57.2, May 2010

The Contribution of Technical Communicators to the User-Centered Design Process of Personalized Systems

Lex van Velsen, Thea van der Geest, and Michaël Steehouder


Purpose: This article discusses how personalization will affect technical communication practitioners’ everyday work, and indicates to researchers which knowledge gaps scientific research needs to fill.

Method: After a description of how personalization exactly works, we demonstrate that the technique is very similar to the approach to personalization as applied in ancient rhetoric. Next, we describe how the history of the concept “the audience,” and how it has been analyzed and approached, has led to the tactic of electronically tailoring communication to individuals. We propose the User-Centered Design approach as an approach that can help the designer get to know the individual user, thereby increasing the fit between personalized systems and users’ needs, wishes, and contexts.

Results: We discuss how the User-Centered Design approach needs to be adjusted to cope with the demands personalization places on the approach. Furthermore, we consider the technical communicator’s role in this design process.

Conclusion: Technical communicators need to devise and lead user studies that inform and evaluate each step of the personalization process. Researchers need to focus their efforts on studies that aid the design of personalized systems, like discerning in which situations personalization is of added value or not, and identifying the factors that influence the acceptance of personalization.

Keywords: personalization, User-Centered Design, usability, user modeling, audience analysis

Practitioner’s Takeaway

  • When designing for personalization, practitioners need to take into account specific usability issues, like a loss of controllability and the risk of a diminished breadth of experience.
  • When designing user studies that inform the design of personalization, one has to take into account the different steps of the personalization process. Each step of this process has to be designed for, and evaluated separately.
  • Technical communicators can be the linking pin in the personalized system design team, who can maximize the fit between users and tailored output.


In 1998, three Amazon.com employees filed a patent for a technique that generated product recommendations automatically (Linden, Jacobi, & Benson, 2001). What makes these product suggestions special is the fact that they are personalized. In the Amazon case, it means that each individual customer receives a unique set of recommendations based on his or her previous purchases. By giving customers proposals on the basis of their previous behavior, Amazon tries to recommend items that are more relevant for the individual client. Ultimately, these recommendations must, of course, lead to higher sales. Since 2001, Amazon recommendations have become a great success and the most well known form of personalization to the public at large.

Besides Amazon’s recommendations, many other forms of personalization have developed. Personal TV guides, pre-filled online forms, and intelligent tourist guides are just a few examples. The widespread introduction of personalized features in (online) systems might make it necessary for technical communicators to reconsider their role in the system design process. Is personalization “business as usual”? Does it have implications for the way in which one must approach an audience? Can one still speak of an audience at all, if everybody receives unique system output? And finally, does personalization influence the application of design and evaluation activities technical communicators normally deploy? This article seeks to provide answers on these questions. In order to do so, we will first discuss in more detail what personalization exactly entails (Personalization: An Overview). Next, in the Section “Personalization, Rhetoric, and the Audience,” we will show that personalization is the logical result of the changing nature of audiences and the ways in which rhetoricians (or, as they are known in the modern communication landscape, communicators) have studied and approached them. In “User-Centered Design of Personalized Systems,” we propose the User-Centered Design approach as a way to design electronic communication for “audiences of one” and set out the implications of personalization for this approach. We complete this article by outlining in the section “Technical Communication and the User-Centered Design Process of Personalized Systems” the role of the technical communication practitioner and researcher in optimizing the design process of personalized systems.

Personalization: An Overview

A Short History of Personalization

The idea of personalizing electronic output arose in the early 1980s (Weibelzahl, 2003). According to Brusilovsky (2001), the first research on personalization dates to the early 1990s, with the amount of research done on the topic taking off after 1996. This was due to the growing popularity of the World Wide Web and the possibilities it offered for creating personalized media content. Furthermore, by then researchers realized that personalization proved to add value and was therefore worth pursuing. Finally, around this time, the commercial sector realized that electronic personalization could be a fruitful replacement of the mass marketing techniques applied up to that point. Hence, the use of personalized marketing features was introduced, thereby offering personalization to the public at large (Kobsa, 2001).

In 2001, Kobsa, Koenemann, and Pohl (2001) identified three emerging and promising forms of personalized output: (1) recommendations, (2) guidance and orientation, and (3) personal views and spaces. Recommendations have indeed turned out to be a successful application of personalization, with the aforementioned recommendations by Amazon.com as the most well known personalized application. The second form of personalization, personalized guidance and orientation, deals with offering users a personal path through a system (e.g., a Web site) by means of displaying personal buttons or creating a personalized tour. Contrary to the expectations of Kobsa and his colleagues, this kind of personalization has not been widely adopted by the market yet. Finally, the third form of personalization, personal views and spaces, provides users with personal home pages on which personally relevant content and links are displayed. This form of personalized output is present on many governmental Web sites (e.g., the Canadian My Government site: http://www.canada.gc.ca). The commercial sector has also adopted this technique. The best known commercial examples include last.fm (with a personal overview of the music a user has been listening to) and the many personal overviews mobile telephone service suppliers offer (displaying a client’s calling behavior).

Although the exemplary systems in this section have different goals, their workings are roughly the same. In the next sections we will elaborate on the two phases that are elemental in the process of creating tailored output: user modeling and personalizing output.

User Modeling

Before system output can be personalized, for each user a file must be created, called a user model. In this model, information about a particular user is stored. On the basis of the information stored in the user model, the system determines if output needs to be tailored for the individual and, if so, in what form. It is also possible to tailor output to a homogeneous group of users. In this case, the personalization of output is based upon a group model: a file containing information about a particular group of users.

