A Framework for Targeting Banner Advertising On the Internet
`
`Katherine Gallagher and Jeffrey Parsons
`Faculty of Business Administration, Memorial University of Newfoundland
`St. John’s, NF, Canada A1B 3X5
`{kgallagh, jeffreyp}@morgan.ucs.mun.ca
`
`Abstract
`
`Constraints that limit accurate targeting of advertising in
`traditional media may not hold in cyberspace. This paper
`presents a model for effectively and efficiently targeting
`hypermedia-based banner advertisements in an online
`information service. The model takes advantage of
`information
`technology
`to micro-target banner
`advertisements based on individual characteristics of users.
`A simple version of the model, which has the virtue of ease
`of development, is presented. Enhancements are also
`proposed. These require more effort to develop, but may
`lead to even more precise targeting of advertisements.
`Implementation of this framework may benefit both online
`advertisers and online consumers.
`
`1. Introduction
`
`Cyberspace is a rapidly growing new medium for
`commerce. To date, a great deal of industry attention has
`focused on electronic transactions over the Internet.
`Although rapid growth is predicted over the next few years
`[10, 17, 21], actual sales thus far have been only moderate:
`users appear to regard the Internet primarily as a source of
`product information--when it comes time to pay, they prefer
`to buy offline by more conventional means [12, 14].
`Responding to consumers’ desire for information,
`businesses in large numbers have developed sites on the
`World Wide Web (WWW or Web). Most commercial Web
`sites describe the firm and its products and/or services, and
`many offer opportunities for visitors to the Web site to
`provide feedback and ask for specific information. As well,
`some Web sites collect information from visitors in order to
`improve future offerings. Some sites also support ordering
`and payment. The interactive potential of Web sites is
`particularly exciting, as it facilitates relationship marketing
`and customer support, eliminating
`the obstacles of
`geography and time [14, 22]. Not surprisingly, then,
`industry and scholarly research has recently focused on
`making Web sites more appealing and useful to visitors [13].
`However, a Web site can only be effective if current and
`prospective customers visit it. Attracting this audience is
`currently a major challenge.
`
`In this paper, we address the challenge of attracting a
`defined target audience to a Web site via banner advertising.
`We propose a framework for effectively targeting banner
`advertising in an electronic marketplace in a manner that
`benefits both advertisers and consumers. It allows
`advertisers to reach consumers who are more likely to be
`interested in the products and/or services offered by the
`company, and exposes consumers to information about
`products and services that they are likely to be interested in
`purchasing. Although the framework is discussed in terms
`of the Internet, we believe it will be relevant to whatever
`form the "information superhighway" eventually assumes.
`The framework takes advantage of the capabilities afforded
`by information technology for collecting and processing data
`about users. The next section examines trends in the
`electronic marketplace. Subsequently, the current state of
`advertising in this medium is discussed. Thereafter, a
`framework for targeting banner advertising, supported by
`appropriate information technologies, is proposed. Finally,
`opportunities for further research are discussed.
`
`2. Marketing and Advertising in an Evolving
`Electronic Marketplace
`
`The Internet began in the early 1970s as a US
`government research project designed primarily for the
`needs of the military. It expanded in the 1980s to serve the
`international academic and research communities [19, 23].
`In the 1990s, businesses began to appear on the Internet.
`Although accurate estimates are obsolete as soon as they are
`made, it is clear that today tens of millions of people have
`access to the Internet [16] through over 100,000 computer
`networks in 150 countries--and the numbers continue to
`increase [14]. Two types of developments are particularly
`noteworthy with regard to this growth.
`First, a large and ever expanding number of affluent,
`educated consumers are using the Internet [11]. This
`concentration of very desirable consumers has led to a surge
`in commercial interest. Prior to 1990, nodes on the Internet
`were predominantly academic institutions. In 1990, about
`1,000 businesses had Internet connections. By June 1995,
`over 21,000 businesses were online, and the growth in
`commercial connectivity shows no sign of slowing [8].
