US20190370831A1 - Method and system for influencing auction based advertising opportunities based on user characteristics - Google Patents

Method and system for influencing auction based advertising opportunities based on user characteristics Download PDF

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US20190370831A1
US20190370831A1 US16/544,059 US201916544059A US2019370831A1 US 20190370831 A1 US20190370831 A1 US 20190370831A1 US 201916544059 A US201916544059 A US 201916544059A US 2019370831 A1 US2019370831 A1 US 2019370831A1
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adjustment
user
value
based
bid
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US16/544,059
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Harry Russell Maugans, III
Cody Alan Carrell
Kenneth Jørgensen
Deepak Gopalakrishnan
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Clickagy LLC
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Clickagy LLC
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Priority to US15/177,204 priority patent/US20160364767A1/en
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Priority to US16/544,059 priority patent/US20190370831A1/en
Assigned to Clickagy, LLC reassignment Clickagy, LLC ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: Jørgensen, Kenneth, CARRELL, CODY ALAN, GOPALAKRISHNAN, DEEPAK, MAUGANS, HARRY, III
Publication of US20190370831A1 publication Critical patent/US20190370831A1/en
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    • G06F2221/2101Auditing as a secondary aspect

Abstract

Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Description

    RELATED APPLICATIONS
  • This is a continuation application of U.S. application Ser. No. 15/177,204 filed Jun. 8, 2016, entitled “Method and System for Influencing Auction Based Advertising Opportunities Based on User characteristics,”, which Applicant claims the benefit under the provisions of 35 U.S.C. § 119(e), U.S. Provisional Application No. 62/173,071, filed Jun. 9, 2015, which are incorporated herein by reference.
  • The following related U. S. patent applications, filed on even date herewith in the name of Clickagy, LLC, assigned to the assignee of the present application, are hereby incorporated by reference:
      • Attorney Docket No. 00279.005-PA-USN-86R, entitled “METHOD, SYSTEM AND COMPUTER READABLE MEDIUM FOR CREATING A PROFILE OF A USER BASED ON USER BEHAVIOR;”
      • Attorney Docket No. 00279.006-PA-USN-86R, entitled “METHOD AND SYSTEM FOR PROVIDING BUSINESS INTELLIGENCE BASED ON USER BEHAVIOR;” and
      • Attorney Docket No. 00279.007-PA-USN-86R, entitled “METHOD AND SYSTEM FOR CREATING AN AUDIENCE LIST BASED ON USER BEHAVIOR DATA.”
  • It is intended that each of the referenced applications may be applicable to the concepts and embodiments disclosed herein, even if such concepts and embodiments are disclosed in the referenced applications with different limitations and configurations and described using different examples and terminology.
  • FIELD OF DISCLOSURE
  • The present disclosure generally relates to bidding of advertisement opportunities based on user characteristics. More specifically, the present disclosure relates to a method and system for adjusting bidding of advertisement opportunities based on user characteristics.
  • BACKGROUND
  • Advertisers are on a constant endeavor to provide relevant information regarding products and/or services to users who may be interested in purchasing those products and/or services. Advertisements may be presented to users through various mediums. For instance, the display device located at public places may be used to visually present advertisements to users in the vicinity of the display device. Similarly, advertisements are generally presented communication channels such as radio, television and audio video players.
  • Most of the advertisements presented to users do not take into account characteristics of the users viewing the advertisements. Accordingly, users may not always be exposed to advertisements relevant to their interests. For example, advertisements presented on television are generally independent of interests of users viewing the advertisement. As a result, a conversion rate of the advertisement presented independent of user interests is very poor. In other words, such advertisements which do not take into account the interests of users are ineffective.
  • Accordingly, several advertisers are beginning to move towards a targeted advertising paradigm where advertisements are presented to users according to user interests. For example, users may be monitored while browsing the Internet and a set of user interests may be identified. Accordingly, based on the set of user interests, relevant advertisements of products and/or services may be identified and presented to users. Such advertisements are commonly known as targeted advertisements.
  • However, privacy of users is a major challenge posing implementation of effective targeted advertising. In order to be able to provide advertisements relevant to users, user interests need to be identified at a granular level. This entails extensive monitoring of user activity, such as for example, online browsing. Further, there are several laws in most countries protecting privacy of users by forbidding collection and/or dissemination of personal information.
  • As a result, advertisers face difficulty in obtaining sufficient data regarding user behavior in order to identify user interests more accurately. Accordingly, although existing targeted advertisements may be more effective than non-targeted advertisements, there is much scope for improvement in effectiveness. Therefore, there is a need for methods and systems for providing targeted advertisements with a greater degree of effectiveness.
  • Further, several methods and systems of advertising online are based on a bidding model where different advertisers may compete to present their respective advertisements at a given advertisement opportunity. For example, a publisher of online content may have an advertisement space on a web page where an advertisement may be displayed. Accordingly, the publisher may invite bids from multiple advertisers to present an advertisement in the advertising space. Further, an advertiser proposing the maximum bid amount may be considered a winner. Accordingly, the advertiser may be allowed to display a chosen advertisement in the advertising space. Such a model is beneficial to both the publisher and the advertisers since the publisher is able to maximally monetize the advertising space while the advertisers are able to control and limit their advertising budget according to their needs.
  • Generally, the bid for an advertising space depends on a context corresponding to the advertising space. For example, a web page containing the advertising space may be related to a particular topic such as, for example, sports. Accordingly, it may be inferred that users viewing the web page may be interested in sports products such as, shoes. Accordingly, the publisher may notify advertisers of an advertising context, such as through a keyword “shoes”. On the other hand, an advertiser may be willing advertise a particular brand of shoes within the advertising space. Accordingly, the advertiser may place a specific bid amount for the keyword “shoes”. As a result, any advertisement opportunity having the keyword “shoes” may be a relevant advertising opportunity for the advertiser.
  • However, presenting such targeted advertisements to users based on bidding may not be effective in relation to advertiser. For instance, such a technique of advertising based on bidding assumes that the entire pool of users viewing the web page is homogeneous. In other words, the advertiser in existing bid based techniques, competes with the same bid amount for each user within the pool of users. However, it is evident that they are significant differences between users within the pool with regard to user interests and/or affinity towards a product, service or a brand. Accordingly, the advertisers may be disadvantaged in competing for an advertisement presented to the pool of users while a conversion rate of the advertisement may vary drastically across users owing to the heterogeneity of user interests within the pool of users. Accordingly, there is a need for improved methods and systems for managing bidding of advertisement spaces while taking into account differences between users with regard to interests and/or affinities.
  • BRIEF OVERVIEW
  • A bidding platform may be provided. This brief overview is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This brief overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this brief overview intended to be used to limit the claimed subject matter's scope.
  • Disclosed are methods and systems for facilitating bidding of advertisement spaces. According to some embodiments, an improved method and system may be provided in order to facilitate advertisers to place bids on advertisements presentable to users according to interests of the users. In other words, advertisers may be enabled to bid for advertisement opportunities based on user behavior data. For example, advertisers may be allowed to specify a set of users in terms of user behavior data such as, for example, webpages visited by users, one or more interests expressed either implicitly and/or explicitly by the users, and so on. Accordingly, an advertiser may specify a particular bid amount for an advertisement opportunity in relation to characteristics of the user viewing the advertisement.
  • Further, in some embodiments, advertisers may be enabled to specify a plurality of big amounts corresponding to a plurality of sets of users satisfying a plurality of criteria based on user behavior data. For instance, a primary criteria based on user behavior data may include an interest towards shoes. Accordingly, a primary set of users may be identified based on past user behavior data indicative of an explicit and/or an implicit interest of the users in shoes. For example, online browsing by users may be monitored and those users who visited webpages related to shoes may be identified as the primary set of users. Accordingly, an advertiser may specify a primary bid amount corresponding to the primary set of users for an advertisement opportunity related to shoes. As a result, an association between the primary set of users and the primary bid amount may be created and stored. Accordingly, when an advertisement opportunity occurs, for example, when a user of the primary set of users is viewing a webpage, the advertiser may bid for an advertisement space on the webpage with the primary bid amount. If the primary bid amount wins the bid, the advertiser may present a desired advertisement to the user.
  • Further, the advertiser may also be enabled to specify a secondary criteria based on user behavior data, such as, for example, an interest towards sports shoes. Accordingly, a secondary set of users may be identified based on past user behavior data indicative of an explicit and/or an implicit interest of the users in sports shoes. Further, in some instances, the secondary set of users may be a subset of the primary set of users. For example, online browsing by users may be monitored and those users who visited webpages related to sports shoes may be identified as the secondary set of users. Accordingly, an advertiser may specify a secondary bid amount corresponding to the secondary set of users for an advertisement opportunity related to sports shoes. As a result, an association between the secondary set of users and the secondary bid amount may be created and stored. Accordingly, when an advertisement opportunity occurs, for example, when a user of the second set of users is viewing a webpage, the advertiser may bid for an advertisement space on the webpage with the secondary bid amount. In some instances, the secondary bid amount may be greater than the primary bid amount. If the secondary bid amount wins the bid, the advertiser may present a desired advertisement relating to sports shoes to the user.
  • As a result, the advertiser may be enabled to prefer one set of users over others while competing to bid for presenting advertisements. For instance, an advertiser of sports shoes may place higher bids for advertising to users whose behavior data indicates a specific interest towards sport shoes as opposed to other users whose behavior data indicates a general interest towards shoes.