User modeling is concerned with the creation of a valid model of an individual user. Based on Kobsa et al. (2001), we list the kinds of data that can be used to create a user model:

  1. User data:
    • Demographic data
    • User knowledge
    • User skills and capabilities
    • User interests and preferences
    • User goals and plans
  2. Usage data:
    • User clicking
    • User viewing times
    • User ratings
    • User tags
    • User purchases or related actions
    • Browser actions (e.g., saving, printing)
  3. Environment data:
    • Software environment
    • Hardware environment
    • User location

These data can be collected implicitly and/or explicitly. If data are collected only implicitly, they are inferred from user behavior. When personalization is based upon implicitly collected user data, the system is adaptive. Users can also explicitly state what they would like the personal output to look like, which is then stored in the user model. In this case, a system is adaptable. Many personalized systems offer adaptive as well as adaptable features (Wu, Im, Tremaine, Instone, & Turoff, 2003).

A personalized system collects one or more kinds of data and then applies rules to interpret these kinds of data. For example, if John uses an online bookstore to purchase biographies of the painters Van Gogh, Monet, and Renoir, the system may deduce that John is interested in books about Impressionist painters. Consequently, this interpretation is stored in John’s user model. To discuss the methods of acquiring and interpreting the kinds of data listed above would be a technical matter and outside the scope of this article. We refer those who are interested to Kobsa et al. (2001).

Personalizing Output

Once a user model is created, it can be used to decide whether or not to tailor output. If the rules in a system lead to the decision to tailor output for an individual, many different techniques can be used. Several overviews of these techniques have been published (Brusilovsky, 1996, 2001; Kobsa et al., 2001; Knutov, De Bra, & Pechenizkiy, 2009) that display a large degree of overlap. Based on these overviews, we list the possible forms of personalized output.

  1. Adaptation of content. This type of personalization deals with tailoring the content of an entire or parts of a communication message (e.g., a Web page or a video), or one or more fragments thereof. In the first case, there will be different messages prepared for different kinds of users, and the system will decide which message will be presented to each user. When one or more fragments of the message will be personalized, there exists a general message that will be presented to all users, but certain parts will be tailored by, for example, leaving out parts or rearranging the text in the message to better suit the receiver. Examples: Amazon’s book recommendations; the adaptable home pages of major search engines like Google ( iGoogle) and Yahoo! (My Yahoo!).
  2. Adaptation of presentation. This type of personalization deals with tailoring the layout of a message or the modality in which it is presented. Examples: A Web site that provides content in different modalities to print-disabled users; a Web site that only shows text when accessed by means of a mobile phone.
  3. Adaptation of navigation. This type of personalization deals with tailoring the way in which a user navigates through a system (e.g., a Web site) or through the Internet in general. In the case of a closed hyperspace like a Web site, the adaptation can take the form of creating personalized tours, hiding links, or sorting links personally. Personalizing navigation in an open hyperspace, like the World Wide Web, is mostly done by means of personalized search engines. Examples: A search engine that removes results that are irrelevant for a specific user; a digital museum guide that only displays art pieces of the user’s favorite artists.
  4. Adaptation of user input. This type of personalization deals with tailoring the text in entry fields, which originally had to be filled in by users themselves. This text can either be incorporated from a user’s user model or be collected from a connected system in which the user also has a user model and the required information is already known. Furthermore, information submitted by the user can be expanded with user-related data. Examples: Pre-filled online government forms; automated tagging of photos uploaded to a photo-sharing service.
  5. Adaptation of collaboration. This type of personalization deals with initiating the interaction between two or more people working with the system. This might be done, for example, by psychologically profiling a large group of users and, on the basis of these profiles, bringing together those personalities that, in theory, will work well together. At this time, this kind of personalization is very novel and has been implemented in only a few systems.

In this section, we have described the generation of personalized system output, a process that requires several steps, such as user modeling and personalizing output. This makes it different from the generation of “traditional” one-size-fits-all output, which is relatively straightforward. Personalization can be seen as a specific way of analyzing the audience and, consequently, tailoring communication. In that sense, personalization is not only a technical process, but also a rhetorical process.

Personalization, Rhetoric, and the Audience

In order to get to the source of personalization, we must go back to ancient Greece. In Phaedrus, which Peters (1999) characterizes as the first book on communication science, Socrates and Phaedrus discuss love and the foundations of rhetoric (Plato, trans. 2005). While discussing these foundations, a fictive Socrates states:

Since the power of speech is in fact a leading of the soul, the man who means to be an expert in rhetoric must know how many forms soul has. Thus their number is so and so, and they are of such and such kinds, which is why some people are like this, and others like that; and these having been distinguished in this way, then again there are so many forms of speeches, each one of such and such a kind. People of one kind are easily persuaded for one sort of reason by one kind of speech to hold one kind of opinion, while people of another kind are for some other sorts of reasons difficult to persuade (p. 271, c10–d5).

Socrates explains here that people are not alike, but are individuals with unique characteristics, or small groups of similar individuals. Each individual or small homogeneous group is best persuaded by applying a tailored rhetorical approach.

After stating that there are different kinds of people who require different kinds of persuasion, Socrates describes the competences a rhetorician needs to create a speech that is tailored to the characteristics of the listener and that thereby achieves successful persuasion.

…when he both has sufficient ability to say what sort of man is persuaded by what sorts of things, and is capable of telling himself when he observes him that this is the man, this the nature of person that was discussed before, now actually present in front of him, to whom he must now apply these kinds of speech in this way in order to persuade him of this kind of thing when he now has all of this, and has also grasped the occasions for speaking and for holding back, and again for speaking concisely and piteously and in an exaggerated fashion, and for all the forms of speeches he may learn, recognizing the right and the wrong time for these, then his grasp of the science will be well and completely finished, but not before that (p. 271, e1–272, a5).

