activity Stream Data User Movement Flow: representative flow of all users with gaze plot
Page Visit Data Page view (PV), unique visitors (UV), Average PV per UV, average PV per UV, Average session length, Churn rate
User Environment Data Monitor resolution distribution, monitor resolution detail, device statistics, country statistics, OS statistics, browser statistics
Segmenting CTA Reports the statistics of users who clicked important elements by backtracking
Inflow and Drop Data
Aggregate step-by-step data from initial user inflow to users' final conversion and focus on the renewal of the pages with relatively high churn rate
<Table 1> User Behavior Data Types with Beusable Solution
3. Case study for developing data-driven new and returning user persona 3.1. Extracting data for new and returning user persona from Google Analytics
Based on the previously described Google Analytics and Beusable, an in-page data visualization analytical tool, we studied the persona type classification variables that can be used to extract common data from both solutions to perform a comparative study after modeling the data-based persona. As a result of solution function analysis, two solutions were able to model commonly used new-returning users, conversion-drop off, and referral site type personas. In this study, persona modeling was performed for both new and returning users. First, the data related to new and returning users provided by Google Analytics was extracted, and the characteristics of each data are summarized as shown in <Table 2>.
Data Types Description
Session Number of sessions per new - returning visitor group
User Number of users per new - returning visitor group
Age New - Returning Visitor Group: 18-24, 25-34, 35-44, 45-54 Age
Groups
Gender Percentage of males and females by new-returning group
Mobile Percent of desktop, mobile, and tablet users by new-returning user Group
Transition Overview Percentage of new-returning visitors who reached the goal specified by the service
Behavior overview Percentage of new-returning visitors reaching a particular submenu Drop-off rate Breakout by new-returning visitor group
Pages per session Percentage of sessions per new-returning visitor group Average session time Average new session time per new-returning visitor group
Pageviews Pages per new-returning visitor group
Active user Active user by new-returning visitor group
<Table 2> New and returning user data provided by Google Analytics
Google Analytics focuses on basic usage data across all web services and demographic information such as goal achievement rate, age, and gender, rather than specific interactions within a specific page. In addition, it has a feature where it is easy to
grasp the overall data flow by visualizing the trend of data when the period is set.
However, Google Analytics shows daily graphs in detail to facilitate the use of data to measure web service marketing effectiveness. However, since data aggregation and trends are important in persona modeling, daily data representation is not used.
3.2. Extracting data for new and returning user persona from Beusable analytics The Beusable solution is one of the in-page data visualization analysis tools. It provides eight types of user behavior data, summarized in <Table 1>, enabling the modeling of a persona in a format that is different from that of Google Analytics.
In particular, a hit map, which is a representative function of the in-page data visualization analysis tool, is displayed for each new-to-visiting user, and page link elements with a high click rate for each new-visiting user group are displayed in order Of Click Rate, Click PV Rate, Hover to Click, Scroll to Click, and Hover> Click Time. Also, the ratio of connected devices, click, mouse move, scroll, and clickstream data can be compared for each new-returningvisitor group. In addition, the connection path (Referrer) of the new-re-visitor can be traced to confirm the top 10 clicks page link rank (Top 10), device access rate, click, move, scroll, and clickstream data for each access route. This in-page data visualization technology provides useful data for services that require page-based user interaction analysis such as homepages and product pages.
On the other hand, Beusable provides basic statistics such as page view, average residence time, average PV per visitor, dropout rate, device statistics, monitor resolution distribution, and country statistics provided by Google Analytics. Only the numbers are provided and only the number of unique visitors can be checked by new-returning visitors group.
