Abstract:
A method of delivering targeted on-line advertising to an end user of a computer network system comprises creating an advertisement campaign having a plurality of dimensions associated therewith, obtaining end user data based upon engagement of the end user with the network system, profiling advertisement content available on the network system to identify specific items of advertisement content that are compatible with the advertising campaign, for each item of compatible advertisement content, utilizing the end user data and metrics associated with the plurality of dimensions associated with the advertisement campaign to develop an effectiveness measurement, and utilizing the effectiveness measurements to select at least one item of compatible advertisement content for display to the end user.

Description:
RELATED APPLICATIONS 
       [0001]    This application is a division of U.S. application Ser. No. 12/623,858, filed Nov. 23, 2009, entitled Multi-Dimensional Framework for Optimized Delivery of Targeted On-Line Brand Advertisements. U.S. application Ser. No. 12/623,858 was a division of U.S. application Ser. No. 11/515,618, filed Sep. 5, 2006, entitled Online Personalized Content and Merchandising Environment, which claimed the benefit of U.S. Provisional App. No. 60/714,325, filed Sep. 6, 2005, entitled Online Personalized Content and Merchandising Environment and U.S. Provisional App. No. 60/715,075, filed Sep. 8, 2005, entitled Online Personalized Content and Merchandising Environment. U.S. application Ser. No. 12/623,858 was also a continuation-in part of U.S. application Ser. No. 12/340,112, filed Dec. 19, 2008, entitled Internet Publishing Engine and Publishing Process Using Ad Metadata to Deliver Ads, which claimed the benefit of U.S. Provisional App. No. 61/117,293, filed Nov. 24, 2008, entitled Multi-Dimensional Framework for Optimized Delivery of Targeted On-Line Brand Advertisements. All of the applications listed above are incorporated by reference in their entirety. 
     
