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 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.

This article is a summary of the behavioral targeting study by Fional Ellis-Chadwick and Neil F. Doherty. You can get the pdf here: Web Advertising: The role of e-mail marketing.

Ellis-Chadwick F, Doherty NF, Web advertising: The role of e-mail marketing, J Bus Res (2011), doi:10.1016/j.
jbusres.2011.01.005

The authors investigate permission-based email marketing and the tactics used in this marketing strategy. Executional tactics can significantly influence the decisions and attention of recipients in this form of marketing campaign.

Permission-based e-mail marketing

The advantages of permission-based email-marketing are the following. It builds customer relationship, It has low setup and distribution costs, and so small and medium-sized businesses can use it. This form of email marketing asks the consent of users to be a part of a businesses’ emailing list. Users will continuously receive ads that they really want to receive, and spam is prevented. Spam, or unsolicited emails, have received numerous complaints in the past from users.

Research Method

The study specifies executional tactics for email marketing messages. These tactics come from the following executional factors: Length of email, frequency and timing of email, illustrations, subject line, headline, message content, brand logo, hyperlinks, interactive features, animation and personalization. For each factor, prevalence, recommendations to how this factor should be investigated, and managers’ comments are identified.

For example, for length of email, the average length is 2.4 pages and only 18 percent of all email marketing messages are 1 page short. It is recommended that to determine the most effective length for a given message, variant testing should be used. Managers suggest that promotional emails should be shorter compared to newsletters, and Easter and Christmas newsletters are important to us. Pictures also make the message more interesting.

Another example is for animation. Only 2 percent of email marketing campaigns use animation. While it can potentially attract attention, its rare usage suggests that its not as effective as one might initially think. A manager mentioned using animations before, but that did not make a significant improvement to click throughs. Yet another example is personalization, which is used by 35 percent of email marketing messages. Personalization can increase click-throughs, but the level of personalization depends on how much personal data a customer shares to the company and customer relationship stage.

For the main study, twenty retailers in the U.K. who used permission-based email marketing participated by sending the researchers their promotional emails for a period of 18 months. Over this period, an average of one email per 5 days were sent to the researchers, and a total of nearly 1000 emails were sent by the retailers. Content analysis was done towards these emails based upon the executional tactics previously mentioned.

Results

For frequency and timing, results show that 13 of 20 firms sent the most number of emails during the fourth quarter (including the Christmas season), and 5 firms sent the most during the second quarter (which includes Spring). Furthermore, there is an increase in number of emails sent as 15 firms sent more emails during the first quarter of 2006 than the first quarter of 2007. Furthermore, most emails were sent during work days (8 am to 4 pm).

E-mail subject line is composed of e-mail sender and subject matter. The participating firms showed five different types of e-mail senders, including company name only, company web address only, etc. Furthermore, 56 percent of the emails contained incentives in their subject matter. Marketing managers said that the subject lines were determined by knowing first what the purpose of the email is. If it is used for sales, then it should be short and relevant, grabbing the interest of the consumer.

The average length of the emails is 2.4 pages. Managers think that consumers “switch off” when the email is too lengthy. Sales are usually short, but newsletters are longer. Nice pictures make emails longer to to allow for space that highlights the firms’ products.

Discussion

These are just some of the results of the study, which deviates from previous related studies by investigating how a wide range of executional tactics can help email marketing campaigns. Previous studies have centralized only on tactics related to web page design. On the other hand, future researchers should investigate which executional tactics combinations should be used to make attracting consumer attention very effective.

This is the summary of an article by Kraig Koch. You can find the original behavioral targeting article here: Regulatory Efforts and Best Practices for the Online Behavioral Advertising Industry

Online behavioral advertising serves as a main component for online marketing, gaining widespread popularity but at the same time, getting lots of critcisms from privacy advocates. A Wall Street Journal report published in 2011 indicated that consumers were “Very alarmed” when they found out companies and advertisers were tracking their online behavior.

This article talks about online behavioral targeting technology, and the opinions of the people that are associated with it. The article also makes suggestions on how to advance self-regulation, which will help make government regulation a last resort.

Online Behavioral Advertising

There are two unique ways in which user behavior is tracked. The first way is called intrasite or first-party collection. This manner of behavior tracking involves a website which works outside the help of an ad network, most of the time using cookies to track user previous search patterns, and the user gets a unique ID stored in a database. Tailored ads are then sent to individual users.