`Second, the emergence of the hypermedia-based WWW,
`
`Proceedings of The Thirtieth Annual Hawwaii International Conference
`on System Sciences ISBN 0-8186-7862-3/97 $17.00 © 1997 IEEE
`1060-3425/97 $10.00 (c) 1997 IEEE
`
`Twitter-Google Exhibit 1011
`Page 1 of 10
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`together with point-and-click multimedia interfaces such as
`Netscape, have greatly increased usability of the Internet for
`persons without extensive computer
`training.
` The
`development of "applet" technology, such as Java, which
`allows programs to run on a variety of platforms, increases
`the transparency of various Internet services. In other
`words, as technology continues to evolve, it is no longer an
`obstacle to, but an enabler of, electronic commerce.
`In this environment, companies are seeking ways to use
`the Internet effectively [1, 3, 13, 22]. One active area in
`electronic commerce involves using the Internet as a
`medium to communicate persuasive product and service
`information via advertisements. These take various forms,
`the most common of which are corporate Web sites and
`banner advertising. We define a banner advertisement as:
`" paid communication (via text, graphics, video and/or
`audio) of information about an organization and/or its
`products and services
`" by an identified sponsor
`" embedded within, and visually distinct from,
`information provided by an online service
`" with hypermedia links to the sponsor’s Web site.
`We distinguish banner advertising from simple hypermedia
`links (paid or not) to commercial Web sites: banner
`advertising conveys a message even if the user does not
`follow the link; simple links can only convey a message if
`the user follows the link. Banner advertisements are also
`distinct from what [14] refer to as "flat ads," single page
`advertisements that do not contain hypermedia links. In this
`paper, we restrict our discussion to banner advertising that
`appears in the course of users’ browsing and searching
`activities on
`information services, such as Yahoo!
`( h t t p : / / w w w . y a h o o . c o m ) a n d E x c i t e
`(http://www.excite.com), that provide an entry point to
`Internet resources.
` Appendix 1 shows a banner
`advertisements by the Saturn automobile company.
`Scant attention has been paid to banner advertising by
`researchers. This may be because banners seem relatively
`insignificant, especially when compared with the interactive
`richness of Web sites. Technical specifications for banner
`advertisements severely limit creative options and preclude
`any consumer-firm interaction beyond the consumer’s
`selection of the hypermedia link to the associated Web site
`(Excite, for instance, specifies that "all banners are 468x60
`pixels, gif format only, maximum file size is 7k" [9]).
`Banner advertisements are, however, very important and
`interesting when viewed as part of a system that converts
`browsers and searchers into Web site visitors and,
`ultimately, customers. In their model of this conversion
`process, Berthon, Pitt and Watson [3] identify a sequence of
`tasks. First, users must be made aware of the Web site, then
`they must be attracted to and locate the site. Once users
`have found the Web site, the task is to turn that hit into a
`
`visit, ensuring there is some meaningful contact between
`the firm and the consumer; then to convert the visit into a
`purchase. The final task is to get purchasers to return to the
`Web site and repurchase. Each task in the sequence is
`dependent on the successful execution of the previous task.
`Our view of the role of banner advertising in this system
`is as a mechanism to make target audience members aware
`of a firm’s Web site and to attract those users to the site. We
`define two concepts critical to understanding this role.
`Attraction effectiveness is the number of target audience
`members who reach a company’s Web site via a banner
`advertisement hypermedia link divided by the number of
`target audience members who use the information service on
`which the advertisement appears. Attraction efficiency is the
`advertising cost per target audience member attracted to a
`company’s Web site via a banner advertisement.
` There is some evidence that the attraction efficiency of
`banner advertising is low. A recent estimate indicates that
`only 1-2% of banner advertisements lead viewers to seek
`additional information (e.g., by selecting a hypermedia link
`to the company’s Web site) [5]. Since information services
`charge advertisers based on number of exposures (e.g., [9,
`24]), the cost of attracting a single target audience member
`to a Web site is at least 50 to 100 times what it would be if
`all users who were exposed to the advertisement selected the
`hypermedia link. (The cost is even higher if some users
`selecting the link are not target audience members.)