  • Furthermore, in some embodiments, the advertisers may also be enabled to control bidding based on a context corresponding to the advertisement opportunity. Accordingly, advertisers may be enabled to specify one or more contextual conditions under which a bid for an advertisement opportunity may be placed. For instance, further to identifying a relevant user currently viewing a webpage, information regarding contents of the webpage may be specified as a contextual condition. Accordingly, an advertisement may be presented to the user provided that, for example, the content of the webpage is relevant to the advertisement. For instance, a user viewing a sports article may be identified as part of the secondary set of users. Further, since the context of the webpage relates to sports, a bid to present an advertisement related to sports shoes may be made. As a result, when presented, the likelihood of the advertisement being noticed and acted upon by the user may increase.
  • Both the foregoing brief overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing brief overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure. The drawings contain representations of various trademarks and copyrights owned by the Applicants. In addition, the drawings may contain other marks owned by third parties and are being used for illustrative purposes only. All rights to various trademarks and copyrights represented herein, except those belonging to their respective owners, are vested in and the property of the Applicants. The Applicants retain and reserve all rights in their trademarks and copyrights included herein, and grant permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
  • Furthermore, the drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the present disclosure. In the drawings:
  • FIG. 1 illustrates a block diagram of an operating environment consistent with the present disclosure;
  • FIG. 2 is a flow chart of a method of creating user profiles based on user behavior data;
  • FIG. 3 illustrates an example of how logic functions may sort specific groups;
  • FIG. 4 illustrates and example of how such logic may be used to provide optimally targeted advertisements;
  • FIG. 5 illustrates a flow chart of a method of bidding for advertisement opportunities in accordance with some embodiments;
  • FIG. 6A illustrates a flow chart of a method of facilitating creation of an audience list in accordance with some embodiments;
  • FIG. 6B illustrates a flow chart of a method of facilitating creation of a sub-audience list in accordance with some embodiments;
  • FIG. 7 illustrates a flow chart of a method of bidding for advertisement opportunities based on user identifiers in accordance with some embodiments;
  • FIG. 8 illustrates a flow chart of a method of bidding for advertisement opportunities based on a contextual variable in accordance with some embodiments;
  • FIG. 9 illustrates an exemplary user interface for receiving bid adjustment based on user behavior data in accordance with some embodiments;
  • FIG. 10 illustrates an exemplary user interface for selecting a bid adjustment parameter in accordance with some embodiments;
  • FIG. 11 illustrates an exemplary user interface for receiving bid adjustment based on user behavior data in accordance with some embodiments;
  • FIG. 12 illustrates a method of bidding for advertisement opportunities based on user behavior in accordance with some embodiments.
  • FIG. 13 illustrates an online user behavior of a user based on which a user profile may be created in accordance with some embodiments;
  • FIG. 14 illustrates an exemplary comprehensive user browsing data based on which a user profile may be created in accordance with some embodiments;
  • FIG. 15 illustrates Natural Language Processing performed on data extracted from webpages visited by a user based on which a user profile may be created in accordance with some embodiments; and
  • FIG. 16 is a block diagram of a system including a computing device for performing the methods of FIG. 2, FIG. 5 to FIG. 8 and FIG. 12
  • DETAILED DESCRIPTION
  • As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may further incorporate only one or a plurality of the above-disclosed features. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the display and may further incorporate only one or a plurality of the above-disclosed features. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.
  • Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself.
  • Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection is to be defined by the issued claim(s) rather than the description set forth herein.
  • Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the ordinary artisan based on the contextual use of such term—differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.
  • Regarding applicability of 35 U.S.C. § 112, ¶6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase “means for” or “step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element.
  • Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.”
  • The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the appended claims. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.
  • The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of data mining for marketing purposes, embodiments of the present disclosure are not limited to use only in this context. For example, the platform may be used to study demographics, psychographics, market behavior, competitor affinity, and expanding markets.
  • I. Platform Overview Consistent with embodiments of the present disclosure, a bidding platform may be provided. This overview is provided to introduce a selection of concepts in a simplified form that are further described below. This overview is not intended to identify key features or essential features of the claimed subject matter. Nor is this overview intended to be used to limit the claimed subject matter's scope.
  • A platform consistent with embodiments of the present disclosure may be used by individuals or companies to perform bidding of advertising opportunities for presenting advertisements to users characterized by one or more interests.
  • Accordingly, the present disclosure provides a platform that enables clients such as, for example, advertisers to create an audience list and place bids on advertisements targeting individuals in the audience List. Additionally, the platform also allows the advertisers to adjust how heavily those bids are placed for different users in the audience list.
  • In order to create the audience list, the advertiser may specify user behavior data in the form of a filtering criteria. More particularly, the advertiser may specify users to be targeted based on a type of online behavior exhibited by the users. For example, the filtering criteria may specify webpages visited, keywords associated with the visited webpages, affinities/importance of the keywords and so on. In other words, the advertiser may specify certain user behavior that may be indicative of interests in one or more topics, products, services etc. Accordingly, users who have exhibited such behavior may be identified and targeted.
  • Additionally, a filtering criteria based on user behavior may also be used to adjust how heavily each user in the audience list gets targeted.
  • For example, subsequent to setting up the audience list for bidding on certain online advertisements, the client may control how heavily the bids will be placed to win an advertising space to relevant individuals on relevant web pages. For instance, the audience list may include individuals who may be the target for a corvette. Further, the client may adjust how heavily the bids may be placed on each of those targeted individuals. For example, the client may add a bid adjustment parameter based on Domain List and specify the domain to be ‘www.autotrader.com’ as exemplarily illustrated in FIG. 9. Accordingly, bids corresponding to individuals in the audience List who have visited autotrader.com may be increased to by, for example, 200%.
  • Further, the client may be enabled to provide a number of such filtering criteria in order to characterize a preferred user behavior. Accordingly, the client may be presented a user interface, as exemplarily illustrated in FIG. 10 in order to specify multiple filtering criteria. For example, the filtering criteria may be based on a plurality of parameters including, but not limited to, IP address, Segment, Keyword, Domain, Page URL, Continent, Country, Region, City, Zip code, Hyperlocal, ISP, Connection type, Device type, Browser, Operating System, Screen dimensions, Browser dimensions, language etc. Further, the client may be enabled to combine as many of these parameters as necessary to determine bid adjustments.
  • Additionally, in some embodiments, the platform may also enable the clients to specify a blacklist of user behavior, corresponding individuals and/or a context of the advertising opportunity. Accordingly, in such cases, the client may reduce the bid by 100% if certain parameters are met. For example, if the in a targeted individual in the audience list is currently on a violent domain and the client is Disney, the client may not want their brand to be affiliated with a violent website. Accordingly, the client may set the bid adjustment to be −100% as exemplarily illustrated in FIG. 16.
  • Further, in some instances, the client may also be enabled to make bid adjustments based on one or more conditions such as, for example, time of day.
  • Accordingly, performance of retargeting may be improved by varying the retargeting process in time. For instance, once a user leaves a client's site, there are moments when the user may be focused on something else and don't want to be bothered, and other times the user may be open to retargeting and willing to convert. An ideal time is when the user returns to the client's market on their own will, casually researching competitors and exploring options. Accordingly, when the user decides to resume shopping on their own, the client's retargeting may improve by hyper-aggressively bidding for displaying advertisement to the user, driving the user home in the critical final hour.
  • For example, as illustrated in FIG. 12, at step 1, the platform may lightly retarget the user across normal browsing. Accordingly, when the user visits general websites such as a news website, a weather website etc., the platform may place a nominal bid amount for presenting the client's advertisement to the user. However, at step 2, the platform may receive a notification of the user currently browsing a webpage relevant to the industry of the client. Accordingly, at step 3, the platform may place higher bid amounts for presenting the client's advertisement to the user. As a result, at step 4, the user may be more receptive to the client's advertisement driving better engagement and conversion rate.
  • Similarly, when the user has previously visited the client's website, the client may wish to retarget the user on other websites with a nominal bid amount. However, when the user is on, for example, a competitor's website, the client may want to increase the bid amount to ensure that the client's advertisement appears on the competitor's site. Accordingly, the platform may receive such bid adjustment conditions from the client and place bids accordingly.
  • As another example, the platform may enable the client to create an audience list of users who exhibited interest in a particular type of car. Subsequently, the platform may allow the clients to specify bid adjusts to a specific set of users within the audience list. For instance, the client, such as for example, Corvette, may increase bids (or adjust bids) on users who have been shopping on competitor websites. Accordingly, the client may be enabled to specific the competitor websites. Alternatively and/or additionally, the client may also specify a particular region, time of day, ISP, Browser type, domain name, keywords, user affinity and so on.
  • Additionally, in some embodiments, the client may be enabled to specify a subset of the audience list by performing logical operations between a plurality of existing audience lists. For example, a user interface may be provided to the client in order to create a subset of the audience list by dragging and dropping audience lists into a workspace area and performing logical operations such as “AND”, “NOT”, “OR” and so on. Further, the client may be enabled to directly manipulate graphical objects such as circles representing audience lists. Accordingly, by overlapping two graphical objects, an intersection of the audience lists corresponding to the graphical objects may be created, such as in a Venn diagram. Based on the intersection, the subset of the audience list may be identified.
  • Additionally, in some embodiments, the platform may enable the client to specify a time-based budget allocation. For instance, advertisement agencies allow advertisers to specify the times during which the advertisers wish to serve the advertisements. Accordingly, the platform may enable dynamic allocation of the budget over time.
  • Further, an exemplary application of the methods and systems disclosed herein, may include performing bid adjustments at a Demand Side Platform (DSP), such as an ad buying/bidding system, to make bidding more efficient. For example, a DSP may have bought a list of a million people who have been identified as being in-market for widgets. Conventionally, a bidder may bid a flat bid amount, for example $4.00 CPM, to serve widget ads to individuals in the list regardless of the website that they individuals may be visiting. In contrast, in accordance with methods and systems disclosed herein, bid adjustments may be made to improve the performance by adding another layer of logic on top of the bidding process.
