This is the summary of an article by Patricia E.M. Covington and Meghan Musselman. You can get the pdf of the behavioral targeting article here: 2010 Privacy and Data Security Developments.

The decade ending 2010 is great for new laws and regulations made for protecting online consumers in terms of secure data and privacy. It started with the GLB act of November 1999 for detailing sharing and collection o fpersonal information among financial institutions. Regulators such as the Federal Trade Comission or FTC also created new laws to punish companies that were unable to protect the information of their consumers. Online activities, data security and privacy seem to be top priority for FTC in the coming years.

Importance of Online Marketing

Wireless internet, social networks, and smart mobile devices have radically changed how consumers spend their time on the Internet. Online shopping has also become more prevalent and there are significant increases in loan production, which is primarily due to improvements in mobile technology, and businesses don’t want to be technologically left behind.

In fact, businesses engage in tracking the online behaviors of consumers to produce targeted ads. FTC and the Congress have prioritized behavioral advertising because this has caused some privacy concerns among consumers who didn’t want to be tracked. From 2009-2010, FTC held Privacy Rountables in search of better technologies for collection user information. Jon Leibowitz, FTC Chairman, stated that consumers are no longer aware that their information is being used because the user agreements are too complex for them to bother reading. FTC hopes that self-regulation will make forward progress and will not be regulating behavioral advertising in the meantime.

Data Pass

Data Pass is a recent issue. As a consumer checks out during an online purchase, he is offered a discount or any offer that gets him to pay recurring fees. A consumer usually doesn’t recognize that this offer comes from a third party who already has his credit card info. In response to this anomaly, Visa has initiated by not allowing third parties to get credit card information. Another response comes from Senator John Rockefeller who legislated a data pass regulation, called the Restore Online Shoppers’ Confidence Act.

Data Security

During 2010, the FTC has continued its campaign to prohibit practices that are unfair to data security. For example, a Twitter settlement was made which was the first action by FTC towards a social networking site. FTC pointed out incidents during 2009 where users have gained access to private Twitter accounts and have made unauthorized tweets. Twitter was unable to protect the information and the system which contains it. As a result, Twitter was asked to maintain “a comprehensive information security program”.

This is the summary of an article by Alan J. Bush, et al. You can get the pdf of the behavioral targeting article here: Sports Celebrity Influence on the Behavioral Intentions of Generation Y.

Sports celebrities are role models in today’s media culture. Advertisers have taken advantage of this, and many of our spokespersons today are famous athletes such as Michael Jordan, Tiger Woods, Kobe Bryant and Shaq. However, there is a lack of research regarding the influence of athletes on a target market. There is also some doubt that sports celebrities are really that effective for meeting the strategic demands of advertisers.

For advertisers, teenagers are one of the most important and challenging targets markets.
Nowadays, they belong to what is called the Generation Y, or those born between 1977 to 1994. Research is being done to understand what motivates this generation and what their behaviors are.

The objectives of this study are the following. First, explore the concept of sports celebrities being role models for Generation Y. Second, investigate if sports celebrities really influence the behaviors and intentions of this generation. Third, explore the influence of sports celebrities on female members of this generation.

Consumer socialization

Ward, 1974, describes consumer socialization as the process by which young people acquire attitudes, skills and knowledge that are important to their roles as marketplace consumers. There are socialization agents that influence young people’s behaviors, motivations and attitudes. Role models, in its conceptual definition, clearly include peers, teachers, parents, and the like. Sports celebrities, on the other hand, offer indirect contact, where their influences comes from their individual outstanding achievements.

Vicarious role model: the celebrity athlete

The entertainment and sports market is huge and fast growing. Advertisers have used celebrity athletes to endorse their products for various reasons. One theory that explains why this preference exists is the following. Sports heroes are highly dynamic, attractive and have many likable attributes. However, consumer socialization may provide the theoretical groundwork to determine the effectiveness of the influence of a sports celebrity athlete.