`Increasing attraction efficiency by reducing wasted
`exposures should therefore be a priority. (An additional
`motivation for improving performance of banner advertising
`in converting searchers and browsers into Web site visitors
`arises from recent events such as the agreement between
`Procter & Gamble and Yahoo! which provides for payment
`based on the number of people who actually seek additional
`information
`(by selecting a
`link
`from a banner
`advertisement) rather than those who are merely exposed to
`the advertisement [20]. Such arrangements are expected to
`pressure online services to eliminate wasted exposures [5].)
`The estimate cited above does not provide evidence on
`the attraction effectiveness of banner advertising. The fact
`that only 1-2% of exposed users select a link to the
`advertiser’s site is irrelevant to effectiveness if all target
`audience members using the information service are among
`this group. However, since banner advertisements on online
`information services are shown selectively to users, there
`will generally be the possibility that some target audience
`members who use the information service will not be
`exposed to the advertisement and, hence, will be unable to
`link to a company’s Web site via it. Depending on the
`strategy used to select advertisements for users, a large
`number of target audience members may be missed.
`We contend that both the attraction effectiveness and
`efficiency of banner advertising can be improved by
`
`Proceedings of The Thirtieth Annual Hawwaii International Conference
`on System Sciences ISBN 0-8186-7862-3/97 $17.00 © 1997 IEEE
`1060-3425/97 $10.00 (c) 1997 IEEE
`
`Twitter-Google Exhibit 1011
`Page 2 of 10
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`precisely targeting advertisements based on characteristics
`and behavior of individual users of information services.
`Moreover, such targeting can be more precise than the
`targeting possible in traditional media. For example, visitors
`to a "Travel" page on an information service may be good
`targets for an advertisement for discount airfares, as would
`readers of the Travel section of a newspaper. But the fact
`that the online visitors have made a series of decisions and
`taken a series of actions (i.e., selecting only a subset of
`highlighted links within a hierarchical menu of categories)
`to reach the Travel page, rather than some other page (e.g.,
`the Home Decorating page) suggests they may have a
`greater
`interest
`in
`travel
`than, say,
`readers who
`unintentionally come upon the Travel section of a
`newspaper and decide to read it. Since these exposures are
`more likely to be target audience members, attraction
`effectiveness can be improved. Targeting individual users
`strategy should also lead to fewer wasted exposures, since
`the advertisement would not be shown to users who have not
`reached the Travel page, thereby improving attraction
`effectiveness. (See Appendix 2 for a similar example.)
`At present, targeting of banner advertising does not
`always occur. For example, Appendix 3 shows an
`advertisement for Honda that appeared when Organic
`Gardening was selected from a hierarchical menu of
`categories. People interested in organic gardening may not
`be the best prospects for automobiles, as they are likely to be
`more environmentally sensitive than the general population
`and may feel that cars unnecessarily harm the environment.
`Nevertheless, online information services do currently
`provide some targeting capability. As of August 1996, both
`Yahoo! [24] and Excite [9] offered advertisers three options:
`general rotation, geographic or content targeting, and
`keyword-based targeting. With "general rotation," banner
`advertisements rotate randomly through user searches and
`browsing on the site. The Honda advertisement that
`appeared on the Organic Gardening page in Appendix 3 was
`probably in general rotation. Restricted rotations permit
`advertisers to purchase space in specified content areas or by
`geographic region. For example, financial institutions can
`limit the exposure of their banner advertisements to users
`searching or browsing Business categories, and Canadian
`advertisers can choose to have their banner advertisements
`shown only to users who are searching or browsing in the
`Yahoo! Canada site. These two options are analogous to the
`targeting offered by traditional media such as newspapers,
`magazines, television, and radio [4].