  • For example, subsequent to running an audience targeting campaign for a week, reports may indicate that when individuals are visiting a website about golf, the conversion rate of the ads is twice as high in comparison to when the individuals are visiting www.answers.com. Accordingly, the DSP add a bid adjustment, such as for example, 150%, in case the ad opportunity is relevant to golf (contextual keyword present on the webpage). As a result, the bid amount may increase from $4.00 to $10.00 CPM. Further, bid amounts for presenting ads on www.answers.com may be reduced by a predetermined percentage value, such as −90%, lowering the bid amount of $0.40 CPM. Further, in some instances, multiple bid adjustments may be combined together according to a predetermined combining function. For example, if an individual in the audience targeting campaign visits a golf related webpage on www.answers.com, the DSP may bid $1.00 CPM ($4.00×1.5×0.1).
  • Similarly, as another example, marketers may target individuals using a type of computers, such as Macs, over others who may be using Windows. Accordingly, the marketers may specify a +20% adjustment of bids to be made if an individual is using “OS X”. Likewise, further such bid adjustment specifications may be specified as follows: Using a smartphone: −15%; located in Texas: +25%; also present in another audience list: +120%; currently a Sunday: +50%; Using Comcast Internet connection: −10%; Gender is male: −80% and so on. Accordingly, bid amounts may be dynamically adjusted accordingly to desirable characteristics of individuals in order to achieve higher conversion rates.
  • Embodiments of the present disclosure may operate in a plurality of different environments. For example, in a first aspect, the platform may receive notice that an individual has visited a webpage. Then, the platform may crawl that page to gather raw data from the page. For example, the platform may use various algorithms, including, but not limited to, for example, natural language processing (NLP) and digital signal processing (audio/image/video data) to search the web page for key words or phrases.
  • Still consistent with embodiments of the present disclosure, the platform may receive raw data as it tracks individuals throughout, for example, an ad network or collection of ad networks. Tracking may include, for example, but not be limited to, a crawling of each visited webpage so as to create a profile for the page. As will be further detailed below, the profile may be generated by, for example, the aforementioned algorithms used to gather raw data for the page.
  • Accordingly, in some embodiments, interaction of a user with a plurality of servers, such as for example, content servers, ad servers and so on may be monitored. For instance, when the user visits a webpage provided by a server, a tracking cookie may be instantiated in order to save information regarding the user and/or the user's interaction with the webpage. For instance, the tracking cookie may be instantiated at the server side and may include information such as a timestamp corresponding to the user's visiting of the webpage and one or more identifiers associated with the user. The one or more identifiers may be for example, a network identifier such as an Internet Protocol (IP) number and/or a MAC number, a device identifier such as an IMEI number, a software environment identifier, such as OS name, browser name etc., user identifiers such as email address, first name, last name, middle name, postal address etc. and values of contextual variables such as GPS location of the device used to access the webpage, sensor readings of the device while accessing the webpage and so on.
  • In some embodiments, the one or more identifiers, such as the IMEI number, may uniquely identify the user while preserving anonymity of the user.
  • In other embodiments, the one or more identifiers may be subjected to encryption or a one way hashing in order to render the one or more identifiers unreadable to other users while maintaining the ability of the one or more identifiers to uniquely identify the user. For example, in some instances, tracking cookie may be instantiated on a client side, where the tracking cookie may reside on a user device, such as a smartphone or a laptop computer. Accordingly, any information collected by the tracking cookie may remain accessible in human readable form only within the user device. However, prior to transmitting the tracking cookie to the server side, the information collected may be subjected to hashing. Accordingly, in some embodiments, information about the user in human readable form may not be available at the server side. Thus, users may be ensured of preserving their privacy.
  • Further, in some embodiments, each of the plurality of servers may adopt a common hashing algorithm such that each of the plurality of servers may compute a common hash value for the one or more identifiers. Accordingly, when information in the tracking cookies from each of the plurality of servers is transmitted to the platform, the information collected by multiple tracking cookies may be identified as being associated with the same user based on the common hash value. Such a technique may allow tracking the user across multiple servers accessed by the user through a common user device.
  • In yet further embodiments of the present disclosure, the raw data may be from purchased data acquired by data aggregators. The raw data may include, for example, a plurality of device specific information (e.g., device serial number, IP address, and the like) along with a listing of websites accessed by the device. The platform may be enabled to identify a plurality of devices associated with a single individual and, subsequently, associated the data aggregated and processed for each device to a single individual profile.
  • For instance, in some embodiments, where the user may access the same and/or different servers through multiple user devices, a correlation of the information collected by the multiple cookies may be performed in order to track the user. For instance, each of the multiple tracking cookies may not include all of the one or more identifiers. For example, the user may access a webpage of a server using a smartphone, while the user may access a webpage of another server using a laptop computer at work. Further, the laptop computer may include additional restrictions that forbid the tracking cookie from collecting some of the one or more identifiers. However, at least some of the information collected by the multiple cookies may still be common. Accordingly, by correlating information across the multiple tracking cookies, it may be ascertained that the multiple tracking cookies are associated with the same user. Further, in some embodiments, a threshold of correlation value may be established. Accordingly, the multiple tracking cookies may be determined to be associated with the user only if a correlation value exceeds the threshold.
  • The platform may then apply the aforementioned algorithms to process the websites accessed by the devices and, in this way, profile the websites as will be detailed below. The profiled website may then be used to characterize an individual who has been detected to access the profiled website. Moreover, and as will be further detailed below, the characterized individual data may then be grouped along with other individuals' data assessed by the platform in a plurality of ways including, but not limited to, geographic, household, workplace, interests, affinities, gender, age, and the like.
  • It should be understood that each individual analyzed by the platform of the present disclosure may be weighted with an ‘affinity’ of relationship to a particular category. For example, for those individuals who have visited websites profiled to be more ‘female’ friendly may be determined, by the platform, to be most likely a ‘female’ based on, either solely or at least in part, the individuals web-traffic of profiled webpages associated with the individuals tracked device.
  • As yet a further example, the platform may identify individuals that visit webpages that include the words “cell phone” and determine that the individuals may be more likely to be shopping for cell phones. Further, by counting the number of times the individuals visit webpages that have predominately iPhones versus webpages that have predominately Android phones, the likelihood that such individuals prefer one phone to the other may be assessed. The platform may group like users to create useful statistical data. For example, the platform may create groups of people that are most likely willing to purchase a specific product (e.g., cell phones, or, more specifically, Android smartphones).
  • Embodiments of the platform may further be used to enable a platform user (e.g., mobile telecommunications company) to better understand its target market. Accordingly, data that has been acquired, aggregated, and processed by the platform may be provided to the user. For example an application program interface (API) may provide statistics about single individuals (e.g., likelihood that an individual prefers Android phones to iPhones), or groups of individuals (e.g., which individuals prefer Android phones to iPhones). Such statistics may be provided in, for example, lists, charts, and graphs. Further, searchable and sortable raw data may be provided. In some embodiments, the data may be provided to licensed users. For example, users that have identified data such as, for example, AT&T, which has a list of known individuals, may use the data to, for example, further market to their known list of individuals or predict churn.
  • In some embodiments, the processed data may be provided to the user as a plug-in. For example, if an individual logs into a website for the first time (e.g., Home Depot), the website owner may be able to customize the display for the first-time individual. In other embodiments, the platform may integrate with a customer relationship module (CRM). In this way, the CRM may be automatically updated with processed data for individuals in the CRM.
  • Both the foregoing overview and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing overview and the following detailed description should not be considered to be restrictive. Further, features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.
  • II. Platform Configuration
  • FIG. 1 illustrates one possible operating environment through which a platform consistent with embodiments of the present disclosure may be provided. By way of non-limiting example, a platform 100 may be hosted on a centralized server 110, such as, for example, a cloud computing service. A user 105 may access platform 100 through a software application. The software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 1600. One possible embodiment of the software application may be provided by Clickagy, LLC.
  • As will be detailed with reference to FIG. 16 below, the computing device through which the platform may be accessed may comprise, but not be limited to, for example, a desktop computer, laptop, a tablet, or mobile telecommunications device. Though the present disclosure is written with reference to a mobile telecommunications device, it should be understood that any computing device may be employed to provide the various embodiments disclosed herein.
  • A user 105, such as, for example, an advertiser, may provide input parameters to the platform. For example, input parameters may include filtering criteria for identifying a plurality of sets of users, such as, for example, an audience list and a sub-audience list based on user behavior data. Additionally, the input parameters may also include a plurality of bid amounts corresponding to the plurality of sets of users. For example, the user 105 may provide a primary bid amount corresponding to the audience list and a secondary bid amount corresponding to the sub-audience list. Furthermore, the input parameters may also include a contextual variable and a bid adjustment parameter to indicate how bidding may be adjusted upon detection of the contextual variable. The contextual variable specified by the user 105 may include a domain on which a user is currently browsing, keywords present on the webpage being viewed by the user, affinities/importance of the keywords to the user, time when the user is viewing the webpage, characteristics of the user device being used by the user and so on.
  • Information relevant to individuals associated with the input parameters, such as, for example, which websites they visited, may be sent to web crawler 115. Web crawler 115 may search webpages and online documents visited by individuals being tracked and gather data associated with the searched webpages and online documents. For example, web crawler 115 may utilize natural language processing and audio, video and image processing to gather information for websites. Web crawler 115 may further perform algorithms and build profiles based on webpages and online documents being searched, such as, for example, constructing ‘affinities’ for websites (further discussed below). Information and website and online document profiles being tracked may be passed back to server 110. Server 110 may further construct profiles for individuals being tracked and groups of individuals being tracked. The individual and group profiles as well as further data (e.g. personally identifiable information (PII), non-PII, de-identified data and website/individual/group affinity) and bids may be returned to user 105.
