Behavioral Targeting – Definition

Cite this article as:"Behavioral Targeting – Definition," in The Business Professor, updated March 4, 2019, last accessed October 25, 2020,


Behavioral Targeting Definition

Target advertising is an online advertisement that involves the use of sophisticated techniques to reach out to the receptive target audience with particular traits. The technique chosen by an online advertiser always depends on the type of product or the person promoted. The traits considered include demographic factors such as age, gender, education level, employment, race, and economic status or psychographic factors which include personality, opinions, attitudes, interests, and lifestyles. Some online advertisers may also consider behavioral variables like purchase history, browser history or any recent activity. Since target advertising focuses on certain traits and only considers consumers with strong preference to a brand, the advertising approach helps to eliminate wastage.

A Little More on Behavioral Targeting

Over the years, online advertisement has come to replace traditional forms of advertising such as print magazines, radio, newspapers, and billboards. With the transformation of the information and communication technology (ICT) space, targeted advertising has spread across several technologies like IPTV and mobile environments. It is projected that targeted advertisements will increase radically across different ICT channels.

The demand for targeted advertising is increasing due to the emerging new online channels, and companies are interested to minimize wasted advertising through the use of these technology. Current targeted advertising uses second-order proxies such as mobile web consumer activities, associating historical web page consumer demographics with web page access, and contextual advertising.

Social media targeting

This is a form of targeted advertising which involves the use of general targeting attributes including geotargeting, behavioral targeting, and socio-psychographic targeting. An example is the case of Facebook; in case a consumer likes a clothing page, they will receive ads based on their liked pages including their areas of residence. This advertising method enables advertisers to be target-specific as they address the needs of consumers in response to their cities and interests.

Social media platforms have made it easier for targeting advertisers to understand the interests and ‘likes’ of consumers on the user profiles. Facebook, for instance, enable advertisers to target consumers based on age, gender, and location.

Mobile devices

Recently, advertising has become more pervasive online in the mobile setting. Using mobile devices for targeted advertising enhances the transmission of consumer information including their location, time, and interests. This enables advertisers to produce adverts that cater to consumer-specific needs and more specific the changing environment.

Content and contextual targeting

Content/contextual targeting is considered the most straightforward targeting approach. It involves placing ads in a specific place relative to the available content. Also known as content-oriented advertising, this targeting method can be applied across different mediums. For instance, an online advert about home purchases may include a different advert within the same context, such as insurance ad. This form of advertising can be achieved through an ad matching system that analyses a page’s content and presents a relevant ad. However, an ad matching system may fail if it fails to recognize the different between negative and positive correlations which can result in placing contradictory adverts.

Technical targeting

This form of advertising is associated with the software or hardware status of a user. In technical targeting, an ad is altered in line with the user’s available network bandwidth. For instance, if a user has a mobile device with limited connection, this ad delivery system will ensure the user receives a smaller ad version that enables faster data transfer rate.

Common delivery systems used in technical targeting is the addressable advertising systems which serve ads based on the psychographic, demographic, and behavioral attributes of the consumers. These systems have digital components and are addressable i.e. they have end points that are capable of rendering an ad independently.

Behavioral targeting

This form of targeting is based on the actions/activities of users and is majorly successful on web pages. Advertisers believe that behavioral targeting produces ads that are relevant to users, and as such enhances the level of consumer influence. For example, if a consumer was looking for plane ticket prices, the targeting system recognizes this and starts showing related adverts to the consumer across unrelated websites. An advantage of this targeting method is that it focuses on the consumer’s interests instead of the target groups of a particular brand/product.

As consumers visit different web sites, site publishers are able to create defined audience segments based on information such as the pages commonly visited, the amount of time spent on a page, links clicked by the consumers, and the things they interact with.

Behavioral marketing can be used independently or with other forms of targeting, also referred to as “audience targeting.”

Some of the key advantages of behavioral marketing include it helps advertisers to reach surfers with affinity, access surfers who are not exposed to media campaign, and in reconnecting with customers or prospects.

