Virtual Special Issue - Opportunities and Challenges of Personalized Advertising Journal of Interactive Advertising

Journal of Interactive Advertising

This virtual issue of the Journal of Interactive Advertising (JIAD) is comprised of a collection of key studies about personalized advertising. Personalized advertising can be defined as “advertising that is tailored to an individual’s characteristics and/or interests or tastes” (Hoy and Milne, 2010; De Keyzer, Dens and De Pelsmacker, 2015 p 125). Personalization is based on many different consumer characteristics. For example, advertising messages can be sent to specific targets on the grounds of their disclosed interests or psychographics, marketers can use personal information like name, age or gender to design more relevant advertisements, or ads can be tailored to a geographic location for targeting consumers.

Advertisers often emphasize the opportunities that personalized advertising offers to consumers. Some studies note that consumers believe that personalized ads have benefits because they reduce information overload, and help consumers in making decisions (Kruikemeijer, Sezgin and Boerman, 2016). Also personalized advertising is generally believed to be more effective than non-personalized advertising because consumers perceive ads as more relevant (De Keyzer, Dens and De Pelsmacker 2015), and pay more attention to personalized ads (Maslowska, Smit and Van den Putte, 2016).

In the past few years, the availability of huge amounts of data, algorithms and machine learning have made it easier to better target consumers at the right place and time with relevant messages (Rosenkrans and Myers, 2018). However, the fact that advertisers are increasingly monitoring consumers online to deliver individually targeted ads is also associated with certain challenges. Most importantly, personalized advertising and the data collection practices that make personalized advertising possible raise concerns about privacy and intrusiveness (Boerman, Kruikemeier and Zuiderveen Borgesius, 2017; Unni and Harmon, 2007; Hoy and Milne, 2010; Limpf and Voorveld, 2015; Rosenkrans and Myers, 2018; Van ‘t Riet et al., 2016). These concerns are rooted in the nature of the data and often being perceived as rather personal because information is collected from highly personal devices, such as smartphones, that are truly embedded in peoples’ everyday lives (Okazaki , Li and Hirose, 2009; Ng and Wakenshaw, 2017).

In the past ten years, the papers in JIAD have particularly focused on personalization associated with location: so called location-based advertising (LBA). In fact, the manuscript by Bruner and Kumar is one of the first academic explorations about mobile location-based advertising. They define LBA as “marketer-controlled information customized for recipients' geographic positions and received on mobile communication devices (Bruner and Kumar, 2007, p 3). The study not only describes the phenomenon of LBA, and the opportunities and challenges that are associated with it, but it also develops a scale that can be used to measure attitude toward location-based advertising.

Around the same time, the study by Unni and Harmon (2007) was also published and has proven to be an influential piece because it makes an important distinction between push versus pull forms of LBA. This distinction is used throughout the literature involving mobile advertising in general and location-based advertising in specific. Unni and Harmon (2007) conclude that pull LBA is more effective than push LBA, that privacy concerns are high, and that the perceived benefits and value of LBA is low.

The distinction between push versus pull LBA was also examined in a study by Limpf and Voorveld (2015). They expanded upon the findings of Unni and Hallman (2007) and further investigate the role of information privacy concerns influencing consumer responses towards LBA. Findings indicate that information privacy concerns influence consumers’ attitude towards LBA only in the case of push, but not pull LBA. Thus, in line with Unni and Harmon (2007), this paper shows that the type of mobile LBA (i.e., push versus pull) is crucial in understanding consumers’ attitudes and acceptance of mobile LBA.

Different than previous work, the van ‘t Riet and colleagues (2016) investigate whether ads that are tailored to consumers' location are indeed more effective than ads that are not. Another distinction is that most earlier studies about LBA rely on scenario-based work, but this research uses a sophisticated virtual reality setting. By utilizing a VR headset, participants could walk around in a virtual supermarket providing participants with a realistic shopping experience, and the researchers with the experimental control needed to manipulate location congruency. An important conclusion from this research is that the location-congruent ads resulted in more purchases than the location incongruent ads, but only when the advertised product was highly relevant for participants because of their goal to purchase. In contrast, location congruence resulted in more negative ad attitudes when the advertised product was of low (goal) relevance to participants. The authors warn that location congruent messages can backfire when the advertising content is perceived as irrelevant or disruptive to a consumer’s activities.

Further, the study by Rosenkrans and Myers (2018) takes a unique methodological approach and is very timely given recent advertising trends. It reports the results of a field study in which big data and predictive analytics (a form of artificial intelligence) are used to optimize mobile location-based ads. The paper makes a significant contribution to the field of advertising by exploring the effectiveness of predictive analytics and technology for macro and micro geofencing mobile ads. An important conclusion is that using predictive analytics to target mobile users in a macro geofenced area is more successful in terms of click-through rates than targeting those users in a micro geofenced area.

Obviously, the location of consumers is not the only characteristic that can be used to serve consumers with ads that are tailored to their personal situation. The last three studies in this virtual special issue broaden personalization to include online behavioral advertising on social networking sites, and personalization based on gender, name, and professional status. Next to investigating different types of personalization, these manuscripts also contribute to a methodological discussion in the research field of personalized advertising. Meaning, they explore whether actual personalization (personalization cues included in an ad), or the perception of personalization by the user (whether the user perceives that an ad fits him or her), should be investigated, and which of the two is most predictive of consumer responses to personalization (Maslowska, Smit and Van den Putte, 2016; De Keyzer, Dens, and De Pelsmacker, 2015).

In turn, Hoy and Milne (2010) provide insight into young adults' awareness of and beliefs regarding usage of their personal profile information for personalized advertising on social networking sites. Results of a survey show that women and, to a lesser extent, men have concerns about advertisers' and marketers' use of personal information for advertising on social networking sites. Furthermore, several differences between men and women were found with regards to the amount and type of proactive self-protective behaviors.

De Keyzer, Dens and De Pelsmacker (2015) on the other hand investigate the effectiveness of personalization based on gender in the context of social media advertising. They argue that perceived personalization is probably more important than actual personalization in terms of influencing consumer responses. By creating one personalized advertisement based on gender and another on non-personalization, their results show that perceived personalization improves responses toward Facebook ads, and that perceived relevance is an underlying mechanism for this relationship. Attitude toward Facebook is a moderator of this relationship but only in which a low involvement product is used.

Lastly, Maslowska, Smit and van den Putte (2016) breaks new ground by explicitly testing the mediating role of perceived personalization as an explanatory mechanism of personalized advertising effectiveness. In the study, three personalization strategies are compared: raising expectation (statements promising a personalized offer), identification (identifying the recipient by name), and contextualization (framing a message in a context meaningful to the recipient with the use of contextual variables). Only the advertisements in which the recipient’s name was included triggered perceived personalization. Perceived personalization then mediates the effects of personalized advertising on attention, cognitive responses, and attitude toward the message.

In total, this virtual special issue highlights eight key studies published in JIAD examining different forms of personalized advertising. It is likely that personalized advertising will only increase in prevalence in the coming years. Thus, there remains an incredible need for future research to identify and explore the opportunities and challenges related to this intriguing type of advertising. I hope you enjoy learning about this influential area of interactive advertising scholarship.

Hilde Voorveld, Ph.D.
Associate Professor of Marketing Communication
Amsterdam School of Communication Research
University of Amsterdam