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For Kids, Media Usage Isn't Either/Or

Dawn Anfuso
For Kids, Media Usage Isn't Either/Or Dawn Anfuso
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Children and teens are spending an increasing amount of time using "new media" like computers, the internet and video games, without cutting back on the time they spend with "old" media like TV, print and music, according to a new study released this week by the Kaiser Family Foundation. Because of the amount of time they spend using more than one medium at a time (for example, going online while watching TV), they're managing to pack increasing amounts of media content into the same amount of time each day.


The study, Generation M: Media in the Lives of 8-18 Year-olds, was released yesterday at a forum that included a keynote speech by Senator Hillary Rodham Clinton and a roundtable discussion featuring FCC Commissioner Michael Copps, Hip Hop artist Common, and top executives from the video game and television industries. CNN's Jeff Greenfield moderated the discussion.


The study -- which measured recreational (non-school) use of TV and videos, music, video games, computers, movies, and print -- examined media use among a nationally representative sample of more than 2,000 third through 12th graders who completed detailed questionnaires, including nearly 700 self-selected participants who also maintained seven-day media diaries.


Results indicate that the total amount of media content young people are exposed to each day has increased by more than an hour over the past five years (from 7:29 to 8:33), with most of the increase coming from video games (up from 0:26 to 0:49) and computers (up from 0:27 to 1:02, excluding school-work).


However, because the media use diaries indicate that the amount of time young people spend "media multi-tasking" has increased from 16 percent to 26 percent of media time, the actual number of hours devoted to media use has remained steady, at just under six-and-a-half hours a day (going from 6:19 to 6:21), or 44 1/2 hours a week. For example, one in four (28 percent) of youth say they "often" (10 percent) or "sometimes" (18 percent) go online while watching TV to do something related to the show they are watching. Anywhere from a quarter to a third of kids say they are using another media "most of the time" while watching TV (24 percent), reading (28 percent), listening to music (33 percent) or using a computer (33 percent).


"Kids are multi-tasking and consuming many different kinds of media all at once," says Drew Altman, Ph.D., president and CEO of the Kaiser Family Foundation. "Multi-tasking is a growing phenomenon in media use and we don't know whether it's good or bad or both."


Media in the bedroom


Children's bedrooms have increasingly become multi-media centers, raising important issues about supervision and exposure to unlimited content. Two-thirds of all eight- to 18-year-olds have a TV in their room (68 percent), and half (49 percent) have a video game player there. Increasing numbers have a VCR or DVD player (up from 36 percent to 54 percent), cable or satellite TV (from 29 percent to 37 percent), computer (from 21 percent to 31 percent), and internet access (from 10 percent to 20 percent) in their bedrooms. Those with a TV in their room spend almost one-and-a-half hours (1:27) more in a typical day watching TV than those without a set in their room.


Outside of their bedrooms, in many young people's homes the TV is a constant companion: Nearly two-thirds (63 percent) say the TV is "usually" on during meals, and half (51 percent) say they live in homes where the TV is left on "most" or "all" of the time, whether anyone is watching it or not.


Parental rules


While prior studies indicate that parents have strong concerns about children's exposure to media, about half (53 percent) of all eight- to 18-year-olds say their families have no rules about TV watching. Forty-six percent say they do have rules, but just 20 percent say their rules are enforced "most" of the time. The study indicates that parents who impose rules and enforce them do influence the amount of time their children devote to media.


Kids with TV rules that are enforced most of the time report two hours less (2:01) daily media exposure than those from homes without rules.


"These kids are spending the equivalent of a full-time work week using media, plus overtime," says Vicky Rideout, M.A., a Kaiser Family Foundation Vice President who directed the study. "Anything that takes up that much space in their lives certainly deserves our full attention."


New media gains in usage


Since 1999 there have been big changes in the percent of eight- to 18-year-olds who have a computer at home (73 percent to 86 percent), have two or more computers at home (25 percent to 39 percent), have internet access at home (47 percent to 74 percent), and go online for more than an hour in a typical day (5 percent to 22 percent).