  • Additionally, the platform 100 may be in communication with an ad-exchange 120. The ad-exchange 120 may be, for example, a sever computer, capable of communicating with the platform over a communication network, such as the Internet. Further, the ad-exchange 120 may facilitate a bidding based advertisement in collaboration with a number of ad-servers (not shown in figure) and content servers. For instance, a content server may host a webpage on tips for buying smartphones. Accordingly, the content server may communicate to the ad-exchange about the availability of an advertising space on the webpage. In response, the ad-exchange may invite bids from multiple advertisers (e.g. smartphone manufacturers) in order to present an advertisement in the advertising space. Accordingly, ad-servers, such as the platform 100, may communicate with the ad-exchange by providing a bid amount in order to present advertisements. For instance, the user 105 may be an administrator of a marketing campaign for a particular brand of smartphones. Accordingly, the user 105 may communicate an audience list and a sub-audience list and corresponding primary and secondary bid amounts to the platform. In addition, the user 105 may also specify conditions under which the bidding may be performed. In response, the platform may transmit bids to the ad-exchange 120 based on the information received from the user 105. Accordingly, based on a win, advertisements selected by the user 105 may be presented to one or more users, such as those in the audience list and/or the sub-audience list.
  • III. Platform Operation
  • FIG. 2, FIG. 5 to FIG. 8 and FIG. 12 are flow charts setting forth the general stages involved in methods 200, 500 to 800 and 1200 consistent with various embodiment of the disclosure for providing a bidding platform 100. Methods 200, 500 to 800 and 1200 may be implemented using a computing device 1600 as described in more detail below with respect to FIG. 16.
  • Although methods 200, 500 to 800 and 1200 have been described to be performed by platform 100, it should be understood that computing device 1600 may be used to perform the various stages of methods 200, 500 to 800 and 1200. Furthermore, in some embodiments, different operations may be performed by different networked elements in operative communication with computing device 1600. For example, server 110 may be employed in the performance of some or all of the stages in methods 200, 500 to 800 and 1200. Moreover, server 110 may be configured much like computing device 1600.
  • Although the stages illustrated by the flow charts are disclosed in a particular order, it should be understood that the order is disclosed for illustrative purposes only. Stages may be combined, separated, reordered, and various intermediary stages may exist. Accordingly, it should be understood that the various stages illustrated within the flow chart may be, in various embodiments, performed in arrangements that differ from the ones illustrated. Moreover, various stages may be added or removed from the flow charts without altering or deterring from the fundamental scope of the depicted methods and systems disclosed herein. Ways to implement the stages of methods 200, 500 to 800 and 1200 will be described in greater detail below.
  • FIG. 3 further illustrates how logic functions may sort specific groups of users. Specifically sorted groups may further enable users to target individuals in the proper context. For example, an individual searching for sports cars may receive advertisements for sports cars when looking at websites related to cars, but not when looking at sports. Accordingly, an advertiser may specify a filtering criteria in terms of user behavior data in order to identify users who may be interested in a particular product associated with the advertiser. Further, the advertiser may have specified a logical combination of multiple filtering criteria. For example, as illustrated in FIG. 3, the advertiser may specify a logical “AND” combination of three keyword based filtering criteria: “sports car”, “convertible” and “safety”. Further, the advertiser may specify logical “NOT” of a keyword based filtering criteria: “video games”. Accordingly, based on the keyword “sports car”, the platform may identify 650,000 users who are associated with user behavior data indicative of an interest in sports cars. Similarly, based on the keywords “convertible”, “safety” and “video games”, 318,000, 85,000 and 1.8 million users respectively may be identified. As per the logical expression specified by the advertiser, a resultant set of 23,000 users may be determined to be the group of users to be targeted.
  • The 23,000 users thus identified may, in some instances, constitute the audience list for marketing purposes. Accordingly, the advertiser may specify a bidding amount associated with the audience list. Accordingly, when a user in the audience list visits a webpage, the platform may detect the presence of the user's identifier in the audience list and accordingly bid for an advertising space on the webpage. For instance, the platform may transmit the bid amount to an ad-exchange in communication with a webserver hosting the webpage. As a result, in the event of a bid win, the advertiser may present a selected advertised to the user viewing the webpage.
  • FIG. 4 illustrates and example 400 of provision of optimally targeted advertisements in accordance with some embodiments. As illustrated, the platform may initially identify the audience list of 23,000 users interested in buying sports cars based on filtering criteria provided by the advertiser. Accordingly, the platform may be configured to track online activities of these users and present advertisements based on bidding.
  • However, in addition to specifying the bid amount, the platform may enable the advertiser to specify one or more conditions under which the bid amount may be modified. For instance, as illustrated in FIG. 4, the advertiser may specify a context corresponding to an advertising opportunity within which the platform may bid for advertising spaces. In the example, the advertiser may specify presence of relevant keywords on a webpage being viewed by a user in order to determine bidding. For instance, when the user is viewing a webpage “sports.com” which does not have any content related to car shopping, the platform may determine an irrelevant context. Accordingly, the platform may not bid for advertising opportunities on “sports.com”. However, when the user is viewing the webpage “acmecars.com”, presence of a relevant keyword such as “leasing” may trigger the platform to bid for advertising opportunities on “acmecars.com”.
  • Further, in some embodiments, the platform may enable the advertiser to additionally specify a bid adjustment parameter associated with the one or more conditions. For instance, the advertiser may specify a nominal bid amount with the audience list for targeting users in the audience list independent of the webpage they may be currently viewing. Further, the advertiser may specify a modified bid amount to be used for bidding provided one or more conditions specified by the advertiser are met. For instance, the advertiser may specify the modified bid amount, for example a higher bid amount, to be used for bidding when a user in the audience list is viewing a webpage containing certain relevant keywords. Accordingly, the platform may bid for an advertising space on the webpage more aggressively in order to ensure that the likelihood of the advertiser's advertisement being presented to the user increases.
  • Further, according to some embodiments, a computer implemented method, such as method 500, of bidding for advertisement opportunities based on user behavior may be provided as illustrated in FIG. 5. An advertisement opportunity in general may correspond to any opportunity where a user may be presented with an advertisement.
  • The presentation of the advertisement may take place in one or more form such as visual, auditory, tactile and so on. Accordingly, one or more presentation devices such as, but not limited to, LED/LCD display devices, loud speakers and braille displays may be used to present the advertisement. Further, in some instances the presentation device may be a public presentation device such as a roadside display device or an electronic billboard. Alternatively, in some other instances, the presentation device may be a personal presentation device such as, for example, a laptop computer, a smartphone or a tablet computer.
  • In some instances, an advertisement opportunity may be in the form of an advertising space within a content. For example, in case a user is viewing a webpage containing a news article, a portion of the webpage may be reserved for presenting an advertisement. Accordingly, the portion of the webpage may constitute the advertisement opportunity.
  • Generally, content providers notify the availability of such advertisement opportunities to other interested entities such as for example, advertisers, ad-servers and ad-exchanges. Accordingly, an advertiser may bid for the advertisement opportunity in order to present a desired advertisement to the user.
  • Accordingly, the platform of the present disclosure enables clients such as advertisers to bid for advertisement opportunities based on user behavior data. In some instances, the user behavior data may be indicative of interests of the user. Accordingly, in some instances, the platform may be configured to monitor user behavior and create a user profile indicating interests of the user. The user behavior may include for example, present and/or past online activity performed by the user such as viewing webpages, online shopping, downloading content from the internet, uploading content to the internet and interacting with a desktop application and/or a mobile application. An exemplary online user behavior data based on which the user profile may be created is illustrated in FIG. 13.
  • Further, in some embodiments, data representing the user behavior may be de-identified. In other words, data representing the user behavior may not include identifiable information such as name, phone number, postal address, bank account number and so on. Accordingly, privacy of users may be preserved. For instance, data representing the user behavior may include a list of URLs visited by the user and a corresponding list of times when the user accessed the URLs.
  • In order to place bids based on user behavior data, the platform may be configured to receive a plurality of bid values corresponding to a plurality of sets of users corresponding to a plurality of filtering criteria based on user behavior data. In other words, a user 105 of the platform, such as an advertiser may specify a set of users based on a filtering criteria including characteristics of user behavior and a corresponding bid value to be used while bidding for presenting advertisements to the set of users. For instance, the platform may be configured to identify a plurality of users constituting an audience list based on a primary filtering criteria. Similarly, the platform may be configured to identify at least one user constituting a sub-audience list based on a secondary filtering criteria. Further, the platform may be configured to associate different bid amounts with the audience list and the sub-audience list.
  • Accordingly, the method 500 may include a step 510 of receiving a primary bid value associated with an audience list. The audience list may include a plurality of users associated with user behavior data corresponding to a primary filtering criteria. In other words, the audience list may include user identifiers of users who may have exhibited a certain user behavior in the past or are currently exhibiting such user behavior. For example, the advertiser may specify the primary filtering criteria to be keywords and corresponding affinity values (e.g. “sports car”; >80% affinity) in order to identify the audience list. Additionally, the advertiser may further specify a domain visited by the users (e.g. www.topgear.com) to be used the secondary filtering criteria. Accordingly, the sub-audience list of users may be created based on analysis of user behavior data of users present in the audience list. Specifically, each user in the audience list who visited the domain specified in the secondary filtering criteria may be identified and included in the sub-audience list. The sub-audience list may present a set of users who may be more relevant to the advertiser. Therefore, the advertiser may specify a higher bid amount to be used while bidding to present advertisements to the sub-audience.
  • Accordingly, the method 500 may include a step 520 of receiving a secondary bid value associated with the sub-audience list. The sub-audience list may include one or more users associated with user behavior data corresponding to the secondary filtering criteria.