Another way of collecting user data is third-party advertising, which includes an ad network that serves as the third party; the one responsible for collecting data which was tracked by the first party websites that belong to its ad network. A first-party website sells its collection of user data to an ad network, and an ad network can create a more comprehensive unique user profile, as it includes behavior of individual users across a number of websites belonging to its network. In contrast, first party collection only tracks the behavior of a unique user in one website.

Nevertheless, third-party advertising is subject to many consumer privacy complaints, as it is possible that these ad networks allow other parties to use their information without consent.

Legal Case Studies: Facebook Beacon

Facebook Beacon was launched during November 6, 2007, and just after about a month, it was shut down after several complains from Facebook users regarding its privacy standards and faulty participation requests. Facebook Beacon had 44 affiliate websites, and as a Facebook user, your activity in one of these websites will be published to your wall. The problem is that Beacon was an opt out platform, which automatically enrolled users whether they liked it in the first place or not. The vivid story goes that Sean Lane, a Facebook user, purchased a ring from www.overstock.com which he will give as a gift to his wife. But that was published on his Facebook wall, which her wife saw, spoiling the surprise.

Another problem with Facebook Beacon was that it displayed information about other people using products from affiliated websites to Facebook, even if these users were not members of the social networking site. December 5, 2007 was when Mark Zuckerberg made a public apology, stating that the program was a failure and their implementation was even worse. A settlement agreement was made during September of 2009 worth 9.5 million dollars, some of that went to establishing a nonprofit Privacy Foundation built for promoting online security, safety and privacy.

Government Regulation

A bill from U.S. Rep. Bobby L. Rush, called “The Best Practices Act of 2010,” looks into consumer privacy issues, and aims at balancing out industry incentives and privacy rights. Some of the key provisions in this legislation proposal include: making users choose how their personal information is disclosed, used and collected, requiring companies to disclose how they are collecting, merging, retaining peresonal information, and explaining the options that users can have with regards to these practices. Among others.

Microsoft, Intel and eBay has expressed support to the Best Practices Act. Although it did not come to vote during 2010, Rush reintroduced this in February 2011 and hopes to get more supporters.

The latest government initiative with regards to consumer privacy was formed on March 2011. The Obama administration desires to create a privacy bill of rights, which discourages all forms of intrusive data gathering. This is in contrast to other Administrations which didn’t want to be involved in Internet consumers concerns.

Industry Self-Regulation

The advertising industry, on the other hand, aims to avoid government regulations, by developing self-regulation systems. During July 2009, the Self Regulatory Principles for Online Behavioral Advertising was released. It consists of seven principles: The Education Principle, The Transparency Principle, The Consumer Control Principle, The Data Security Principle, The Material Changes Principle, The Sensitive Data Principle, and The Accountability Principle.

This is the summary of an article by Jeff Sonderman. You can find the original behavioral targeting article here: For location-based mobile ads, hype exceeds interest.

The Millenial Media ad network reports only 20 percent of mobile ad campaigns use location targeting so far, during the second quarter of 2011. 19 percent use gender or age targeting, known as demographic targeting, while 6 percent use behavioral targeting. The remaining majority of ads do not use targeting at all.

The report also shows that 14 percent of consumers using mobile devices are in favor of getting ads based on their location. 2,000 adults from the United States of America were surveyed for this study.

Indeed, this shows that location-based ads are nothing more than hyped, at least for now. Majority of mobile device users, in particular, 60 percent, said they want ads that are personalized in terms of their interests, for example cooking, sports, among others. 70 percent also find time-based promotions appealing.

Assaf Baciu, the product management senior vice president for Upstream, a mobile marketing firm, mentioned that location-based advertising is still very important, although the high conversion rates are obtained through ads based on mobile user interests. Still, there’s hope for location-based mobile advertising, and all other firms of ad targeting in the mobile industry. This avenue is very much alive and it is highly possible for location-based advertising to be a much stronger market than it is today several years from now.

JiWire, a location advertising company, conducted another survey this year, which may be biased with its results, but shows that over half of their respondents have no problem with sharing the location of their devices for targeted advertising. Going back to The Millenial Media survey, majority of the industries interested in location-based advertising are restaurants and finance industries. Travel, entertainment, automotive and pharmaceuticals are industries wherein mobile advertising is huge as well.