Hypotheses

The following are the hypotheses of the study. First hypothesis states that teenagers’ athlete role model influence has a positive relationship with complaint behavior and product switching. Second hypothesis states that Teenagers’ athlete role model influence has a positive relationship with favorable word-of-mouth behavior. Third hypothesis states that teenagers’ athlete role model influence has a positive relation to brand loyalty. Finally, the fourth hypothesis states that the athlete role model influence of female teenagers’ has a more positive relationship to complaint behavior and product switching, positive word of mouth behavior and brand loyalty than male teenagers.

Summary of Methodology

Teenagers from Generation Y were the sample for this study, because of their huge population, the fact that they are currently acquiring brand loyalties and product preferences, and because they will spend huge amounts of money in the future. All in all, there were 218 teenagers who participated in the study, with 54 percent male and 46 female.

Role model influence was assessed using the Rich (1997) role model scale, which determines the level of agreement a respondent has on statements regarding an athlete’s behavior which the respondent may want to emulate.

Intentions and behaviors are measured using a purchase intentions scale by Zeithaml, Berry and Parasuraman (1996). A 12-item 7-point scale test composed of several questions related to purchase and behavioral intentions. Confirmatory factor analysis was also used for assessing the multidimensionality of the scale used for behavioral intentions.

Results, Discussion and Implication

Results for the first hypothesis show that there is no significant relationship between athlete role models and complaining behavior or product switching. For the second hypothesis, there is a positive relation between favorable word-of-mouth communication and the role models. There is also a positive relation for hypothesis 3, with regards to brand loyalty.

For the fourth hypothesis, there is only partial support. For product switching and complaint behavior, girl and boy teenagers don’t differ much. However, female teenagers agree that athlete role models influence then to talk about good things about a product or brand, recommending and encouraging relatives and friends to purchase the product.

Most interestingly, this research shows that for adolescents, celebrity sports role models are important to them when they make choices and when they talk positively to a certain brand or product.

This is the summary of an article by Marious Koufaris. You can get the pdf of the behavioral targeting article here: Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior.

Behaviors of consumer on the Internet are unique in terms of the two main players. Online consumers are also computer users, while the businesses are now virtual stores. This kind of online transaction has been given a name, NEOs, or Net-enabled organizations. This study focuses on B2C or business to consumer e-commerce and the purchases in this framework that are unplanned. Study is also done for customer retention and how these two are influenced by the perceptions of an online consumer toward a Web store.

Theoretical Framework and Study Measures

Do online consumers have a different way of thinking and acting in comparison to offline consumers? Well, one of the differences between these two is that online consumers have lower customer loyalty than offline counterparts because they have more power, are more demanding, etc. The vendor has less power than the consumer. Another difference is that online consumers are afraid of online risks such as vendors asking for your credit card number for all the wrong reasons.

What attitudes or metrics influence how users behave in an e-commerce environment? Definitely, online consumers have similarities with offline consumers, but they have their own unique concens because online marketing is a different kind of environment.

Customer Intention to Return

One of the primary goals of companies is to convince their clients to return and establish costumer retention. NEOs seem to have a harder time developing customer loyalty because it is very easy to switch to another vendor, and would even go back to buying from physical stores if they find the experience not so delightful and convenient. This study approximates long periods of data taking for accurate customer loyalty measurement by conducting a survey to measure behavioral intention.

Why Customers Return

Online consumers have a double identity of being a computer user and a traditional shopper. As a traditional shopper, an online consumer’s desire to return will be based on traditional marketing / psychological variables, such as emotional responses, pleasure, arousal and dominance, to the environment. Another model is called flow, which is described as consumers being absorbed with what they are doing. Flow has also been studied in terms of computer environments.

Hypotheses of the Study

Flow can be measured in terms of shopping enjoyment. However, the importance of this factor has been challenged by previous researches, but some researches also point out that enjoyment may determine the loyalty of an online customer. The first hypothesis states that intention to return has a positive relation to shopping enjoyment. Intention to return is also hypothesized to be positively related to concentration and perceived control.

Hypothesis two states that Perceived usefulness and ease of use of the Web store has a positive relation to intention return. The third hypothesis states that consumers that have higher shopping enjoyments are more likely to make unplanned purchases, while those with higher perceived control are less likely to do so. High likeliness to make unplanned purchases is also related to consumers with higher concentrations.

