US20140304066A1

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Publication number: US20140304066A1
Application number: US14/016,804
Authority: US
Inventors: Meir Zohar; Nitzan Ben-Zvi
Original assignee: eXelate Inc
Current assignee: eXelate Inc
Priority date: 2009-04-16
Filing date: 2013-09-03
Publication date: 2014-10-09
Also published as: US8554602B1

Abstract

System and method for automatic definition and tuning of user segment combinations used for targeted advertisement and marketing. Automated analysis and feedback modules may provide, ad hoc, optimized market segments definitions that maximize the conversion ratio for advertisements. The system and method may optimize of segment data based on a learning-running process built around a data exchange within a real-time bidding (RTB) media exchange system. In identifying qualified combinations for segments on-going loops or iterations may analyze the correlation between the performance of an advertisement or “creative” and user data, based on a set of parameters. As a part of data segmentation, embodiments of the present invention find combinations of parameters that may predict lift. Segment combinations are optimized by, for example combining a segment combination with one or more other segment combinations and/or dividing a segment combination into two or more different combinations.

Description

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Provisional Ser. No. 61/169,966, filed on Apr. 16, 2009 (and titled “BEHAVIORAL SEGMENT OPTIMIZATION BASED ON A DATA EXCHANGE”) which is incorporated in its entirety herein by reference.

FIELD OF THE INVENTION

This invention relates to the fields of targeted marketing, targeting advertisement content, automatic segment enhancement for advertising and marketing, and advertising through targeted marketing on the World Wide Web using market segment data.

BACKGROUND OF THE INVENTION

Targeting and data collection techniques provide advertisers and other marketing organizations with market segment data related to advertising viewers, including, for example, computer users who view advertising on the World Wide Web (Web) or Internet. For advertising viewers such as Internet users, the available information related to each user depends, for example, on his or her historical Web behavior and, for example, on his or her origin environment, such as the user's computing platform, service provider, country, time of day, etc. A “market segment” or “segment” is a subset, or partial portion of a group that can be characterized in some way; a segment may also be a data object describing such a group.

Advertisers and other marketing organizations may create segment definitions to define groups of potential marketing targets (e.g., users) and direct advertising to those groups, such as groups of users on the Internet. “Data publishers” (or “data sellers”) may sell information concerning targets or people, such as Internet users, and their behaviors, which advertisers and other marketing organizations may use to create, for example, behavioral segment definitions. An Internet user may access a Web site of a data publisher, such as a bicycling interest Web site, for example, and be identified as a user “interested in bicycling”. Other attributes, such as time and location of the person's access, may also be identified. Data publishers may sell the identifying information about users who access their sites and receive income from sales based on this information's use.

User identification (ID) data from data publishers can be used to create segment definitions. In general, segment definitions may be characterized by specific values for available properties. For example, segment definitions might exist for categories such as “Gender”, “Age” and “Nationality” and one segment combination might be defined with three properties as, “Male, 35-40, European.” Once identified (e.g., from information from a data publisher (data seller)), a user who fits the characteristics of “Male, 35-40, European” can be grouped into and/or associated with this segment combination. An advertisement can be exposed to (or placed) with users identified with the segment combination, and data can be collected to determine how the users identified with that segment respond. Behavioral segment definitions for “Shopping Interest”, “Running Interest” and “Web surfing interest” can be defined and Behavioral attributes, such as “likes to shop”, “intensely likes running” or “Web surfs in the evening” can also be included in segment combinations. Segment combinations can have attributes that are purely behavioral, purely non-behavioral or a mixture of behavioral and non-behavioral.

The efficiency of a given advertisement depends on the match between the content of the advertisement (advertising content) and the market segment to which the content is exposed. In practice, a numeric “conversion ratio” value describes the efficiency or “success” relationship between the advertising content and target segment. A high conversion ratio value can show, for example, by various measures or various methods of determining or collecting such data, that a given advertisement or advertising campaign (group of advertisements) is well received by a given target segment.

It is perceived within the advertising and marketing industries that, in general, better and more accurate segment targeting capabilities could improve conversion ratios. High conversion ratios for advertisements, on the Internet and in other advertising venues, such as, e.g., print, outdoor, direct are desirable. Identification, for example, of a large user group with a high response rate to advertising and with members who respond in stable and predictable manners over time is desirable.

Within Internet marketing, serving systems for organizations executing advertisement placement in advertising campaigns may execute “media optimization” when placing an advertisement on a particular Web site or with a particular media publisher. Media optimization may include analyzing parameters in segment combinations to identify values for each parameter that may yield the “best results” for each advertisement the serving system runs. A serving system may be a networked computing system that enables an operator to place advertisements on particular Web pages. Serving systems place advertisements on behalf of an advertiser or advertising agency, and can be operated by a number of entities such as an independent operator working with an advertiser or advertising agency.

With the development of the Internet advertisement market, it is thought that bid based media purchasing may become more widely used. In such a system “real time bidding” (RTB) bidders acquire spaces from media publishers on a “real time bidding” (RTB) exchange. An RTB bidder in such a model could operate one or more serving systems More accurate and efficient segmentation or grouping capabilities can provide an advantage to the bidders and allow them to more greatly maximize their profit in this arena. In such a bidding environment, response time becomes a critical element and it is perceived that automatic tools, including those with segment-identifying capabilities may improve operational efficiency.

Serving systems using available media optimization algorithms have distinct limitations. Automatic systems exist, but with such systems, segment content does not get improved with the time. Such systems, generally, use “raw” segment data as received from a data publisher (data seller). An algorithm for media optimization looking at such data can, for example, identify that a given creative works well (e.g. gets a high conversion ratio) when displayed to users on weekends. Based on that high weekend response, a media optimization using such live user data may show that the creative in question should be used mostly on weekends. This type of algorithm ignores past data, however, so, for example, the algorithm may not identify the fact that the campaign related to the creative works better for people who expressed an interest in technology gadgets within the last 30 days. Such an algorithm, for example, also does not allow for improvements as more data is amassed.

Another drawback in current systems is that such systems provide only limited centralization. A centralized system may, for example, allow handling of different data types, such as the different types of data provided by different types of data publishers (“data sellers”). Using behavioral aspects from a wide group of data publishers may provide an advantage in manipulating a wide variety of data and could enhance segment groupings. Such a centralized system, may also, when accessed by “data buyers”, further be able to improve the segment data over time and provide it in a manner that permits further automation of data buyer and data seller systems. Such automation may desirable for system such as an RTB media exchange system.

In general, there is a need for improved techniques for media optimization, in the advertising and marketing fields in general and, in particular, with regard to Internet advertising.

SUMMARY OF THE INVENTION

Embodiments of the present invention provide a system and method for automatic definition and tuning of user behavioral segments, by, for example, combining segment combinations, dividing or splitting segment combinations or otherwise altering segment combinations. In this description, a segment may be a data structure, e.g. stored on a computer, which holds attributes or parameters used to characterize groups of people, e.g. a sub-set or portion of a larger group, and which may link statistical and other information about the groups' members, such as, for example, how many members (e.g. the percentage of users) belonging to this group. A segment definition may be a data structure, e.g. on a computer, which sets attributes and parameters (or sub-segments) that identify the nature, properties or meanings of the segment (for example, “frequency of reading news”, “Origin country”, “gender”). Segments, based on the segment definitions, can be combined together into data structures, e.g. on a computer, called segment combinations. A “segment combination” holds attributes and parameters that identify behaviors for more than one segment. For example a segment for people who “read news every 3 hours or more” and a segment for people who “like fictional books” can be combined into a segment “combination” (The relation in combination can be “and” or “or”). Segment combinations can also be referred to herein as segments.

Embodiments of the present invention utilize automated analysis and feedback modules to provide optimized market segments designed to maximize conversion ratios for provided advertisement content. Within the definition and tuning of user segments as described herein, embodiments of the present invention provide for behavioral segment combination optimization based on, for example, a data exchange, or other methods. In this description, a “behavioral segment” may be a segment definition (herein a data structure, implemented on a computer), having definitional attributes that pertain to subjective aspects of people's behavior such as for example, “tendency to read news”, “common working hours”. When comparing such aspects to other aspects such as “age” or “origin country” or “gender” Behavioral segments can be combined into further data structures on a computer with either non-behavioral segments or other behavioral segments to form “behavioral segment combinations”.

One embodiment may operate through an exchange system in connection with a serving system of an organization that places advertisements, such as an advertiser, an advertising agency or, in the example of Internet bid-based media placement, a real-time bidding (RTB) organization (an “RTB bidder”) within a real-time bidding (RTB) media exchange system. Such an embodiment provides a configuration where pluralities of serving systems, for example those of RTB bidders, use, for segment optimization, a centralized exchange system also connected to or associated with systems of one or more data publishers (data sellers). The centric setup may bring together “data buyers” on the one hand, such as serving systems who execute advertisement placement (e.g. an RTB bidder system) and “data sellers” on the other hand, such as data publishers who user identification data that can be associated with particular behavioral segments. Optimizing of segments according to some embodiments can be used outside of an RTB media exchange system and does not need to be used with a system including data buyers, data sellers, or with the specific architectures disclosed herein.

Embodiments of the present invention may further allow optimized segment combinations to be made available to a serving system organization (such as an RTB bidder) for their use in making advertising placement determinations (e.g. automatically through the serving system's existing placement engines). An operator of a serving system of an RTB bidder, for example, may request optimization services, as a client, from an embodiment of the exchange system. The exchange system may “tag” users (e.g. with a “buyers tag”), identifying them through “cookies” (e.g., located on the user's computers) with segment identifications naming the client. Other methods and systems for identifying users with segments may be used. “Tagging” may be accomplished by writing, saving, or otherwise storing an identifier of the new segment combination, using a computer server connected to the network, on computers of a plurality of new computer users at the time each new computer user access a Web site of a data publisher to identify the computers of the new computer users with the new combination. Such a tagging process occurs, for example, when a user accesses a Web site of a “data seller” such as a data publisher. The serving system client, then may be able to expose an advertisement (e.g. in its test runs of an advertisement) to groups of users who can be identified to these exchange system-provided segments.