3.3. Developing and evaluating new and returning data-driven user persona This study conducted persona modeling mainly with the data of Google Analytics and Beusable that were summarized in the previous chapter. The purpose of persona modeling was to model the perception of the user as a basic tool to show the data that can indicate the user’s basic characteristics and behavior characteristics of the usage data of the current site and to grasp design problems through it. This study focused on behavior (motivation and activity), use environment, frustrations and pain points, demographics related to behavior, and data that can be extracted as quantitative data based on behavior-related technology, experience and ability, behavior-related attitudes and emotions can be included in a persona. In addition, we were able to grasp the basic goal of user perception and problems at a glance and help design decision making. In this study, two types of persona were modeled. As shown in <figure 3>, the first one is a two-page type persona that has more space than a one-page persona, so the data is more readable, and user behavior, confusion and insights are explained in detail. the second persona modeling result is a one-page persona type that compresses the contents and gives a view of all the data with one page <Figure 4>.
In this study, a 1:1 expert interview and evaluation method was used. Eight experts with 2 or more years of UX design experience in the field of Internet service reviewed the two types of persona in order to evaluate the utilization of a data-based persona and the appropriateness of the one- and two-page type personas. Expert
<Figure 3> Two-page type persona model
interviews were carried out on how to utilize personas and how to improve them.
Expert evaluation was done by collecting opinions and evaluating usability, clarity, and validity on a 7-point scale. The evaluation results showed that two-page type evaluation scores and opinions were superior to the one-page type. In detail, the two-page type was superior in clarity and usability, and while validity was slightly better with the one-page type, the two-page type was generally preferred by the experts.
<Figure 4> One-page type persona model
Variables Types Grades Expert Opinion
Usability
o n e - p a g e
type 3.75
-- Key data of key interest and key patterns and key action patterns can be seen at a glance, making it easy to identify usage issues and design issues. (same)
-- It is convenient to view all the data and contents at a glance, but it is inconvenient to find the relation between data and data interpretation contents.
-- It is convenient to compare the new and returning user personas.
t w o - p a g e
type 6.125
-- Key data of key interest and key patterns and key action patterns can be seen at a glance, making it easy to identify usage issues and design issues. (same)
-- Interpretation of data, user needs, and behavioral patterns are displayed next to each other, so data interpretation is convenient.
-- It is displayed on two-pages, but data infographics are represented so that the configuration is not complicated and easy to use.
Clarity
o n e - p a g e
type 3.25
-- Data is generally clear, but the infographics at the bottom are small in size, making it difficult to get a clear sense of meaning.
-- It is difficult to identify 75%, 50%, and 25% line positions in the scroll data.
t w o - p a g e
type 6.125
-- It is relatively easy to grasp the data clearly because there is a relatively large space.
-- It is easy to grasp 75%, 50%, and 25% line position in scroll data.
Validity
o n e - p a g e
type 6.125
-- The contents of user needs and behavior analysis through data are reasonable.
-Compared to the existing qualitative data persona, so it is possible to understand the usage pattern clearly (same)
t w o - p a g e
type 5.625
-I would like to have a procedure to create or modify the contents jointly through the workshop.
-It is based on the quantitative data compared to the existing qualitative data persona, so it is possible to understand the usage pattern clearly. (same)
Utilization
o n e - p a g e
type 5
-- It is useful for understanding the current status of web site usage and design problems. It can be used to improve UI for new visitors and return visitors. (same)
-- If the persona is updated regularly, it will be useful. (same) -- Data is valid, but user confusion and design issues are not clear, making it difficult to promote decision-making by team.
t w o - p a g e
type 6.125
-- It is useful for understanding the current status of website usage and design problems. It can be used to improve UI for new visitors and return visitors. (same)
-- If the persona is updated regularly, it will be useful. (same) -- It is easy to promote understanding and promotion of decision making among the members because the contents about user behavior are abundant and the confusion and insight part are specified as compared with one-page persona.
Total
o n e - p a g e
type 4.53
-- Clarity and usability are poor compared to the two-page type.
The feasibility is rather good, but it is not enough because it does not provide user confusion and insight contents.
t w o - p a g e
type 6 -- It is not unreasonable if the two-page type is superior in, clarity, and usability, feasibility, confusion, and insight.