Product involvement is the motivational state of a person to an object activated by its relevance or importance. Hypothesis 4 states that there is a positive relation between product involvement and concentration and shopping enjoyment. The fifth hypothesis states that there is a positive relation between perceived skills and perceived control, shopping enjoyment and concentration.

Product search mechanisms are called for because consumers want to have more control and easily find what they are looking for. Hypothesis 6 states that the use of value-added search mechanisms has a positvie relation to concentration, shopping enjoyment and perceived control. Finally, hypothesis seven states that the level of challenges of a Web store is positively related to concentration, shopping enjoyment and perceived control.

Discussion

Results show that both shopping experience and enjoyment are significant variables to determine the intention of return of an online consumer. How much a consumer believes a Web store is useful also determines his future visits, as well as an emotional resoponse to the store. Consumers don’t have to expect to enjoy while they shop online, but when they do they are most likely to come back. This means that online consumers are closer to offline consumers than we previously thought.

There are surprising results as well, such as no relationship between unplanned purchases and perceived control, shopping enjoyment and concentration. Perhaps there are other variables that are in play here that actually have a significant relationship with unplanned purchases.

Other results show that consumers are more likely to enjoy shopping online if they feel comfortable and confident with the Web store.

As a practical implication, online web stores should provide hedonic and utilitarian value to their stores, catering to not only increasing customer convenience but also making sure that emotional experiences such as shopping enjoyment are incorporated as well to retain customers.

Opinions about behavioral targeting are divided. Some are pro-behavioral targeting while others are anti-behavioral targeting. A recent study on individuals that understand and work in the online ad industry was conducted. However, these individuals may or may not fully understand the whole concept of behavioral targeting. The study shows that around 25 percent of individuals in the USA are not in favor of online behavioral tracking. Around 40 percent of these individuals delete the cookies in their cache or web histories. Only 11 percent are in favor of behavioral targeting.

Behavioral Targeting is not a new concept

Did you know that behavioral targeting already existed more than a hundred years ago? When Montgomery Ward catalog came out in 1872, the concept of behavioral targeting started. When a catalog arrives in front of your door, you are already their target customer, and you wonder how they are able to send you a catalog that you wanted. This is similar to the concept of online targeting.

Pro-cookies and for behavioral targeting

Individuals who are pro-cookies say that tracking cookies are a good thing. There is no need to delete it in the trail, mail or cache. They know that cookies do not contain any personal identification information. Cookies do not contain any material to know who you are; name, age, address, etc. The advertisers do not actually care about any personal information about you. All they care about is the profile of their target customer and the ads that they can flash in front of you.

Anti-cookies and not for behavioral targeting

On the other hand, there are people who think tracking cookies is not a good thing. There is a need to delete it in their mail or cache. These people are scared of the possibility of knowing any personal information about them. They do not want their online behavior to be followed or be tracked. Target online advertisement is an idea that they are not comfortable with.

Self-assessment for behavioral targeting

Are you now thinking if you are a pro or anti behavioral targeting? There may be a lot of things going on your head right now. You may be puzzled if you are going to delete those cookies in your email or cache or you should retain it. It all depends on your desire to be followed on your online activity or not. You may be bothered for security reasons. You just need to weigh the pros and cons, and read more on details about behavioral targeting.

This is an article which reviews practitioner and academic literature regarding several consumer segmentation trends, written by Brownwyn Higgs and Allison C Ringer. You can get the pdf of the behavioral targeting article here Trends in Consumer Segmentation.

Consumer generated media, such as product review sites and social networking sites have allowed online consumers the chance to influence what kind of content they want to see online. This, in turn, influences how online business deals with its transactions. In addition, there are more intelligent consumers now than ever before, with the particular characteristic that they want to see content and purchase goods and services based on their individual needs. Demand has indeed become individualized, and so mass-customization is born.

Communication channels that have interactive features are now everywhere, and they continue to grow in number and sophistication. Interactivity allows consumers to talk to marketers and vice versa. These transactions are documented and used to improve how markets respond.

