The ability to deliver personalized advertising messages has long been a major objective in marketing since it allows marketers to meet heterogeneous consumer needs and target their messages more effectively (Arens & Bovee, 1994). However, traditional one-to-many marketing approaches applied in mass media suffer from their inability to meet this objective (Dibb, 1998; Hoffman & Novak, 1997). In order to increase the efficiency of their strategy, marketers identify homogeneous groups of consumers (market segmentation) which they target according to their marketing objectives. Thus, market segmentation has become the most important marketing tool for targeting purposes (McBurnie & Clutterbuck, 1998), also utilized in the TV advertising domain in conjunction with domain-specific features such as time zones and/or program typologies. However, this strategy has admittedly little to offer towards the ultimate goal of one-to-one communication, since the targeted unit is the segment rather than the individual consumer, and therefore individual needs cannot be satisfied. In the broadcasting television advertising domain, media coverage either exceeds the targeted market segment or leaves potential customers without exposure to the message, thus reducing its cost effectiveness (Belch & Belch, 1995). At the same time, TV viewers have to deal with a vast amount of available advertising information. The issue of information overload, typical in information theoretic terms, is also experienced in the case of TV advertisements as advertising clutter, which has been identified as one of the significant factors associated with the negative attitude of viewers towards advertising and can have a negative impact on television advertisement recall or recognition (Mord & Gilson, 1985). Relevant surveys reveal that 80% of the viewers feel that there is “too much advertising in television” (Elliott & Speck, 1998), while more than 75% of consumers are not happy with the broadcasted advertisements (Hawkins, Best, & Coney, 1998 ). Current target marketing methods are limited in their ability to efficiently target consumers at the individual level, particularly in mass media such as television. Thus, personalization of advertisements provides marketers with the opportunity to increase advertising effectiveness by targeting consumers who are most likely to respond positively to the advertising message. The present article investigates appropriate personalization methods for the domain of digital television advertisements by examining relevant methods utilized for personalized Web applications. In addition, it is concerned with the design of the interactive elements of a typical 30-second advertisement in support of the personalization process. The two objectives of this article are interrelated: the selection of a personalization technique affects the design of interactive advertisements since it indicates the type of interaction data that should be collected in order to enable personalization. The next section of this article opens up the discussion on personalization from a theoretical point of view and in the following section specific personalization techniques are compared. Next, the types of interaction data required to achieve personalization are discussed and the article concludes with further discussion and conclusions.
Adaptive hypermedia and adaptive Web-based systems are systems that adapt their content, structure, or presentation to the goals, tasks, interests, and other features of individual users or groups of users (Brusilosvsky &Maybury, 2002). The term hypermedia denotes interactive systems that allow users to navigate a network of linked objects (for example Web pages). However, the usefulness of these systems extends to any application area with diverse users and reasonably large space of possible options (Brusilovsky, 1996, 2001). Indeed, adaptive hypermedia systems provide the scientific framework for the personalization research (Ardissono & Goy, 2000; Kobsa, Koenemann, & Pohl, 2001).
Key Terms in this Chapter
Interactivity: A two-way communication model providing users with power over the presented content.
Personalization: The process of providing content tailored to individuals.
User Modeling: The development or updating of the user model.
Recommender systems: Systems that provide personalized recommendations according to the user’s interests.
Collaborative Filtering: A recommendation method that exploits similarities between the users of a recommender system.
Rating: A quantification of a user’s evaluation on observed information items.
Adaptive Hypermedia Systems: Systems that adapt their content, structure, or presentation to the features of their users.
User Model: A representation of user features.