Data concerning the user's responses to the advertisement can be collected for users identified with these segments. In one embodiment, the serving system may collect the user response data and provide summary reports to the exchange system. In another embodiment, the serving system may arrange for all user requests to be routed through exchange system, and in such an embodiment, the exchange would receive the user responses “live” and in real time. In one embodiment, the exchange system may receive responses from many clients (e.g. many advertising placement organizations) concerning the segments.

The exchange system may tune or update the segment definitions through an optimization process, based on the user response data received. In one embodiment, the exchange system may modify or change the segment definitions. The exchange system may, for example, combine two or more segment combinations, or divide a segment combination into two or more other segment combinations. Such embodiments may provide systems and methods for improving segment combinations, where each segment combination may comprise information characterizing behaviors by individuals identifiable to specific computers in a computer network.

The optimized segment combinations may then be output by the exchange system, in an automated fashion to identify or “tag” users for the RTB bidder (the client), and for other clients as well, where users are now associated with one or more of the optimized segment definitions. The serving engines within the serving system (e.g. of an RTB bidder) may then execute “media optimization” and target the advertisement to large numbers of users identified with the optimized segment combinations. The process can continue and data from further runs of the advertisement may be used to further optimize segment combinations. Data from many advertisements may be used to optimize segment combinations. Embodiments may perform optimization on data from many clients and many advertisements (or many kinds of advertisements). Optionally, other data configurations such as optimization on the data of individual clients (on their own specific data) are possible.

One goal of optimized targeting may be to achieve an increase in conversion ratios for an advertisement or advertising campaign. A tuned or optimized segment combination may identify a group of users that behaves predictably to advertising. When large, predictable groups are identified, such as when many segment combinations are combined, such segments identifications may be valuable to data “buyers” such as an RTB bidder. Use of user data in large combined segments may be also profitable to data “sellers”. Embodiments of the exchange system bring together such groups and further provide optimization that may enable further automation of data seller and data buyer processes.

Embodiments may perform segment optimization using a number of processing threads or other computer-based processes that examine data, for example, in on-going loop processes that may be updated periodically. A first thread (e.g. a “Thread I”) may perform an analysis for a given advertisement on the user response data (such as data in a buyer report table) received for that advertisement to identify or “tag” certain segment combinations for further processing in the combine/divide/alter process. When user data shows that a given segment combination has a achieved a “good” or high response level, for example, that segment combination may be tagged as an “approved” or “qualified” combination. A second thread (e.g. a “Thread II”) may analyze “qualified” combinations and further combine two or more qualified combinations to create, for example, a “unified combination”. A unified combination, for example, may identify or describe a larger population of users who behave similarly in response to advertising. A third thread (e.g. a “Thread IR”) may compare the unified combination table entries with the current behavioral/combined segment definitions (such as may be located, for example, in a behavioral/combined segments table) and may update the existing segment combinations, such as by adding (or activating) new optimized segment combinations and “de-activating” or (removing from use) segment combinations that do not provide a high response rate.

One embodiment divide and/or alter segment combinations, and may include two additional threads (other methods of dividing and altering segments, not using these threads, may be used). A fourth thread (e.g. a “Thread IV”) may analyze user data received (and, for example, stored in a buyer report table) to identify “poor” or low performing segment combinations, tagging them as “candidates” for segment dividing or alteration. Such a thread may also operate to create possible divisions of a segment combination. For example, a segment combination which includes a segment for Age: 20-30 could be possibly split into two segments combinations with different age segments, e.g. 20-24 and 25-30. Thread IV may add or link additional sub segments elements, e.g. 20-24 and 25-30 to the segment combination with age segment 20-30 and then tested user response data against these new “possible” combinations. After a pre-designated period, a fifth thread (e.g. a “Thread V”) may perform analysis to determine if it would be desired to split the segment and, if desirable, updates the behavioral segment definitions e.g. adding (or activating) two new divided segment combinations and de-activating (or removing from active use) the original segment combination.

Embodiments, with thread processing and otherwise, provide both systems and methods of improving segment combinations comprising information characterizing behaviors of groups of users of computers in a computer network. Such processing may comprise, for example, receiving, at a computer server, user response data concerning an advertisement exposed to a first plurality of computer users operating computers connected to the computer network, the identities of the computers of the users being identified with a segment combination based on the computers' previous accessing of a data publisher Website, the user response data providing a user response rate value indicative the responsiveness of the users of the computers to the advertisement. Such processing further may comprise identifying from the user response data, those segment combinations within the user response data that have a user response rate surpassing a pre-determined value measured according to one or more user events and also creating at least one segment combination as a new combination from permutations of the identified segment combinations wherein the resulting new combination has an associated value indicative of statistical unification that surpasses a threshold value and provides an indication of the behavioral similarity of the combined group in response to the advertisement.

With a new combination identified, the system then may write, save or store an identifier of the new segment combination, using for example a computer server connected to the network, on computers of a second plurality of computer users at the time each second plurality computer user access a Web site of a data publisher to identify the computers of the second plurality users with the new combination, where the new segment combination is written to the computers to be accessible and identifiable to a serving system of an advertisement placement organization. Other embodiments of combining and dividing segments another statistical data are possible.

In embodiments of the present invention, segments or groups can be output by an exchange system either as “virtual” segments or “real” segments. When outputting the exchange system may, for example, “tag” a user with cookies (e.g., located or saved on the users' computers) or other methods or devices that identify a segment in either a “virtual” or “real” way. In one embodiment when the segments are provided as “virtual” segments, the serving system may receive cookies from a user that, for example, identify a segment by number (e.g. “Exchange System,Segment #5”) and provide no further attribute-descriptive or other information. In another embodiment, the serving system may receive the ‘real’ segments information (e.g. codes or texts) providing an explanation or meaning for the segment. Providing “virtual” segments (e.g. having only identification or limited conversion ratio data only), may facilitate advertising placement through, for example, automated systems, where for example, a serving system, such as a serving system of an RTB bidder, places advertisements based on a “media optimization” process or algorithm without knowing the attribute details of the segment combination. The serving system may rely on the optimization process which seeks to identify user groups who have high response levels and/or who behave reliably.

When the segments are provided as “real” segments, the exchange system may “tag” a user with content that describes the segment attributes (e.g. text or codes that describe behavioral and other preferences—“Male”, “25-30”, “likes to swim”). That data can be used by, for example, the RTB bidder, if desired for processing. The meaning may be reported a data buyer, e.g. an RTB bidder in other ways, outside of receiving it through a cookie.

Such combining or dividing of segments or other data sets describing users, and providing segments as virtual or real segments need not be used exclusively with the system disclosed herein, including specific entities or specific architectures, and can be use with other entities and architectures.

In addition, embodiments of the exchange system may operate in connection with data publishers (data sellers) in ways that further support their seller-side automation. As data publishers are integrated to the exchange system in embodiments of the present invention, the exchange system may offer better monetization of their data, easy management of multiple serving systems and ability to control access to their data such as through a bidding system connected to the exchange system. In one embodiment, the exchange system provides an auto registration component that is based, for example, on registration and set-up, a qualification phase and then an active run phase. Automated billing may be supported, and in one embodiment of the present invention, the exchange system calculates income due to the data publishers based on the exchange's logged data and pre-set deal terms. Further to the above, the embodiments of the present invention provide a fraud detection component to monitor various angles of the data to identify fraudulent cases and provide warnings or cut-off data sources

Embodiments of the present invention may be implemented in both computer hardware and software with the software for execution on computer hardware components. The invention in embodiments is implemented also in a combination of computer hardware and software elements.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings in which:

FIG. 1 depicts an embodiment of the present invention implemented as part of a bid-based media purchasing and system.

FIG. 2 provides an overview depiction of an exemplary RTB service flow, according to an embodiment of the present invention.

FIG. 3 depicts a set of elements for an embodiment of the exchange system, according to an embodiment of the present invention.

FIG. 4A depicts an exemplary process for the exchange system to “tag” a user (e.g. with a buyer tag) on the user's access to a data publisher Web site, according to an embodiment of the present invention.

FIG. 4B depicts an exemplary process for using a buyer tag for obtaining segment information from a user's access to an advertisement on a Website of a media publisher, according to an embodiment of the present invention.

FIG. 5 depicts a mapping of a user request for advertisement data, according to an embodiment of the present invention.

FIG. 6A depicts exemplary processing threads for segment combining and dividing/alteration processes according to an embodiment of the present invention.

FIG. 6B depicts data tables used within the database of the exchange system and accessed by exemplary processing threads for segment combining and dividing/alteration processes, according to an embodiment of the present invention.

FIG. 6C depicts exemplary relationships for data base tables of an exchange system according to an embodiment of the present invention.

FIG. 7A depicts an exemplary process flow for the qualified combinations, according to an embodiment of the present invention.

FIG. 7B depicts an exemplary process flow for the unified combinations procedure, according to an embodiment of the present invention.

FIG. 7C depicts an exemplary process flow for updating behavioral segments after segment combining, according to an embodiment of the present invention.

FIG. 7D depicts an exemplary process flow for dividing/altering behavioral segments according to an embodiment of present invention.

FIG. 7E depicts an exemplary process flow for updating behavioral segments after segment dividing/alteration, according to an embodiment of the present invention.