    
     TECHNICAL FIELD 
       [0002]    The present invention relates to the presentation of personalized content and merchandising to an end user in an online environment. More particularly, the present invention is directed to multi-dimensional systems and methods for providing superior branding effectiveness for on-line display advertisements. 
       BACKGROUND 
       [0003]    The present invention relates to the presentation of personalized content and merchandising to an end user in an online environment. More particularly, the present invention is directed to multi-dimensional systems and methods for providing superior branding effectiveness for on-line display advertisements. 
         [0004]    Above-cited U.S. application Ser. No. 11/515,618 discloses a database-based content management system for a network system that provides a straightforward process for publishing content and advertisements from advertisers and affiliates associated with the network system to a website. The disclosed content management system implements a workflow-based approach that allows different types of network content contributors and other sources to push and feed data to the different sections of the website properties. More specifically, it provides a centralized data repository and a set of content contributor web-based interfaces so that non-technical resources can jointly and concurrently work on the publishing of the sites. In this way, editors can log into the system and create database objects that hold the initial metadata and mapping for the stories to be published, merchandisers can manage and import collections of products and their availability information from a very large number of records, made available to the network system via automated data feeds that are updated daily, designers can prepare a custom layout for each page of a custom collection and upload the different graphical assets that will be required for building the layout, and production specialists can gather all of the assets, lay them out and publish the website. 
         [0005]    Those skilled in the art appreciate that, before a determination can be made regarding how to increase the return-on-investment (ROI) from on-line advertising, the objectives for the on-line advertising campaign must be determined This is necessary because on-line advertising often serves multiple purposes. For example, on-line ads can help to increase brand awareness, brand perception and user engagement with the ad (e.g., clicks), as well as ultimately lead to higher sales of the products and services being advertised. 
         [0006]    While traditional media (e.g., magazines and TV) measure reach, awareness and product sales as the primary metrics for ad campaign effectiveness, on-line advertising has used ad impressions delivered, clicks and conversions as its primary metrics. However, recent research reports indicate that only sixty percent of on-line end users actually see the ads above the fold and there is no correlation between display ad clicks and brand metrics. Furthermore, only one percent of on-line end users click on ads and there is no connection between measured attitude towards a brand and the number of times an ad for that brand was clicked. 
         [0007]    As more advertisers shift their ad campaigns to on-line digital media, it is clear that brand advertising is not possible with small text ads that typically appear next to search results. Also, the majority of on-line advertising has focused on performance-based campaigns with “click” as the primary measurement metric. Graphical ads have now moved beyond static banner ads to include flash animation, video and sound, thus making it more compelling to present brand ads in more attractive and engaging formats. With Web advertising expected to surpass $60 billion by 2013, and with display and video ads to account for more than a third of that amount, there is increasing demand for more effective systems and methods for delivering on-line brand advertising. 
       BRIEF SUMMARY 
       [0008]    The present invention provides systems and methods for delivering on-line brand advertising that go beyond clicks and offer new metrics and capabilities to help advertisers increase the effectiveness of their on-line brand ad campaigns. These metrics combine the best of both traditional media and new digital media while allowing advertisers to assign higher weights to specific metrics that are more important for the success of their brand ad campaign. Utilizing the concepts of the present invention, advertisers are able to create, execute, analyze and optimize their brand ad campaigns more effectively and efficiently. 
         [0009]    An embodiment of a method of delivering targeted on-line advertising to an end user of a computer network system in accordance with the concepts of the present invention comprises creating an advertisement campaign having a plurality of dimensions associated therewith, obtaining end user data based upon engagement of the end user with the network system, profiling advertisement content available on the network system to identify items of advertisement content that are compatible with the advertisement campaign, for each item of compatible advertisement content, utilizing the end user data and metrics associated with the advertisement campaign to develop an effectiveness measurement, and utilizing the effectiveness measurements to select at least one item of compatible advertisement content for display to the end user. 
         [0010]    The features and advantages of the various aspects of the present invention will be more fully understood and appreciated upon consideration of the following detailed description of the invention and the accompanying drawings, which set forth illustrative embodiments in which the concepts of the invention are utilized. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a diagram illustrating five primary dimensions of a method of delivering on-line brand advertising in accordance with the concepts of the present invention. 
           [0012]      FIG. 2  is a block diagram illustrating an embodiment of a network platform for delivering on-line brand advertising in accordance with the concepts of the present invention. 
           [0013]      FIGS. 3 and 4  combine to provide a flow chart setting forth a sequence of steps for implementing a method of delivering on-line brand advertising in accordance with the concepts of the present invention. 
           [0014]      FIG. 5  illustrates the flow between a client ad selector and a network ad selector in the process flow of  FIGS. 3 and 4 . 
           [0015]      FIG. 6  provides an example of an ad campaign analytics dashboard that can be developed in accordance with the concepts of the present invention. 
           [0016]      FIG. 7  is an example of audience segmentation for users who clicked on an on-line brand ad for a home computer provided in accordance with the concepts of the present invention. 
           [0017]      FIG. 8  is an example of a web-based campaign planning tool for delivering on-line brand advertising in accordance with the concepts of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0018]      FIG. 1  presents a visualization of five primary dimensions associated with a brand ad campaign in accordance with the invention: audience, context, time, placement and engagement. For each of these dimensions, a brand ad campaign has specific values associated with the metrics in that dimension. For example, the “audience” dimension has metrics that measure the reach and quality of a targeted audience, while the “placement” dimension has metrics that deal with the quality, size and surrounding of a brand ad on a web page. An illustrative list of metrics for these five dimensions is shown in Table I below: 
         [0000]    
       
         
               
             
               
             
           
               
                 TABLE I 
               
               
                   
               
               
                 BRAND AD DIMENSIONS 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 Audience 
               
               
                 Demographic: age, affinity, income, ethnicity, marital status, education, 
               
               
                 etc. 
               
               
                 Geographic: country, DMA/region, city and zip-code 
               
               
                 Psychographic: personality, values, attitudes, interests, and lifestyle 
               
               
                 Behavioral: frequently visited content, search intent, ads clicked on, etc. 
               
               
                 Intent: content visited, search intent, segment behavior, etc. 
               
               
                 Context 
               
               
                 Channel: Beauty, health, sports, etc. 
               
               
                 Type: news, email, stories, gossip, social networking, etc. 
               
               
                 Time 
               
               
                 Time zone: reaching active audiences across and within countries 
               
               
                 Intraday: most impactful times: early morning email, lunch-hour surfing, 
               
               
                 etc. 
               
               
                 Seasonal: relevant products and services in tune with seasons 
               
               
                 Special events: benefiting from expected traffic surges for special events 
               
               
                 Sequenced: higher impact with time-sequenced, related ad impressions 
               
               
                 Placement 
               
               
                 Position: above the fold, on the sides, pre-, mid- or post-roll for video, etc. 
               