What's more, as new technologies have become available, young people have been quick to make use of them, changing how they use media as well as which media they use. For example, 64 percent have downloaded music from the internet; 48 percent have streamed a radio station through the internet; 66 percent use instant messaging; 39 percent have a cell phone; a third (34 percent) say they have a DVR such as TiVo in their homes; 32 percent have created a personal website or webpage; 18 percent have an MP3 player; and 13 percent have a hand held device that connects to the internet.


Teen internet usage patterns


comScore Media Metrix reports that more than 14 million Americans just between the ages of 13 and 17 accessed the internet in January. The firm found that sites that facilitate or promote teen communication, such as BuddyProfile.com, QuizYourFriends.com, Xanga.com and LiveJournal.com, were among the highest ranked properties by composition of users age 13 to 17.


Not surprisingly, educational sites, such as FreeTranslation.com and Sparknotes.com, which allow teens to use the internet for help with schoolwork, are disproportionately popular among this segment. Teens make up 29 and 23 percent of these sites audiences, respectively.













































































































Top Properties by Composition of Visitors Age 13-17
January 2005
Total U.S. Home, Work and University Internet Users
Source: comScore Media Metrix

 

Unique Visitors (000)
Age 13-17


% Reach Among Users Age 13-17


% Composition Unique Visitors Age 13-17


Composition Index

Total Internet Population – Age 13-17

14,243


100


8.8


100


DEVIANTART.COM


276


1.9


29.5


335


PUREVOLUME.COM


254


1.8


29.2


333


FREETRANSLATION.COM


387


2.7


28.8


328


BUDDYPROFILE.COM


623


4.4


26.5


302


XANGA.COM


1,693


11.9


25.4


289


FUNNYJUNK.COM


347


2.4


23.8


271


QUIZYOURFRIENDS.COM


412


2.9


23.7


269


PICTURETRAIL.COM


433


3


22.8


260


SPARKNOTES.COM


473


3.3


22.7


258


Bolt


574


4


22.5


257


Alloy


585


4.1


22.4


255


NEOPETS.COM


805


5.6


22.3


254


LimeWire


885


6.2


21.8


248


LYRICS.COM


648


4.5


20.2


230


LIVEJOURNAL.COM


1112


7.8


20.1


229


Teens also favor an interactive online retail experience. More than 70 percent of teen internet users visited at least one Retail category site in January 2005. 


Specialty apparel retailers targeting teens, such as HollisterCo.com (owned by Abercrombie), HotTopic.com and Alloy, are among the Retail sites with the highest composition of 13- to 17-year-old users. Many of these sites offer promotional enticements such as contests and giveaways, as well as chat rooms and other communication tools, which encourage teen consumers to make the brand a part of their everyday life, not just a site for purchasing.  


Teens also tend to be heavy consumers of music content and merchandise. Three music-related properties, CDUniverse.com, MusiciansFriends.com and Roxio, ranked among the top 15 sites by composition of users age 13 to 17.



















































































































Top Retail Properties by Composition of Visitors Age 13-17
January 2005
Total U.S. Home, Work and University Internet Users
Source: comScore Media Metrix

 

Unique Visitors (000)
Age 13-17


% Reach Among Users Age 13-17


% Composition Unique Visitors Age 13-17


Composition Index


Total Internet Population – Age 13-17


14,243


100


8.8


100


Retail Category Visitors – Age 13-17


10,137


71.2


7.7


88


HOLLISTERCO.COM


192


1.3


29.6


338


HOTTOPIC.COM


235


1.7


26.9


307


BABYPHAT.COM


114


0.8


24.7


281


Alloy


585


4.1


22.4


255


PACSUN.COM


149


1


21.9


250


AE.COM


313


2.2


17.5


199


ABERCROMBIEANDFITCH.COM


205


1.4


17.1


194


CDUNIVERSE.COM


128


0.9


14.7


168


MUSICIANSFRIEND.COM


198


1.4


14.5


165


Foot Locker Sites


313


2.2


13.8


157


EBGAMES.COM


150


1.1


13.6


155


NFLSHOP.COM


114


0.8


13


148


BARTLEBY.COM


153


1.1


12.4


141


SHOES.COM


114


0.8


11.8


135


Roxio, Inc


315


2.2


11.5


131

Say you go through the entire process like we’ve been looking at here: search campaigns, banner campaigns, email campaigns, site-side targeting, event-based targeting, network-based targeting and ad server targeting. How do you possibly tie all of that response data together so that you can make actionable decisions? 