FIG. 8 depicts an embodiment of the exchange system with a data publishers (data sellers) management unit as part of the exchange system.

It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE PRESENT INVENTION

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention.

To assist in further explanation the presented embodiments of the invention, term explanations are provided in the “Explanation of Terms” section at the end of the detailed description.

RTB Media Exchange System

Embodiments of present invention can be implemented as part of a bid-based media purchasing and system, for example as is depicted inFIG. 1. In other embodiments, other systems, including other entities and other hardware, may be used. Such a system may include entities such asadvertisers14,advertising agencies16,RTB bidders18, RTB exchanges20,media publishers22, anddata publishers24. In one embodiment, anexchange system10 with an administrative andcontrol interface12 communicates with one ormore RTB bidders18 and one ormore data publishers24. Other entities, and other hardware devices, may be used. Theexchange system10 interfaces with the data publishers24 (and users accessingdata publisher24 Websites) to obtain data to create segments (or groups) and tag users with segment or group identifications. Theexchange system10 further provides segment optimization services toRTB bidders18. An overview of the participants within such an environment follows; other or different parties may participate, and in different manners.

In an exemplary system as depicted inFIG. 1,media publishers22 sell advertising space toadvertisers14 and theirrespective advertising agencies16.Media publishers22, for example, are organizations having sites on the World Wide Web (“Web sites”) that allot Web page space to advertisements. For example, companies such as the New York Times, Games.com or Facebook sell space on their Web pages for creative advertisements such as banner advertisements.Media publishers22 provide their content to consumer users alongside of advertisements (e.g. banner advertisements) placed in the Web page advertising spaces.

Advertisers

14 can include companies who publish and promote products and/or services.Advertisers14 promote their products and services though, for example, advertisements onmedia publisher22 sites on, e.g., the World Wide Web. As an example, anadvertiser14 may be company such as Ford, American Express, American Airlines or Macy's with a product or service to sell.Advertisers14 use advertising campaigns and advertisements, such as Web banner advertisements, for product and service promotions. In this process,advertisers14 work withadvertising agencies16 to develop advertisements and marketing campaigns for their products and services.Advertising agencies16 represent theadvertisers14 and publish advertisements on behalf of theadvertisers14, for example, on Web sites such as themedia publisher22 Web sites.Advertisers14 can also work independently from anadvertising agency16 and publish advertisements, for example, on themedia publisher22 Web sites.

In the embodiment ofFIG. 1, theadvertisers14 and/oradvertising agencies16 may obtain advertising space ofmedia publishers22 through a “real time bidding” (RTB) exchange system for selling Web page advertising space. In such an embodiment,RTB bidders18 are organizations that mediate betweenadvertisers14 and/oradvertising agencies16 and theRTB Exchange20 in bid transactions formedia publisher22 advertising spaces. Other methods of obtaining space, other than RTB, may be used, and different entities may exchange information and advertisements. Examples of organizations that may beRTB bidders18 include, for example, companies such as inviteMedia, MediaMath and Turn.RTB bidders18 interface with one ormore advertisers14 and/oradvertisement agencies16 concerning specific advertisements and specific marketing campaigns. TheRTB exchange20 represents one ormore media publishers22 in the sale of advertising spaces toadvertisers14, either directly or as represented byadvertisement agencies16, via bid transactions. In such an embodiment, theRTB bidder18 operates a serving system26 and uses marketing segments to perform media optimization in placing the creatives or other advertising content it receives from either the advertisers or advertisement agencies.

Referring again toFIG. 1, data publishers24 (data sellers and/or other entities) may collect user access data concerning user's access Web sites or Web pages dedicated to or focused on a specific user interest, such as for example Web sites for “fishing equipment”, “chopper” motor cycles, or “minivans”.Data publishers24 sell or market user identification data and other parameter data related to the user's access of these Web sites. Theexchange system10 may use the user identifications and related parameters to define segments and place the users into segment groups.

Embodiments of the present invention may enableRTB bidders18 to receive tuned or optimized behavioral segments that are automatically updated by theexchange system10.

Hardware

An exemplary theexchange system10 may comprise a networked computer system, coupled to and communicating with computer systems of one ormore RTB bidders18. Theexchange system10 may also be coupled to and communicate with computer systems of one ormore data publishers24. Theexchange system10, in one embodiment, is further coupled to and communicates with a control/administration interface12, which is used to monitor and control the operations of theexchange system10.

In one embodiment, theexchange system10 may include a plurality of networked computers making up a server system. A system of networked computers is known in the art and many different types of computers and hardware configurations can be suitable for an embodiment of theexchange system10. The configuration of computers may depend upon the load of the data moving through the exchange system. In one embodiment the exchange system may be implemented on a standard personal computer or PC, e.g. with an UNIX/Windows-based operating system. Additional networked servers and processors may be used, e.g. depending on the load. The computer configuration, e.g., networked computer system, of theexchange system10 communicates with the computer systems of theRTB bidders18 and the data publishers24 (and users who access the data publisher Websites) via a communication interface such as the Internet or a dedicated interface (for example TCP/IP based). Such a communication configuration may employ a standard HTTP (Hyper Text Transport Protocol) interface which may be used by thedata publisher24 and/orRTB bidder18 to send or receive data. The standard interface may also be used when the exchange system updates the “cookie information” on a user browser when the user accesses a data publisher site.

Thenetworked computer system10 may include a database and database management system. Sufficient computer memory may be provided to store and manipulate computer data in generating the segments. For example, a standard dual CPU (or dual core) system with 4 Gigabyte memory may be used for processors in the system. Database storage and manipulation systems for high speed access to data are known and any number of known such systems such as MySQL™, Oracle™, or Microsoft SQL™ server can be used to implement embodiments of the present invention.

Data Exchange Overview

FIG. 2 provides an overview depiction of an exemplary RTB service flow, according to an embodiment of the present invention. The figure shows a number of process phases, with different steps executed or performed by different actors in an RTB media exchange system. In a system preferences/set upphase200, anRTB bidder18 may request optimizingservices205 and theexchange system10 may configure the system resident on theexchange system10 servers. In this phase theexchange system10 may pedant′ a data set up for theRTB bidder18. Such data configurations may be stored on the exchange system servers in adatabase370.

Users may access Web content and receive (via the users' computers) and transmit “cookie” data in two exemplary phases: a “User Session I”phase202 and a “User Session II”phase204. In the User Session I phase202 the user may receive “cookies” (e.g., to be located or saved on the user's computers) that a client, such as anRTB bidder18 can use to identify the user. Atstep52ainFIG. 2, auser50, operating a computer, opens a browser program, e.g. such as Safari™, or Windows Explorer™ or Firefox™ and accesses a site of adata publisher24, such as a Web site on the World Wide Web (WWW). The action generates a user request for content on theWeb server212aof thedata publisher24. As noted above,data publishers24 provide users access to Web sites having distinct focuses such as Websites for “bicycling” or “knitting” or “finance”. The discernable interests of these sites allow for a user to be identified with the interest. By agreement between the owners of theexchange system10 anddata publisher24, the user request for content transmitted by the user can be “redirected” to the servers of theexchange system10.

In one exemplary embodiment, theexchange system10 upon receiving such a re-direct request will download (e.g. from the database370) to the browser of theuser50, “cookies” that will associate the user with one or more of the segments maintained the exchange system. Theexchange system10 may, for example, place or save that cookie on the browser of the user in the name of theRTB bidder18 and show that the cookie is “for” theRTB bidder18. Thus, the user will carry a cookie “tagged” for the RTB bidders and will identify the user with a particular segment, such as “interested in finance”. The information “interested in finance” could be written within the cookies as “real” information (with for example “interested in finance” being spelled out in the “cookie”, such that the RTB bidder knows what the identified interest is. In an alternative embodiment, the information “interested in finance” could be provided virtually, such that the cookie only identifies the user with a segment identifier such as “ExchangeSystem Segment #5”.

In the UserSession II phase204, the user brings the “cookie” information to theRTB bidder18. Atstep52bthe user, again, operating a computer, opens his or her browser program and accesses a Web site. However, unlike the instance in “User Session P”202, where theuser50 accesses a Website of adata publisher24, in this case, theuser50 may access a Web site on aWeb server212bof a media publisher with frames dedicated for advertising. In one exemplary embodiment, the user request for content may cause a “re-direct” to occur (for a defined frame ‘hosting’ advertisement content). Such a re-direct will be directed to an advertisement serving system220 (e.g. within a servingsystem118 of an RTB bidder18). The re-direct will also “′forward” or transmit to theRTB bidder18 the user's “cookies” placed by theexchange system10 on the user's computer. The cookies identify the user with one or more segments of the exchange system. The segment information may be “real” or “virtual”.

The servingsystem118 of theRTB bidder18 may use the “cookie” information to target an advertisement to the user. Thereafter, if the user takes an action concerning the presented advertisement, such as by “clicking” on the advertisement to link to a Website pertaining to the advertisement, the Web coding of the advertisement may track the user's action. The information may identify the advertisement and identify the response, as well as other information such as time and location of the action. InFIG. 2 theadvertising serving system220 may store the user responses, compile data and provide reports to theexchange system10, for example, concerning the performance of the exchange system segments (e.g. —did the users identified with the exchange system segments respond?) Such information may be stored in a memory, e.g., indatabase222 of the RTB bidder18 (e.g. within memory319) and in database370 (within memory307) when received by the exchange system10 (seeFIG. 3). In another embodiment, theexchange system10 may receive the user responses directly (e.g. through a re-direct configured to route to the exchange system10). At224, theexchange system10 processes the information and performs the optimization process, updating the segment settings of theexchange system10 by, for example, combining or dividing segment combinations or otherwise altering them. The revised segments may then be used, for example, for tagging users in, e.g. thefirst user session202.