               
                 Size: size of the ad based on IAB standards, such as 300×250 
               
               
                 Brand proximity: Effective combinations of banner ads on the same page 
               
               
                 Engagement 
               
               
                 Interactions: clicks, mouse-overs, video play/rewind/pause/etc. 
               
               
                 Conversions: on-line and off-line transactions tied to the ad 
               
               
                 Feedback: survey results, ranking, etc. 
               
               
                 Viral: sharing, forwarding, etc. 
               
               
                   
               
             
          
         
       
     
         [0019]    For a given brand ad campaign, an “effectiveness” measure is in turn computed based on a combination of the weighted values of the metrics associated with that campaign. Advertisers have control over the weights assigned to each metric in order to emphasize specific objectives of a brand ad campaign. This measure is referred to herein as Prime Target Rank (PTR) and can be used to compare brand ad campaigns with similar objectives. For example, two rich media movie ads targeted for similar audiences and placed in similar web pages can be compared based upon their respective PTRs. Furthermore, a PTR index can be established for a category of brand ads, e.g., “organic food,” to facilitate evaluating the effectiveness of brand ads in that particular category. 
         [0020]    PTR is computed as follows:
       Prime Target Rank of an on-line brand ad campaign is a positive real number defined as:       
 
         [0000]      PTR= w   1   *A+w   2   *C+w   3   *T+w   4   *P+w   5   *E  ( w   1   +w   2   +w   3   +w   4 =1.0)   A=Audience (positive real number)   C=Context (positive real number)   T=Time (positive real number)   P=Placement (positive real number)   E=Engagement (positive real number)     
         [0027]    The value for each of the above parameters is computed based upon the following:
       Audience to whom the brand ad campaign is delivered is defined as:       
 
         [0000]        A=w   1   *AR+w   2   *AS+w   3   *AV ( w   1   +w   2   +w   3 =1.0)   AR=Audience Reached (impressions —  delivered/forecasted inventory) (between 0&amp;1)   AS=Audience Scarcity 1-(targeted_audience/total_audience) (between 0&amp;1)   AV=Audience Value (average CPM for campaigns targeting similar audience)     
         [0032]    Context of the page within a web site in which the brand ad is displayed is defined as: 
         [0000]        C=w   1 *EQ+ w   2 *UV+ w   3 *PV+ w   4 *TV+w 5   *R 1+ w   6 *PR ( w   1   +w   2   +w   3   +w   4   +w   5   +w   6 =1.0)   EQ=Editorial Quality (computed based on metrics defined in Appendix C)   UV=Unique Visitors (per month) (integer)   PV=Page Views (per month) (integer)   TV=Avg. Time spent per Visit (in seconds)   RI=Revenue per ad Impression (in cents)   PR=Page Rank (based on Google) (1: lowest 10: highest defined per Google PR algorithm)
       Time at which the brand ad campaign is delivered is defined as:   
         
         [0000]      CE= w   1 *IV+ w   2 *TPI+ w   3 *CTR+ w   4 *SR ( w   1   +w   2   +w   3   +w   4 +1.0)   IV=Impressions Viewed (impressions_abovethefold/total_impressions_rendered)
       Placement of a creative for a brand ad campaign is defined as:   
         
         [0000]        P=w   1 *CS+ w   2 *CP+ w   3 *CQ+ w   4 *CT ( w   1   +w   2   +w   3   +w   4 =1.0)   CS=Creative Size (total area of the ad creative in pixels)   CL=Creative Location (4: northwest, 3: north or northeast, 2: east or west, 1: south)   CQ=Creative Quality (1: simple &amp; low-quality 10: elegant &amp; high-quality)   CT=Creative Technology (1: text 2: image 3: rich-media 4: video)
       Engagement with the brand ad campaign is defined as:   
         
