Patent Publication Number: US-2012030007-A1

Title: Online advertisement profiles

Description:
BACKGROUND 
     Various techniques are used in optimization of online advertising and advertising campaigns, and other related areas. Techniques used in, for example, optimizing advertisement selection, and matching of online advertisements in connection with serving opportunities, can lead to enhanced advertisement performance, greater profitability and return on investment for online advertisers, and a more efficient and optimized online advertising marketplace for all participants. Such techniques include, among other things, targeting techniques such as behavioral targeting, time-based targeted, and geotargeting. 
     However, there is a need for techniques for use in online advertising, including, for example, techniques for use in optimizing aspects of online advertising and online advertising campaigns. 
     SUMMARY 
     Some embodiments of the invention provide systems and methods in which information is obtained regarding a set of one or more online advertisements. Information is also obtained relating to performance of advertisements of the set, including information regarding locations of user recipients of the advertisements and information regarding times of serving or receipt of the advertisements. Using the obtained information, an advertisement profile is built, relating to the set of advertisements, providing an indication of varying advertisement performance in connection with differing times of serving or receipt, and differing locations of user recipients. The advertisement profile may be used, for example, in optimization of online advertising or advertising campaigns, or in selection or ranking of advertisements, or for other purposes. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a distributed computer system according to one embodiment of the invention; 
         FIG. 2  is a flow diagram illustrating a method according to one embodiment of the invention; 
         FIG. 3  is a flow diagram illustrating a method according to one embodiment of the invention; 
         FIG. 4  is a flow diagram illustrating a method according to one embodiment of the invention; 
         FIG. 5  is a flow diagram illustrating a method according to one embodiment of the invention; and 
         FIG. 6  is a flow diagram illustrating a method according to one embodiment of the invention. 
     
    
    
     While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and the invention contemplates other embodiments within the spirit of the invention. 
     DETAILED DESCRIPTION 
       FIG. 1  is a distributed computer system  100  according to one embodiment of the invention. The system  100  includes user computers  104 , advertiser computers  106  and server computers  108 , all coupled or able to be coupled to the Internet  102 . Although the Internet  102  is depicted, the invention contemplates other embodiments in which the Internet is not included, as well as embodiments in which other networks are included in addition to the Internet, including one more wireless networks, WANs, LANs, telephone, cell phone, or other data networks, etc. The invention further contemplates embodiments in which user computers or other computers may be or include wireless, portable, or handheld devices such as cell phones, PDAs, etc. 
     Each of the one or more computers  104 ,  106 ,  108  may be distributed, and can include various hardware, software, applications, algorithms, programs and tools. Depicted computers may also include a hard drive, monitor, keyboard, pointing or selecting device, etc. The computers may operate using an operating system such as Windows by Microsoft, etc. Each computer may include a central processing unit (CPU), data storage device, and various amounts of memory including RAM and ROM. Depicted computers may also include various programming, applications, algorithms and software to enable searching, search results, and advertising, such as graphical or banner advertising as well as keyword searching and advertising in a sponsored search context. Many types of advertisements are contemplated, including textual advertisements, rich advertisements, video advertisements, etc. 
     As depicted, each of the server computers  108  includes one or more CPUs  110  and a data storage device  112 . The data storage device  112  includes a database  116  and Online Advertisement Profile Program  114 . 
     The Program  114  is intended to broadly include all programming, applications, algorithms, software and other and tools necessary to implement or facilitate methods and systems according to embodiments of the invention. The elements of the Program  114  may exist on a single server computer or be distributed among multiple computers or devices. 
       FIG. 2  is a flow diagram illustrating a method  200  according to one embodiment of the invention. At step  202 , using one or more computers, a first set of information is obtained, including information regarding a set of one or more online advertisements. 
     At step  204 , using one or more computers, a second set of information is obtained, including information regarding historical performance of the set of one or more online advertisements, including information regarding times of serving or receipt and information regarding locations of user recipients. 
     At step  206 , using one or more computers, based at least in part on the second set of information, an advertisement profile is generated for the set of one or more online advertisements, in which the advertisement profile includes an indication of varying advertisement performance based at least in part on time of serving or receipt, and in which the advertisement profile includes an indication of varying advertisement performance based at least in part on user recipient location. 
     At step  208 , using one or more computers, the advertisement profile is stored. 
       FIG. 3  is a flow diagram illustrating a method  300  according to one embodiment of the invention. Steps  302 ,  304 ,  306  and  308  are similar to steps  202 ,  204 ,  206  and  208  of the method  200  depicted in  FIG. 2 . 
