Abstract:
A computer system and computer-implemented method for placing an advertisement on one or more social networks wherein advertisements are generated and deployed on one or more social network advertising platforms and based on performance data, adapting the parameters of the advertisement or placement of the advertisement until the performance criteria of the advertiser are met.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    This invention relates to the field of online advertising optimization. 
         [0002]    In recent years the art of creating and placing advertisements on internet web sites such as Yahoo, Bing, Google, and the like has become very sophisticated. Generally the advertiser pays the host web site a fee based on one or more measures such as number of times the advertisement is “clicked” on by users, the number of times the advertisement is displayed to a user, and/or the number of times a user purchases a product or service after clicking on an advertisement. The advertiser provides the content of the advertisement to the host. In the case of search engines, the advertiser can provide one or more key words to the host which, when found in search results, will cause the advertisement to be displayed to a searching user. For example, a seller of drugstore items may select toothbrush, floss, toothpaste, and flu as some of its keywords. 
         [0003]    Many advertisers will place a single advertisement on several different hosts such as Google, Yahoo, Bing, as well as social networks such as Facebook, Linkedin, Myspace, and others, and will use various methods to allocate their spending based on varying results. A “social” network is described as a web property that collects first party information that is volunteered by users and can be used to target advertisements against them on a first party level. Also these networks describe and have created ad platforms which utilize custom elements unique to each web property to better increase the experience and utility of the web platforms themselves into the ad units that are being distributed on each social ad platform. Examples of this include: Facebook ad units with “like” buttons, Twitter ad units with promoted tweets. Such ad units are not standard IAB approved ad units. 
         [0004]    Systems and methods for bidding for ad placement on publisher web sites such as Google and Yahoo are well known. Pisaris-Henderson, et al., US 2003/0220866 A1, for example, describe a bid amount chargeable to a participating advertiser upon response to at least one biddable advertisement determined by open auction conducted by the service provider or publisher. As responses by users to the biddable by the service provider or publisher. As responses by users to the biddable advertisement are received by the service provider, they are provided to the associated participating advertiser and that participating advertiser is charged the bid amount for the response. The advertiser associated with a biddable ad can change in real time based on the auction and associated mechanisms used by the service provider/publisher, such as a bid weighted rotation which associates the biddable ad to a number of participating advertisers. 
         [0005]    Systems for ranking the quality of internet traffic from one web site to another have been published. For example, Fishteyn, et al., U.S. 2004/0190448 A1 disclosed a system and method of determining a quality ranking of user traffic directed from at least one traffic producer Web site to a traffic consumer Web site. A reference for the traffic consumer is established on a Web site of the traffic producer. The reference includes a link from the traffic producer to a traffic quality intermediary and a unique identifier to identify the traffic consumer. The traffic quality intermediary receives user traffic data associated with the user traffic directed from the traffic producer and determines a quality ranking of the user traffic based upon the user traffic data. 
         [0006]    For example, Schwarz, et al., US 2010/0293047 A1 disclosed an ad network system for optimizing the purchase of online display advertisement inventory which includes an advertiser management system to manage and acquire data for a set of advertising campaigns for a set of advertisers and a publisher management system to manage and acquire data for inventory at publishers&#39; sites and applications. A media buying system runs a two-part optimization to determine an allocation of available inventory and an inventory purchase plan is based on the data acquired by the advertiser management system and publisher management system. 
         [0007]    Flake, et al., US 2008/0103953 A1 disclosed a tool for optimizing advertising across disparate online advertising networks wherein a participant can specify goals and/or constraints for participating and the tool can automatically optimize advertising expenditures for advertising transactions across the different networks while also tracking the performance of the participant across the different networks and dynamically tunes the participant&#39;s advertising expenditure based on such performance as well as the changing conditions of the marketplace. 
         [0008]    Another online advertising optimization system was disclosed by Torigoe, et al., US 2010/0250361 A1, wherein features of the advertisement are defined and a predicted click through rate (CTR) corresponding to the advertisement based in part on the set of feature is generated and the advertisement is ranked and served based on the ranking. 