`The third option, keyword-based targeting, makes greater
`use of the targeting potential of information services. A
`company can buy keywords so that whenever a user enters
`one of those keywords during a search, s/he will be exposed
`to the company’s banner advertisement. This ensures that
`the banner advertisement is presented only to people with a
`
`demonstrated interest in the area. For instance, a marketer
`of golf equipment might buy the keyword "golf." Every
`time a user enters "golf" in a search, a banner advertisement
`for the equipment would appear. This is analogous to the
`more precise targeting provided by magazines.
`While these are useful strategies, they fail to take full
`advantage of the targeting potential of banner advertising.
`Current technology provides the capability to develop
`sophisticated and detailed profiles of individual users of
`information services based on individual characteristics and
`past patterns of behavior in using the information service.
`The next section proposes and describes informally two
`versions of a model for targeting banner advertising by using
`the information technology on which an online information
`service is built.
`
`3. A Model for Targeted Advertising
`
`In traditional media, the quality of the information
`available constrains an advertiser’s ability to target
`advertising effectively and efficiently. For example, many
`media buying decisions are based on data provided by
`research bureaus such as the Audit Bureau of Circulations
`(ABC), Business Publication Audit of Circulation (BPA),
`Arbitron, and A.C. Nielsen, which collect data on the
`demographics and media habits of consumers, and
`sometimes on product usage and brands [4]. These survey
`data are cross-tabulated to develop a profile of the audience
`of each media vehicle. The audience profile is then
`compared to the target audience profile identified by the
`advertiser to determine where there is a good match. For
`instance, an automobile manufacturer might identify the
`target audience for an advertisement for a particular model
`of car as middle-income females, 18 to 34, with busy
`lifestyles. Based on research bureau data, as well as the
`experience and judgement of the media planner, media
`vehicles with good reach in that demographic group would
`be chosen. Realistically, though, this type of targeting is
`usually very approximate. For instance, no matter how well
`the media vehicle audience profile matches the target
`audience profile, it is likely that only a portion of the
`audience would be in the market for a new car.
`Online banner advertising may be able to overcome this
`problem. It is possible to target users very precisely because
`data can remain associated with individuals, so advertisers
`can select exactly the users to whom they wish their
`advertising to be exposed. It may be possible, for example,
`to identify which users will be in the market for a new car in
`a particular year. The remainder of this section describes
`two versions of a model for targeting banner advertising by
`taking advantage of the technological capabilities of the
`online environment. The model is designed to be
`appropriate for use by information services which sell
`
`Proceedings of The Thirtieth Annual Hawwaii International Conference
`on System Sciences ISBN 0-8186-7862-3/97 $17.00 © 1997 IEEE
`1060-3425/97 $10.00 (c) 1997 IEEE
`
`Twitter-Google Exhibit 1011
`Page 3 of 10
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`advertising space.
`
`3.1. Basic version
`
` The basic version of the model requires that users be
`assigned unique identifiers (e.g., user accounts) when they
`first connect to the information service. Subsequently, they
`provide these identifiers each time they connect. Users also
`complete an online questionnaire the first time they use the
`information service.
`
`(Incentives
`to complete
`the
`questionnaire may be provided by informing users that the
`information will be used to filter out advertising for products
`in which they are likely not to be interested.) The
`questionnaire allows data to be collected on several
`dimensions, including: (1) demographic attributes such as
`geographic location, income, family lifecycle stage,
`occupation, and sex; (2) psychographic attributes such as
`travel patterns and hobbies; and (3) product and brand usage
`attributes. This element of the basic model permits a banner
`advertisement to be directed to users (and only those users)
`who fit certain criteria, assuming data were collected on
`relevant attributes. For instance, a banner advertisement for
`baby strollers could reach parents of children under five
`years old--and only individuals in that group.