         [0000]      CE= w 1*IV+ w 2*TPI+ w 3*CTR+ w 4*SR ( w 1 +w 2+ w 3+ w 4=1.0)   IV=Impressions Viewed (impressions_abovethefold/total_impressions_rendered)   TPI=Avg. Time Per Impression (total_user_ad_view_time/total_impressions)   CTR=Click Through Rate (total_clicks/total_impressions_rendered)   SR=Success Rate (total_desired_user_ctivities/total_impressions_rendered)     
         [0051]    The targeting component deals with finding the most appropriate end user and environment that meet an ad campaign&#39;s specific targeting profile. Targeting is based upon the metrics associated with the five dimensions, as described above. For audience targeting, a behavioral interest taxonomy facilitates profiling end users based on their various activities across the network system. An illustrative taxonomy is as follows: 
       Behavioral Interest Taxonomy 
     Beauty (B) 
       [0052]    Beauty.Cosmetics (BC) 
         [0053]    Beauty.Cosmetics.BodySkinCare (BCB) 
         [0054]    Beauty.Cosmetics.FaceCareMakeup (BCF) 
         [0055]    Beauty.Cosmetics.Perfume (BCP) 
         [0056]    Beauty.HairCareStylesColoring (BH) 
       Entertainment (E) 
       [0057]    Entertainment.CelebrityNewsGossip (EC) 
         [0058]    Entertainment.Films (EF) 
         [0059]    Entertainment.Gaming (EG) 
         [0060]    Entertainment.Music (EM) 
         [0061]    Entertainment.RestaurantsBars (ER) 
         [0062]    Entertainment.ShowsTheatres (ES) 
         [0063]    Entertainment.TV (ET) 
       Fashion (F) 
       [0064]    Fashion.Apparel (FA) 
         [0065]    Fashion.Cosmetics (FC) 
         [0066]    Fashion.Hair (FH) 
         [0067]    Fashion.Jewelry (FJ) 
         [0068]    Fashion.PursesBags (FP) 
         [0069]    Fashion.Shoes (FS) 
       Health (H) 
       [0070]    Health.AllergiesAsthma (HA) 
         [0071]    Health.BabylnfantHealth (HB) 
         [0072]    Health.CaridoHealth (HC) 
         [0073]    Health.DentalHealth (HD) 
         [0074]    Health.Diabetes (HI) 
         [0075]    Health.HeadacheMigraine (HH) 
         [0076]    Health.MentalHealth (HM) 
         [0077]    Health.Oncology (HO) 
         [0078]    Health.SexualHealth (HS) 
         [0079]    Health. SexualHealth.BirthControl (HSB) 
         [0080]    Health.VisionHealth (HV) 
       Living (L) 
       [0081]    Living.Autos (LA) 
         [0082]    Living.Crafts (LC) 
         [0083]    Living.EcoGreen (LE) 
         [0084]    Living.Food (LF) 
         [0085]    Living.Food.Cooking (LFC) 
         [0086]    Living.Food.OrganicHealthyFood (LFO) 
         [0087]    Living.Food.WineBeerCocktail (LFW) 
         [0088]    Living.GamesHobbies (LG) 
         [0089]    Living.Home (LH) 
         [0090]    Living.Home.Decorating (LHD) 
         [0091]    Living.Home.Entertaining (LHE) 
         [0092]    Living.Home.Furniture (LHF) 
         [0093]    Living.Home.InteriorDesign (LHI) 
         [0094]    Living.Home.Pets (LHP) 
         [0095]    Living.Home.Remodeling (LHR) 
         [0096]    Living.Home.TechAppliances (LHT) 
         [0097]    Living.MoneyFinance (LM) 
         [0098]    Living.News (LN) 
         [0099]    Living.ParentingMoms (LP) 
         [0100]    Living.ParentingMoms.ChildCare (LPC) 
         [0101]    Living.ParentingMoms.Pregnancy (LPP) 
         [0102]    Living.RomanticRelationships (LR) 
         [0103]    Living.Sports (LS) 
         [0104]    Living.Sports.Golf (LSG) 
         [0105]    Living.Sports.Ski (LSS) 
         [0106]    Living.Sports.Tennis (LST) 
         [0107]    Living.Travel (LT) 
         [0108]    Living.Travel.Airlines (LTA) 
         [0109]    Living.Travel.BeachesIslandsCruises (LTB) 
         [0110]    Living.Travel.DoemsticDestinations (LTD) 
         [0111]    Living.Travel.HotelsLodging (LTH) 
         [0112]    Living.Travel.InternationalDestinations (LTI) 
         [0113]    Living.Travel.SpasMassages (LTS) 
         [0114]    Living.Weddings (LW) 
       Shopping (S) 
       [0115]    Shopping.Apparel (SA) 
         [0116]    Shopping.BeautyFragrance (SB) 
         [0117]    Shopping.Electronics (SE) 
         [0118]    Shopping.HomeGarden (SH) 
         [0119]    Shopping.JewelryWatches (SJ) 
         [0120]    Shopping.KidsBabyNeeds (SK) 
         [0121]    Shopping.Luxury (SL) 
         [0122]    Shopping.ShoesAccessories (SS) 
         [0123]    SocialMedia (M) 
         [0124]    SocialMedia.Network (MN) 
       SocialMedia.Community (MC) 
       [0125]    Wellness (W) 
         [0126]    Wellness.Aging (WA) 
       Wellness.DietNutrition (WD) 
       [0127]    Wellness.EcoGreen (WE) 
         [0128]    Wellness.FitnessExercise (WF) 
         [0129]    Wellness.HolisticHealth (WH) 
         [0130]    Wellness.OrganicFood (WO) 
         [0131]    Wellness.Relaxation (WR) 
         [0132]    Wellness.Spa (S) 
         [0133]    Wellness.Spirituality (WP) 
         [0134]    Wellness.StressManagement (WM) 
         [0135]    Wellness.Yoga (WY) 