         [0009]    More recently social network advertising has become a greater percentage of online advertising than previously. Examples of social networks which have only recently begun displaying advertisements are Facebook.com, Linkedin,com, Twitter.com, Ning.com, myspace.com, reddit.com, StumbleUpon,com. Social network advertising differs from search engine site advertising in that much more data about particular users is available to advertisers and advertising agencies and aggregators than with search engine sites, newspaper sites, and the like. However, to date there has been much room for improvement in the art of customizing advertisements and placing them on the various social networks, measuring results and effectiveness, and dynamically improving the advertisements to increase their effectiveness, while carrying out the advertisers&#39; objectives within the advertisers&#39; budgets. 
         [0010]    It is an object of this invention to provide an improved method and system for such customizing advertisements and placing them on the various social networks, measuring results and effectiveness, and dynamically improving the advertisements to increase their effectiveness, while carrying out the advertisers&#39; objectives within the advertisers&#39; budgets. 
       SUMMARY OF THE INVENTION 
       [0011]    These objects and others which will become apparent from the following disclosure are achieved by the present invention which comprises in one aspect computer system comprising, a processor, a computer readable medium in communication with the processor, the computer readable medium having encoded thereon a set of instructions executable by the computer system to perform one or more operations, the set of instructions comprising instructions for
       a) receiving an order from an advertiser client to place an advertisement on one or more social networks;   b) receiving one or more advertisement performance criteria for the advertisement from the advertiser client;   c) receiving data regarding a product or service to be described in the advertisement;   d) outputting an advertisement for the product or service having a first set of format parameters;   e) deploying the advertisement for the product or service having a first set of format parameters on at least one social network advertising platform;   f) receiving performance data for the advertisement for the product or service having a first set of format parameters;   g) calculating whether the performance data meets the advertisement performance criteria for the advertisement received from the advertiser client;   h) outputting the advertisement with a second set of format parameters;   i) deploying the advertisement with the second set of format parameters on at least one social network;   j) repeating steps g), h), and i) until the performance data at least meets the advertisement performance criteria for the advertisement received from the advertiser client; and   k) reporting the performance of the advertisement to the advertising client.       
 
         [0023]    In another aspect, the invention comprises a computer method comprising
       a) receiving with a computer an order from an advertiser client to place an advertisement on one or more social networks;   b) receiving with the computer one or more advertisement performance criteria for the advertisement from the advertiser client;   c) receiving with the computer data regarding a product or service to be described in the advertisement;   d) outputting with the computer an advertisement for the product or service having a first set of format parameters;   e) deploying with the computer the advertisement for the product or service having a first set of format parameters on at least one social network advertising platform;   f) receiving with the computer performance data for the advertisement for the product or service having a first set of format parameters;   g) calculating with the computer whether the performance data meets the advertisement performance criteria for the advertisement received from the advertiser client;   h) outputting with the computer the advertisement with a second set of format parameters;   i) deploying with the computer the advertisement with the second set of format parameters on at least one social network;   j) repeating with the computer steps g), h), and i) until the performance data at least meets the advertisement performance criteria for the advertisement received from the advertiser client; and   k) reporting with the computer the performance of the advertisement to the advertising client.       
 
         [0035]    In some embodiments the one or more social networks have format requirements and the system is programmed to change the format parameters of the advertisement to meet the format requirements of the one or more social networks on which the advertisement is to be deployed. In certain cases the one or more social networks receive advertisements for Web, mobile, video, audio, or offline channels. Optionally the system can be programmed so that the one or more social networks on which the advertisement is to be deployed can be selected either by the advertiser client or by the system. Also, the system can output the advertisement with second and subsequent sets of format parameters selected from size, scale, placement cost, URL requirements, title length, and body length, the sets of format parameters calculated to improve the performance of the advertisement on one or more selected social advertisement platforms. 