  • Further, the method 500 may include a step 530 of transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The bid may be transmitted to entities such as, for example, ad-exchanges, ad-servers and/or content servers.
  • For instance, the platform may transmit the bid in response to a notification of an advertisement opportunity provided by a content server. The notification may, in some instances, also indicate a current context of the advertisement opportunity. For example, user identifiers corresponding to users currently viewing a webpage provided by the content server may be transmitted to the platform. Accordingly, the platform may compare the user identifiers with those present in the audience list and/or the sub-audience list.
  • When a user of the webpage is detected to be present in the audience list but not in the sub-audience list, the bid transmitted at step 530 may include the primary bid value. However, when a user of the webpage is detected to be presented in each of the audience list and the sub-audience list, the bid transmitted at step 530 may include the secondary bid value, which may be for example, higher or lower than the primary bid value.
  • In order to create the audience list and/or the sub-audience list, the platform may be configured to execute methods 600A and 600B as illustrated in FIG. 6A and FIG. 6B. Accordingly, the method may include a step 610 of receiving the primary filtering criteria for creating the audience list. Further, the method may include a step 620 of filtering a set of users based on the primary filtering criteria. Further, each user in the set of users may be associated with user behavior data. Furthermore, the primary filtering criteria may be based on one or more characteristics of user behavior data as described earlier. Additionally, the method may include a step 630 of identifying the plurality of users from the set of users based on filtering of the set of users. The plurality of users may then constitute the audience list.
  • Likewise, the method may include a step 640 of receiving the secondary filtering criteria for creating the sub-audience list. Further, the method may include a step 650 of filtering the plurality of users based on the secondary filtering criteria. Furthermore, the method may include a step 660 of identifying one or more users in the audience list based on filtering the plurality of users. The one or more users may then constitutes the sub-audience list.
  • According to some embodiments, prior to transmitting bids, the platform may receive notifications from entities such as ad-exchanges about the availability of advertisement opportunities. Accordingly, the platform may be configured to execute methods 700 and 800 as illustrated in FIG. 7 and FIG. 8.
  • Accordingly, the method may include a step 710 of receiving a notification of the advertisement opportunity from, for example, the ad-exchange. Further, the notification may include a user identifier corresponding to a user. For instance, a user associated with the user identifier may be currently viewing a webpage on a content server. Accordingly, the content server may notify the ad-exchange of the presence of an advertisement opportunity towards the user. In response, the ad-exchange may communicate the user identifier to the platform.
  • Further, the method may include a step 720 of detecting presence of the user identifier in one or more of the audience list and the sub-audience list. Further, the method may include a step 730 of transmitting the bid for the advertisement based on presence of the user identifier in one or more of the audience list and the sub-audience list.
  • For example, when the user of the webpage is detected to be present in the audience list but not in the sub-audience list, the bid transmitted may include the primary bid value. For instance, the primary bid value may be a nominal value that the advertiser may be willing to spend in order to target users in the audience list who may have a general interest towards a product/service associated with the advertiser.
  • However, when a user of the webpage is detected to be presented in each of the audience list and the sub-audience list, the bid transmitted may include the secondary bid value, which may be for example, higher or lower than the primary bid value. For instance, in case the sub-audience represents users who have shown specific interest in the product/service, the advertiser may be willing to provide a higher bid amount in order to ensure that the user is exposed to an advertisement of the product/service.
  • Further, in some embodiments, the platform may also be configured to place bids on current context associated with advertisement opportunities. Accordingly, as illustrated in FIG. 8, the method may include a step 810 of receiving a notification of the advertisement opportunity along with a contextual variable. The contextual variable may indicate one or more contextual conditions associated with the advertisement opportunity. For example, the contextual variable may indicate nature of content being viewed by the user, demographic/psychographic characteristics of the user, characteristics of a user device through which the user is consuming the content, state of the user device, sensor data obtained from the user device, current time, place, a physiological and/or psychological state of the user while consuming the content and so on.
  • Accordingly, the method may include a step 820 of evaluating the contextual variable based on one or more of the primary filtering criteria and the secondary filtering criteria. Further, each of the primary filtering criteria and the secondary filtering criteria may include a preferable contextual variable. For instance, the advertiser may have specified a primary filtering criteria as presence of certain relevant keywords on a webpage being viewed by the user. Similarly, the advertiser may have specified a secondary filtering criteria as OS type (e.g. Windows) executing on the user device used to view the webpage. Additionally, the method may include a step 830 may include a step of transmitting the bid for the advertisement based on evaluation of the contextual variable. For instance, if it is determined that a user is currently viewing an article related to iPhone through a Windows based smartphone, a desirable context of a windows phone user viewing content about iPhones may be detected. Accordingly, the advertiser may bid for displaying an advertisement for an iPhone to the user. Further, in some instances, the bid amount transmitted may depend on evaluation of the contextual variable in relation to the primary and the secondary filtering criteria. For instance, if the contextual variable satisfies only the primary filtering criteria (i.e. webpage relates to iPhones), the platform may place a nominal bid amount for presenting the advertisement to the user. However, if the contextual variable satisfies each of the primary filtering criteria and the secondary filtering criteria (i.e. user using windows phone), the platform may place a bid more aggressively by specifying a higher bid amount in order to ensure that the likelihood of presenting the advertisement to the user increases.
  • Further, in some embodiments, the platform may be configured to collect and maintain rich user behavior data by monitoring user behavior across multiple websites. Accordingly, the platform may create and maintain user profiles corresponding to different users. The user profiles may contain, for example, keywords representing user interests and affinity values representing the importance of the keywords to corresponding users.
  • In order to create the user profile, the method may include a step of receiving a plurality of Universal Resource Locators (URLs) corresponding to a plurality of webpages visited by the user. Further, the method may include a step of retrieving content from each of the plurality of webpages based on the plurality of URLs. For instance, a crawler program may be executed on a processor to automatically retrieve content from each of the plurality of webpages by accessing the plurality of URLs.
  • Subsequent to retrieving the content, the method may include a step of analyzing the content from each of the plurality of webpages. In some embodiments, analyzing content from a webpage may include analyzing content corresponding to each content type present on the webpage. For example, both textual content and non-textual content such as audio, images, video and multimedia on the webpage may be analyzed.
  • Further, in some embodiments, the analyzing may include performing Natural Language Processing (NLP) of a textual content in the webpage. Additionally, in some embodiments, in case the webpage consists of non-textual content, a step of converting the non-textual content into textual content may be performed. Subsequently, the NLP may be performed on the converted content.
  • For instance, as illustrated in FIG. 15, analyzing content of the webpage using, for example, NLP may result in identification of a category of content, such as “Entertainment”. Further, NLP may also identify brand affinities of the webpage, such as for example, “Star wars” that may provide a greater contextual relevance and brand awareness to users. Additionally, NLP may also include event detection involving identification of specific time-sensitive triggers, such as for example, an upcoming “New Movie”. Further, NLP may also identify important topics addressed in the content of the webpage and associate those topics as concept tags with the webpage, such as for example, “Cinema”. Further, NLP may also include entity extraction involving identifying relevant proper nouns like people and/or brands.
  • Additionally, the method may include a step of identifying a plurality of keywords corresponding to the webpage based on the analyzing. For instance, an exemplary set of keywords identified for a user based on the user's interaction with various webpages is illustrated in FIG. 14. For example, based on the user's visiting of a webpage related to sports news, the keywords “Football” and “Basketball” may be identified and associated with the user.
  • Furthermore, the plurality of keywords may be associated with a plurality of affinity values. The plurality of keywords and the plurality of affinity values may constitute the profile of the user. For instance, an affinity value of the keyword on a webpage may represent how strongly the content of the webpage relates to the keyword. In other words, the affinity value may represent a relative importance of the keyword in the content. Accordingly, in some instances, keywords that appear either in important sections of the webpage such as title, abstract, sub-headings, table of contents, index, main image and so on may be associated with a relative larger affinity value as compared to those keywords that appear elsewhere in the webpage. Likewise, keywords that appear often within the content of the webpage may be associated with a relatively larger affinity value as compared to those keywords that appear only once or a few times. Additionally, keywords that may appear in different media types present on the webpage, such as text, image and audio/video may be associated with an even higher affinity value.
  • Further, in some embodiments, the method may further include a step of determining an aggregated affinity value corresponding to a keyword based on a first affinity value of the keyword corresponding to a first webpage and a second affinity value of the keyword corresponding to a second webpage. In other words, the aggregated affinity value may represent an overall affinity of the keyword to the user based on the user's interaction with a plurality of webpages containing the keyword.
  • Further, in some embodiments, the aggregated affinity value may further be based on a time decay value associated with each of the first affinity value and the second affinity value. For instance, each of the first affinity value and the second affinity value may be weighted based on a time decay value. Accordingly, an impact of an affinity value on the aggregated affinity value may be controlled according to for example, a “freshness” associated with the affinity value. For instance, an affinity value of the keyword associated with a first webpage visited a week ago may be weighted more than an affinity value of the keyword associated with a second webpage visited a month ago.
  • An exemplary method 200 of creating user profiles based on user behavior data in accordance with some embodiments, is illustrated in FIG. 2. Method 200 may begin at starting block 205 and proceed to stage 210 where platform 100 may receive data from an individual's internet use. For example, the platform may receive information about a webpage that the individual visited or a Microsoft Word document or PDF that an individual downloaded. Information may include the URL of the webpage. Further information may be received, including IP address of the individual, search history of the individual, and geolocation of the individual.
  • From stage 210, where platform 100 receives data from an individual's Internet use, method 200 may advance to stage 220 where platform 100 may further gather information associated with the individual's Internet use. For example, the platform may crawl the webpage that the individual visited. For example, the platform may search for specific key words or phrases. In some embodiments, if the webpage has already been crawled, the webpage may be skipped.