Exchange System Elements

FIG. 3 depicts a set of software and database elements for an embodiment of anexchange system10 with group or segment-optimizing. Within theexchange system10, anRTB server interface300 may be responsible for communication and interface with client systems such as the servingsystem118 of theRTB bidder18. TheRTB server interface300 may allow, for example, theexchange system10 to receive summary data, such as for example a performance summary of the segment combinations, aggregated, for example, per advertising campaign. On the RTB bidder-side (18), there may be an RTB/Exchange system module315 (or set of modules) that, for example, transmits data (such as summary user response data) to theexchange system10. As also shown inFIG. 2, the servingsystem118 of anRTB bidder18 may also operate anadvertising serving system200 which transmits advertisements to users as Web page content and collects user response data, which may be stored in adata base222. One ormore computer processors317 coupled to one ormore computer memories319 may be used in such an embodiment to execute the RTB/Exchange system modules315 and other elements, e.g.118,220,222. For example,processor317 may execute modules for theweb server212b,advertising serving system200 andRTB system315.Database222 as well as the computer code for

modules

212b,200 and315 may be stored inmemory319. InFIG. 3, theprocessor317 andmemory319 are depicted to encompass elements of the servingsystem118 as a whole.

Anadministrative interface module320 within theexchange system10 may be responsible, in one embodiment, for the administration aspects, user permission and access, client configuration (e.g., such as configurations with the servingsystem118 of the RTB bidder18), system administration and communication (e.g. with the control/administration interface12). The control/administration interface12 may provide in one embodiment, asystem administration module330, for system administration and status monitoring, and acustomer administration module340, for customer configuration and status monitoring. Within (or part of) thecustomer administration module340 may be anRTB administration module342 for performing RTB customer configuration and monitoring. Anothermodule343 may performdata publisher24 system configuration and monitoring. To execute the procedures of the control/administration interface12 in one embodiment, one ormore computer processors345 coupled to one ormore computer memories347 may be used. For example,processor345 may execute modules for the customer administration340 (includingRTB administration342 and data publisher administration343) andsystem administration330.Memory347 may store computer code formodules330,340 (including342 and343). InFIG. 3, theprocessor345 andmemory347 are depicted to encompass elements of the control/administrative interface12 as a whole.

Within theexchange system10, asegment planner module350 processes the collected information (e.g. within the boundaries of a system policy, such as the “tagging policy” described further below) and may create new groups or segments for output. To implement such processing, thesegment planner module350 may operate within, as part of, or in connection with anRTB system module365 in optimizing segments. Thesystem database370 may hold, for example, customer-provided information (“buyer” information), data publisher information (“seller” information), segment definitions, segment combinations and segment processing data, and may be accessed byRTB system module365 andsegment planner module350. In one embodiment, theRTB system module365 along with thesegment planning module350 may execute processes for analyzing user response data, performing combining, dividing and other alteration of the segments and other aspects of theUser Session II204 andPeriodic Update206 processes, such as those described with reference toFIG. 2. Also within theexchange system10, a dataformat mediator module310, as an optional procedure, may mediate, for example, between customer data formats exchanges and a system (generic) data format which may be used by theexchange system10, e.g. for formatting and maintaining segment information. In implementing elements of theexchange system10, one ormore computer processors305 coupled to one ormore computer memories307 may be used in one embodiment. For example,processor305 may execute modules for the RTB system365 (including segment planner350),administrative interface320, datapublisher system module321,user cookie update322 anddata format mediator310.Database370 and the computer coder for

modules

310,320,321,322 and365 (including350) may be stored inmemory307. InFIG. 3, theprocessor305 andmemory307 are depicted to encompass elements of theexchange system10 as a whole.

For interfacing with user access to the Websites of data publisher server systems, theexchange system10, in one embodiment, provides adata publisher module321 and a usercookie update module322. Thedata publisher module321 may for example receive requests (e.g. on redirect) from users who have accessed adata publisher24 Website. Thedata publisher module321 may also access thedata base370 to determine one or more segment identifications for the user commencing the request. The usercookie update module322, in one embodiment, may set the user's “cookie”information323, including for example, cookies that contain a segment identifier for an RTB bidder. Thedata publisher24 may communicate with users via theweb server212aand with theexchange system10 via a data publisher/exchange interface317.FIG. 3 also shows aprocessor318 and amemory319 for the system of thedata publisher24 to execute its computer processing. For example,processor318 may execute modules for the data publisher/exchange module317 andweb server212a.Memory319 may contain the software code for

modules

212aand317. InFIG. 3, theprocessor317 andmemory319 are depicted to encompass elements of the computer system of thedata publisher24 as a whole.

The different parts of theexchange system10 communicate using various known methods, for example using standard LAN/WAN communication techniques and hardware. Inter-communication between the modules may be LAN based; in other embodiments other communications methods and systems may be used. In one embodiment, for example, the communication with customer systems for the data transfer, such as theRTB bidder18, may be over the Internet (e.g., a WAN). Both theexchange system10 and system of theRTB bidder18 may use, for example, TCP/IP-based protocols. Theexchange system10 may also receive information, such as user response data, and communications with an RTB bidder.18 (and data publishers24) via standard HTTP (Hyper Text Transport Protocol) interfaces. Other communications methods may be used.

The procedures in the exchange system10 (see, for example,202,204 and206,FIG. 2) may be ongoing processes that analyze the customer's new data (e.g. user response data from the RTB bidder18) and identify users in new, optimized segment combinations. Due to the amount of data and processing needs presented by those tasks, multiple concurrent modules such as those depicted inFIG. 3 may be used and may be implemented on more than one server, using for example more than oneprocessor305 and more than onememory307. Thus, theexchange system10, presented in one embodiment, may comprise a plurality of severs with which, for example thedatabase370 and segment-related processes (350,365) operate.

User Tagging Details

For each client, such as anRTB bidder18, theexchange system10, for example during the User Session I process (202) may tag a user with cookies to identify the user for a client such as theRTB bidder18, with a segment definition maintained on thedatabase370. All of such data may be stored in memories, such as indatabase370 within memory307 (SeeFIG. 3). Such a tag may be called a “buyer's tag”.FIG. 4A depicts an exemplary process for anexchange system10 to receive a user request, re-directed from adata publisher24 Web server (e.g.212a) and provide the user with cookies to associate him or her with a segment for theRTB bidder18. InFIG. 4A, theInternet user50 is a common person, for example, using ahome computer51 connected to theInternet54 and using abrowser program52 to search for Web sites. Thecomputer51 may comprise aprocessor56 to operate thebrowser program52 and amemory57 to store the code for the browser program52 (inFIGS. 4aand4b

processor

56 andmemory57 are depicted to relate tocomputer51 as a whole). Theuser50 may open thebrowser52 and accesses a Web site, by, for example typing for example the Universal Resource Locator (URL) of a Web site of one of the data publishers24 (see alsoFIG. 1). Theuser50 in this example intends to open aWeb page100 of a Web site which has information concerning a specific subject, such as fantasy baseball, so that by accessing this site adata publisher24 can associate this user as “interested in baseball”.

Through, for example, an agreement between the owners of theexchange system10 anddata publisher24, the Web site of the data publisher24 (e.g. onWeb server212a) may be arranged to re-direct user requests content requests to theexchange system10. For example, the code (e.g. HTML code) for the Web page may contain a code, such as for example a known “HTML re-direct” code, to make user'sbrowser52 request re-direct to the servers of theexchange system10. It is known to set up such a request by having the data publishers'Web page100 require some content, for example, one pixel of content from theexchange server10. In this scenario, the re-direct130 creates a request on theexchange system10 to send the pixel content information to thebrowser52. Once this request is accepted, for example, by the data publisher interface321 (FIG. 3) of theexchange system10,segment information140 may be written to the client cookies under the RTB bidder Web site space (more then one buyer cookie can be set at this session). Thecookie53, containing information, may identify theRTB bidder18 and may also provide data to associate the user with a segment (e.g. an initial segment or later an optimized segment) that corresponds to this user interest. Such a cookie may be written, using standard techniques for writing or creating “cookies” (e.g. text to a file with delimiters, in a format according to the data buyer).

On thecomputer51 of the user, the cookie may be stored in thememory57 and can be accessed for example, when running thebrowser program52. “Real” or “virtual” information may be written to the cookie. The cookie, once written to the computer of the user, may associate the user with the segment for his or her subsequent access to any RTB bidder-controlled Web page advertisement space. Other methods and devices of associating a user with a segment may be used.

Collecting User Response Data

FIG. 4B depicts an exemplary process for obtaining segment information by theRTB bidder18 andexchange system10 from a user's access to an advertisement on the Web site of amedia publisher22. The user50 (e.g., the person using thehome computer51 connected to theInternet54 and using the browsing program52 (stored inmemory57 and operated, e.g. by processor56) accesses aWeb page105 of a Web site run this time by amedia publisher22, by, for example, typing in the URL of a selected site of themedia publisher22.

As part of the site content (e.g. the content of theWeb page105, written in HTML for example) aframe122 may be allocated to advertisements (for example by themedia publisher22, the site owner). Theframe122, in this example, has been programmed with for example a HTML re-direct procedure that, for example, may have been provided by an RTB Exchange20 (see alsoFIG. 1). TheRTB bidder18 made (and won) a bid for the frame as advertising space through theRTB Exchange20. In such an example, the redirect then redirects500 to theRTB bidder18, who obtained the space. TheRTB bidder18 may supply the content for the advertising space,content user50 ultimately sees. The redirect comes to theserver118 of theRTB bidder18 as a request for frame content. Other redirect or advertising methods may be used with embodiments of the present invention.