The competences that Socrates mentions also describe the steps by which a rhetorician must tailor a speech. First, the rhetorician has to identify the individual listener (“this is the man”). The rhetorician then needs to get to know and understand this individual listener (“ this [is] the nature of person […] now actually present in front of him”). For each individual listener, the rhetorician can decide upon a suitable goal to be achieved by means of rhetoric (“to persuade him of this kind of thing”). Taking the individual listener’s characteristics and the goal to be achieved into consideration, the rhetorician needs to decide upon a suitable communication strategy (“he must now apply these kinds of speech”). And even these strategies can be tailored into specific presentation forms (“apply these kinds of speech in this way in order to persuade him”). In short, the steps to create a personalized message are, according to Socrates:

  • Identify the individual.
  • Get to know the individual.
  • Set a communication goal for the individual.
  • Tailor the rhetorical approach to the individual.
  • Tailor the communication content to the individual.

Interestingly, these steps resemble the steps in the personalization process as performed by many personalized systems. In Table 1, we have listed the rhetorical steps to personalization side by side with the steps of the technical personalization process, as characterized in Paramythis and Weibelzahl (2005). The table shows that in both approaches to personalization, first, the user is identified. Then, the rhetorician has to get to know him or her, or a user model has to be created. Next, a communication goal is set, while in the technical counterpart it is decided whether personalization is appropriate in a given situation and what this personalization should entail. And finally, the actual content of the message is tailored.

Table 1. A comparison of rhetorical steps and the personalization process

Rhetorical Steps

Personalization Process

Identify the individual

Identify user

Get to know the individual

Collect user data

Interpret user data

Set a communication goal for the individual

Decide upon personalization

Tailor the rhetorical approach to the individual

Apply adaptation

Tailor the communication content to the individual

Although the steps in both processes are very similar, the means by which the personalized message is conveyed are very different. Socrates argued that tailoring a speech to the individual can only be done by means of personal conversations (Peters, 1999). The written word, or broadcasting in general, is to be considered an inferior means of communication, as the message to be communicated cannot be geared to the characteristics of an individual, and thereby loses persuasive strength.

And when once it is written, every composition trundles about everywhere in the same way, in the presence both of those who know about the subject and of those who have nothing at all to do with it, and it does not know how to address those it should address and not those it should not (p. 275, e1).

Socrates believed personalized messages to be more persuasive than general ones. And for many centuries, face-to-face communication was the only means to guarantee that personalization could be successful. However, the possibilities for tailoring mediated messages to an audience (or to audience segments) have changed due to the evolving nature of audiences, new methods of analyzing these audiences, and advances in technology. Ultimately, this has led to a situation in which personalization can be achieved electronically. In the next sections, we will set out how the view on “the audience” has evolved. This will show how the ancient starting point (personalization by means of face-to-face communication) has changed into the current situation (personalization by means of interactive media), and what consequences this has for the design of systems that aim at an audience of one.

The Audience

Audience is the term that originally was used for the spectators in ancient Greek and Roman theaters and arenas, gathered to view a play or spectacle. Different kinds of events would attract different kinds of audiences, varying in, for example, education or social status. In the last 500 years, technological innovations have transformed the way in which we approach and perceive audiences, who have evolved from relatively small and homogeneous groups of people into large and heterogeneous masses catered to by the mass media. This process primarily started in 1456 with the invention of printing, which allowed communicators to communicate their message to a larger and often unknown audience. Several centuries later, the industrial revolution and urbanization created a situation in which large geographically concentrated audiences could be reached more easily by means of newspapers and movie theaters. In the 1920s, the introduction of commercial broadcasting further reduced the limitations of the mass media’s dependence on location. National radio shows, and a few decades later television shows, created nationwide audiences. Finally, the growing availability of Internet connections in the 1990s created the possibility for communicators to reach people, unconstrained by any geographical boundaries.

Creating one definition of “audience” to fit all the different strands of research that focus on addressing audiences is impossible (Webster, 1998). With this in mind, McQuail (1997) constructed a typology of “audiences” that spans the different research focuses. His typology classifies the research focuses on audiences by using a societal or a media perspective and subsequently a macro- or micro-level view.

On a macro-societal level, an audience is a group of people who can be considered a collective before their identification as an audience. An example of such an audience are the employees of an organization who are addressed through a company newsletter. The audience on a micro-societal level is the individual who chooses for himself or herself which TV program to “consume” or which Web site to visit. This view of the audience is central in the uses and gratifications theory, originally developed by Katz, Blumler, and Gurevitch (1973). According to the uses and gratifications theory, each media consumer consciously chooses the medium and message he or she wants to consume in order to fulfill a certain need (e.g., being informed of the latest news or being entertained).

McQuail’s other perspective on audience, the media perspective, approaches people as a mass. On a macro level, a media audience consists of all the people who consume media content transmitted by one particular medium (e.g., the television audience or the book-reading public). More specific is the media audience on a micro level. This is the audience of one particular medium transmission. What binds these people is their consumption of a certain medium transmission (e.g., Monday night’s eight o’clock news) and not their shared psychological or demographical characteristics.

The societal perspective on audiences can be characterized as a bottom-up perspective and focuses on the individual’s motivations to consume certain media content or the small group’s commonalities that makes them interesting as a media audience. The media perspective is a top-down one. Instead of perceiving the individual or small group as the main party in the act of media consumption, the media perspective perceives the medium or a single transmission as the instigator of media consumption to which an audience is drawn. This perspective is prominent in media research and the design of media content (McQuail, 1997). In order to grasp commonalities among audience members, and to gear their communication toward these commonalities, players in the media analyze their audiences.