Many advertisers are at this stage right now. They have engaged the technologies and are generating the data, but they are still evaluating and making decisions about each initiative within the technical silos that they reside. The potential is there to know that an individual person who is landing on your homepage for the first time was (1) previously there before because of a search click, (2) previously saw four banners from an ad campaign and (3) is now responding to an event-based targeting campaign on a network.
 
The techniques in practice today have offered advertisers advantages in the areas of prospecting, identifying better sites and better messaging for generating preferred responses. They have enhanced the ability to position products and offerings on websites to increase the purchasing habits on websites, and they have empowered marketers with insight into how users will behave both externally and internally with respect to messaging.


But now we need to be able to tie it all together.


Put the puzzle together
Event-based targeting can begin with banners that are served across the web by having the serving history and the advertiser navigation patterns written to the advertiser’s first-party cookie. Banner serve decisions can be made based on customer segments assigned by eCRM written by the advertiser to the first-party cookie. Banner serve decisions can also be made just like with traditional network behavioral targeting, but across the entire web, using an ad server that can read the first-party cookie. CMS can react to banner serving history written to the advertiser’s first-party cookie by the ad server. eCRM can enhance the customer record based on information written by the site-side navigation tracking history of the analytics vendor using the advertiser’s first-party cookie.


So the commonality of the first-party cookie enables the advertiser to combine the various forms of behavioral targeting technologies, create more focused segments of both customers and prospects and create a data asset from which actionable decisions can be made.

Site-based targeting, event-based targeting, network-targeting and ad server-based targeting all have data that is collected and analyzed separately. The data is in different formats, associated with different vendors and is not translatable into one common format so that it can be manipulated easily. Some vendors offer costly data synchronizations, but these processes are historic. And no real-time decisions can be made. It is not easy to translate banner performance to search performance, or network behavioral targeting to site-side targeting. Each is analyzed separately as a result. The optimization decisions are made for each component without consideration for the performance of the other.


Behavioral targeting techniques produce analytical data from which an advertiser can make optimization decisions. The knowledge gained from one technique can have a benefit on another one with the translation of knowledge and integration of the relationships of the behaviors together. Event-based targeting enables you to show specific ads to someone based on pages they have seen in the past (network and ad server behavioral targeting). But now you can render pages on your website based on all of the ads a visitor saw before they finally clicked on an ad.


Coremetrics, for example, has gone first-party almost all the way. Integrate them with a first-party ad server and you would know not only what marketing message produced a lead, but all of the messages that did not produce a reaction as well. That information will help you to build the perfect page of products and offers to maximize desired actions on the first page.


Without that kind of cross-technique, actionable integration, advertisers are getting to the point that they have too much to look at. With so much data, they can’t help but deem the entire process as either too daunting or simply not effective. The truth is, the problem lies in the disparity of the data and siloed nature of the techniques.



Too many cookies
Earlier I said the answer lies within the cookie, a common technical thread between the technologies that will enable data to flow together, right? A common report, or the ability for data to flow between reports in real or near-real-time so that each technique can be evaluated holistically?


CMS uses cookies, site-side analytics uses cookies, ad serving uses cookies, event-based targeting uses cookies and networks use cookies. Each vendor uses their own third-party cookie and collects data based on that cookie. That is why the data does not flow. The advertiser uses their own first-party cookie for their eCRM. That is how they track someone once they have logged on; they can track behavior internally.


If every technology operated on the same cookie type, the data would be capable of flowing between the techniques. Each behavioral targeting technology could read the values written by the other and act off of the decisions made by one another.