         [0136]    Furthermore, various audience segments are defined as aggregations of multiple behavioral interests that collectively identify end users with similar interests, such as “gadget lover.” Each brand ad campaign in turn may have multiple targeting parameters associated with it and the ad server of the network system ensures that the brand ad is delivered in the right context, to the appropriate audience, at the correct time, with the desired engagement. 
         [0137]    The serving component is responsible for handling the requests from the web server to return the most appropriate brand ad for a given web page, ad position and size, end user, and time, for example. The ad server makes its decision based upon several criteria including, for example, the targeting characteristics of the ad campaign, its priority compared to other running ad campaigns, and pacing requirements based on daily impressions that need to be delivered to meet the campaign goal. Therefore, the ad server utilizes a multi-dimensional arbitration and optimization scheme to select a brand ad that meets the delivery requirements of the ad campaign. 
         [0138]    In accordance with one of its aspects, the present invention provides a network system platform that is focused on providing on-line brand advertising in accordance with the concepts discussed above. The main components of an embodiment of an ad platform that provides the on-line brand advertising are graphically depicted in the  FIG. 2  block diagram and described in greater detail below in conjunction with the flow charts provided in  FIGS. 3 ,  4  and  5 . 
         [0139]    An embodiment of a multi-dimensional ad delivery method in accordance with the concepts of the present invention comprises the following steps: 1) the creation of an advertising campaign; 2) the profiling of the content; 3) the measurement of the matching brand ad dimensions; and 4) the selection of the ad for presentation to the end user. 
         [0140]    The Creation of the Advertising Campaign 
         [0141]    With reference to the  FIG. 2  block diagram and the  FIG. 3  flow chart, an Order Manager Module  200 , along with information provided by an Inventory Forecaster  202 , allows an Advertiser Planner or Sales Team  203  to create an advertising campaign within a network system that fulfills the needs of the Advertiser/Sales Team  203  (steps  1   a  and  1   c ). These needs are expressed as a series of targeting criteria, all of which map, for example, to the five primary brand advertisement dimensions discussed in detail above: audience, context, time, placement and engagement. As an example, an advertisement can be targeted to a specific audience (e.g., women, 30 to 45 years old, with a household income greater than $70K/year), while they are in the context of researching for advice on how to educate their children, during the times of the day when they are likely to be more receptive because they are online connected from home, only for above-the-fold ad placement and set to only reach visitors who have a history of engaging with asthma condition content or have clicked on allergy medicine ads. A Yield Manager  204  allows the system to calculate a desired PTR and price and ad accordingly (step  1   b ). The Order Manager  200  then saves the information about the ad in a Campaign Database  206  (step  1   d ). 
         [0142]    Profiling the Content 
         [0143]    With continuing reference to  FIGS. 2 and 3 , a series of automated web spiders or Crawlers  207  indexes and categorizes every single page url of available Network Content  208  available in the network system (step  2   a ) and feeds that data to a Content Profiler  210  (step  2   b ). From this information, the Content Profiler  210  creates a Content Database  211  that lists every single url that it identifies as a potential target for the ad campaigns that are live (step  2   c ). The Content Database  211  is then partitioned into Interest Categories  212  to expose the different types of content or subjects with which the end user is likely to interact and made available to the Inventory Forecaster  202  (step  2   d ). It is important to note that only the content that matches the targeting objectives of the ads that are live in the network system will be created in the Content Database  211 . As discussed in greater detail below, this allows the network system to manage a reduced data set during the real time selection of the ad, resulting in an architecture that can scale better and easier and faster than other comparable systems. As a result of this architecture, a network system with only twenty or forty servers that match certain key configurations can process the same counts of requests/second as an equivalent network system that requires hundreds of servers. It is estimated that the server to requests per second ratio for the system architecture disclosed herein is the highest in the industry and is at least ten times higher than that provided by currently known system architectures. 
         [0144]    Measurement of the Matching Brand Ad Dimensions 
