This is the summary of a blog entry in archive.ciol.com. It talks about how in the space of mobility, understanding the customer is most important. As such, mobile businesses have understood that the most important aspect that consumers from India look for are those that concern battery life. That is because power outages are quite common in the region, whereas in the United States, some people don’t even know what power outages are. Here’s the link to the original article: Understanding customer to make it big in mobility.
This is the summary of a blog article from Marketwire.com. It talks about Digilant’s multicultural segmentation strategy for real time advertising, providing the ability to really zero in on highly specific persons to send the advertising message across effectively. Here’s the link to the original behavioral targeting article: Digilant Introduces Digilant Multicultural Segmentation Technology for Real-Time Advertising.
This is the summary of a behavioral targeting article by NetMBA.com. It talks about the importance of market segmentation, and the fact that for market segments to work they should be durable, substantial, accessible, identifiable and caters to unique needs. Here’s the link to the original behavioral targeting article: Market Segmentation.
This is the summary of an article by Tom Ryan. It talks about how consumers tend to be defensive when they are responsible for a product or service failure, and complain against the company instead to protect their sense of self worth. You can read more from the original behavioral targeting article here: Study: Consumers Complain More When its Their Fault.
This is the summary of an article by Marketing Plan.net. It talks about the history of the multinational firm, business philosophy, pillars of marketing strategy, and pay per click advertising as the company’s marketing campaign black sheep. Here’s the link to the original behavioral targeting article: Marketing Strategies of Amazon.com.
A media contacts ebook on Behavioral Targeting reports the history of behavioral targeting, which is summarized in this article. Here’s the link to the original behavioral targeting e-book: MC Insight: Behavioral Targeting.
This is the summary of an article by Joseph Turow. It talks about the workings of corporations in people’s relationships to the becoming apparent of the digital media system. It also discusses the new contours of the digital marketing ecosystem, which use online user data without their permission. You can get the pdf of the behavioral targeting article here: How Should We Think About the Digital Age?.
This is the summary of an article by Leslie Harris. It talks about how silicon valley and Washington entities have attacked “Do Not Track,” saying it is not good for the advertising-supported World Wide Web. The attacks are even more surprising given the fact that there was already voluntary agreement from advertising companies that they would employ Do Not Track by the end of 2012. Here’s the link to the original behavioral targeting article: The Bizarre, Belated Assault on Do Not Track.
The “Do Not Track” policy has been talked about by advocates and the industry for almost two years now. Basically, it proposes a browser setting that limits the gathering of online users’ personal information, while at the same time still allowing companies to serve up ads.
Just recently, during a meeting of the World Wide Web Consortium (W3C) in Amsterdam, a number of representatives from the advertising industry, consumer advocacy groups, and creators of browsers met up to talk more about the said setting.
However, lately it has also been observed that the Do Not Track is being criticized and described as a move that would destroy the Internet, which is greatly supported by advertising.
A Surprising Development
The sudden “attack” of criticism on the Do Not Track seems rather surprising, especially since by the end of the year, the ad industry has expressed agreement regarding its deployment.
Just last February, the Digital Advertising Alliance, an umbrella network that includes the Network Advertising Initiative, the Interactive Advertising Bureau, the Better Business Bureau and other groups, expressed their commitment in honouring browser based settings that regulate online data collection and behavioral advertising.
Furthermore, the ad industry has in fact been honouring “opt out” requests for years; however, since these requests are “cookie-based,” they also get cleared when cookies are cleared. Hence, all stakeholders agreed that the Do Not Track was supposed to address this concern.
An Opposing Letter
Despite these occurrences, a letter was sent by a number of House Republicans to the Federal Trade Commission or FTC. They berated the commission for getting involved with the W3C in a move that might control online advertising without the explicit approval of the Congress.
In the letter, they invoked House Resolution 127, a resolution which states that a global Internet that is free from the control of the government must be promoted.
On the other hand, it can also be noted that the W3C is in line with the said resolution, as it is a group composed of advocates and companies that basically seek to set-up “non-legal” standards for the Web. Without it, the national government and international governmental bodies could be the ones seeking to regulate the Internet.