         [0036]    Preferably, the system is programmed to deploy and redeploy the advertisement on one or more networks based on network performance. In certain preferred embodiments the system and related method receive and calculate volume and relevancy of conversation data from social platforms to output parameters of an advertisement and on which social network where the advertisement will have the highest probability of meeting the performance criteria received from the advertising client. In some embodiments of the invention weight is assigned to conversation data from a social network and format parameters, number of advertisement placements on the social network, or advertising spend on the social network is determined by the system based in part on said assigned weight. A metric value can be calculated according to a formula which considers one or more factors selected from clicks, click through rate (CTR), impressions, average cost per click (avg. CPC), cost, and conversions values and advertisements are ordered on one or more social networks which yield the highest metric, depending on the embodiment. In certain preferred embodiments the system can be programmed to dynamically and periodically assign weights to title, image, body, targeting, geographical, and demo data, deploy a set of advertisements on one or more social networks which have varying weights, and removing advertisements within the deployed set which do not meet performance criteria. 
         [0037]    The computer system can be programmed to output the advertisement with second and subsequent sets of format parameters selected from filter, border, color, overlay, red-blue-green balance (RBG), and edge. The system can also be programmed to calculate trends and number of occurrences of a brand on a social network and output a viral value for the social network, and deploy the advertisement on a social network having the highest viral value. 
         [0038]    According to some embodiments, the system will scan a social network for conversation and dialogue data relating to the product or service, assign a weight to each conversational element, and to deploy an advertisement on a social network if the assigned weight exceeds a threshold value. In more advanced embodiments the system will receive real time event data, dynamically modify an advertisement upon receipt of the real time event data, wherein the resultant modified advertisement includes a reference to a real time event within the received real time event data. 
         [0039]    An order from an advertising client can comprise a spending budget and advertisements are deployed on one or more social network so that the cost of deployment does not exceed the budget according to an algorithm which calculates an index for price based on chosen segments based on monitoring average prices for each individual metric over time. The method executed on the system of the invention will, in most cases, deploy advertisements on one or more social networks so that the cost of deployment does not exceed a predetermined cost limit or budget. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0040]    Certain embodiments of the invention are illustrated by way of figures in the accompanying drawings in which: 
           [0041]      FIG. 1  is a flow chart of a generalized process of one embodiment of the invention. 
           [0042]      FIG. 2  is a flow chart of an aspect of certain embodiments wherein the system automatically deploys ad content on social networks. 
           [0043]      FIG. 3  is a flow chart of an aspect of certain embodiments wherein the system returns performance data. 
           [0044]      FIG. 4  is a flow chart of an aspect of certain embodiments wherein ads deploy across different self-serve ad platforms. 
           [0045]      FIG. 5  is a flow chart of an aspect of certain embodiments wherein the system assigns values or weights to different social networks for performance. 
           [0046]      FIG. 6  is a flow chart of an aspect of certain embodiments wherein the system tracks performance and weight for each ad campaign. 
           [0047]      FIG. 7  is a flow chart of an aspect of certain embodiments wherein the system automatically reallocates spend and/or ad units based on network performance. 
           [0048]      FIG. 8  is a flow chart of an aspect of certain embodiments wherein one ad template is used to generate multiple ad unit variations repetitively. 
           [0049]      FIG. 9  is a flow chart of an aspect of certain embodiments wherein one ad template is used to generate multiple ad unit variations repetitively and the ads are genetically evolved. 
           [0050]      FIG. 10  is a flow chart of an aspect of certain embodiments wherein the social networks are monitored for trends and trend (“viral”) data are used as coefficients causing the ad to be placed on a social network with the highest viral coefficient. 
           [0051]      FIG. 11  is a flow chart of an aspect of certain embodiments wherein the social networks (“webs”) are monitored for trending items and ads are placed based on viral coefficients. 
           [0052]      FIG. 12  is a flow chart of an aspect of certain embodiments wherein ads are purchased based on budgets rather than bids and the system returns all data to advertiser. 
       
    
    
       [0053]    The invention is not limited to the illustrated embodiments. 
       DETAILED DESCRIPTION 
       [0054]    Referring now to the drawings wherein certain embodiments of the invention are illustrated in flow charts,  FIG. 1  illustrates the general framework of a system starting with step  101  wherein an advertiser submits a product or service to advertise. Using the programmed computer of the invention, the system receives an order from an advertiser  101  and determines  102  which social networks the ad should be placed on, places the ads  103 , returns performance data  104 , and reallocates  105  the advertising spend based on which network performed the best in terms of the criteria set by the advertiser client. The system uses a set of variables to determine and optimize performance of an ad based on parameters and targeting available from social network platforms. 