Once the request arrives at theserver118 of theRTB bidder18, processors connected to theserver118 may analyze the “cookie” or other information related to the request. Thecookie53, e.g. stored inmemory57 were read by thebrowser program52 and transferred to theserver118 as part of the attempt to access the Web site of themedia publisher22. In this example, this cookie information (shown at53) contains the segment identification as was provided inFIG. 4A.

The cookie information may be used by theadvertising serving system220 of theRTB bidder18 to determine what advertising campaign and advertisement may be assigned to theuser50. Other advertisement assignment methods may be used. According to this determination, specific advertising campaign content (a specific advertisement, e.g.510) is returned to the frame area (frame122) of theWeb page100. TheRTB bidder18 may document the data provisioning, making a record of theadvertising campaign content510 that was sent to theuser50.

If and when theuser50 clicks (see520) on the advertisement (e.g., using a pointing device to move an on-screen cursor and indicate that the advertisement is selected or accessed) onframe122, to link to anew webpage540, part of that link, for example, part of the HTML link command, may include a redirect (see530) to theserver118 of theRTB bidder18. The redirect may inform theRTB bidder18 that theuser50 made a response for this particular advertisement (e.g. it may report that the user “clicked”). TheRTB bidder18 documents this information, for example, storing a record of it on theserver118. Measures of success or response other than “clicks” may be used. The serving system of the RTB bidder18 (e.g. via the RTB/Exchange module315), will transmit data in summary form from this user's access and others to theexchange system10. Optionally, as mentioned above, all user requests (e.g.500,530) may, in an alternative embodiment be routed to theexchange system10, instead of the servingsystem118 of theRTB bidder18. Such routing may enable theexchange system10 to perform optimization in real time.

Segment Definitions/Database

Theexchange system10, in tagging users with segment or group identifications may use segment combinations based on definitions maintained in a table (e.g. a segments definitions table) in thedatabase370, which may be stored in a memory, e.g.,memory307. To create the segments or groups, theexchange system10 may collect data, and such data can come from for example data publishers24 (data sellers) (FIG. 1), although other sources may be used.

For example, segments might concern subject areas such as sports or music. A segment can be specific, such as for example segments concerning interests in subject areas like basketball or jazz. Segment definitions can include segments of different types, including combination (compound) segments. As an example a group or segment may contain:


	(1)Jazz
	(1.1) does not relate at all
	(1.2) rare
	(1.3) occasionally
	(1.4) Intensive

Such a segment provides a subject such as jazz and provides parameters concerning jazz interest, identifying a user's interest in jazz as “intensive” for example. Specific users are associated with the parameters. This jazz interest segment may be an example of a “behavioral” segment and can be combined with other types of segments, e.g. user location, or user age segments to form segment combinations.

A combination (e.g., compound) segment may include segments grouped together according to an analysis process, such as, for example, the grouping of the segments ‘Intensive Jazz’ and ‘football fan’ into a combination.

The operator of the exchange system may generate segment combinations to be used for “tagging” users. The segment combinations may include behavioral segments.

The structure of the behavioral segments table in one embodiment has the form (other forms may be used, and other content may be used):


	Data Buyer

Behavioral Segment-1

User Parameter-1,1

Parameter Value-1,1,1

. . .

Parameter Value-1,1,n

. . .

User Parameter-1,n

Parameter Value-1,n,1

. . .

Parameter Value-1,n,n

Behavioral Segment-2

. . .

Behavioral Segment-n . . .

	(non-behavioral segments can be included, instead of or along with
	behavioral segments)

For example, a behavioral segment combination based on users that “get updated with news every 3 hours or more” and “go to sport event every week or more”. for example:


	User Parameter-1 = Exposures to sport events

	Parameter Value-1,1 = daily basis
	Parameter Value-1,2 = weekly
	Parameter Value-1,3 = daily Monthly
	Parameter Value-1,4 = monthly basis
	Parameter Value-1,5 = rare

User Parameter-2 = Frequency of news exposure

	Parameter Value-2,1 = every 3 hours or more
	Parameter Value-2,2 = 3 hours to 1 day
	Parameter Value-2,3 = 1 day or more

Other structures for segments or groups, and other data, may be used. Additional segments definitions, such as behavioral segments, e.g. “likes to swim”, or “surfs the Web in late evening” may also be added. Additional parameters such as “status” can also be maintained as part of the behavioral segments table structure, such as can be seen in this further example:


Column	Description	Example

Segment ID	Identification of the segment	3344556
Minimum value	Value range of thissegment	1
Maximum value		5
Creation date	The data that this segment was	13 Apr. 2010
	defined
Status	Usage status of this segment -	Active
	Active/Not Active

The above example shows the record values for the “User parameter 1” above.

Theexchange system10 may identify each user with one or more segments. For example, when a user accesses a Web site of a data publisher, each user request may be mapped to the relevant segments according to the user profile and collected data. In one example, a single user request for advertisement data can be mapped to tag 3, 7 (age 23-75, like music) or any other combination. InFIG. 5, one user request for content data from adata publisher24 Web site may, for example, be mapped to tag 3,7 (age 23-75, likes music) (seeFIG. 5 at650,655). Other combinations for mapping are possible.

Service Request Details

An operator of a serving system, such as for anRTB bidder18 may execute a service request (e.g.205,FIG. 2). A service request from a client, such as anRTB bidder18 may include setting parameters for the cookies definitions that theexchange system10 will use to “tag” users (and thereafter the buyer (RTB bidder18) will received and process). The service request may also include additional (optional) settings for the segments that the buyer (RTB bidder18) would like to use (such as for example filtering). The following parameters may be used (other parameters and other structures may be used):


Name	Description

Minimum calculated	the segment variety(as calculated by the
variety	server while creating/tracking the segment)
Maximum segment age	Maximum number of days (segment age) allow
	for this bidder
Maximum number of	maximum number of supported segments (for
segments	single user)
Segment KEY	Identification range (for example 10000 . . .
identification range	19999)

Following the transmission of a service request (205,FIG. 2), theexchange system10, may perform a system configuration (210,FIG. 2), where the exchange system operator may set parameters and variables for the optimization analysis. For example, the exchange system operator may perform a data set up. Values may be set, for example, to use compound segments with variety better than “XXX” percent.

In data setup, various data points may be set based on, for example, default values or on creative-specific data. In one embodiment, data points can be set such as:

- Min (Minimum) Decision Data: A value which may reflect what is considered a high enough volume to make a decision. This may be based on a few data points, such as for example:
  - Min (Minimum) Qualification Volume: A value which may reflect what is the minimum needed amount of data for each behavioral segment to determine the quality of that segment for a given creative or other advertisement. For example this value can be 25 clicks which means that when the users of any of the behavioral segment generate a total of at least 25 clicks on the creative, the segment quality is reached for that creative.
  - Min (Minimum) Qualification Level: A value which may reflect what is the minimum needed performance level to define a given behavioral segment as qualified. For example the MinQualification Level can be a CTR (where CTR may be a value reflecting the percentage of those clicking on a link out of the total number who viewed the link or text advertisement) of at least 0.5%.
  - Min Qualified Segments for Optimized Run: A value which may reflect what is the needed amount of behavioral segments that where identified based on the above parameters as qualified segments to start the optimized run.

In one embodiment, data setup can happen based on a number of methods, such as by sending data from theserver system118 of theRTB bidder18 directly via the serving system based on a protocol such as XML. This direct sending could occur, for example as an automatic process where the data is located and sent upon the transmission of the service request. Additionally, the data set up could occur manually, such as for example, the data being entered manually once and/or adjusted for every new creative.

User Session II (Learning Runs/Testing)

TheRTB bidder18 may need to identify, for each new advertising campaign, the segment combinations or groups that most efficiently maximize (or combinations or groups that maximize to a certain standard, possibly not the most effective degree) the conversion ratio for the advertisements of the campaign. In deciding segment combinations to choose and rely upon in placing advertisements, anRTB bidder18 may test an advertising campaign and its related advertisements (and through this test the exchange system can optimize by “learning” optimized segment combination configurations) For example, assuming a campaign budgeted for 1,000,000 exposures or impressions of campaign advertisements on various Web pages, theRTB bidder18 may, for example, allocate as smaller amount, say 5% of the exposures or impressions (50,000) as testing data to find out what may be the best segment combination.

The advertisements in tests may be exposed to users who can be associated with one or more segment combinations. For example an advertisement could be tested to users associated with one segment, such as, Segment 1: “male”, “age: 25-30”, “likes to travel”. The advertisement can also be tested to users associated with another segment, Segment 2: “female”, “female: 25-30”, “likes to travel”. Based on the results of the testing, theRTB bidder18 may find that the particular advertisement has a better response with users inSegment 1. The remaining exposures may be routed to what are identified as the most effective segment combinations (e.g. those segment combinations from the tests which are thought to provide the highest conversion ratio). The determination of the most effective combination of segments may be made according to the constraints, such as available time frame for the campaign, income model, expected income, existing alternatives and other factors.