So engage your behavioral targeting vendors to use your advertiser first-party cookie. Use an ad server like TruEffect, which uses the advertiser’s first-party cookie to serve ads. Have the site-side analytics provider use the advertiser’s cookie, as well, the way WebTrends is capable of doing. Or have the CMS system that is integrated with the site-side tool that is offered by WebSideStory use the advertiser’s cookie. Have the ad server or advertiser drop the first-party cookie on search campaigns so that those campaigns can also be tied together.

The kitchen sink approach to behavioral targeting introduces another conundrum. When attempting to put the right message in front of the right person at the right time, through multiple forms of behavioral targeting, a significant workload implication arises… a lot more strategic thinking, media planning and creative are required.


Event-based ad-server and network targeting all require additional thought, planning and creative. There are advertisers that create as many as five customer segments with behavioral targeting, with different messages for different behaviors. Plus they have the traditional aspects of the campaigns for non-targeting to manage. When you put it all together, there is non-targeting creative, targeting creative and additional creative for optimizing both campaigns. If you’re using an ad agency, it can be costly for just one month’s worth of banners.


Think a bit more about this; it is not just a matter of simply creating more banners. First the advertiser has to determine what it wants each of the behavioral segments to do when they encounters them, both out on the web as well as when they land on the website. Kitchen sink approaches consider the targeting externally, driving prospects, recurring transactions, referral programs, re-enrollments, et cetera, and internal transactions. Messaging has to be created and creative built. This is an expensive proposition.


The circular strategy
First-party marketing benefits the advertiser because they can deploy a circular strategy. Start anywhere in the cycle and the messaging and the "if/then" processes all remain the same:




  1. Customer segments are created internally based on profiled data, shopping patterns and anonymous site-side behavioral analytics.

  2. Existing customers are recognized and re-targeted online through first-party ad serving. Users are driven back to the site, while prospects are tagged and driven to a different rendering of the advertiser’s site (dynamic content management). A great example here is Coremetrics.

  3. Content-management tools distinguish customers from prospects and position messages and products accordingly and leverage the information written to the cookie during the advertising campaign -- banners seen, sites visited sequentially -- to optimize conversion rates.

  4. Site-side analytics reads the first-party cookie data, which includes eCRM on existing customers, anonymous ad serving history information on both existing customers and prospect browsers from the first-party ad server and content management decisions used to serve, track and optimize site navigation.

  5. eCRM improves customer segment models based on improved conversions and enhanced site navigation designs resulting from site-analytics and content management, as well as the holistic web-wide view into acquisition marketing courtesy of the first-party ad serving.

All steps read and write to the same first-party cookie -- the advertiser’s cookie -- and therefore each technology benefits from and strengthens the next one in the cycle. The messaging becomes altogether more simplistic to manage with first-party. There are existing customer segments and prospects. You can recognize and distinguish both site-wide and web-wide. Messaging and creative strategy become fully integrated based on acquisition, revenue, recurring sales and cyclical objectives.

Behavioral targeting can go beyond “did someone see that banner before?” or “did someone go through a specific navigation pathway?” It can even go beyond purchase patterns on a retail website. Some advertisers have looked to coordinate offline initiatives with online to create multi-channel behavioral targeting processes. The more data that an advertiser possesses about its customers, the more it converts that data into actionable knowledge, and the more it can integrate multi-channel behavioral targeting.


The most aggressive online advertisers are beginning to see that they can pull various sources of information about a customer together. For those companies that already have all of the information in one system, such as eCRM, extracting it to create models of customers is arguably an easier step. Many direct marketers -- such as the catalogers -- already do this.


Companies like NextAction and Abacus (now Epsilon) combine profiles of people that you provide from the offline world and return more comprehensive profiles of the customer by combining your data with that of many other marketers. From there you have great customer segments with which you can create models, such as high-value customers or low-value customers, groupings by shopping habits, product preferences, et cetera. Marketers leverage this information to deploy holistic behavioral targeting, targeting preferences internally and externally: site-side and web-side.


Unfortunately, few advertisers have this competency and it is the unnecessary isolation of these technologies that is keeping the rest of us from advancing more quickly.