Overview of Market Segmentation

Market segmentation was developed during the mid 20th century because at that time, purchasing and demographic data, and even distribution and advertising channels was only made for consumer groups. Segmentation is composed of four methods, the traditional ones, post-hoc and a-priori, and the flexible ones, componential and dynamic.

A-priori method is such that an analyst selects a segmentation base and does his or her analysis, while post-hoc does the analysis first before forming bases. On the other hand, componential segmentation focuses on making predictions and deemphasizes on partitioning, while dynamic segmentation models simulated conditions in which analyses can be done on how consumers respond to the characteristics of test products.

Segmentation is important because it aims to identify segments that vary in terms of market behavior, aspirations and purchasing power. The kinds of data that are obtained to properly segment consumers include consumption, purchasing and attitudes toward products or services. As a result, most segmentation techniques are brand-driven and tactical.

Segmentation has several limitations, including its inabilty to narrow down groups into sufficiently small custers. Another criticism of this process is that it relys heavily on one off surveys. Ideally, continuous data collection hhelps prevent certain marketing dynamics difficulties in the long run. Still, segmentation is endeared by many practitioners and there is a lot of research going to make analysis more sophisticated and improve on segmentation approaches.

Specialized Segmentation

Certain types of segmentation, including those used for advertising, have diverged in terms of development and improvement because of their unique purposes and goals. A different set of methodologies and procedures for analysis are employed as well, and unique instruments are carried out to engage in segmentation studies. Aside from advertising segmentation, CRM and direct marketing segmentation also evolve unique segmentation strands, creating new frameworks, segmentation techniques which employ highly extensive mining of data.

Finer and Hyper-segmentation

Finer segmentation is used to group markets into narrow clusters more precisely, and has been improved by advances in information technology over the years. Hyper-segmentation, on the other hand, is used to identify an individual consumer’s segment. There are two common methods for hyper-segmentation, progressive profiling and addressable marketing.

Progressive profiling involves collecting data through interactive websites, asking consumers a question or two during transactions in a continuous process, gathering rich data about individual consumers and his or her preferences. Addressable marketing, on the other hand, uses digital communication services to collect information regadrding online behaviors like advertising exposure, content involvement, site engagement and site visitation.

Behavioural Based Targeting

Behavioural-based targeting or BBT involves aggregating the market rather than partitioning it, by determining the behavior and patterns that a user forms across the web. Two types of data are used for Behavioural-based targeting: first is sample populations of site visitors and a target users list. With BBT, marketers can identify valuable information regading user concentrations across websites.

Implications and Conclusion

These segmentation trends will improve the quality of data one can get for marketing purposes, but at the expense of increased complexity in processing among others. Certainly, as marketing improves so will the number of segmentation approaches increase.

Online Marketing Düsseldorf 2007
This is a video about behavioral targeting with Frank Wagner. Frank Wagner tells how he explains to his mother what his job in behavioral marketing is all about.

www.youtube.com/watch?v=26FSZ6RRJRE

This is the summary of an article by Michele Ybarra, et al. You can get the pdf of the behavioral targeting article here: Internet Prevention Messages: Targeting the Right Online Behaviors.

The Internet is a place for 9 percent of online youth being harrassed which could lead to emotional distress and other psychosocial problems. Advocates have called for the youth to stop sending out personal information about themselves online, and refrain from talking to strangers. While it seems logical to do so, there is little evidence to either support or refute this claim.

This study investigates 5 online behaviors: downloading images through file-sharing, sexual behavior, talking to online strangers, aggressive behavior, and sharing of personal information. Four questions will be asked. First, What are the prevalence rates and characteristics of online behaviors commonly referred to as “risky”? Second, Are behaviors targeted in Internet safety and prevention messages associated with increased likelihood of online interpersonal victimization? Thirds, Do psychosocial and personal behavior problems account for these associations? Fourth, does the total number of online behaviors engaged in affect the association between specific behaviors and victimization online?