     At step  310 , using one or more computers, the advertisement profile is utilized in optimization of online advertisement selection and/or ranking for serving in connection with online advertisement serving opportunities. 
       FIG. 4  is a flow diagram illustrating a method  400  according to one embodiment of the invention. As depicted, information is stored in a database  406 , including information regarding a set of one or more online advertisements  402  and historical advertisement performance information including time and location information  404 . 
     At step  408 , the information  402 ,  404  is utilized in generating or building an advertisement profile relating to the set of one or more online advertisements, including time and location correlations with performance, such as differing performance levels associated with different time and location parameters. 
     At step  410 , the advertisement profile, or information obtained from the advertisement profile, is utilized as input to an online advertisement selection and/or ranking model, which may select and rank advertisements for serving, such as sponsored search advertisements, ranked in terms of prominence or order of visual presentation, for example, in reply to a user search query. 
     At step  412 , online advertisement serving takes place in accordance with the selection and ranking. 
       FIG. 5  is a flow diagram illustrating a method  500  according to one embodiment of the invention. The method  500  provides an example of aspects of advertisement profile building according to some embodiments of the invention. 
     At step  504 , advertisement performance information, which can include historical information including click information, such as click through rate information, is obtained from a database  502 . 
     At step  506 , the advertisement performance information is divided and may be organized by time and location. 
     At step  508 , utilizing the divided and organized advertisement performance information, profiles are built for sets of one or more advertisements. 
     For instance, as just a simple example, performance information may be organized by various geographical regions where the receiving user was located, and performance information may be aggregated for each region. Furthermore, performance information may be organized by time-related parameters, such as month of year, weekday or weekend, day of the week, hour or period of the day, special event period, etc., during which serving or receipt of an impression took place. Using this organized and aggregated information, advertisement profiles can be built for individual advertisements or sets of advertisements, such as sets of related advertisements. Each advertisement profile may include performance levels associated with different geographical areas, different time parameters, etc. 
     In some embodiments, a machine learning model  510  may be utilized in building advertisement profiles, or building some advertisement profiles. For example, in some embodiments, the model  510  may be used in determining or assessing advertisement performance levels for advertisements or sets of advertisements for which direct historical performance information is not available, or performance levels in connection with particular time, location or other parameters. For example, associations between features of advertisements and known historical performance information associated with those advertisements can be used to train the model  510 , and the model  510  can then be used to assess performance levels for advertisements for which direct historical performance information is not available or is not sufficiently available, based at least in part on the features and associated performance levels. 
     At step  512 , the advertisement profiles are used in online advertising optimization. This can broadly include online advertising planning and management by an advertiser, as well as other online advertising-related activities by various parties such as marketplace facilitators, marketmakers or publishers, etc. 
       FIG. 6  is a flow diagram illustrating a method  600  according to one embodiment of the invention. The method  600  provides an example of aspects of advertisement selection, ranking and serving, according to some embodiments of the invention. 
     At step  604 , serving opportunity information is obtained from a database  602 , including time and location information associated with the serving opportunity. 
     At step  606 , selected advertisements are obtained, or information regarding the selected advertisements is obtained, in connection with a user search query and associated serving opportunity. Of course, the advertisements may also be selected based in part on a wide variety of information and targeting or optimization parameters, including inventory management and availability parameters, campaign parameters, behavioral and user targeting parameters, geotargeting parameters, etc. In various embodiments, advertisement profiles may be used in advertisement selection and ranking, or may only be used in selection or ranking. 
     At step  610 , historical click information, such as click through rate information, is obtained from advertisement profiles  608  associated with each selected advertisement. The historical click information can include various information that provides an indication of click through rates, ranges, or other measures of click through rate increase or decrease, for example, associated with different location and time parameters, for each advertisement (or set of advertisements). 
     At step  612 , the historical click information, such as click through rate information, is used as input to an advertisement ranking model, such as a model that ranks sponsored search advertisements, for presentation to a user. 
     At step  614 , advertisements are ranked in accordance with ranking determined using the model, and served. 
     Some embodiments of the invention include a recognition that known techniques for leveraging advertisement performance and parameters, such as in optimization advertisement selection, ranking, and serving, among other things, have not been optimized or ideal. For example, techniques exist in which advertisers may select geolocation or time-based parameters required for serving of particular advertisements or groups of advertisements. Also, targeting, selection or ranking can be performed based on categorization, user search queries, user profiles, including demographic or behavioral profiles, etc. Furthermore, advertisements can be targeted, selected or ranked based on a host of factors, including location and time-based factors, and techniques exist in which advertisers can, for example, specify such parameters for certain advertisements or advertisement groups. 