`In contrast, research bureau data uses demographic
`correlates (e.g., males and females, 18 to 34) to identify
`media vehicles that attract a relatively large proportion of
`the people in the identified demographic group [4]. The
`media vehicles thus chosen may miss members of the target
`group (e.g., older parents) and reach consumers not in the
`target group (e.g., people who are between 18 and 34 but do
`not have young children). Even audience data based on
`cross-tabulations, while they supply information on more
`variables, still cannot isolate individuals who are in the
`target audience. (For example, research bureau data may
`allow an advertiser to identify a magazine whose audience
`includes a large number of people between 18 and 34 who
`have young children, but there will still be some readers who
`are not in the target market.)
`The second element of the basic model involves eliciting
`the target audience profile from advertisers. An advertiser
`can specify a target audience using any number of attributes
`about which data have been collected. These can be
`expressed conjunctively and/or disjunctively. For example,
`a specification may indicate that an advertisement is to be
`presented to all users who (1) have household incomes over
`$50,000, and (2) either work in a job that involves travel at
`least four times per year or have travelled on vacation in at
`least four of the past five years.
`In this version of our model, the questionnaire
`determines the data collected about each user. The content
`of the questionnaire will vary depending on the nature of the
`information service, expected users, and expected
`
`advertisers. However, it is imperative to design the
`instrument carefully, in consultation with advertisers based
`on anticipated relevant target audience attributes.
`The final element of the model consists of a mechanism
`to select banner advertisements to display to users. The
`target audience profiles supplied by advertisers provide a
`screening mechanism over users. Each time a user connects,
`his/her profile is compared to all target audience profiles
`from all advertisers. The user’s profile will actually match
`some subset of those profiles. If the number of matches is
`small (and the session is long), it will be feasible to display
`all banner advertisements associated with the matched
`profiles during the user’s session. However, if the number
`of matches is larger (or the session is short), presenting all
`advertisements associated with the matched profiles may
`overwhelm the user. In such a case, it will be necessary to
`present only a selection of
`the
`identified
`target
`advertisements. A rationing system would be needed so that
`users are not deluged with banner advertisements while
`advertisers are assured of access to users who match the
`target audience profile.
`In summary, the basic model has three elements:
`individual user profiles, individual advertisement target
`audience profiles, and a selection mechanism for presenting
`advertisements to specific users who match the target
`audience profile. This framework potentially eliminates
`wasted exposures and provides the capability to reach every
`single user who matches the target audience profile (this
`may not be realized if a rationing system is used). Users
`also benefit, since they will see advertisements only for
`products likely to be of interest to them.
`
`3.2. Enhanced Version
`
`The basic version of the model relies on users
`completing a questionnaire when they initially use an
`information service. This is a straightforward mechanism to
`collect data about user characteristics for the purpose of
`targeting advertisements. A similar approach has been
`incorporated in a commercial product for use with online
`catalogs to direct shoppers to products in which they are
`interested [15]. However, the advantage of simplicity is
`offset by several potential limitations.
` First, such
`information may become outdated, sometimes quickly, as
`user preferences and characteristics change. To some
`extent, information can be kept up-to-date by either
`readministering the questionnaire periodically or giving the
`user the opportunity to update her/his information (e.g., by
`a menu option or hypertext link) each time s/he connects to
`the information service. However, each of these strategies
`is intrusive and may impose an unwarranted burden on users
`in order to maintain currency of information.
`A second, and perhaps more serious limitation of the
`
`Proceedings of The Thirtieth Annual Hawwaii International Conference
`on System Sciences ISBN 0-8186-7862-3/97 $17.00 © 1997 IEEE
`1060-3425/97 $10.00 (c) 1997 IEEE
`
`Twitter-Google Exhibit 1011
`Page 4 of 10
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`questionnaire strategy is that it is subject to two potential
`types of bias. First, the questionnaire designer will want to
`identify as many user attributes relevant to potential
`advertisers as possible. As the number of attributes
`increases, so does the length of the questionnaire, creating
`the possibility of higher mortality in completing the
`questionnaire (especially since it may be more difficult to
`induce users to complete it because they are both physically
`and psychologically remote), thereby
`increasing
`the
`potential nonresponse bias [7]. Second, the questionnaire
`method is plagued with well-known problems, such as errors
`due to inaccurate recall, telescoping, social desirability
`concerns, and cognitive biases, as well as ambiguity,
`intimidation, confusion, and incomprehensibility [2].