References for Behavioral Targeting

Academic Research on Behavioral Targeting

  • How much can behavioral targeting help online advertising?, Yan, J., Liu, N., Wang, G., Zhang, W., Jiang, Y., & Chen, Z. (2009, April). In Proceedings of the 18th international conference on World wide web (pp. 261-270). ACM. This article illustrates how Behavioral Targeting (BT) can be used to increase campaign and ad effectiveness in the online advertising market. To address this question, the authors provide an empirical study on the click-through advert logs collected from a commercial search engine. Based on the study, it is concluded that: BT results in the increase click-through rates (CTR) of ads, using short term user behavioral targeted advertising is more effective compared to long term user behaviors, and users clicking similar ads on a Web exhibit same behaviors.
  • The value of behavioral targeting, Beales, H. (2010). Network Advertising Initiative, 1. This study analyses the effects of behavioral targeting on advertising rates and revenues. It involves a survey of 12 ad networks to obtain quarter CPM data, conversion rates, and revenues across different advertising segments. The findings include: advertising rates increases significantly among behaviorally targeted ads; behavioral targeting advertising is more successful compared to standard run of network advertising; and majority of network advertising revenue is spent on acquiring publishers’ inventory.
  • Probabilistic latent semantic user segmentation for behavioral targeted advertising, Wu, X., Yan, J., Liu, N., Yan, S., Chen, Y., & Chen, Z. (2009, June). In Proceedings of the Third International Workshop on Data Mining and Audience Intelligence for Advertising (pp. 10-17). ACM. This article recognizes the role of behavioral targeting in delivering the most appropriate ad to the most appropriate users. With the key challenge of not being able to automatically segment users for ads delivery, the article proposes the use of user segmentation strategies to improve click through rates (CTR). In particular, Probabilistic Latent Semantic User Segmentation (PLSUS) has been proposed as the best segmentation algorithm to improve behavioral targeted advertising.
  • How effective is targeted advertising?, Farahat, A., & Bailey, M. C. (2012, April). In Proceedings of the 21st international conference on World Wide Web (pp. 111-120). ACM. This article acknowledges the fact that few studies have proposed an appropriate methodology to analyze the effectiveness of a targeted advertising campaign. As such, the authors have proposed ta difference-in-difference estimator that decomposes the effect of targeting into selection bias and treatment effects components.
  • An economic analysis of online advertising using behavioral targeting, Chen, J., & Stallaert, J. (2010). This study illustrates the economic implications of behavioral targeting by using a horizontal differentiation model to capture the fit between consumers and the ad displayed. The authors identify different factors that influence the publisher’s revenue, the advertisers’ payoff, and social welfare.
  • Online display advertising: Targeting and obtrusiveness, Goldfarb, A., & Tucker, C. (2011). Marketing Science, 30(3), 389-404. This study uses a large-scale field experiment data to explore the factors that influence the effectiveness of online advertising. It is revealed that matching an ad to website content and increasing obtrusiveness of the ad independently can significantly increase the intent of purchase. However, the study also notes that using the two strategies, in combination, is always ineffective.
  • The online advertising industry: Economics, evolution, and privacy, Evans, D. S. (2009). Journal of Economic Perspectives, 23(3), 37-60. This article analyses the growing online advertising industry. According to the authors, online advertising accounts for about 9% of all US advertising, and the expansion is expected in few years. Online advertising is provided by series of interlocking multisided platforms that promote the matching of advertisers and consumers. However, some of these methods create privacy concerns, and as such require balancing between benefits and protecting consumer data.
  • Beliefs and behaviors: Internet users’ understanding of behavioral advertising, McDonald, A., & Cranor, L. F. (2010). This paper provides an overview of the American adult internet users’ knowledge and perception on internet advertising techniques. The authors conducted an online survey and in-depth interviews focusing on the views of participants on online advertising and their ability to make decisions regarding privacy tradeoffs. The study reveals that users have misconceptions about the role of cookies and effects of clearing them.
  • The effect of banner advertising on internet purchasing, Manchanda, P., DubĂ©, J. P., Goh, K. Y., & Chintagunta, P. K. (2006). Journal of Marketing Research, 43(1), 98-108. This article illustrates how banner advertising impacts internet purchasing. The authors used a behavioral database to measure the impact of banner on the current customers’ probability of repurchase. The authors use a survival model to model the probability of a consumer making purchase in a particular week, since the last purchase.
  • Targeted online advertising: Using reciprocity appeals to increase acceptance among users of free web services, Schumann, J. H., von Wangenheim, F., & Groene, N. (2014). Journal of Marketing, 78(1), 59-75. This article illustrates how targeted online advertising (reciprocity) can be used to increase acceptance among free web service users who depend on advertising revenues and powerful marketing tools to promote their business models. According to the authors, experiments have found that using normative reciprocity argument in advertising is more effective compared to the utilization of utilitarian argument. However, it is also noted that this dominance may switch depending on the characteristic of a website including website utility and user-generated content level.
  • Behavioral Advertising: The Offer You Can’t Refuse, Hoofnagle, C. J., Soltani, A., Good, N., & Wambach, D. J. (2012). Harv. L. & Pol’y Rev., 6, 273. This journal outlines the benefits and risks of behavioral advertising, taking into consideration political debates that surround website behaviors. The article also illustrates the significance of online privacy and the assumptions that surround the merits of privacy law.

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