Analyzing the Audience

The goal of audience analysis is “to identify its needs, document the perceived costs and benefits of addressing the needs, and formulate a program that addresses the needs in the most cost-beneficial manner to both the [receiver] and the [sender of the message]” (Lefebvre & Flora, 1988, p. 303). Napoli (2008) has outlined the evolution of audience analysis, a process strongly influenced by technological innovations. In the pioneering days of the mass media, audience analysis was performed by means of what Napoli calls the intuitive model: Communicators applied their common sense and “gut feeling” to characterize their audience and to determine how it could be served best. After the Great Depression in the United States, the need for a better understanding of the audience arose as movies were becoming more expensive to produce and competition among media was growing. Therefore, a more systematic approach to audience analysis was applied. Sources such as box office figures, radio sales, or letters of complaint were used to deduce who was receiving the message and how it was appreciated. In the 1970s, the introduction of electronic information systems facilitated new ways of analyzing audiences. Large quantities of data could be easily collected (by means of sales systems or television set-top boxes), analyzed, and interpreted; and, as a result, a shift in focus took place. Instead of focusing on the number of people who had received a message and on their reception of the message, audience analysis increasingly focused on the demographics of the audience.

With the growing use of the Internet and the development of technologies like data mining, audience analysis has reached a whole new stage. The technological developments have provided an opportunity to collect data about individual audience members and to scrutinize their behavior at an extremely detailed level. It is, for example, possible to track and record an online bookstore customer’s behavior via mouse clicks, viewing times, purchases, book ratings, etc. Subsequently, these data can be used to create a user model that states this user’s tastes in literature, inferred on the basis of, for example, owned books. In short, user modeling has made it possible to analyze audiences at a more detailed level than was possible before.

Targeting Audience Segments

As audience analysis was becoming a systematic undertaking, communicators—marketers in particular—realized that they could communicate more successfully if they addressed a small homogeneous segment of an audience instead of a large and heterogeneous population (Haley, 1968). In order to create advertisements that would have a higher persuasive effect with a specific subsection of the audience, Smith (1956) introduced “audience segmentation.” Audience segmentation has been defined as “the process of identifying groups of customers who are relatively homogenous in their response to marketing stimuli, so that the market offering can be tailored more closely to meet their needs” (Brennan, Baines, & Garneau, 2003, p.107). Audience segmentation, and the subsequent targeting of communication and product design at each segment, is done to find new, previously unaddressed target groups and to improve upon the communication to (potential) clients (Beane & Ennis, 1987). Ultimately, it has the potential to cater to the specific needs of customers and thus increase customer satisfaction and customer loyalty (Van der Geest, Jansen, Mogulkoç, De Vries, & De Vries, 2008). According to Baines, Fill, and Page (2008), there are three kinds of criteria by which an audience can be segmented:

  1. Behavioral criteria—e.g., similar purchases or similar technology usage
  2. Psychological criteria—e.g., similar lifestyle or attitudes
  3. Profile criteria—e.g., similar demographics or socioeconomic characteristics

Although segmentation has been reported to be beneficial when marketing products, it has also been heavily criticized by scholars. The major criticisms of dividing an audience into segments are that there is no a priori segmentation approach that yields the best results, audience segments are often not discriminating and overlap, and, finally, segments are not stable, as people’s characteristics and interests change constantly (Hoek, Gendall, & Esslemont, 1996). These drawbacks have led communicators to consider other ways of targeting their communication, mostly by focusing on individuals and addressing their unique characteristics, needs, and contexts (Kara & Kaynak, 1997).

In the area of mediated communication, the possibilities of targeting communication at individuals have grown rapidly with the introduction of user modeling. Based upon a user model, an intelligent system can tailor output to each individual’s unique needs, wishes, and context: personalization. Together with user modeling, personalization changes the way in which communicators perceive and communicate with their audience. As a result, one can wonder what the importance and meaning of a concept like “audience” entails in this context. When the audience at large is replaced by a collection of individuals who are to be addressed with an individual message, do we even need a concept of “audience”?

Witnessing the End of the Audience as We Know It

Driven by advances in technology, the role of the individual audience member has transformed from a receiving party to the individual who is actively involved in the creation of a message. This shift is made possible by technological advances like hypermedia, cross-media, and user-generated content. Hypermedia has introduced a way of media consumption in which the individual audience member has gained control over the order in which content is consumed (Cover, 2006). And due to another innovation, cross-media, a message is not distributed by means of only one medium, but by different media that augment each other. For example, a television channel broadcasts a documentary about genetically modified rice, after which a Web site facilitates a discussion on the topic between experts and viewers of the television broadcast. At the moment of writing, the latest development that has transformed the role of the audience is user-generated content (UGC). The Organisation for Economic Co-operation and Development (2007) has defined UGC as publicly available user content in which creative effort has been invested and that is created outside of professional routines and practices. Well-known examples of UGC collections are Flickr (www.flickr.com), where Web site visitors can place and tag (label) photos, Wikipedia (www.wikipedia.org), a Web site where users can coauthor and coedit an encyclopedia, and Yahoo! Answers (www.answers.yahoo.com), a Web site that offers people the possibility to pose all sorts of questions and publish answers to other persons’ questions.

Newly available technologies have enabled individuals to publish and personalize their own media content. As a result, the audience has transformed from a collective mass, traditionally addressed with one-way communication media, to unique individuals who are offered a more and more active role in the construction of a message (Livingstone, 2003; Tauder, 2005). This transformation is reflected in three changes in the traditional roles of communication senders and receivers and their relationships with each other (Bruns, 2007):

  1. Senders do not consist of selected individuals or groups anymore, but of (a community of) different people with their own geographical location, knowledge, etc.
  2. One person may assume different roles: generating the message at one moment, and consuming it at the other.
  3. A message is continuously being created and is never finished.