Methods

The survey was conducted by telephone towards 1500 youth in what was called the Second Youth Internet Safety Survey on June 11, 2005. English speaking youth who used the Internet during the past 6 months on a monthly basis were allowed to do the survey. The makeup of the respondents correspond to the Internet population on a national survey level.

The respondents were asked how often they performed one of 9 online behaviors that are said to make one prone to online victimization. These behaviors were determined through messages about Internet safety and documents regarding youth victimization online. One example is sexual behavior, which are divided into two types: that which involves someone you don’t know talking to you about sexual matters and intently entering an x-rated website.

Online Interpersonal Victimization

When people are harrassed online in a sexual manner, that’s called online interpersonal victimization. The respondents are asked three questions to determine whether they have been baited into talking about sexual matters. They were also asked if they had any online relationship and whether it turned sexual in any way. Aside from that, harrassment was asked to find out whether the Internet was used against them for harrassment. Bivariate analyses was done to determine how harassment, unwanted sexual allurement and online behaviors were related to each other.

Psychosocial and Behavioral Problems

Questions from the Juvenile Victimization Questionnaire were used to ask the respondents if they have been abused physically or sexually during the year prior. In addition, the Youth Self-report of the Child Behavior Checklist was used to assess child emotional and behavioral problems.

Internet Use and Demographics

The youth were asked to estimate how much time they spent online in terms of number of hours in a day and days in a week. They were also asked to assess how well they are familiar with using the Internet and how important the Internet is to them. They also answered questions regarding chat rooms, instant messaging, and blogging.

Results

The results show that 20 percent of youth mentioned they have experienced online interpersonal victimization during the previous year. Three fourths of the respondents said they experienced at least one of the online behaviors linked with increased victimization. Most common was posting personal information online and least common was talking to online strangers.

All of the nine online behaviors are found to have a significant relationship with online interpersonal victimiation. The behaviors which have the strongest association are talking about sex with a person you only know online, meeting online people, and embarrassing someone deliberately.

Commonplace Risky Online Behaviors

A lot of online behaviors are becoming commonplace, such as posting personal online information. More than 30 percent of youth have friends they don’t really know in person. It is important to recognize the risky online environment that our youth are immersed in and find ways to reduce these risks.

Meeting people online is rightfully considered as a behavior that may heighten Internet harassment. On the other hand, sharing of personal information should be given more consideration. It is also found that youth that exhibit four or more kinds of risky online behaviors are more than a tenfold times likely to be victims. Health professionals and child experts should collaborate with parents to assess the behavior of their children online.

This video from Youtube is about social marketing strategies and how you can use the power of Facebook, Twitter and all those other social networks to drive people to your blog. Perry Belcher also explains why making friends is so important in today’s online business world.

www.youtube.com/watch?v=zn1cspHx7DU

This is the summary of an article by Markus Svensen, et al. You can get the pdf of the behavioral targeting article here: Broad vs Narrow: Modelling Strategies for Online Behavioural Targeting.

The estimated value of online advertising for the year 2011 is 28.5 billion dollars. With online advertising, you can reach a wide audience that are most interested in the products you offer. One kind of online advertising is display ads. For display ads to work, one can use contextual advertising in which ads put up in websites are contextually similar to the content of the site.

Behavioural targeting, on the other hand, can help advertisers reach their consumers with ads that are not necessarily contextually similar, but a user will respond to because they are relevant to his or her interests. Behavioural targeting divides online users into specified segments in a form of audience segmentation. There are many ways to do behavioral targeting. This article talks about several proposed models.

Behavioural Targeting Using Click Prediction

Click Prediction states that a user click on an advertisement is the only indication that the user is interested in the product being advertised. User click is a very observable quantity and really weighs a lot in terms of how advertisers can evaluate the effectiveness of their campaigns.

Matchbox is a probabilistic Bayesian model which is used to match users and items and group users that have a similar interest for certain items, and group items that are similarly rated by users. A Matchbox model is composed of two sub-models, a linear model and a bi-linear model. The linear model is responsible for modelling bias effects and the bi-linear model models the interaction between users and advertisements.