         [0055]      FIG. 2  illustrates a process flow chart wherein an the system receives  201  parameters submitted by an advertiser client and looks  202  for any previous campaigns with similar product or service keywords, then queries  203  a database for a match. If there is no match, the user inputs  204  ad information and selects network for deployment. The system automatically deploys  205  the ad content on sites. If there is a match in the database, the system uses psychological targeting parameters to associate  206  user behaviors on different social sites. The database is again queried  207  for a match and depending on whether or not there was a match, the system recommends  208  which network(s) the advertiser should chose. Upon confirmation or revision by advertiser client, the system automatically deploys  209  the ad on social network sites. The system, according to the invention, tracks  210  performance on each network, reallocates  211  spend/ad units based on network performance, and reports  212  information and data to the advertiser. 
         [0056]    As shown in  FIG. 3 , the system can take a designated ad from advertiser and place that ad on a collection of fragmented self-serve ad platforms. These platforms can include but are not limited to Facebook, Myspace, linkedin, reddit, and plenty of fish, among the currently popular social networks, with other networks expected to become popular and useful in the future. The system can deploy the same piece of ad unit across multiple mediums (web, mobile, video, audio, offline). The system can return data and performance from all channels and all mediums back to advertiser. In step  301  an ad is received from an advertiser and then the advertiser or the system can chose  302  which self-serve social networks the ad will be deployed on, and then normalize  303  the ad form parameters across all platforms and mediums, and normalize  304  the targeting parameters across all platforms and mediums. The system then deploys  305  the ad across web, mobile, video, audio, and offline channels and finally returns  306  performance scores to the advertiser. 
         [0057]      FIG. 4  illustrates an embodiment wherein ads are deployed across different self-serve social networks starting with receipt  401  of parameters of a product or service to advertise and selection  402  of networks for deployment. The system then takes a template ad unit and morphs  403  it into each selected network by changing  404  the advertisement using an algorithm which modifies an ad in view of size, scale, budget requirements, URL requirements, title length, and body length, and connects  405  to multiple social ad platforms  406 , reports  407  success or failure. If success  407 , the system continuously and repetitively tracks  408  performance on each network, reallocates  409  spend/ad units based on network performance, returns  410  all information and data through an analytics platform to the advertiser client. If failure  408 , the process repeats by connecting  405  to multiple ad platforms. 
         [0000]      FIG. 5  illustrates the weighing performance of a given ad platform or given click from a given ad network and using the information to better target and optimize an advertiser&#39;s ad unit across multiple self-serve ad platforms wherein the system connects  501  to many self-serve ad platforms and monitors 502  individual networks for performance, then places  503  an index or quality score for network or for network&#39;s clicks to optimize for goal. The system automatically, continuously, and repetitively optimizes  504  performance against quality of clicks based on weights. 
         [0058]    Referring now to  FIG. 6 , the system again is connected  601  to multiple social self-serve ad platforms  602  which return  603  statistics and performance metrics, including in this embodiment clicks, click through rate (CTR), impressions, conversions, cost, average cost per click (CPC), and product keywords and type  604 . The platforms also return  609  performance by ad unit in terms of network, image, title/body, and type of variant  610  which, along with the statistics and performance metrics  604  are processed  605  by the system according to an algorithm and the system tracks  606  performance and weight for each campaign, reallocates  607  spend/ad units based on network performance and returns  608  resultant data and information through the analytics platform to the advertiser client. 
         [0059]    In the embodiment shown in  FIG. 7 , the system is configured to take an inputted ad unit consisting of an image, title, and body and permute on those three inputs to generate any number of additional similar ad units. The system can then take all generated ad units and deploy them across multiple self-serve ad networks to determine which ad units were the most successful, upon which the system will remove underperforming ads and continue to optimize ads that are successful. Starting with step  701 , the advertiser submits to the system a product/service to advertise and the system analyzes  702  three variables, title, image, and body, and targeting, geo, and demo data. The system permutes  703  all the variables using algorithms and databases and deploys  704  the resultant ad units across multiple platforms, removing  705  ads which underperform and continuing to run successful ads, which repetitively analyzing the variables  702 . 