         [0145]    With continuing reference to  FIGS. 2 and 3 , data regarding every single interaction that the end user had with any Ads or Content from the Network Content  208  (steps  3   a  and  3   b ) is saved by a User Engagement Tracker  214  in Tracking Logs  216  (step  3   c ). That data is then processed by a User Profiler  218  (step  3   d ) to select a series of matching ads that the end user is likely to be matched against and this information is stored in a User Database  220  (step  3   e ), which information is also made available to the Inventory Forecaster  202  (step  3   d ). In parallel, certain patterns are recorded and organized into a Data Warehouse  222  that will allow an Analytics Reporter  224  to expose insightful trends to the Advertiser or to the Client. Other real time brand ad dimensions are tracked and identified by an Ad Server  226  using mechanisms such as, for example, session information stored on a local computer using cookies, time on the client computer itself and x-y coordinates of the ad on the page. The information saved in the Tracking Logs  216  is made available to the Yield Manager  204  (step  3   g ). 
         [0146]    Selection of the Ad 
         [0147]    With reference to  FIGS. 2 and 4 , the last step in the process is performed by an Ad Selector  228 , a module of the network system that runs processing both on an ad selector on the Client Computer  230  and the Ad Server Computer  232 , as discussed in greater detail below. The Ad Selector  228  on the Ad Server Computer  232  is fed information from the end user&#39;s computer (steps  4   a ,  4   b ) and from the Content Database  211 (step  4   d . 1 ), the User Database  220  (step  4   d . 2 ) and the Campaign Database  206  (step  4   d . 3 ) in order to select the ad that will be presented to the end user. At this point, a PTR is calculated for each ad in the list of matching ads and the system selects the ad that is best suited based upon the Yield required by the Advertisers. As discussed below, using client-side technologies such as Java and Javascript, the Ad Selector  228  performs a portion (e.g., 50%0 of the processing required to select an ad on the Client Computer  230 ; the remaining portion (e.g., 50%) is performed on the Server Computer  232 . As mentioned above, this distributed hardware architecture hugely reduces the number of servers required to scale the system. 
         [0148]    Ad Selector—Client Server Workflow 
         [0149]    As stated above, the Ad Selector  228  ( FIG. 2 ) utilizes an innovative distributed processing model that splits the ad selection workload between a Client Computer  230  and a network Server Computer  232 . This model allows for the ad selection to occur as a four step process where the use of both the Client Computer  230  and the Server Computer  232  are maximized. 
         [0150]    With reference to  FIG. 5 , the distributed processing is implemented as follows: 
         [0151]    (1) Client Request for Ad 
         [0152]    A Javascript enabled computer makes a first call to the Ad Server  226 , using the &lt;script src= . . . &gt;language construct for remote script retrieval, to obtain the first set of ad selection directives (Publisher Client in  FIG. 5 ). The Javascript call sends key parameters, such as the required ad size and the affiliate/publisher id that identifies the network site on which the ad is being displayed. 
         [0153]    (2) Custom Targeting Values and Settings 
         [0154]    Upon the initial call, the ad server returns a series of Javascript language directives that are dynamically generated based upon the meta data contained in the Content Database  211 . The Javascript directives include elements that will determine important ad selection criteria such as: whether or not the site supports and desires different types of ad creatives (e.g., banners, expandable ads, rich media, brand research ads, video ads); whether or not the site supports and desires different types of monitezation models for their ads (e.g., CPM—Cost per mile, CPC—Cost per click, CPA—Cost per customer acquisition); url specific keyword characteristics that were stored in the Content Database  211  and identified for that particular url (e.g., fashion specific content, historical high performance CPC ads on that page for Luxe Automotive advertisers). Then, the dynamically generated Javascript executes itself on the Client Computer  230  and collects more information about the environment on which it is running, such as for example, screen resolution of the computer, operating system and browser type and location of the ad (x,y coordinates, above the fold/below the fold). After the environment has been identified, the Javascript runs a first pass at the ad selection criteria (Ad Selector—Client side) and concludes which ad server within the ad server network is best suited to deliver the highest relevant ad of the desired type. After the ad server type has been identified, the Client Computer  230  makes a request to that Ad Server  226  (Ad Server stub—using the &lt;script src= . . . &gt;language construct for remote script retrieval) in order to execute a request for ad content matches. 