  • During the crawl, the platform may perform, for example, natural language processing (NLP) to further process the context of the words and phrases in the text. In addition, the platform may utilize image recognition, audio recognition, and/or video recognition to gather data about the individual's Internet use. For example, image, video and audio information may be acquired from a webpage “www.example.com” to provide the individual's Internet use information. For instance, images may be scanned with optical character recognition (OCR). The OCR scanning may generate words or phrases for characterizing the webpage. Further, image recognition software may be used to characterize the webpage. For example, artificial intelligence (AI) software may be used to determine whether an image is showing for example, a dog or a tree. Audio files from the webpage may be scanned, using, for example, voice recognition software, to further provide information to characterize the webpage. Video files from a page may be converted to a series of images from periodic individual frames and scanned in the same manner as an image. In addition, the audio associated with the video files may be scanned to provide data about the webpage. Likewise, text from the webpage may also be extracted and analyzed based on NLP. The combination of text, image, audio and video recognition may provide a human-style “view” of what the webpage provides. The human-style “view” may enable the platform to optimize characterization of the webpage.
  • Information that is acquired from the crawl may further be associated with how recently such information was associated with the webpage (e.g., newer information may be given a higher relevance than older information). The platform may receive further information, for example, that is purchased from various data aggregators (e.g., aggregators that track specific IDs.) In addition, information may be tracked from an existing individual base. For example, if the individual clicks (“I Agree”) on certain terms and conditions, the platform may place a tracking cookie on the individual's device to further gather information. In some embodiments, stages 210 and 220 may comprise 207, where platform 100 receives general data. The general data may include, for example, data from webpages (e.g., text, image, audio, and video data associated with the webpage) and data from individuals (e.g., which websites the individuals have visited, information from the individuals' social media profiles, and the like).
  • Once platform 100 further gathers information associated with the individual's Internet use in stage 220, method 200 may continue to stage 230 where platform 100 may analyze the information. In some embodiments, the platform may perform natural language processing (NLP) as well as image, audio and video recognition to analyze the information. For example, the platform may use specific keywords and phrases, as well as keywords associated with image, video and audio files, found on each webpage and attach a plurality of ‘affinities’ to each page. For example, for a news article about iPhones, the platform may return hundreds of ‘keywords’, including “Apple” with 94% affinity, “cell phone” with 81% affinity, and “screen” with 52% affinity. The platform may then interpret the information based on the individual's Internet use to create a profile associated with the affinities.
  • For example, an individual may visit a number of webpages that have high affinity for keywords like “truck”, “football”, and “Scotch”. Such an individual may be statistically more likely to be a male. As another example, another individual may visit a number of webpages that have high affinity for keywords like “nail polish”, “Midol”, and “Pinterest.” Such an individual may be statistically more likely to be female. Such statistical predictions may be associated with a confidence level. Further, statistical predictions may be made for an abundance of other characteristics, such as, for example, but not limited to, age, marital status, parental status, approximate household income, industry of employment, sport preference, automobile preference, and phone preference.
  • After platform 100 analyzes the information for each individual in stage 230, method 200 may proceed to stage 240 where platform 100 may group users based on certain characteristics. For example, individuals likely to be of a certain characteristic, such as, for example, gender, age, marital status, parental status, approximate household income, and industry of employment, may be grouped together. Additionally, individuals may be grouped together based on their preferences, such as, for example, sport preference, automobile preference, and phone preference.
  • IV. Platform Architecture
  • The bidding platform 100 may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device. The computing device may comprise, but not be limited to, a desktop computer, laptop, a tablet, or mobile telecommunications device. Moreover, platform 100 may be hosted on a centralized server, such as, for example, a cloud computing service. Although methods 200, 500 to 800 and 1200 have been described to be performed by a computing device 1600, it should be understood that, in some embodiments, different operations may be performed by different networked elements in operative communication with computing device 1600.
  • Embodiments of the present disclosure may comprise a system having a memory storage and a processing unit. The processing unit coupled to the memory storage, wherein the processing unit is configured to perform the stages of methods 200, 500 to 800 and 1200.
  • FIG. 16 is a block diagram of a system including computing device 1600. Consistent with an embodiment of the disclosure, the aforementioned memory storage and processing unit may be implemented in a computing device, such as computing device 1600 of FIG. 16. Any suitable combination of hardware, software, or firmware may be used to implement the memory storage and processing unit. For example, the memory storage and processing unit may be implemented with computing device 1600 or any of other computing devices 1618, in combination with computing device 1600. The aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned memory storage and processing unit, consistent with embodiments of the disclosure.
  • With reference to FIG. 16, a system consistent with an embodiment of the disclosure may include a computing device, such as computing device 1600. In a basic configuration, computing device 1600 may include at least one processing unit 1602 and a system memory 1604. Depending on the configuration and type of computing device, system memory 1604 may comprise, but is not limited to, volatile (e.g. random access memory (RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or any combination. System memory 1604 may include operating system 1605, one or more programming modules 1606, and may include a program data 1607. Operating system 1605, for example, may be suitable for controlling computing device 1600's operation. In one embodiment, programming modules 1606 may include affinity calculating modules, such as, for example, webpage affinity calculation application 1620. Furthermore, embodiments of the disclosure may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated in FIG. 16 by those components within a dashed line 1608.
  • Computing device 1600 may have additional features or functionality. For example, computing device 1600 may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated in FIG. 16 by a removable storage 1609 and a non-removable storage 1610. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory 1604, removable storage 1609, and non-removable storage 1610 are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device 1600. Any such computer storage media may be part of device 1600. Computing device 1600 may also have input device(s) 1612 such as a keyboard, a mouse, a pen, a sound input device, a touch input device, etc. Output device(s) 1614 such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are examples and others may be used.
  • Computing device 1600 may also contain a communication connection 1616 that may allow device 1600 to communicate with other computing devices 1618, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection 1616 is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, and other wireless media. The term computer readable media as used herein may include both storage media and communication media.
  • As stated above, a number of program modules and data files may be stored in system memory 1604, including operating system 1605. While executing on processing unit 1602, programming modules 1606 (e.g., platform application 1620) may perform processes including, for example, one or more of methods 200, 500 to 800 and 1200's stages as described above. The aforementioned process is an example, and processing unit 1602 may perform other processes. Other programming modules that may be used in accordance with embodiments of the present disclosure may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.
  • Generally, consistent with embodiments of the disclosure, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the disclosure may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. Embodiments of the disclosure may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
  • Furthermore, embodiments of the disclosure may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip containing electronic elements or microprocessors. Embodiments of the disclosure may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the disclosure may be practiced within a general purpose computer or in any other circuits or systems.
  • Embodiments of the disclosure, for example, may be implemented as a computer process (method), a computing system, or as an article of manufacture, such as a computer program product or computer readable media. The computer program product may be a computer storage media readable by a computer system and encoding a computer program of instructions for executing a computer process. The computer program product may also be a propagated signal on a carrier readable by a computing system and encoding a computer program of instructions for executing a computer process. Accordingly, the present disclosure may be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). In other words, embodiments of the present disclosure may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. A computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
  • The computer-usable or computer-readable medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific computer-readable medium examples (a non-exhaustive list), the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CD-ROM). Note that the computer-usable or computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
  • Embodiments of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
  • While certain embodiments of the disclosure have been described, other embodiments may exist. Furthermore, although embodiments of the present disclosure have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, solid state storage (e.g., USB drive), or a CD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the disclosure.
  • All rights including copyrights in the code included herein are vested in and the property of the Applicant. The Applicant retains and reserves all rights in the code included herein, and grants permission to reproduce the material only in connection with reproduction of the granted patent and for no other purpose.
  • V. Aspect
  • The application includes at least the following aspects:
  • Aspect 1. A method of bidding for advertisement opportunities, wherein the method is computer implemented, the method comprising:
  • a. receiving a primary bid value associated with an audience list, wherein the audience list comprises a plurality of users, wherein the plurality of users is associated with user behavior data corresponding to a primary filtering criteria;
  • b. receiving a secondary bid value associated with a sub-audience list, wherein the sub-audience list comprises at least one user, wherein the at least one user is associated with user behavior data corresponding to a secondary filtering criteria; and
  • c. transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value, wherein the advertisement is presentable to the at least one user.
  • Aspect 2. The method of aspect 1, wherein each of the primary filtering criteria and the secondary filtering criteria is based on at least one characteristic of user behavior data.
  • Aspect 3. The method of aspect 2, wherein the user behavior data comprises data corresponding to online activity performed by a user, wherein the at least one characteristic comprises at least one of a domain name, a webpage identifier, a keyword, an affinity value of the keyword, a demographic variable, a psychographic variable, device data and a time corresponding to capture of the user behavior data.
  • Aspect 4. The method of aspect 3, wherein, the device data comprises at least one of a device identifier associated with a user device, indicator of screen size of the user device, a network identifier associated with a communication network used for performing the online activity, an Internet Service Provider (ISP) associated with the communication network, an Operating System (OS) identifier of an OS installed on the user device and a browser identifier of a browser installed on the user device.
  • Aspect 5. The method of aspect 1, wherein the primary bid value comprises a default bid amount, wherein the secondary bid value comprises an adjustment value, wherein the bid for the advertisement comprises a transmitted bid value obtained by adjusting the default bid amount according to the adjustment value.
  • Aspect 6. The method of aspect 1 further comprising:
  • a. receiving the primary filtering criteria for creating the audience list;
  • b. filtering a set of users based on the primary filtering criteria, wherein each user in the set of users is associated with user behavior data, wherein the primary filtering criteria is based on at least one characteristic of user behavior data; and
  • c. identifying the plurality of users from the set of users based on filtering of the set of users, wherein the plurality of users constitutes the audience list.