These changes cast a new light on the traditional roles that senders and receivers have been allocated in communication theory in the past. People can be senders and receivers at the same time and later become only receivers again. The roles of senders and receivers were conceived to be predefined and static, but are now dynamically assigned, depending on the task at hand. Communication has become a collaborative effort. As a result, professional communicators—and especially professional communicators working in the field of new media—should ask themselves whether they should still consider their target groups as audiences, as collective masses to be reached with one general message. Might it not be better to take a micro-societal view of the audience, the individual, and to reconsider the role of the individual in message construction and consumption?

The aforementioned changes in mediated communication make the term user more appropriate than audience member for characterizing the individual interaction with novel communication techniques like UGC and personalization. A user is an individual who can take on different communicative roles within one specific situation of use, like receiving and contributing content. In contrast, audience members are part of a mass, are primarily on the receiving end of communication, and are relatively passive during information consumption.

The shift of focus from a collective audience to individual users, served by personalization, requires a change in message design. The tools on which communicators have relied for decades are to be replaced; user modeling takes the place of audience analysis; and segmentation is put to its extreme in the process of personalization. As personalized messages are extremely sensitive to a correct correspondence with the individual’s needs, wishes, and context (Kara & Kaynak, 1997), a heavy focus on the individual user throughout the design process is conditional (Canny, 2006). One way to ensure this correspondence is User-Centered Design.

User-Centered Design of Personalized Systems

In the mid-1980s, two publications introduced the User-Centered Design (UCD) approach (Gould & Lewis, 1985; Norman, 1986). In essence, UCD is a design approach in which the (prospective) user is the focus of attention and is consulted in all phases of the system design. In their landmark article, Gould and Lewis (1985) list three principles of UCD:

  1. An early focus on users and tasks. Users should be consulted as early as possible, before system design, about their characteristics, needs, and wishes.
  2. Empirical measurement. Studies should focus on actual user behavior and be conducted empirically.
  3. Iterative design. Every substantial new version of the system should be tested with users, and the results of these studies should be incorporated in the next version of the system.

Later, they added a fourth principle, stating that systems should not be designed in isolation, but that all system aspects affecting usability (e.g., help functions or using multiple channels) should be designed in accordance and under one management body (Gould, Boies, and Lewis, 1991). These principles remain very abstract. In order to increase the practical value of the approach, Maguire (2001) divides the system development process into five phases (e.g., requirements engineering, design, and formative evaluation) and for each phase lists the methods that can be of value in a UCD process.

Technical communicators, with their UCD skills as well as their understanding of communication, are excellent candidates to take on a lead role in the UCD process of personalized systems: as the user’s advocate. This is a role technical communicators have often occupied, so how is it different for personalized systems? Traditionally, design has centered on abstractions of users, like audience segments or personas. System output had to comply with the needs, preferences, and contexts of these groups. When dealing with personalization, technical communicators’ focus should be on the individual user. They have to ensure that personalized output is usable and useful for every individual working with the personalized system.

Preventing and Identifying Usability Problems

Several authors have discussed how one can evaluate personalized systems. Gena (2005) and Gena and Weibelzahl (2007) have listed the methods that one can possibly apply during the UCD process of a personalized system. And although these overviews are a good reference point for the decision of which method to use at a given moment, they do not present a coherent approach in which multiple methods are used and geared toward each other. These overviews and several other publications, for example Höök (1997) and Weibelzahl (2005), have listed some pitfalls and ways to overcome them. The majority of these issues concern the design of a valid effectiveness measurement of a personalized system. The issue of applying UCD design methods for understanding how users experience personalized output, and how this experience can be improved upon—as in, for example, Van Velsen, Van der Geest, and Klaassen (2007)—is rarely addressed in the literature.

Two publications that give shape to the user experience with a personalized system have been written by Jameson (2003, 2007). In these book chapters, he lists seven usability issues that have a critical influence on users’ satisfaction with personalization. These usability issues are not new, but with the rise of personalization, they have acquired a new meaning and increased importance. They are:

  • Predictability. Users must be able to predict the consequences of their actions for the generation of personalized output.
  • Comprehensibility. Users must be able to understand how user modeling and the tailoring of system output works.
  • Controllability. Users must be able to control their user model and the generation of personalized output.
  • Unobtrusiveness. Users must be able to complete their tasks without being distracted by personalization features.
  • Privacy. Users must not have the feeling that the generation of a user model infringes on their privacy.
  • Breadth of experience. Users must not lose the possibility of discovering something new because output only complies with their user model.
  • System competence. Users must not have the feeling that the system creates an invalid user model or does not personalize output successfully.

In order to ensure that a personalized system is designed such that it counters the possible negative effects of these issues, they have to be taken into account throughout the design process. This means an extension of the responsibilities for technical communicators involved in the design of personalized systems. They have to make sure that activities deployed before and during design, as well as evaluations, take the pitfalls and usability issues for personalization into account.

A Layered Approach to Designing Personalized Systems

Design or evaluation activities of personalization should not approach the personalization process as a whole. Rather, the process should be “broken down” into several steps, so as to make it possible to prevent or pinpoint problems (Brusilovsky, Karagiannidis, & Sampson, 2001; Paramythis & Weibelzahl, 2005). Each step, then, will have to be designed on the basis of a user study, or should be evaluated separately. Such design and evaluation activities should have the goal of keeping to a minimum any errors made while interpreting information about the user, or reasoning on the basis of this information. The steps of the personalization process, listed by Paramythis and Weibelzahl (2005), can serve as the basis for breaking up the process of personalization:

  1. Identify the user.
  2. Collect user data.
  3. Interpret user data.
  4. Decide upon personalization.
  5. Apply adaptation.