In order to determine the best or an optimal segment combination, theRTB bidder18 may use, as one method, a segment matrix. Servers employing such systems may generate different combinations of segment definition parameters. For example, from segment or group definitions may have a structure such as:


			(4) Exchange
(1)Age	(2)Gender	(3) Time of day	Server Segments

(1.1) 10-15	(2.1) Male	(3.1) Morning	(4.1) Seg 1
(1.2) 16-25	(2.2) Female	(3.2) Noon	(4.2) Seg 1
(1.3) 26-39		(3.3) Night	(4.3) Seg 1
(1.4) 40-120			(4.4)Seg 1

Segments from theexchange system10, such as the segments from column (4) in the example above, can be included in the segment combinations. For example, a “media optimization” algorithm may have identified these segment combinations, based on data previously collected from user requests for advertising content. For example, the calculations may have been computed “new” for each campaign, without old data, for example, from historic campaigns. However, segment combinations may have been identified in other ways.Exchange system10 segments can include behavioral segments, alone or in combination with other segments. The segment matrix of the servingsystem118 of theRTB bidder18 may create a list of combinations such as the following and allocate exposures to each one:


Segment Combination	Exposures

1.1 - 2.1 - 3.1 - 4.1	<number of exposures n the campaign> *
	<percentage allocated for the test>/<number
	of combinations>
1.1 - 2.1 - 3.1 - 4.2
1.2 - 2.1 - 3.1 - 4.1
. . .

In the example above, each combination may be tagged with or stored with, or have added to it, a unique ID and document (e.g. a file or database record) to allow analysis of the responses. As the servingsystem118 of theRTB bidder18 performs its combination tests (exposing the advertisement to identified groups), it may collect user response data. Each user who carries cookies can be mapped to a one of the segment combinations, and can be served an exposure to the advertisement.

TheRTB bidder18 may collect the responses and summarize them, for example, to report to theexchange system10 concerning the responses gathered from users carrying the exchange system segments. An automatic interface in one embodiment allows theRTB bidder18 to report on segment behavior, such as hourly, daily or every week. The summary report may, include records showing for example, an advertisement ID, a segment ID, a count of the users who participated or provided responses and a conversion ratio for the advertisement based on the participants. Other data may be included. Such a report may have a record format such as:


Field	Explanation

Campaign unique ID	Identifier of the campaign
Segment identification	The segment identification, provided by the
	segment-providingsystem 10 that was used
Conversion ratio	A numeric value of conversions
Group size	Number of responses actually counted

Following the format above, responses for one advertisement may look like the following:


	Advertisement	Responses

	ID	Segment	Percentage	Group Size

1	1	0%	123
	2	0.005%	22345
	3	23%	12
	4	0.04%	0
	5	1%	0
	6	0	0
	7	0	121
	8	0	333
	9	2%	432
2
3

The example above may be transmitted to theexchange system10 from the servingsystem118 of theRTB bidder18. In some embodiments, a group size value may indicate or show the number of users with the segment exposed to the advertisement. In some embodiments a value may be included indicating or showing the percentage of users who responded to the advertisement.

Embodiments of the present invention may further provide for a “complete” or full data producing system, where all transactions are routed thorough theexchange system10, such as through the RTB system module365 (FIG. 3) and where they are not routed through the servingsystem118. In such an embodiment, user requests for content and user responses may be routed through theexchange system10. Using this method, theexchange system10 can receive all information that pertains to a segment and can perform optimization in real time. In such an embodiment, theRTB system module365 may process without any statistic report from the RTB bidder18 (data buyer).

Optimization Process Details

After receiving data from a servingsystem118, theexchange system10 may analyze the data and create new, optimized segment or group combinations or groups of the exchange system-provided segments, such as by combining segment combinations, or by dividing or altering a segment combination. One goal of segmentation is to find large groups of users who respond consistently and reliably to advertising. It is currently thought that the best sets of behavioral segment combinations are ones that have the highest probability of identifying high performing users for any campaign. In performing segment optimization, theRTB system365 may call or operate in conjunction with asegment planner module350. (SeeFIG. 3).

FIG. 6A depicts exemplary processing threads that may be used, for example, within asegment planner350 of the RTB system365 (FIG. 3) to implement segment optimization, such as combining segment combinations and dividing or altering a segment combination. InFIG. 6A, a first process (e.g., “Thread I”)600 when executed by a processor (e.g.,processor305,FIG. 3), may analyze the test data received concerning an advertisement and prepare that data for combination and/or division/alteration processing. The first process may use this data to identify those segment combinations that were the most “successful”, such as, for example, if the segment has conversion ratio data that shows that the segment in question gave the advertisement a response level (e.g. conversion ratio) that was above a certain, pre-designated minimum performance lift value. The first process (“Thread I”)600 may mark a particular segment combination that achieved a high response as “approved for qualification”. Such “approved” or “qualified” segment combinations may, for example, be combined with other segment qualified combinations to form “unified” combinations, as is described below. Other combinations, such as for example combining “qualified” and non-qualified segments are possible.

A second thread (e.g., “Thread II”)602 may when executed by a processor (e.g.,processor305,FIG. 3), seek, for example, to examine all identified “qualified combinations” (e.g. a segment combinations marked as “approved”) and then seek to group such “qualified combinations” into combined segment combination groups (e.g. “unified combinations”). In one embodiment, qualified combinations of related advertising of a multitude of data buyers could also be grouped and searched for unified combinations. For example, all data received from all data buyers (e.g. many RTB bidders18) may be processed together.

In an exemplary embodiment, a unified combinations table may hold data concerning the grouping or combining of qualified segment combinations into unified segments. A unified combinations table may hold the aggregated data from the qualified combinations of all advertisements currently being processed at a current point in time. In one exemplary embodiment, the second processing thread (e.g. Thread II)602 may compute a “grade” or score representing the quality of a given unified combination as a potential behavioral segment. Such a grade may show what quality of behavioral segment would have that is based on this combination and potential unified combinations can be deleted or kept off the unified combinations table based on this grade.

A third thread in an exemplary combining process (e.g., “Thread III”)604 may, when executed by a processor (e.g.,processor305,FIG. 3), update the current set of behavioral segments being used for a given advertisement according to the new unified combinations. In such an exemplary process, the thread may determine matches between ones of the current behavioral segments and the unified combinations. A match might be found, for example, for a unified combination that has the highest percentage overlap between users included in the behavioral segment and the users included in the unified combination. When a match is found, theexchange system10 may compare the entries from the united combinations table with the current set of behavioral segments, e.g. the set of initial segments, and update and/or delete the current behavioral segment definitions based on the comparisons.

An exemplary embodiment may employ two additional threads for segment dividing and altering. One thread (e.g. a “Thread IV”)606 may, when executed by a processor (e.g.,processor305,FIG. 3), examine user response data (e.g. data from the buyer report table) and identify segment combinations which may be candidates for dividing or other alteration. For example, such a thread may identify segment combinations that have low Var values (e.g. low lift variation) but have a minimum response volume (e.g. include reported data from at least “X” number of users). Other dividing criteria are possible. For those segment combinations that are identified as “candidates” for division, the thread may analyze the data base records that include the sub information of the segment to determine if it may be possible to divide the segment combination in question into two or more other segment combinations. For example, assuming a behavioral segment combination “X” has poor results and the combination carries an age segment parameter, e.g., Age: 20-30, theexchange system10 may add or link to the segment (if the other segments parameters are available or creatable), smaller age group segment parameters, for example “segment A: 20-24” and “segment B: 25-30. Theexchange system10 may also receive additional user data on the segment combination and may process the user response data according to both the “existing” larger age segment parameter of 20-30 and also to “candidate”, smaller, segment parameters of 20-24 and 25-30. After a pre-determined time period, theexchange system10 may evaluate the results comparing, for example, the results of the segment within existing segment parameter and candidate segment parameters, and determine if the segment in question should be divided.

For example, a subsequent thread (e.g. a “Thread V”)608 may when executed by a processor (e.g.,processor305,FIG. 3), compare the segment combinations in a divided combinations table with the current behavioral segment combinations and update, replace or change the current segment combinations.

Optionally, when a unified combination (e.g. created in the combining process described above) is not performing well, that unified combination can also be broken again into its constituent approved combinations. Segments combinations which are candidates for division may be identified and stored on a table (e.g. a divided/altered combinations table). As data is further collected (using the original, non-divided segment, for example) that data can also be applied, as applicable, to the “candidate” segment possibilities. In one embodiment,Thread IV606 may also identify segment “candidate” combinations, e.g. to the operator of theexchange system10, in a report.

FIG. 6B depicts exemplary processing threads e.g. threads I-V (600-608) and shows data tables that may be used within thedatabase370 during such processing in one embodiment.

Indatabase370, an initial set of behavioral segment or group definitions for a client, such as anRTB bidder18, may be found in a behavioral segments definition table610, where, for example, behavioral segment definitions may be stored (e.g. in tree form). Such tables may be stored in a memory such as, for example, memory307 (FIG. 3). Example embodiments of structures for a behavioral segments definition table610 are described herein (see, e.g., section: “SEGMENT DEFINITIONS/DATABASE”).