`In view of these potential problems, it is appropriate to
`enhance the model so that it does not rely on user self-
`reports, can accommodate changing user characteristics and
`preferences, and is less constrained by the choice of
`questions. Fortunately, information technology may provide
`assistance in each of these areas.
`Current technology allows a considerable amount of data
`about user search activities (both deliberate search and
`browsing) to be collected unobtrusively and analyzed to
`determine patterns. (We are dealing here only with the
`capabilities of the technology, not with the ethical issues
`such capabilities raise. However, we recognize that ethical
`issues must be considered explicitly in the design of systems
`based on our model. For instance, we believe users should
`be aware that such information may be collected, and how
`it may be used, and consent to this activity before using an
`information service.) In the enhanced model, we propose
`that patterns of search and browsing behavior exhibited by
`users while using an information service determine which
`advertisements are shown to that user during current or
`future sessions. In the remainder of this section, we provide
`a general overview of this approach.
`As before, this model relies on assigning a unique
`identifier to each user for recording her/his searching and
`browsing activities while using the information service.
`Each session constitutes a "record", consisting of data such
`as: sites visited in order; pattern of navigation through a
`hierarchical category structure (as in Yahoo!); choice of
`search terms in keyword-based searches; and reaction to
`previously exposed targeted banner advertisements (e.g.,
`which linked Web sites are selected and visited by the user
`and which ones ignored). The aggregate of such records for
`each user provides a profile from which preferences can be
`implicitly generated. As a simple example, if a user has
`made several searches using keywords such as "Atlantic
`salmon" and "fly fishing", and has visited the site of the
`A n g l i n g C l u b L a x - a o f
`I c e l a n d
`(http://www.ismennt.is/fyr_stofn/lax-a/uk/angl_uk.html),
`s/he may be targeted for a banner advertisement for a fishing
`
`lodge in Alaska. However, if a user has previously been
`exposed to the same or similar banner advertisements but
`has not visited linked Web sites when there was an
`opportunity to do so, s/he may not be shown these banner
`advertisements in future.
`This version of the model has the advantage of
`transparency. A user simply visits a service for whatever
`purpose s/he has in mind. Data are collected unobtrusively
`in the course of the visit. Moreover, the data reflect actual
`user behavior, rather than attitudes, intentions, or reported
`behavior captured through a questionnaire. Hence, the
`quality of data derived from user behavior should be
`superior to that of questionnaire data, for purposes of
`targeting advertisements.
`A disadvantage of this model is the preparatory work
`involved on two fronts. First, it is not clear how to structure
`the data collected during visits so that useful information can
`easily be coded for storage and later extraction. Research is
`needed to develop useful and efficient coding mechanisms
`for storing such data as sequences of visits and search terms
`used. We expect this can be handled using conventional
`database structures such as relations (tables); however, the
`design of a relational database for this purpose is itself a
`distinct research issue. Second, the ability to store the
`required data does not necessarily mean useful information
`can be extracted from it. Further research is required to
`determine the types of analyses that yield insights into user
`characteristics and preferences hidden in the data.
`The enhanced model should be used in conjunction with
`the basic model. A questionnaire may be very effective for
`identifying various demographic data relevant to advertisers
`but impossible to ascertain simply from users’ online search
`and browsing behavior. However, since demographic data
`has limitations for effectively targeting consumers of most
`products, the enhanced model of data collection may yield
`complementary data on preferences from patterns of online
`search and browsing behavior.
`implementation
`The next section describes an
`architecture for the basic version of the model. Extensions
`that support the enhanced version of the model remain as
`future research.