  • Aspect 7. The method of aspect 1 further comprising:
  • a. receiving the secondary filtering criteria for creating the sub-audience list;
  • b. filtering the plurality of users based on the secondary filtering criteria; and
  • c. identifying at least one user in the audience list based on filtering the plurality of users, wherein the at least one user constitutes the sub-audience list.
  • Aspect 8. The method of aspect 1, wherein the secondary bid value is based on a contextual variable associated with the advertisement opportunity.
  • Aspect 9. The method of aspect 1 further comprising:
  • a. receiving a notification of the advertisement opportunity, wherein the notification comprises the contextual variable; and
  • b. evaluating the contextual variable based on at least one of the primary filtering criteria and the secondary filtering criteria, wherein each of the primary filtering criteria and the secondary filtering criteria comprises a preferable contextual variable, wherein transmitting the bid for the advertisement is further based on evaluation of the contextual variable.
  • Aspect 10. The method of aspect 1 further comprising:
  • a. receiving a notification of the advertisement opportunity, wherein the notification comprises a user identifier corresponding to a user; and
  • b. detecting presence of the user identifier in at least one of the audience list and the sub-audience list, wherein transmitting the bid for the advertisement is based further on presence of the user identifier in at least one of the audience list and the sub-audience list.
  • Aspect 11. The method of aspect 10, wherein the bid for the advertisement comprises the primary bid value, wherein the user identifier is present in the audience list and absent in the sub-audience list.
  • Aspect 12. The method of aspect 10, wherein the bid for the advertisement comprises the secondary bid value, wherein the user identifier is present in each of the audience list and the sub-audience list.
  • Aspect 13. The method of aspect 1, wherein the secondary bid value comprises one of a positive value and a negative value, wherein the bid for the advertisement is transmitted provided the secondary bid value is a positive value.
  • Aspect 14. The method of aspect 1 further comprising:
  • a. receiving a notification of a win based on the bid; and
  • b. transmitting the advertisement to the at least one user based on receiving the notification of the win.
  • Aspect 15. The method of aspect 1, wherein the user behavior data is anonymous.
  • Aspect 16. A system for facilitating bidding of advertisement opportunities, wherein the system comprises a communication module and a processing module communication module coupled to the communication module, wherein the communication module is configured to:
  • a. receive a primary bid value associated with an audience list, wherein the audience list comprises a plurality of users, wherein the plurality of users is associated with user behavior data corresponding to a primary filtering criteria;
  • b. receive a secondary bid value associated with a sub-audience list, wherein the sub-audience list comprises at least one user, wherein the at least one user is associated with user behavior data corresponding to a secondary filtering criteria; and
  • c. transmit a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value, wherein the advertisement is presentable to the at least one user.
  • Aspect 17. The system of aspect 16, wherein each of the primary filtering criteria and the secondary filtering criteria is based on at least one characteristic of user behavior data.
  • Aspect 18. The system of aspect 17, wherein the user behavior data comprises data corresponding to online activity performed by a user, wherein the at least one characteristic comprises at least one of a domain name, a webpage identifier, a keyword, an affinity value of the keyword, a demographic variable, a psychographic variable, device data and a time corresponding to capture of the user behavior data.
  • Aspect 19. The system of aspect 18, wherein, the device data comprises at least one of a device identifier associated with a user device, indicator of screen size of the user device, a network identifier associated with a communication network used for performing the online activity, an Internet Service Provider (ISP) associated with the communication network, an Operating System (OS) identifier of an OS installed on the user device and a browser identifier of a browser installed on the user device.
  • Aspect 20. The system of aspect 16, wherein the primary bid value comprises a default bid amount, wherein the secondary bid value comprises an adjustment value, wherein the bid for the advertisement comprises a transmitted bid value obtained by adjusting the default bid amount according to the adjustment value.
  • Aspect 21. The system of aspect 16, wherein the communication module is further configured to receive the primary filtering criteria for creating the audience list, wherein the processing module is configured to:
  • a. filter a set of users based on the primary filtering criteria, wherein each user in the set of users is associated with user behavior data, wherein the primary filtering criteria is based on at least one characteristic of user behavior data; and
  • b. identify the plurality of users from the set of users based on filtering of the set of users, wherein the plurality of users constitutes the audience list.
  • Aspect 22. The system of aspect 16, wherein the communication module is further configured to receive the secondary filtering criteria for creating the sub-audience list, wherein the processing module is configured to:
  • a. filter the plurality of users based on the secondary filtering criteria; and
  • b. identify at least one user in the audience list based on filtering the plurality of users, wherein the at least one user constitutes the sub-audience list.
  • Aspect 23. The system of aspect 16, wherein the secondary bid value is based on a contextual variable associated with the advertisement opportunity.
  • Aspect 24. The system of aspect 16, wherein the communication module is further configured to receive a notification of the advertisement opportunity, wherein the notification comprises the contextual variable, wherein the processing module is configured to evaluate the contextual variable based on at least one of the primary filtering criteria and the secondary filtering criteria, wherein each of the primary filtering criteria and the secondary filtering criteria comprises a preferable contextual variable, wherein transmitting the bid for the advertisement is further based on evaluation of the contextual variable.
  • Aspect 25. The system of aspect 16, wherein the communication module is further configured to receive a notification of the advertisement opportunity, wherein the notification comprises a user identifier corresponding to a user, wherein the processing module is configured to detect presence of the user identifier in at least one of the audience list and the sub-audience list, wherein transmitting the bid for the advertisement is based further on presence of the user identifier in at least one of the audience list and the sub-audience list.
  • Aspect 26. The system of aspect 25, wherein the bid for the advertisement comprises the primary bid value, wherein the user identifier is present in the audience list and absent in the sub-audience list.
  • Aspect 27. The system of aspect 25, wherein the bid for the advertisement comprises the secondary bid value, wherein the user identifier is present in each of the audience list and the sub-audience list.
  • Aspect 28. The system of aspect 16, wherein the secondary bid value comprises one of a positive value and a negative value, wherein the bid for the advertisement is transmitted provided the secondary bid value is a positive value.
  • Aspect 29. The system of aspect 16, wherein the communication module is further configured to:
  • a. receive a notification of a win based on the bid; and
  • b. transmit the advertisement to the at least one user based on receiving the notification of the win.
  • Aspect 30. The system of aspect 16, wherein the user behavior data is anonymous.
  • VI. Claims
  • While the specification includes examples, the disclosure's scope is indicated by the following claims. Furthermore, while the specification has been described in language specific to structural features and/or methodological acts, the claims are not limited to the features or acts described above. Rather, the specific features and acts described above are disclosed as example for embodiments of the disclosure.
  • Insofar as the description above and the accompanying drawing disclose any additional subject matter that is not within the scope of the claims below, the disclosures are not dedicated to the public and the right to file one or more applications to claims such additional disclosures is reserved.

Claims (16)

The following is claimed:
1. A method of bidding for advertisement opportunities, wherein the method is computer implemented, the method comprising:
receiving a specification of a first audience list based on primary filtering criteria, wherein the first audience list comprises a plurality of users having user behavior data corresponding to the primary filtering criteria;
receiving a primary bid value associated with the first audience list;
specifying an adjustment to the primary bid value based on at least one contextual variable, wherein specifying the adjustment to the primary bid value based on the at least one contextual variable comprises:
specifying an adjustment filtering criteria, wherein specifying the adjustment filtering criteria comprises receiving a selection of the following:
an adjustment filter type and an adjustment filter parameter,
wherein receiving the selection the adjustment filter type comprises receiving a selection of at least one from a group consisting of the following:
an additional audience list based adjustment,
keyword based adjustment,
affinity based adjustment,
web traffic history based adjustment,
location based adjustment, and
time based adjustment;
wherein receiving the selection the adjustment filter parameter comprises receiving a selection of at least one from a group consisting of the following:
at least one additional audience list,
at least one keyword,
at least one interest,
at least one web domain,
at least one web page,
at least one location, and
at least one time, and
specifying an adjustment value to be applied to the primary bid value when the at least one contextual variable is detected in association with an advertisement opportunity, wherein specifying the adjustment value comprises receiving a specification of the following:
a percentage of adjustment for the primary bid value, wherein the percentage is configured to be either of a positive value and a negative value, wherein an adjustment to the positive value indicates a multiplier to the primary bid value, and wherein an adjustment to the negative value indicates a divider of the primary bid value;
tracking an online behavior of the plurality of users in the first audience list to detect the at least one contextual variable, wherein tracking the online behavior of the plurality of users comprises:
accessing a web page that has been accessed by a user of the plurality of users,
performing natural language processing on the web page to determine keywords associated with the web page, and
associating the keywords with the user; and
transmitting a bid for the advertisement opportunity, wherein transmitting the bid for the advertisement opportunity comprises:
calculating the bid, when the at least one contextual variable is detected in association with the advertisement opportunity, by adjusting the primary bid value by the following:
the multiplier associated with the adjustment value when the adjustment value is positive for the at least one contextual variable, and
the divider associated with the adjustment value when the adjustment value is negative for the at least one contextual variable.
2. The method of claim 1, wherein each of the primary filtering criteria and the adjustment filtering criteria is based on at least one characteristic of user behavior data.
3. The method of claim 2, wherein the user behavior data comprises data corresponding to online activity performed by a user, wherein the at least one characteristic comprises at least one of the following: a domain name, a webpage identifier, a keyword, an affinity value of the keyword, a demographic variable, a psychographic variable, device data and a time corresponding to capture of the user behavior data.
