Behaviorally targeted location-based mobile marketing

Marketers increasingly use behavioral targeting in location-based mobile marketing (LBMM). However, highly personalized marketing messages like this may backfire by eliciting consumer reactance. We suggest that LBMM efficacy depends on its potential to minimize consumer reactance, which can be achieved by effectively combining location targeting (in-store vs. out-store), behavioral targeting (based on consumers’ product category involvement [PCI]), and the type of promotion offered (price vs. non-price promotion). Results of a field study, a virtual reality experiment, and two online experiments show that although in-store mobile ads are generally more effective in increasing sales than out-store mobile ads, this is only the case if consumers have low PCI with the advertised product category, because this decreases their reactance. To attract consumers to stores by out-store LBMM, we show that firms should offer price promotions to consumers with low PCI and non-price promotions to consumers with high PCI, because these combinations of location targeting, behavioral targeting, and type of promotion elicit the least reactance and therefore result in a higher probability to buy.

Makna Simbolik dalam Iklan Cerdas dan Target Perilaku Khalayak

This article aims to discuss the concept of smart advertising, especially with regard to the goals and effects of smart smart advertising to form the top of the mind of the audience. This research was conducted with library research (library research) with a descriptive analysis approach, namely collecting data, compiling or clarifying, and interpreting it. The technique used in this research, namely by analyzing advertisement sentences, seen from the use of interference. The data in this study were obtained from several sources in which there was interference. This study concludes that one of the most widely used strategies to increase the value of advertising is "smart advertising" or advertising that is carried out with behavioral targeting. By using information about online behavior, including sites visited and interest in certain types of content, behavior targeting seeks to serve ads that are more attractive to certain groups of consumers. Despite the potential for a dramatic increase for online advertisers and publishers, some users and user advocacy groups have expressed concern over the privacy concerns posed by behavioral targeting

Targeting and Privacy in Mobile Advertising

Mobile in-app advertising is now the dominant form of digital advertising. Although these ads have excellent user-tracking properties, they have raised concerns among privacy advocates. This has resulted in an ongoing debate on the value of different types of targeting information, the incentives of ad networks to engage in behavioral targeting, and the role of regulation. To answer these questions, we propose a unified modeling framework that consists of two components—a machine learning framework for targeting and an analytical auction model for examining market outcomes under counterfactual targeting regimes. We apply our framework to large-scale data from the leading in-app ad network of an Asian country. We find that an efficient targeting policy based on our machine learning framework improves the average click-through rate by 66.80% over the current system. These gains mainly stem from behavioral information compared with contextual information. Theoretical and empirical counterfactuals show that although total surplus grows with more granular targeting, the ad network’s revenues are nonmonotonic; that is, the most efficient targeting does not maximize ad network revenues. Rather, it is maximized when the ad network does not allow advertisers to engage in behavioral targeting. Our results suggest that ad networks may have economic incentives to preserve users’ privacy without external regulation.