The initial set of segments or groups can be set based, for example, on manual definitions of the exchange system operator. The segment combination data of an exemplary behavioral segments table610 may also be based on events data, such as data provided by a data publisher24 (or data seller) or events data previously received from the same client or from other clients (for example in a system that permitted data sharing between clients). Optionally, the initial set of segments may be based on data provided by the servingsystem118. InFIG. 6B, a native segments table620 may hold definitions of user parameters as provided by, for example,data publishers24. The exemplary native segments table620 may have a structure such as (other structures and other data may be used):


	Column	Description

	Parameter ID	Identification of the segment
	Publisher ID	Value range of this segment
	Data range	Min-max (minimum-maximum) values of the data
		provided by this publisher

Information concerning users from the native segments table620 may be referenced in the behavioral segments definition table610 through relationships in the behavioral segments values definition table615, which, in one embodiment, has a structure such as (other structures and other data may be used):


Column	Description

Behavioral Segment ID	Identification of the segment
Behavioral Segment Value
Native Parameter id	Pointer to the source parameter (native)
	that received from the data publisher
Native Parameter Range	Value range that mapped to this segment
	value
Creation Date	The date that this parameter was defined
	for the first time
Status	Usage status of this segment - Active/Not
	Active

With regard to the

processing threads

600,602,604,606 and608,FIG. 6B also shows a buyer report table640. In one embodiment, the buyer report table640 may contain user response data received, for example, by a servingsystem118 of anRTB bidder18. Examples of user response data sent from a client, such as anRTB bidder18 are provided herein. Such data may be mapped to a record in the buyer report table640 with a format such as (other structures and other data may be used):


Column	Description

Date	Data origin date
Buyer id	Identification of the buyer(e.g. serving system or
	RTB Bidder 18)
Campaign ID
Lift
Var
Volume
Qualification Status	Status of this record:
	Not processed - reported by the buyer but
	was not evaluated by the system (yet)
	Approved for qualification - this record
	should be used for qualification
	Rejected for qualification - this record
	should not be used for qualification
	Processed - this record has been processed
	by the system.(has been qualified)
Division Status	Status of this record:
	Not processed - reported by the buyer but
	was not evaluated by the system (yet)
	Approved for Division - this record should
	be used for Division procedure
	Rejected for qualification - this record
	should not be used for division
	Processed - this record has been processed
	by the system.

In embodiments where live user response data comes to theexchange system10, a buyer report table640 may also include individual records of user responses to test advertising which are then further compiled to segment data for analysis (such as the summary data provided by a serving system). For such embodiments, it is noted that each user with a User ID can belong to between 0 to N behavioral segments. In one embodiment that incorporates theexchange system10 receiving live user data, rather than (or in addition to) summary reports from a servingsystem118, the User ID (or other data) may be used to connect the data in the buyer report table640 with the data in native segments table620. In other embodiments other linking data items may be used. For example, in another embodiment User ID may not be kept and the system can link to data that tracks the number of users who have matched to conditions belonging (or defined by) the segments making up the combinations.

Data from the buyer report table640 may be used in an optimization process for combining, dividing or otherwise altering segment combinations. Threads I and IV, for example,600 and606, may use the buyer report table640 entries for tagging segment combinations as candidates for combining or as candidates for dividing/alteration. In one embodiment, segment combinations which are identified as candidates for combining are further identified as “good” or “qualified combinations”, or are given other descriptions. A qualified combination may have a status that is “approved” and in further processing may be combined with other qualified combinations to create a “unified combination.”

A unified combinations table650, a unified combinations members list table652 and a certified unified combinations table653 may be used, for example by thread II602, for creating unified combinations, which may be combinations of qualified segment combinations. Thread IV may also access the unified combinations table650 and the unified combinations members table652 (e.g. if acting to divide a unified combination).

In one exemplary embodiment the unified combinations table650 may have the following structure (other structures and other data may be used):


	Column	Description

	Date	Data origin date
	Unified combination id	Identification of the record
	Grade

and the unified combinations members table652 may have the following structure (other structures and other data may be used):


Column	Description

Unified combination record id	Identification of the parent record
Qualified record id	Single member of this unified record

For optimization involving dividing and altering segment combinations, a thread IV may access a divided/altered combinations table655 and a divided/altered combinations members list table657, for example for identifying segment combinations in the buyer report table640 that may be candidates for segment dividing or altering. An exemplary structure for the divided combinations table655 may be (other structures and other data may be used):


	Column	Description

	Date	Data origin date
	Divided combination id	Identification of the record
	Request Type	Operation type:
		Remove Segment
		Add Segment

and an exemplary structure for a divided combinations member list table657 may be (other structures and other data may be used):


Column	Description

Divided combination record id	Identification of the parent record
Qualified record id	Single member of this unified record

FIG. 6C depicts exemplary data relationships between: 1) the divided combinations tables (655,657) and the buyer report table640; 2) the unified combinations tables (650,652) and the buyer report table640; and 3) Behavioral segments definition table610 and theNative segments680 and behavioral segments values table670. At664 and665, ID entries or other entries in the divided contributions members table657 may provide associations so that a record (676) in the buyer report table640 can associate or be associated with a record (672) in the divided combinations table655. The divided combinations member table record keys at664 and665 provide the link.

InFIG. 6D,

ID entries

668 and669 in the unified combinations member table650 provide an association so aqualified combination record676 can be included as aunified combination674.

Behavioral segment information may also be associated in this same way. For example, to fieldentries670 and671 in the behavioral segments values table615 may enable that entry to associate abehavioral segment definition678 with anentry680 from the native segments table620. Other ways of associating records in a database, however, can be used.

In operating exemplary threads I-IV600-608, thesegment planner module350 may use a number of parameters concerning the operation of the threads600-608 and the data tables610-657. For example, an exchange operator may have a user interface (UI), which is part of thesegment planner module350, and which may be executed by, for example, processor305 (see, e.g. administrative interface320), and the operator may set parameters such as:

- Minimum Segment CV (Coefficient of Variation) which may be the minimum value that will allow a definition of a segment.
- Minimum Behavioral Segments Unified Combinations % Overlap;
- Minimum Qualified Combinations % Overlap, which may be the minimum variation overlap between the two segments that can be counted;
- Num of Combinations Grade Array[ ]—a array holding a grade for each possible number of qualified combinations in a certain unified combination;
- Minimum Pre User Contribution Grade;
- Minimum Post User Contribution Grade;
- Maximum Num of Segments—the maximum number of behavioral segments that the specific Serving System supports;
- Minimum Segment Size—the minimum number of exchange users that a behavioral segment can have;
- Number of Segments, Minimum Segment Size;
- Value Volume %—what percentage of the users is each parameter value relevant to. The value volume percentage is calculated based on the number of monthly unique users with that value that theexchange system10 counted in the last month;
- Value Volume % Reduction—how much to reduce the weight of the data of that creative for every 1% below X %. The system may also support non linear values;
- Creative Age Reduction—how much to reduce the weight of the data of that creative by age in days. The system may also support non linear values;
- Desired Creative Age Cut Off—From which age of creative data the algorithm should ignore the data of that creative;
- Consistency Reduction—for each creative that thesegment planner350 has enough data to calculate the P-Value for two different time intervals that are at least N days apart from each other, it compares the two P-Values (N is set by the Exchange Operator). Based on the difference between the two P-Values a consistency reduction value is set. It can be set to 100% (=no reduction) or values which are below 100% (=there is reduction) or values which are above 100% (there is a lift). The transformation between the P-Values difference and the consistency reduction value is based on pre-set table that its parameters are controlled by the exchange operator;
- Other Data Buyer Reduction—How much to reduce the weight of the data from creative that run in the same serving system but with different data buyers/clients;
- Other Serving System Reduction—How much to reduce the weight of the data from creative that run in different serving systems and different data buyers/clients;
- Segment Editing/Updating Grade—from which Segment Grade the Behavioral Segment should be edited (meaning that below that grade the segment/combination is not changed); and
- Segment Deletion (de-activating) Grade—from which Segment Grade the Behavioral Segment should be deleted (meaning that below that grade the segment/combination is (de-activated, put out of used, archived, deleted after a time or otherwise status-edited).

Thread I: Qualified Combinations/Buyer Report Table

Thread I600 e.g., of thesegment planner350, may identify all “qualified combinations” for a creative by analyzing the recent received user response data and finding, for example, those segment combinations that received a response level above a value for the minimum number of users reported in this group. The may occur, for example, if the “results” (response ratio) for a certain segment were above the minimum value.

In one embodiment, e.g. where theexchange system10 operates as a computer server, user response data concerning an advertisement that was exposed to a plurality of computer users operating computers connected to the computer network may be received. The users may be identified, connected or associated by the identities of their computers to one or more of a plurality of segment combinations based for example on the respective computers' previous accessing of a data publisher25 Website. The user response data may provide a user response rate value indicative the responsiveness of the users to the advertisement, where the advertisement may have been transmitted to the computers of the users for exposure to the users from an advertising serving system (e.g. the servingsystem118 of an RTB bidder18) connected to a computer network according to requests generated from a browser program operated by each user (see e.g.52,FIG. 4B). Thread I600 in one embodiment, identifies from the user response data, segment combinations having achieved a user response rate surpassing a pre-determined value measured according to one or more user events.

A qualified combination may be indicated as approved with a field marked for the segment combination in the buyer report table640. A record in the buyer report table640, following the example structure set forth above, may contain the following attributes (other structures and attributes may be used):


	Data Buyer
	Creative
	Qualified Combination-1

	Behavioral segment-n, Parameter value-a
	Behavioral segment-m, Parameter value-b
	. . .
	Lift - for this creative, what is the average response rate that this
	combination got for a particular advertisement
	divided by the global average response rate.
	The response is based on the creative's Optimized Event (e.g.
	clicks).
	Var - Lift variation is a statistic defining the variability of the
	lift.
	(i.e. Coefficient Of Variation (CV) - the CV)of the data of this
	qualified combination with this creative
	Volume - what are the number of the “cookied” users that have
	fit with this combination.

Qualified Combination-2

. . .

	Qualified Combination-n . . .