     Some embodiments of the invention provide, for example, a more optimal, sophisticated, flexible, and/or granular approach than previous techniques. For example, in some embodiments, individual advertisements or groups of advertisements are profiled. Such profiles can be sophisticated and granular, leveraging a wealth of historical performance information and associated circumstances and parameter information, and/or other information. For instance, very accurate and detailed tracking is possible today, so that advertisement performance can be tracked along with a host of associated parameters. 
     In some embodiments, time and location parameters are utilized, but some embodiments contemplate use of any number of other parameters that may be associated with advertisement serving or performance. This could broadly include, for example, a variety of contextual or circumstantial parameters, or other information or parameters directly or indirectly associated with advertisements or advertisement performance. For example, some parameters could be associated with the context of serving of an advertisement, such as the associated Web page, content of the page, etc., as well as parameters associated with search results, other advertisements, parameters associated with the user to whom the advertisement is presented, other users in a social network of the user, etc. Furthermore, other parameters could be associated with downstream performance and downstream context or circumstances associated with advertisements, such as context or circumstances when a user clicks on an advertisements, when a user makes a purchase associated with the advertisement, or other online or offline action or information, etc. Of course, many other examples are possible. While embodiments of the invention are described primarily with regard to advertisement profiles including time-based and location-based parameters, some embodiments of the invention contemplate profiles that include, incorporate or make user of any of a variety of other parameters instead or as well. 
     Embodiments of the invention contemplate various types of advertisement profiles. For example, in some embodiments, profiles may include a spectrum of values or ranges for a particular parameter, in association with a spectrum of performance levels or ranges associated, where each performance level or range may be associated with a particular parameter value or range. The advertisement profile may also include tables, indices or other data structures including such information. For example, time-related parameter values may include time of day, and each of a range of times can be associated with a level of performance, such as click through rate, based on historical performance information and/or other information. Where historical performance information is inadequate or lacking, performance information for similar advertisements, or similar parameter values, or both, may be used to approximate values for the advertisement or advertisements with which the profile is associated. Furthermore, in some embodiments, machine learning models or techniques may be used, in combination with advertisement features, for instance, in filling in gaps, etc., based on collected available performance information. Furthermore, in some embodiments, advertisement can have additional layers of complexity and sophistication such as mathematical or other models or constructs based at least in part on, or incorporating information as described above, which may aid in advertisement profile use, flexibility or efficiency. 
     In addition to the above, other more complex or granular advertisement profiles are contemplated. For example, some advertisement profiles may contain multidimensional associations, tables or other data structures, in which combinations of values or ranges for multiple parameters are associated with particular performance levels, based on available performance information. Furthermore, embodiments of the invention contemplate use of various types of information in addition to or other than performance information in advertisement profiles, or in building advertisement profiles. 
     Some embodiments of the invention contemplate advertisement profiles that not only include advertisement performance and parameter information and association information, but also include any of various other types of profile information, which can be any information relating to the advertisement or advertisements, or the character, nature, or associated circumstances or context therefore. 
     Some embodiments of the invention further contemplate advertisement profiles that include or make use of prediction or forecast information associated with advertisements, such as predicted or forecasted performance information. 
     As described, some embodiments of the invention include utilizing advertisement profiles in generating suggestions, recommendations, or automatic adjustments to advertisers or other parties regarding advertising campaigns, including targeting suggestions or budgetary suggestions, advertisement-associated bidding suggestions, or keyword selection or bidding suggestions, for example. As one simple example, if an advertisement profile indicates better performance on weekends, this information can be used in determining a suggestion to an advertiser to increase budget spend or bidding relating to that advertisement in connection with weekends. 
     Some embodiments of the invention further contemplate use of advertisement profiles in data mining and other information gathering, analysis, and usage efforts. 
     Some embodiments of the invention contemplate automated activities including automated advertisement profile building, automated advertisement campaign suggestions or actually implemented adjustments based on advertisement profiles, automated usage of profiles in marketplace operations including advertisement selection and ranking, etc. Furthermore, in some embodiments, advertisement profiles are updated or automatically updated based on newly available performance information or other information. 
     Embodiments of the invention contemplate usage of advertisement profiles in combination with other profiles, including, for example, user-associated profiles. Some embodiments further contemplate larger or broader profiles including advertisement profiles as well as other profiles, or integrated profiles including advertisement profile aspects as well as other aspects. 
     While the invention is described with reference to the above drawings, the drawings are intended to be illustrative, and the invention contemplates other embodiments within the spirit of the invention.