However, it is best to approach personalization in the steps that correspond with the steps in the personalization process, applied by a particular system. In the literature, such a broken-down approach is called a layered approach. Each step has to be designed and evaluated in isolation and in each step the technical communicator can be of great help by applying a user-centered focus. We will now discuss the technical communicator’s role in each step, based upon the overview of the layered approach as discussed in Paramythis and Weibelzahl (2005).

The first step deals with identifying the user. For technical communicators, this means that they must be able to define suitable groups for which output can be personalized. This applies when personalization is targeted at groups and not at individuals. Such a decision can be based upon a study that has the goal to identify homogeneous subgroups in a heterogeneous population. Once a working prototype of the system is available, technical communicators must be able to design evaluations that tell whether the identification of relevant subgroups was correct. In the case of personalized book recommendations targeted at groups of customers, for example, one can decide that a relevant subgroup consists of “people who buy cookbooks with recipes for pasta dishes.” However, a more relevant group might be “people who buy cookbooks on Italian cuisine,” as they will receive more diverse, but still relevant, recommendations.

The correct collection of user data is often a technical matter and outside the expertise of technical communicators. For example, if a system uses gaze data to determine what a certain user is looking at, correct collection of user data deals with whether the recorded data correspond with what the user was looking at. However, also in this step, the technical communicator can add value. Product recommendations, for example, can be based upon age, as they might provide 50-year-olds with music from the ’60s, while teenagers are predominantly recommended music that is popular at the current moment in time. To inform the system, users might be able to enter their birth date in an electronic form, but might do this wrong. This leads to incorrect user data and as a result, the user may be recommended music that is not very interesting. Technical communicators have to make sure that user errors that lead to incorrect data collection are prevented.

In the third step data are interpreted. Technical communicators have to aid the design team when deciding on which data the system has to use and how they will be interpreted. When designing book recommendations, for example, one will have to decide upon a set of data that can model the user’s reading preferences. Are past book purchases indicative of reading preferences? Or is browsing behavior in the store’s collection more informative? Or should one opt for a combination of both sets of data? Such decisions can be made on the basis of a user study that should determine which data set (or combination of data sets) models reading preferences best. For example, in a Wizard of Oz study one could simulate the acquired user models for several users, using different data sets. Then, these users can be asked to judge the quality of the interpretations about their person, stored in the different models.

The fourth and fifth steps are deciding upon personalization and applying personalization. Technical communicators have to make sure that personalization at a certain moment adds value and that the reasoning on the basis of the user model as performed by the system is correct. Evaluations of (prototypical) personalized systems have to point out at what time personalization adds value. For example, can book recommendations be based on the purchase data of two books? Or does one need to have bought 20 books before the user model is “rich” enough to lead to useful recommendations? And if the moment is appropriate, evaluations have to point out the quality of personalization. Is this book one I would like to read? Is this book a new discovery, or do I already know of its existence?

During evaluations it is important that results allow the technical communicator to pinpoint problem areas. If an evaluation only indicates that book recommendations do not fit the individual user, it is difficult to state where the problem lies. Is the system modeling the user incorrectly by interpreting the purchase of books on the history of Japan as an interest in the history of Asia, while the user is only interested in Japan? Or is the user model correct, but the fault lies in the fact that the system reasons that a user who is interested in the history of Asia will also be interested in the economy of Asian countries? Without breaking up the personalization process in underlying steps, and evaluating each step separately, an evaluator will never know. Therefore, evaluators need to create an evaluation design that informs them on the different steps of the personalization process. For example, one can ask participants to interact with the system. Then, participants can be asked to judge the quality of the recommendations and to give their rationale behind this judgment. Finally, evaluators can create printouts of the acquired user models and can ask participants to comment on their user models. This way, more detailed feedback on the personalization process can be generated.

Technical Communication and the User-Centered Design Process of Personalized Systems

UCD for Personalization and the Technical Communication Practitioner

Communication professionals will have to lead user studies that inform the digitalization of the different steps of the personalization process. They will have to devise and carry out studies that determine which input data are necessary for generating valid information about the user and to optimize its collection. Programmers can then convert the results of these studies into algorithms and system code. Technical communicators also have to be able to design evaluations that inform the design team on the personalized system’s performance on the different steps of the personalization process, and how this performance can be improved upon. During all these activities, the communication professional must keep the usability issues for personalization in mind.

The design team of a personalized system is preferably interdisciplinary. The technical communicator can be the linking pin of the design team who can maximize the fit between users and tailored output and, consequently, ensure the success of a personalized system. However, a thorough understanding of the personalization process and knowing how to evade common design and evaluation pitfalls are critical.

A Technical Communication Research Agenda for Personalized Systems

For practitioners involved in the design or evaluation of personalized systems from a user perspective to successfully carry out their work, having the right tools and insights on personalization is a necessity. Therefore, communication researchers need to focus their attention on several matters, so as to give technical communicators these tools and insights.

First and foremost, the added value of personalization for different kinds of systems (e.g., prefilled government forms or personalized tourist guides) needs to be assessed from a user perspective. However, in the literature, effectiveness studies of personalized systems rarely focus on user effects (Van Velsen, Van der Geest, Klaassen, & Steehouder, 2008). In order to decide whether it is worth the effort to design a personalized system for a given task, it needs to be clear whether or not it is a worthwhile financial investment. Summative evaluations investigating the users’ perspective on the quality and the usefulness of personalized systems, combined with the personalized system’s performance on effectiveness metrics, can inform decision makers about whether such an investment is sensible (Díaz, García, & Gervás, 2008). These kinds of evaluations, combining a user and a system perspective, are lacking in the current studies on personalized systems.