For the models used in this study, the user features are unique numerical ID, AgeBand, Country, Gender, and several variables related to query and page views. For Advertisements, the features are ID, Type, Industry and Size (or width and height).

Data

15 days worth of data from Microsoft display network and Bing was used. Impressions and clicks were counted daily. Overall there were 1.8 million users, 606 thousand clicks and 284 million impressions for 3,270 advertisements. Implementing more conditions, such as excluding impressions that corresponded to a small number of ads which may bias the results, the data set now comprises 174 thousand clicks, 78 million impressions, 127 thousand users and 2,793 ads.

Page views and queries are the behavioural data, and every one of these is assigned a specific category related to one another in a forest like structure.

Experiments

An experimental setup was designed to come up with the best user, advertisement and context features combination. A computer cluster was used to run many experiments and scoring various fitted models using three different performance measures called area under the receiver operator characteristic curve (AUCROC), area under the precision-recall curve (AUCPR), and marginal log-likelihood or llh.

For the first M days, data was used for training data, and the remainfrel scoring.

Results and Discussion

The Bayesian logistic regression type model came out on top as the best model among the several Matchbox models used in this study. The best strategy for using training data is to make it correspond to a single topic, as compared to when they are used to include multiple topics. The study addressed a better way to measure the various interests of individual users for more effective behavioural targeting. These information can then be used for data obtained from mobile devices, social networking sites and the like, provided that privacy concerns are resolved first.

This is the summary of an article by Gavin Dunaway. You can get the original behavioral targeting article here: Suit Filed Against KISSmetrics And Pubs Over ETag Tracking.

During the summer of 2009, Ashkan Soltani, a privacy research wrote a report about the undesirable use of Adobe Flash cookies that use respawning HTTP cookies that remove user privacy controls in essence. Scott Kamber’s law firm filed law suits against three companies using this technology: Specific, Clearspring, and Quantcast. Clearspring and Quantcast responded to lawsuits filed against them for using Flash cookies by promising they won’t use it again, along with a 2.6 million dollar settlement.

The summer of 2011 also saw KISSmetrics and Hulu being reported by Soltani and friends for using Flash cookies plus ETags in respawning and tracking, and Kamber Law Firm is back on the plaintiff’s bench. This law firm also filed against the Beacon advertising from Facebook, Google’s toolbar, demographic profiling by Interclick, and Apple for mobile apps that give unique IDs to advertisers. He is the biggest privacy issue suit-happy police.

KISSmetric has ETag tracking codes that are being used by around 400 websites. Kamber has filed against some of these websites, including AOL, Kongregate.com, Hasoffers.com and AOL. Ad technology companies such as SEOmoz and Conduit are part of the lawsuit as well. It is expected that Hulu will agree on a settlement like Clearspring and Quantcast, for its use of Flash cookies and cookie respawning technology.

Kamber’s lawsuit against KISSmetric and codefendant websites is much more complicated. The use of Flash cookies and ETags violates the Unfair Competition Law from the California Business and Professional Code, the Computer Crime Law of the California Penal Code, and the Electronic Communications Act.

Kamber argues that KISSmetric stores coded tracking information specified for each individual user, much like cookies but in a kind of shadow tracking mechanism. In response, Hiten Shah, the CEO of KISSmetric said the y never used ETags or other persistent tracking technologies. As they are only a small company composed of 17 people, their technology doesn’t have the ability to track users across various websites.

Shah mentioned that Soltani seems to send the message that only browser cookies are valid for tracking website activity, but the truth is many online companies use various tracking technologies, including the ones targeted by Soltani in his papers.

Gap Between Do Not Track and Online Behavioral Advertising

Users can opt out of behavioral advertising because of self-regulation. But cookies are still being used after opt-out for internal publisher and advertiser metrics. There are enforcement mechanisms to this and violations will be duly punished for non-compliance. Ad technology and media companies want to use ETags, Flash cookies, and other “persistent” tracking mechanisms for analytics and essentially harmless tracking. But privacy advocates argue that tracking is still tracking, and these persistent tools are especially harmful as they go around user’s control of privacy.