         [0155]    (3) Request for Ad Content Matches 
         [0156]    At this point in the process, the request to the Ad Server for ad content matches sends a series of specific key values (name=value pairs that describe a series of simple positive matching criteria) that will be used to find a specific ad. Upon receipt, the Ad Server selects the highest priority available ad that matches some or all of the targeted key values and returns the matched creative for the ad call. 
         [0157]    (4) Matched Creative for the Ad Call 
         [0158]    Since the second call to the Ad Server is a remote Javascript retrieval, the Client Computer  230  executes the instructions so that it can render the banner, text or rich media ad that was selected. 
         [0159]    In the above-described model, unlike conventional models for server side ad selection that consider the client computer to be a dumb terminal that can only display content, the local processing abilities of the client computer are used to execute an algorithm that does the pre-selection of the ads. This initial step (Publisher Client Stub) reduces the universe of applicable ads from, for example, about 100,000 to a mere 1,000. By the time that the second call is made to Ad Server (Ad Server Stub), the processing time required to load the available ads and select 1 out of 1,000 takes less than 10 ms. Based upon the speed of the internet connection and the CPU type from the client computer, the time to run the two ad calls performed to select the ad can be an average of about 250 ms total. A server farm comprised of twenty ad servers (e.g., dual core Pentium 2.8 GHZ, 8 GB RAM, running Linux Centos 5.2) using the above-described technology, can serve over 50,000 concurrent ad requests. Using a conventional “server side only” model, it would take at least two hundred ad servers to ensure a response time of 250 ms on a similar 50,000 concurrent ad requests load. 
         [0160]    Various data about the delivery and user engagement for an ad campaign are recorded in log files and subsequently processed and analyzed to provide insight about the performance of the ad campaign. The same metrics of the above-discussed five dimensions of the ad platform are used as the basis of collecting and analyzing this data, thus providing a consistent framework for targeting and analytics. The reports are in turn presented to the analysts in a Web-based dashboard, an example of which is shown in  FIG. 6 . 
         [0161]    Furthermore, brand ad campaigns can be further analyzed based upon a specific dimension, such as audience segmentation of the user engagements. An example of audience segmentation for the users who clicked on a brand ad for a home computer is shown in  FIG. 7 . 
         [0162]    Based on the historical log data collected about the past brand ads along the metrics of the disclosed ad platform&#39;s five dimensions, forecasts of inventory for ad impressions across the network are provided by means of a Web-based campaign planning tool. An example of this tool is presented in  FIG. 8 . Using this tool, a sales planner can select various targeting criteria for a brand ad campaign and obtain a visual report of the available impressions inventory that meet those criteria. 
         [0163]    It should be understood that the systems and methods described herein can use any appropriate technology, such as HTML, JavaScript, SQL server, etc., as known in the art for serving and displaying the material discussed herein. The information can be delivered by any appropriate means, such as over an Internet connection, over a wireless connection, offline from a computer readable medium, or via any other appropriate signal or information delivery mechanism. The material can be viewed on any appropriate device, such as a computer, television, mobile phone, handheld gaming device, or other device capable of displaying the information. The code and images for the site, as well as any other content, can be contained on a central server, on many different servers, on a number of networked computers, on a single computer, on a computer readable medium, or on any other appropriate device known or used for containing such information. The computer readable medium can be any appropriate medium, such as may include flash memory, hard disks, optical discs, CD-ROMs, DVDs, memory sticks, memory drives, memory chips, magnetic media, transmission signals, and any other appropriate medium. Portions of the sites and the tools for creating and displaying these sites can be implemented in software, hardware, or a combination thereof, using any appropriate technology known or used in the art. 
         [0164]    It should be recognized that a number of variations of the above-identified embodiments will be obvious to one of ordinary skill in the art in view of the foregoing description. Accordingly, the invention is not to be limited by those specific embodiments of the present invention shown and described herein. Rather, the scope of the invention is to be defined by the following claims and their equivalents.