`
`4. An Implementation Architecture
`
`The architecture required to implement the basic version
`of the model consists of two parts: data structure to
`represent user profiles and target audience profiles, and an
`algorithm to select banner advertisements to display to a
`user. This section describes these components.
`
`Proceedings of The Thirtieth Annual Hawwaii International Conference
`on System Sciences ISBN 0-8186-7862-3/97 $17.00 © 1997 IEEE
`1060-3425/97 $10.00 (c) 1997 IEEE
`
`Twitter-Google Exhibit 1011
`Page 5 of 10
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`4.1. Data Structure
`
`To target banner advertisements, two types of profiles
`are needed: profiles describing users of the information
`service; and profiles describing the target audience for
`advertisements, as defined by advertisers. Each profile can
`be modeled as a set of attributes.
`We assume there is a finite "universe" of attributes, A
`= <a 1,...,aN>, that may potentially characterize users or target
`audience members.
`
`4.1.1. User Profile. Each user, ui, of the service can be
`described by a record consisting of values of the universe of
`attributes, Ri = <a1(ui),...,aN(ui)>, where an(u i) (n=1,...,N)
`denotes the value of attribute an for user ui. This may be
`implemented in a relational database in which a table is
`defined whose primary key is a user identifier, and
`remaining attributes are those in A. Each row in the table
`contains the profile of one user. (A more elaborate data
`structure is needed to support the enhanced model, since
`data must also be kept about the pattern of behavior of a
`user over one or more sessions.) All attributes need not be
`applicable or relevant to a particular user; hence, null values
`are permitted.
`A simple example serves to illustrate this structure.
`Consider a universe consisting of three attributes: age,
`income, and number of dependents. Suppose there are two
`users of a service. When those users have completed a
`profile questionnaire, the resulting data may be stored in a
`relational table as:
`
`USER
`user_id age
`26
`u1
`u2
`45
`
`income
`34000
`54000
`
`dependents
`0
`2
`
`4.1.2. Target Audience Profile. A target audience profile
`is associated with each banner advertisement. A profile may
`be expressed as:
`(1) A characterization of an "ideal" target audience
`member.
`Such an ideal can be described by a record consisting of
`values of the universe of attributes, Ti = <t1,...,t N>, where
`tn (n=1,...,N) is a specific value of attribute an. Some
`values may be null, indicating that any values of those
`attributes are permitted for the ideal; and/or a
`(2) A characterization of the "acceptable" target audience.
`Generally, an advertiser is interested in reaching those
`within specified ranges of the attributes of interest.
`Given N attributes of interest, acceptability can be
`thought of as a region in N-dimensional space. This
`region can be defined by specifying ranges of acceptable
`values
`for various attributes
`in
`the universe.
`
`Combinations of attributes may be expressed:
`conjunctively, indicating that users in the target region
`must satisfy all
`the conditions or restrictions;
`disjunctively, indicating that acceptable users must
`satisfy one of a set of conditions; or using a combination
`of disjunctions and conjunctions.
`Note that "distance" from the ideal point may become
`relevant if an advertiser has to choose a subset of users
`whose profiles fall within the acceptable region.
`Operationally, profiles for ideal or acceptable users can
`be maintained in a relational database structure. In the case
`of ideal profiles, a table can be defined in which each row
`describes the ideal target audience member for each
`advertisement. The primary key for this table consists of an
`identifier for the advertisement, while the remaining
`attributes are those of the universe of attributes of interest.
`Since not all attributes may be relevant in specifying an
`ideal, null values are permitted.
`To illustrate, consider a simple example in which there
`are two advertisements, each with a different target audience
`profile, designated T 1 and T2. The ideal target profile for T1
`is users aged 35 with incomes of $50,000 (no restrictions on
`number of dependents), while that for T2 is users aged 25
`with incomes of $25,000 and no dependents. These profiles
`are shown in the following relational table.
`
`TARGET
`ad_id
`T1
`T2
`
`age
`35
`25
`
`income
`50000
`25000
`
`dependents