Second, studies that identify the factors that affect the acceptance and adoption of personalized systems need to be conducted. With knowledge of these factors, those who practice UCD can deploy meaningful and value-adding requirements engineering, design, and evaluation sessions. In the literature, overviews of these factors of personalized systems are virtually nonexistent, except for the case of personalized e-government services, where Pieterson, Ebbers, and Van Dijk (2007) have listed several user and organizational obstacles. Researchers need to identify factors that influence user acceptance and adoption of personalization.

Third, the requirements engineering stage of a UCD approach is focused on generating requirements that align with prospective users’ needs, wishes, and contexts. For the case of personalization, Gena and Weibelzahl (2007) have listed several methods that can be deployed here. However, these methods are discussed in isolation, while the requirements engineering process is a process that needs to be iterative and that needs to apply various methods geared to each other. Requirements not only need to be elicited from prospective users, their implementation in system design also needs to be checked with prospective users. An integrated, multimethod requirements engineering approach for personalized e-government services has been suggested and demonstrated by Van Velsen, Van der Geest, Ter Hedde, and Derks (2009). Future research has to point out how to deal with the specific intricacies of personalization in this stage of system design for the different types of personalized systems.

Fourth, design guidelines that inform the interface and interaction design of personalized systems are required but scarce. Some high-level guidelines regarding the specific usability issues for personalized systems are reported by Jameson (2003, 2007). On a more specific level, Tintarev and Masthoff (2007) list several guidelines for the design of explanations of personalized recommendations, and Schiaffino and Amandi (2004) discuss the interaction design of personalized interface agents. However, apart from a very limited number of overviews, the designer of personalized systems is left to his or her own insights in the design phase. Research should point out how the interface and interaction of personalization should be designed in such a way that the specific usability issues for personalization can be prevented.

Fifth, during the formative evaluation stage of a personalized system (focused on gathering input for redesign), one needs to choose an applicable method for gathering the data one requires. The (dis)advantages of traditional evaluation methods have been extensively studied, but these studies have taken the assumption that system output is the same for every user in every context. In the case of personalized systems, this assumption no longer holds. It is unclear how suitable the traditional methods are to assess the specific usability issues for personalization. Future research has to point out to what degree we can use traditional or novel evaluation methods for the evaluation of personalized systems.

Concluding Remarks

This article is meant as a call to action, both for communication professionals and communication researchers. We believe it is time for the technical communication community to embrace personalized technology and to make it part of their practical expertise and research focus. The development of, and research into, personalization has, until now, been dominated by a technical point of view. By contributing from their user-centered perspective, technical communication professionals can make a huge difference in the usability and added value of personalization.


Baines, P., Fill, C., & Page, K. (2008). Marketing. Oxford: Oxford University Press.

Beane, T.P., & Ennis, D.M. (1987). Market segmentation: A review. European Journal of Marketing, 21(5), 20–42.

Brennan, R., Baines, P., & Garneau, P. (2003). Contemporary strategic marketing. Houndmills, England: Palgrave Macmillan.

Bruns, A. (2007). Produsage: Towards a broader framework for user-led content creation. Paper presented at Creativity & cognition, 13–15 June, Washington, DC.

Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction, 6(2/3), 87–129.

Brusilovsky, P. (2001). Adaptive hypermedia. User Modeling and User-Adapted Interaction, 11(1/2), 87–110.

Brusilovsky, P., Karagiannidis, C., & Sampson, D. (2001). Benefits of layered evaluation of adaptive applications and services. In S. Weibelzahl, D. Chin, & G. Weber (Eds.), Proceedings of the empirical evaluation of adaptive systems workshop. Sonthofen, Germany.

Canny, J. (2006). The future of human-computer interaction. ACM Queue, 4(6), 24–32.

Cover, R. (2006). Audience inter/active: Interactive media, narrative control and reconceiving audience history. New Media & Society, 8(1), 139–158.

Díaz, A., García, A., & Gervás, P. (2008). User-centred versus system-centred evaluation of a personalization system. Information Processing and Management, 44(3), 1293–1307.

Gena, C. (2005). Methods and techniques for the evaluation of user-adaptive systems. The Knowledge Engineering Review, 20(1), 1–37.

Gena, C., & Weibelzahl, S. (2007). Usability engineering for the adaptive Web. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The adaptive Web (pp. 720–762). Heidelberg, Germany: Springer-Verlag.

Gould, J.D., Boies, S.J., & Lewis, C. (1991). Making usable, useful, productivity. Enhancing computer applications. Communications of the ACM, 34(1), 74–85.

Gould, J.D., & Lewis, C. (1985). Designing for usability: Key principles and what designers think. Communications of the ACM, 28(3), 300–311.

Haley, R.I. (1968). Benefit segmentation: A decision-oriented research tool. Journal of Marketing, 32(3), 30–35.

Hoek, J., Gendall, P., & Esslemont, D. (1996). Market segmentation: A search for the Holy Grail? Journal of Marketing Practice, 2(1), 25–34.

Höök, K. (1997). Evaluating the utility and usability of an adaptive hypermedia system. Paper presented at Intelligent User Interfaces, Orlando, FL.

Jameson, A. (2003). Adaptive interfaces and agents. In J. A. Jacko & A. Sears (Eds.), Human-computer interaction handbook (pp. 203-230). Mahwah, NJ: Erlbaum.

Jameson, A. (2007). Adaptive interfaces and agents. In J. A. Jacko & A. Sears (Eds.), Human-computer interaction handbook (2nd ed., pp. 433-458). Mahwah, NJ: Erlbaum.

Kara, A., & Kaynak, E. (1997). Markets of a single customer: Exploiting conceptual developments in market segmentation. European Journal of Marketing, 31(11/12), 873–895.

Katz, E., Blumler, J.G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 27(4), 509–523.

Knutov, E., De Bra, P., & Pechenizkiy, M. (2009). AH 12 years later: A comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia, 15(1), 5–38.