<Table 3> Expert Evaluation about Data-Driven Persona
Looking closely at the results of the expert evaluation, both the one-page and
two-page type personas showed usability, usefulness, effectiveness, and clarity from the data-based perspective. However, in terms of usability and clarity, the two-page type persona is superior to the one-page type persona. This is because the two-page type persona is useful in understanding the meaning of the data because the original data and its interpretation are on the same line. The validity of the one-page type persona was slightly better than that of the two-page type persona due to user confusion and lack of insights. Experts commented that the confusion and insights of the two-page format were very useful if many experts go to workshops for generating user confusion and insights. In terms of utilization, the two-page persona was superior, so we could get results from supplementing it with the method of collecting user confusion and insights from a derivation process in an expert workshop while using it.
4. Conclusion
In this study we explored data analysis tools for data service-based on-line services and proposed a persona modeling system using these tools. In addition, we conducted case studies for new and returning users and suggested appropriate measures,
utilization plans, and improvement plans with expert evaluation. Specifically, the following two conclusions were drawn.
First, we presented the data structure with the utilization value of a data-based persona and data analysis tools. The advantages and disadvantages of a traditional qualitative research-based persona and the utility value of a data-based persona were identified. In addition, we analyzed the current state of data analysis tools for web services and constructed a data structure for personas based on the Beusable solution that can visualize usage patterns in a page along with the most widely used service, Google Analytics.
Second, we selected the Beusable homepage, which can acquire data, as a case study, and performed persona modeling for new and re-visitor persona types by combining Google Analytics and Beusable data. The results of persona modeling were verified by usability, clarity, validity, and utilization with the evaluation of UX experts. The two-page type persona received excellent evaluations overall, and a utilization plan was derived. In particular, it showed that the data-based perspective is useful for grasping user behavior and frustrations.
Future research tasks include studying the use of professional workshop processes to derive user confusion and insight factors in persona modeling, and studying periodic perspectives and updating methods to establish a system for continuously analyzing trends in persona studies.
References
Chapman, Christopher N, Milham, Russell P, The Personas' New Clothes: Methodological and Practical Arguments against a Popular Method, 2014
Chapman, CN, Milham, R, The Personas' New Clothes, Human Factors and Ergonomics Society, 2006
Cooper, Alan, Reimann, Robert and Cronin, David, About Face 14: The Essentials of Interaction
Design, Wiley, 2014
Cooper, Alan, The Inmates Are Running the Asylum: Why High-Tech Products Drive Us Crazy and How to Restore the Sanity, Sams – Pearson Education, 1999
Lee, Ji Hyun, Tomimatsu, Kiyoshi, A study on developing a data-based referrer persona for web services, Korean Digital Design Studies, Vol.17 No.2, 2017
Lidwell, William, Holden, Kritina, Butler, Jill, Universal Principles of Design, Rockport Publishers Mozyrko, Bartosz, Visual Analytics: uncovering the why in your data, UX Camp Europe 2016,
2016
Pruitt, John and Adlin, Tamara, The Persona Lifecycle: Keeping People in Mind Throughout Product Design (Interactive Technologies), Morgan Kaufmann, 2006
Dorsey, Miora, How Design Personas Differ from Typical Customer Segmentation Models, Forrester Research Report, 2006
Zang, Xiang, Brow, Hans-Frederick, Shankar, Anil, Data-driven Personas: Constructing Archetypal Users with Clickstreams and User Telemetry, Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016
https://www.e-nor.com/blog/google-analytics/google-analytics-and-google-tag-manager-domin ate-fortune-500-in -2015
https://www.e-nor.com/blog/google-analytics/google-analytics-maintains-lead-with-fortune-50 0-in-2014
https://www.google.com/intl/ko_ALL/analytics/features/index.html https://www.nngroup.com/articles/analytics-persona–segment