A Controversial Move
Meanwhile, Microsoft’s decision to steer Internet Explorer users toward using a “Do Not Track” option could also be the cause of the sudden backlash against the policy. How the W3C or the advertising industry would respond to this move is yet to be seen. The end result could be a major conflict between advertisers and privacy advocates, which the Do Not Track aims to avoid in the first place.
Clearly, the Do Not Track still needs to be carefully and thoroughly discussed by all stakeholders involved. A compromise between the privacy advocates and advertisers needs to be reached.
This is the summary of an article by Neha Gupta. This paper demonstrates how past exposure and user interest plays a role in modelling user response. The model was tested over hundreds of real advertising campaigns. This joint model has also been found to target users better, in comparison to traditional targeting models. You can get the pdf of the behavioral targeting article here: Factoring Past Exposure in Display Advertising Targeting.
To achieve effective behavioral targeting, one must have a deep understanding of the factors that influence how a user responds to an advertisement. When it comes to advertising campaigns, there are two such factors: interest match, and past exposure. Past exposure refers to constantly showing ads to users to bring forth a positive response.
The interest match factor is more intuitive compared to the past exposure effect. Most previous studies show, however, that increasing past exposure also increases the likelihood of a user to purse the advertisement. Past exposure might help increase brand-awareness and product familiarity, and even increases the chances that a user might see a product. However, several studies have shown that past exposure might lead to an advertisement fatigue effect, where users might get tired of seeing the same ads over and over again.
Past Exposure and Interest Match Interplay
This study aims to improve user behavioral targeting by accounting for the obscure interplay of the factors “past exposure” and “interest match”. This is a difficult task because of the following reasons. First, if a user responds positively to an ad campaign, we’re not sure how much of that decision is influenced by either of the two factors. Second, it is difficult to characterize both factors because of their complexity. Third, various advertising campaigns may be affected differently by these factors.
Targeting With Past Exposure and Interest Match
Mathematical models were created in this study to deal with the previously mentioned issues. These models use certain attributes of a given user and its interaction with advertising campaigns, user profile, and others.
This study focuses on performance-based display advertising. The goal of each campaign is user identification and targeting those who are likely to be converters.
Proposed Factor Model
For an advertising campaign, user conversion comes from a process that has two stages. Roughly, the first stage is repeated exposure to ads so that it gets the attention of a user. This leads to user awareness, and increase in trust, but excessive repetition leads to negative sentiments regarding the advertiser. The second stage involves the user evaluating the advertisement and deciding whether he or she likes to pursue it or not. The decision is based on several factors. This model in general is callled Exposure-based Factor Model for User Targeting or EFM.
Parameter Estimation for Factor Model
The model parameters are then estimated better for this factor model. Two different approaches are used, namely Alternate Maximization method and Expectation Maximization method. Refer to the original article for further mathematical details on these methods. Experimentation was then done which involves data description, and the evaluation methodology.
Results and Discussion
The paper has presented a factor model framework based on ad-exposure. This model was aimed at determining the joint effect of user interest and past ad exposure on conversion inclination for display advertisements. A huge set of two hundred real-world online advertisements were used for the experiments, providing significant insight into prediction a user’s response to a campaign based on his or her past exposure.
Compared to traditional targeting schemes that does not effectively use past exposure or not at all, the EFM model obtained a 4 to 7 % gain in Area Under Curve (AUC). AUC gives the probability that the targeting model assigns a higher score to a random positive example than a random negative example.
This capability can be used to have a more accurate targeting of potential converters. Interestingly, advertisers can also use this technique to understand which parts of their campaign strategy can be improved to target an attracted user.
This is the summary of an article written by Ruben Corbo. It talks about how blending behavioral targeting with social media marketing increases conversions and sales, and recognizes that both social media marketing and behavioral targeting are very powerful tools. Social media has a vast amount of consumer resources which can then be utilized with behavioral targeting to “get in the mind of the user.” Here’s the link to the original behavioral targeting article: How to Blend Behavioral Targeting and Social Media Marketing.