4. The method of claim 3, wherein, the device data comprises at least one of the following: a device identifier associated with a user device, indicator of screen size of the user device, a network identifier associated with a communication network used for performing the online activity, an Internet Service Provider (ISP) associated with the communication network, an Operating System (OS) identifier of an OS installed on the user device and a browser identifier of a browser installed on the user device.
5. The method of claim 1, further comprising:
establishing a profile for the user, wherein the profile comprises the keywords;
determining the user profile satisfies the at least one contextual variable; and
adding the user to a second audience list upon a determination that the at least one contextual variable is satisfied.
6. The method of claim 1, further comprising:
receiving a notification of the advertisement opportunity, wherein the advertisement opportunity is associated with the user having profile data satisfying the at least one contextual variable; and
evaluating the at least one contextual variable based on at least one of the primary filtering criteria and the adjustment filtering criteria, wherein each of the primary filtering criteria and the adjustment filtering criteria comprises a preferable contextual variable, wherein transmitting the bid for the advertisement is further based on evaluation of the preferable contextual variable.
7. The method of claim 6, further comprising:
receiving a notification of the advertisement opportunity, wherein the notification comprises a user identifier corresponding to the user; and
detecting presence of the user identifier in at least one of the first audience list and the second list, wherein transmitting the bid for the advertisement is based further on presence of the user identifier in at least one of the first audience list and the second list.
8. The method of claim 10, wherein the bid for the advertisement comprises the primary bid value-when the user identifier is present in the first audience list and absent in the second audience list.
9. The method of claim 10, wherein the bid for the advertisement comprises the secondary bid value, wherein the user identifier is present in each of the first audience list and the second audience list.
10. The method of claim 1, further comprising:
receiving a notification of a win based on the bid; and
transmitting the advertisement to be based on receiving the notification of the win.
11. The method of claim 1, wherein the user behavior data is anonymous.
12. A method of bidding for advertisement opportunities, wherein the method is computer implemented, the method comprising:
creating an audience list, wherein creating the first audience list comprises:
receiving primary filtering criteria for creating the first audience list;
filtering a set of users based on the primary filtering criteria, wherein each user in the set of users is associated with user behavior data, wherein the primary filtering criteria is based on at least one characteristic of user behavior data; and
identifying a plurality of users from the set of users based on filtering of the set of users, wherein the plurality of users constitutes the first audience list;
receiving a primary bid value associated with the first audience list;
specifying a first adjustment to the primary bid value based on a first contextual variable, wherein specifying the adjustment to the primary bid value based on the first contextual variable comprises:
specifying an adjustment filtering criteria, wherein specifying the adjustment filtering criteria comprises receiving a selection of the following:
an adjustment filter type and an adjustment filter parameter,
wherein receiving the selection the adjustment filter type comprises receiving a selection of at least one from a group consisting of the following:
an additional audience list based adjustment,
keyword based adjustment,
affinity based adjustment,
web traffic history based adjustment,
location based adjustment, and
time based adjustment;
wherein receiving the selection the adjustment filter parameter comprises receiving a selection of at least one from a group consisting of the following:
at least one additional audience list,
at least one keyword,
at least one interest,
at least one web domain,
at least one web page,
at least one location, and
at least one time, and
specifying an adjustment value to be applied to the primary bid value when the first contextual variable is detected in association with an advertisement opportunity, wherein specifying the adjustment value comprises receiving a specification of the following:
a slider for specifying a percentage by which to adjust the primary bid value, wherein slider is configured to be manipulated in a positive direction and negative direction such that the percentage can be specified as a positive value and a negative value, wherein an adjustment to the positive value indicates a multiplier to the primary bid value, and wherein an adjustment to the negative value indicates a divider of the primary bid value;
specifying a second adjustment to the primary bid value based on a second contextual variable, wherein specifying the adjustment to the primary bid value based on the second contextual variable comprises:
specifying an additional adjustment filtering criteria, wherein specifying the additional adjustment filtering criteria comprises receiving a selection of the following: an additional adjustment filter type and an additional adjustment filter parameter, and
specifying an additional adjustment value to be applied to the primary bid value when the second contextual variable is detected;
specifying logical operations for combining the first adjustment and the second adjustment to the primary bid value when calculating a bid;
tracking an online behavior of the plurality of users in the first audience list to detect one of the following: the first contextual variable and the second contextual variable, wherein tracking the online behavior of the plurality of users comprises:
accessing a web page that has been accessed by a user of the plurality of users,
performing natural language processing on the web page to determine keywords associated with the web page,
associating the keywords with the user, and
establishing a profile for the user; and
receiving a notification of the advertisement opportunity, wherein the advertisement opportunity corresponds to the user having the profile that satisfies at least one of the following: the first contextual variable and the second contextual variable;
transmitting the bid for the advertisement opportunity, wherein transmitting the bid for the advertisement opportunity comprises:
calculating the bid, when the first contextual variable and the second contextual variable are detected in association with the advertisement opportunity, by adjusting the primary bid value by the adjustment value and the additional adjustment value in accordance to the logical operations specified for combining the first adjustment and the second adjustment.
13. The method of claim 12, wherein transmitting the bid for the advertisement opportunity further comprises:
calculating the bid, when only the first contextual variable is detected in association with the advertisement opportunity, by adjusting the primary bid value by the following:
the multiplier associated with the adjustment value when the adjustment value is positive for the at least one contextual variable, and
the divider associated with the adjustment value when the adjustment value is negative for the at least one contextual variable.
14. The method of claim 12, wherein transmitting the bid for the advertisement opportunity further comprises:
calculating the bid, when only the second contextual variable is detected in association with the advertisement opportunity, by adjusting the primary bid value by the following:
the multiplier associated with the additional adjustment value when the adjustment value is positive for the at least one contextual variable, and
the divider associated with the additional adjustment value when the adjustment value is negative for the at least one contextual variable.
15. A method of bidding for advertisement opportunities, wherein the method is computer implemented, the method comprising:
creating a first audience list, wherein creating the first audience list comprises:
receiving primary filtering criteria for creating the first audience list;
filtering a set of users based on the primary filtering criteria, wherein each user in the set of users is associated with user behavior data, wherein the primary filtering criteria is based on at least one characteristic of user behavior data; and
identifying a plurality of users from the set of users based on filtering of the set of users, wherein the plurality of users constitutes the first audience list;
receiving a primary bid value associated with the first audience list;
specifying a first adjustment to the primary bid value based on a first contextual variable, wherein specifying the adjustment to the primary bid value based on the first contextual variable comprises:
specifying an adjustment filtering criteria, wherein specifying the adjustment filtering criteria comprises receiving a selection of the following:
an adjustment filter type and an adjustment filter parameter,
wherein receiving the selection the adjustment filter type comprises receiving a selection of at least one from a group consisting of the following:
an additional audience list based adjustment,
keyword based adjustment,
affinity based adjustment,
web traffic history based adjustment,
location based adjustment, and
time based adjustment;
wherein receiving the selection the adjustment filter parameter comprises receiving a selection of at least one from a group consisting of the following:
at least one additional audience list,
at least one keyword,
at least one interest,
at least one web domain,
at least one web page,
at least one location, and
at least one time, and
specifying an adjustment value to be applied to the primary bid value when the first contextual variable is detected in association with an advertisement opportunity, wherein specifying the adjustment value comprises receiving a specification of the following:
a slider for specifying a percentage by which to adjust the primary bid value, wherein slider is configured to be manipulated in a positive direction and negative direction such that the percentage can be specified as a positive value and a negative value, wherein an adjustment to the positive value indicates a multiplier to the primary bid value, and wherein an adjustment to the negative value indicates a divider of the primary bid value, and
a blacklist indicator, wherein the blacklist indicator, upon activation, is configured to set the primary bid value to zero;
specifying a second adjustment to the primary bid value based on a second contextual variable, wherein specifying the adjustment to the primary bid value based on the second contextual variable comprises:
specifying an additional adjustment filtering criteria, wherein specifying the additional adjustment filtering criteria comprises receiving a selection of the following: an additional adjustment filter type and an additional adjustment filter parameter, and
specifying an additional adjustment value to be applied to the primary bid value when the second contextual variable is detected;
tracking an online behavior of the plurality of users in the first audience list to detect the first contextual variable, wherein tracking the online behavior of the plurality of users comprises:
accessing a web page that has been accessed by a user of the plurality of users,
performing natural language processing on the web page to determine keywords associated with the web page,
associating the keywords with the user,
establishing a profile for the user based on, at least in part, the keywords,
associating the user with the first contextual variable when profile data for the user corresponds to the first contextual variable, and
associating the user with the second contextual variable when profile data for the user corresponds to the first contextual variable; and
generating a second audience list having users associated with the first contextual variable;
generating a third audience list having users associated with the second contextual variable;
specifying logical operations for combining the first adjustment and the second adjustment to the primary bid value;
receiving a notification of the advertisement opportunity, wherein the advertisement opportunity comprises a user identifier;
determining whether the user identifier is associated with a user at least one of the following: the first audience list, the second audience list, and the third audience list;
transmitting the bid for the advertisement opportunity, wherein transmitting the bid for the advertisement opportunity comprises:
calculating the bid, when the user identifier is associated with the first audience list, the second audience list, and the third audience list, by adjusting the primary bid value by the adjustment value and the additional adjustment value in accordance to the logical operations specified for combining the first adjustment and the second adjustment.
16. The method of claim 15, wherein specifying logical operations for combining the first adjustment and the second adjustment to the primary bid value when calculating the comprises graphical user interface manipulations within a workspace, wherein the graphical user interface manipulations correspond to at least one of the following:
dragging and dropping of at least one audience list, and
generating an overlap in a Venn diagram like workspace interface.
US16/544,059 2015-06-09 2019-08-19 Method and system for influencing auction based advertising opportunities based on user characteristics Pending US20190370831A1 (en)

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