Kobsa, A. (2001). Generic user modeling systems. User Modeling and User-Adapted Interaction, 11(1/2), 49–63.

Kobsa, A., Koenemann, J., & Pohl, W. (2001). Personalised hypermedia presentation techniques for improving online customer relationships. The Knowledge Engineering Review, 16(2), 111–155.

Lefebvre, R.C., & Flora, F.J. (1988). Social marketing and public health intervention. Health Education Quarterly, 15(3), 299–315.

Linden, G.D., Jacobi, J.A., & Benson, E.A. (2001). U.S. Patent No. 6,266,649 B1. Washington, DC: U.S. Patent and Trademark Office.

Livingstone, S. (2003). The changing nature of audiences: From the mass audience to the interactive media user. In A. N. Valdivia (Ed.), A companion to media studies (pp. 337-359). Malden, MA: Blackwell Publishing.

Maguire, M. (2001). Methods to support human-centred design. International Journal of Human-Computer Studies, 55(4), 587–634.

McQuail, D. (1997). Audience analysis. Thousand Oaks, CA: Sage Publications Inc.

Napoli, P.M. (2008). Toward a model of audience evolution: New technologies and the transformation of media audiences. Bronx, NY: The Donald McGannon Communication Research Center.

Norman, D.A. (1986). The design of everyday things. New York: Basic Books.

Organisation for Economic Co-operation and Development. (2007). Participative Web and user-created content. Paris: Organisation for Economic Co-operation and Development.

Paramythis, A., & Weibelzahl, S. (2005). A decomposition model for the layered evaluation of interactive adaptive systems. In L. Ardissono, P. Brna, & A. Mitrovic (Eds.), Proceedings of the 10th international conference on user modeling (438-442). Heidelberg, Germany: Springer-Verlag.

Peters, J.D. (1999). Speaking into the air. A history of the idea of communication. Chicago: University of Chicago Press.

Pieterson, W., Ebbers, W., & Van Dijk, J. (2007). Personalization in the public sector. An inventory of organizational and user obstacles towards personalization of electronic services in the public sector. Government Information Quarterly, 24(1), 148–164.

Plato. trans. (2005). Phaedrus (C. Rowe, Trans.). London: Penguin Books.

Schiaffino, S., & Amandi, A. (2004). User-interface agent interaction: Personalization issues. International Journal of Human-Computer Studies, 60(1), 129–148.

Smith, W.R. (1956). Product differentiation and market segmentation as alternative marketing strategies. Journal of Marketing, 21(1), 3–8.

Tauder, A.R. (2005). Getting ready for the next generation of marketing communications. Journal of Advertising Research, 45(1), 5–8.

Tintarev, N., & Masthoff, J. (2007). Effective explanations of recommendations: User-Centered Design. Paper presented at ACM Conference on Recommender Systems, October 19-20, Minneapolis, MN.

Van der Geest, T., Jansen, J., Mogulkoç, E., De Vries, P., & De Vries, S. (2008). Segmentation and e-government: A literature review. Enschede, the Netherlands: Telematica Institute.

Van Velsen, L., Van der Geest, T., & Klaassen, R. (2007). Testing the usability of a personalized system: Comparing the use of interviews, questionnaires and thinking-aloud. Paper presented at the IEEE Professional Communication Conference. Seattle, WA.

Van Velsen, L., Van der Geest, T., Klaassen, R., & Steehouder, M. (2008). User-centered evaluation of adaptive and adaptable systems: A literature review. The Knowledge Engineering Review, 23(3), 261–281.

Van Velsen, L., Van der Geest, T., Ter Hedde, M., & Derks, W. (2009). Requirements engineering for e-government services: A citizen-centric approach and case study. Government Information Quarterly, 26(3), 477–486.

Webster, J.G. (1998). The audience. Journal of Broadcasting & Electronic Media, 42(2), 190–207.

Weibelzahl, S. (2003). Evaluation of adaptive systems. Freiburg, Germany: Pedagogical University of Freiburg.

Weibelzahl, S. (2005). Problems and pitfalls in the evaluation of adaptive systems. In S. Y. Chen & G. D. Magoulas (Eds.), Adaptable and adaptive hypermedia systems (pp. 285-299) . Hershey, PA: IRM Press.

Wu, D., Im, I., Tremaine, M., Instone, K., & Turoff, M. (2003). A framework for classifying personalization scheme used on e-commerce Web sites . Paper presented at the 36th Hawaii international conference on system sciences, Waikoloa, HI.

About the Authors

Lex van Velsen is working toward a Ph.D. degree in the Technical and Professional Communication Department at the University of Twente, Enschede, The Netherlands. His research interests include personalized communication systems, user-centered design, e-Service design, and Web 2.0. Contact: l.s.vanvelsen@utwente.nl.

Thea van der Geest is an associate professor at the communication studies/technical and professional communication department of the University of Twente, The Netherlands. She teaches courses in interface and interaction design and research methodology. Her research focuses on information and document design for interactive media, with a special interest on the design and evaluation process and user-centered research methods. She has conducted and supervised numerous user studies on requirement analysis, acceptance, personalization, usability, and accessibility of systems. Her recent research projects are focusing on requirements engineering for and evaluation of e-government services and systems for Dutch and international government agencies. Contact: t.m.vandergeest@utwente.nl.

Michaël Steehouder holds the chair of Technical Communication at the University of Twente, The Netherlands, where he leads a research group with interests in user support, organizational communication, and document design. He is a senior member of STC and IEEE, vice-president of INTECOM, the international platform for Technical Communication, and former president of STIC, the Dutch Society for Technical Communication. He published and edited several Dutch books about communication skills, forms design, and technical communication. Contact: m.f.steehouder@utwente.nl.