         [0060]    In  FIG. 8 , a process is shown wherein one ad template is used to generate multiple ad unit variations in a circuit fashion in a manner analogous to genetic evolution. Starting again with receipt  801  from an advertiser of a template ad with image, title, body, and keywords, the system looks up  802  in an approved database related images and queries  803  the database for a match. A variant analysis is then performed  804  where the variables are color, border, filter, edge, blue-green or blue green red balance, and overlay, which is used to generate  806  variations and inject  807  keywords into the title and or body. The system is connected  808  to multiple social self-serve ad platforms  809  and tracks  810  performance and weight for each campaign, reallocating  811  spend per ad units based on network performance, returning  812  information and data through an analytics platform to the advertiser client. The system can ping  813  a genetic algorithm for the next set of ad units based on current performance and generate  814  variations of the ad, while continuously tracking  810  performance and weight, etc. ( 811 - 813 ). 
         [0061]    Referring to  FIG. 9  illustrates an embodiment wherein campaign and associated data is received from an advertiser and social networks are monitored  902  for trends, referred to in the art as “going viral,” wherein the system takes viral coefficients for the advertiser&#39;s content and finds  903  a match which is automatically  904  placed on the social network with the highest viral coefficient. In this process the system receives data and processes it to determine how many users of a social network are talking about a brand, for example. 
         [0062]    The viral coefficient calculated in the process shown in  FIG. 9  is illustrated in more detail in  FIG. 10  wherein after the advertiser&#39;s parameters of product or services is received  1001 , the web is scanned  1002  for conversations and dialogue related to the product or service. For example, if may users of Facebook are entering comments or likes about a Chevy Volt, the conversational elements are assigned  1003  a weight in terms of volume, impact, sentiment, and type of site, for example  1004 , the data is processed and the system recommends  1005  which networks the advertiser should use based on the viral metrics. The system can automatically deploy  1006  ad content on recommended sites based on viral metrics. Since the system is connected  1007  to multiple social self service ad platforms  1008 , the system can track  1009  performance on each network, reallocate  1010  spending per ad units based on network performance and return all the information and data through the analytics platform to the advertiser. 
         [0063]    In the embodiment illustrated in  FIG. 11 , the system receives  1101  campaign and associated data from the advertiser, uses  1102  a custom index for price based on chosen segments, and combines  1103  and deploys ads against these segments to fulfill target budget instead of bidding. In this way the system does not use bids to purchase ads but uses correlated prices of certain demographic and targeting segments valued by network to charge advertiser clients for ad buys on social properties. 
         [0064]      FIG. 12  illustrates an embodiment wherein the system receives  1201  campaign and associated data from the advertiser, uses  1202  a custom index for price based on chosen segments, the custom index created  1203  by monitoring average prices for each individual metric over time and creating the index for these prices by segment. The system, as noted earlier, is connected  1204  to multiple social self-serve ad platforms and deploys ads based on budgets rather than on bids, finally returning  1205  all information and data through the analytics platform to the advertiser. In this way the system does not use bids to purchase ads but uses correlated prices of certain demographic and targeting segments valued by network to charge advertiser clients for ad buys on social properties. 
         [0065]    The present invention, therefore, is well adapted to carry out the objects and attain the ends and advantages mentioned, as well as others inherent therein. While the invention has been depicted and described and is defined by reference to particular preferred embodiments of the invention, such references do not imply a limitation on the invention, and no such limitation is to be inferred. The invention is capable of considerable modification, alteration and equivalents in form and function, as will occur to those ordinarily skilled in the pertinent arts. The depicted and described preferred embodiments of the invention are exemplary only and are not exhaustive of the scope of the invention. Consequently, the invention is intended to be limited only by the spirit and scope of the appended claims, giving full cognizance to equivalents in all respects.