The cookie battle
Tagging users based on an anonymous membership to a customer segment and targeting those customers behaviorally anywhere on the web requires a first-party cookie. The brick wall that separates each behavioral targeting technique must come down for all of this to work. Third-party cookies keep the technologies isolated because no two vendors can read the information form or write information to another vendor’s cookie without the delegated permission to do so. Tacoda is not granting the rights to Omniture to read/write the Tacoda cookie. Why? For one thing, Tacoda builds a data asset associated with its cookie. Every individual it tags through pixels online becomes a profile in the network, and Tacoda accumulates a data asset of profiles across its network for future advertisers. Omniture is not letting Advertising.com read the Omniture cookie because that would enable them to have access to the Omninture clients’ site-wide data. Vendors are not playing nicely with others, and the advertisers are suffering as a result. There is more to it actually, but for the purposes of this article this suffices as an explanation.


The cookie solution
First-party cookies, however, offer a different opportunity. If everyone were to use the advertiser’s cookie, none of the vendor-specific data assets would be exposed. Revenue Science would not have to worry about the exposure of its data assets generated by all of its other advertisers. With first-party cookies, WebTrends would not have to worry about all of its other clients’ data being compromised, and it could leverage the information written to one advertiser’s cookie by other associated vendors to further extend the targeting and analytical application.


Push your vendors to consolidate their delivery mechanisms on your first-party cookie. Delegate the right to read and write information to your cookie. CRM systems do this today. So do content management tools. Ad server TruEffect, my former employer, leverages information written to the first-party advertiser cookie by the CRM system to define targeting buckets. That drives both new customer acquisition and customer re-targeting.



Adopting that sort of implementation opens doors for you. Then you can make your other vendors get in line.

When it comes to behavioral targeting, advertisers are deploying multiple techniques in an attempt to further capture the potential that behavioral targeting has to offer.


The problem advertisers face today is that each technology currently operates within a silo environment. That is the result of different third-party cookies. The data generated from each technique is not readily transferable, and therefore the ability to react to the results from one technique combined with another does not exist. They are all speaking different languages. There is no communication between the CRM design and ad server targeting. Event-based network targeting has no communicative impact on site-level content management, and site-side analytics has no external view into acquisition marketing generated by advertising.


When an advertiser leverages a network like TACODA or Advertising.com to conduct event-based behavioral targeting, they are attempting to capture prospects or existing customers based on anonymous behavioral events, such as a succession of page experiences.


The dislocated architecture of the advertiser and network prevents integration. Advertisers rely on site-side analytics and content management systems (CMS) to promote behavioral targeting capabilities on their websites. These technologies also largely use third-party cookies and cannot connect with the event-based targeting that is conducted by the networks on their behalf out on the web. So a WebTrends has no view into what a Revenue Science is doing. It’s like a brick wall between the techniques.


Advertisers also use third-party ad servers to deploy behavioral targeting. DoubleClick’s Boomerang is a great example of this. Unlike a network, the ad server is web-wide. But again, almost all ad servers are relying on their third-party ad server cookies, like the ad network cookie and the site-side targeting cookie, which cannot communicate with another technology.


The kitchen sink
This mentality describes advertisers who know there are more ways to deploy behavioral targeting and choose to deploy them all to figure out what works. It may not even be intentional, but the result is a dysfunctional set of technologies that don’t integrate or benefit from the value of one another. At first it may have been just Tacoda, Advertising.com, Boomerang or a site-side technology like Omniture, WebSideStory or WebTrends. But now these technologies are all in play at the same time.


The silos between the technologies are producing discrepancies, and the inherent limitation of each technology renders an advertiser blind in the decision-making process from one technique to the next when they could be leveraging each other to make very powerful decisions.


The solution
The solution is a common cookie that is used by the advertiser, written by each technique and read by each technology so that all may benefit from the information generated by the other. First-party cookies imply the utilization of the advertiser’s primary cookie by all associated vendors: ad servers, networks, CRM, site-side analytics and content management. A first-party cookie can serve as a common translator with which each technology can communicate. It can break the silo walls and enable an advertiser to integrate various techniques and leverage the knowledge from one technology to benefit the other.

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