For example for a certain creative a qualified combination might be all users who are Hispanics, between the ages of 18 to 34, and live in the west coast of the United States. Other combinations, using other modalities, are, of course, possible. One example of a qualified combination record is:


	Creative = x
	User Parameter-1 = Ethnicity

Parameter Value-1,1 = Hispanic

User Parameter-2 = Age

	Parameter Value-2,1 = Age Group of 18-24
	Parameter Value-2,2 = Age Group of 25-34

User Parameter-3 = Geo Area

Parameter Value-3,1 = USA, West Coast

	Lift = 78%
	Var = <24%
	Volume = 1.34 million users

FIG. 7A depicts an exemplary process flow for an exemplary segment combination approval procedure (e.g. a “Thread I)600 in an embodiment of the present invention. Instep710,thread600 begins an on-going loop or iteration. The first part of this loop identify the advertisement or other creative that has the largest amount of new optimized events since the last time it was analyzed. In this identification procedure,thread600 may give an advantage, e.g. X % percentage advantage, to an advertisement that has not yet been analyzed. Instep720,thread600 begins an inner loop, such that, for the identified advertisement, the thread identifies combinations where:

- The number UU (e.g. Unique Users, such as the number of users that were exposed to this content) they define is greater than (>) Minimum Segment Volume (step730); and
- Lift is greater than (>) Minimum Segment Lift+K*F(Lift Variation) (step740) (where K is a parameter that may be used to tune the condition according to the actual system behavior on a “real life” situation).

Other combinations may be identified using, for example, other variables. Instep750,thread600 may loop or iterate to identify all such combinations passing the above tests. Each record in the buyer report table640 identified bythread600 as a “qualified combination” (e.g. according to rules described above, such as for example if the volume is bigger than 10,000 and the “lift” is high) will be tagged as “approved for qualification”. Instep760,thread600 may delete qualified combinations with a date less than (<) the creative cut off date. Instep770,thread600 may loop or iterate and return to the first step (710) of the on-going loop.

In one embodiment of the “Thread I”process600, each report received from a client/data buyer, such as for example the servingsystem118 of anRTB bidder18, is inserted into the buyer report table640. Optionally, asthread600, processes, it may also calculate the “Var” (e.g. variable lift) field for each recorded in the buyer's record table. It may further mark each new record as “not processed” for division (which enables later processing by threads IV and V (606 and608). Ifthread600 determines that a record is not suitable for qualifying, theexchange system10 may keep it (for historical and/or statistical later analysis). However, in an exemplary embodiment,thread600 will tag such a record as “rejected for qualification”. Obsolete records, such as “rejected” records and “approved” combination records that are out of date, as mentioned above, may be deleted.

Thread II: Unified Combinations

Thread II (602), in one embodiment, may analyze the buyer report table640 and based on that analysis may update the unified combinations table650 that holds the aggregated data from the qualified combinations of all the relevant advertisements at the current point in time. In such an embodiment, unified combinations may be calculated using the combined data of many different clients/buyers (e.g. all clients (data buyers) running advertisements to users identified with the same exchange system segment combination at a point in time). In other embodiments, the unified combinations may be calculated separately for each specific data buyer/client

As described herein, the unified combinations table650 (e.g.FIG. 6B) may hold data for all clients or for one client. Following the exemplary table structure described above, entries in the unified combinations table650 may have the following form (and other data may be stored):


	Unified Combination-1

	Source Buyer
	Qualified Combination-1

. . .

Qualified Combination-n

. . .

Grade.

	Unified Combination-2
	. . .
	Unified Combination-n . . .

FIG. 7B depicts an exemplary process flow for a unified combinations procedure (a Thread II602), according to an embodiment of the present invention. Instep800,thread602 may begin an on-going loop. Instep810,thread602 may sort all qualified combination records in the buyers report table640 by buyer/campaign. Instep820,thread602 may begin a processing loop. For each buyer, buyer/campaign,thread602 instep830 may begin an analysis to locate those qualified combinations which “match” on a behavior basis, such that in a matching pair, triple, quadruple, etc. there is a closely matching response behavior to each of segments in the matching group.

Many different computer techniques can be used to generate “matches”. In one embodiment,thread602 may store (in step840) all permutations of all record combinations on temporary table (shown at850) and may, additionally, calculate for each stored record (of a combination) a “Unification Grade” value that may indicate “how much this unified segment (composed of all users who belong to the segments of each qualified combination record included in this permutation) may behave in a unified way (and can be expected respond to advertising content in the same way as they have done so in this sample). Although generating permutations is one way of finding “matches”, other techniques such as using recursive algorithms to match pairs, then pairs with other segments, etc. can also be used. After generating the permutations instep830, the temporary table of records (stored in step840) may have the form:


	Temporary id
	List of segments included in this group
	Unification Grade

In the calculations ofstep840,thread602 may use only those records from the buyers report table640 that are “qualified combinations” in that, for example, they have a minimum number of users that were exposed to content and also minimum lift value (represent minimum number of user's responses). In other embodiments, other minimum criteria may be selected.

Once a temporary table (e.g.850) is ready (for a specific buyer/campaign),thread602 instep860 may select those records from the temporary table850 that surpass a minimum “unification grade” value and may insert them into the unified combinations table650 as candidates for unification. Instep870,thread602 may complete the processing for one set of buyer/campaign records and return to step820 to process another set. When loop820-870 is completed,thread602 loops instep880 and returns to800 as part of an on-going process and will repeat the analysis, e.g. periodically, to find “matches”.

The “Unification Grade” which may be determined by thread602 (e.g. in step830) may calculate a value representing how tightly or how “well” the group is unified. The unification grade, for example, may show the group identified in a unified combination could be how reliably and predictably the group behaves. For example, such a value may indicate if the group could respond with the same response numbers to (high or low) to any (or the next) advertisement content. In one embodiment the unification grade is calculated as the gap (in percentage) between the minimum and maximum VAR of the group members.

One sample of sorted records from the buyers report table640, used for analysis bythread602 may be appear as follows (the ID column is used only for the example explanation):


Id	Buyer	Campaign	Segments	Lift Var

1	B1	C1	S1		2%
2	B1	C1	S3		8%
3	B1	C1	S34	8.1%
4	B1	C1	S12	9.1%

And from those records,thread602 may generate permutations which may be appear as follows (the ID column is used only for the example explanation):


Id	Members	Grade [%]

1	S1,S3	6
2	S1, S34	6.1
3	S1, S12	7.1
4	S3, S34	0.1
5	S3, S12	1.1
6	S12,S34	1
7	S1, S3, S34	6.1
8	S1, S3, S12	7.1
9	S1, S34, S12	7.1
10	S3, S34, S12	1.1
11	S1, S3, S34, S12	7.1

Thread III: Updating Behavioral Segments (Unification)

Thread III (604) may update the behavioral segments definitions (e.g. in table610) according to new unified combinations.FIG. 7C depicts an exemplary process flow for a behavioral segments update process (e.g. a “Thread III”).

Instep900,thread604 may go over all unified combinations and prepare the records for the unification procedure. During this process, sub set of records “certify unified combination” list will be created and used later on in this thread. The process of preparing this list may in one embodiment:

Go over the records and group them by the segment combination they describe.

If the total number of members (records in the database that describe the same segment group) is smaller than a minimum pre defined number, stop the certification (for this segment group).

If the gap in the grade between all members is larger than a defined parameter, stop the certification (for this segment group).

If the group passes the above ‘tests’, a single ‘Certified Unified combination’ record is inserted to the table (e.g. the certified unified combination table653 (seeFIG. 6B). This record may be used in the following processes ofthread604.

Instep905,thread604 may set for each behavioral segment corresponding matching certified unified combination that has the highest overlap percentage (%)(with the behavioral segment definition in question. In one embodiment percentage (%) overlap is equal to the number of users that are included in the behavioral segment and in the certified unified combination divided by (/) number of users that are included in the existing behavioral segment. Other criteria for percentage overlap correspondence can be used.

Instep910 the thread may update the segment definition with the new segment examine each of the unified combinations and may look for unified combinations that are matching combinations for more than one behavioral segment definition (source from another buyer/campaign or another buyer). In this process,thread604 may instep920 set a unified combination as the matching unified combination only for the behavioral segment definition that has the highest percentage (%) overlap with it. For each of the other behavioral segment definitions,thread604 may instep930 set as the matching unified combination the unified combination that has the next highest percentage (%) overlap with that behavioral segment definition. Instep940,thread604 may loop or iterate, and may return to perform steps910-930 again until no cases of a unified combination with more than one match to a behavioral segment definition are found. Some behavioral segment definitions may have no matching correspondence with a unified combination. After this process, some qualified combinations may also not be matched to any of the behavioral segment definitions.

In making matches, the value “minimum behavioral segment unifies percentage (%) overlap” may represent a parameter for tuning or optimization. Behavioral segment definitions that have a correspondence to one or more unified combinations through this value may be considered “relevant”. Each existing behavioral segment definition that could not be “verified” or matched to one of the unified combinations with this data may be deleted, for example, as a segment that is “not relevant”.

In one exemplary embodiment, each existing segment definition that was not matched within a time period (e.g. a “maximum verification time” value) may be deleted. Instep950,thread604 may examine each of the non-matched behavioral segment definitions and may delete them, for example, if their time stamp values have expired. In such an embodiment, an aging mechanism is applied to ensure that non-relevant behavioral segment definitions have an opportunity to demonstrate their “relevancy” but will be also removed in time. Each behavioral segment definition record may, for example, include a time stamp value. The time stamp value may show, for example, the last time the behavioral segment definition was modified, and this may include an indication of modification based on the whether a match was found for the behavioral segment definition in steps910-940 above.

Instep960,thread604 may examine each of the remaining, non-deleted behavioral segments definitions remaining and may update the data. For example,thread604 may create a new segment to replace the previously used segment. The “old” segment may be kept for a defined time period and then removed. In one embodiment, the exchange system may use the new segment (e.g. for tagging users) from the moment of creating the new segment.

Instep970,thread604 may examine each of the unified combinations were not matched to a non-deleted behavioral segments and may set each one as a new behavioral segment.

Thread IV: Division Analysis

In one embodiment, Thread IV (606) may analyze records in the buyer report table640 and identify segment combinations that are candidates for division or alteration. For example,thread IV606 may process records in the buyer report table640 having a division status of “not processed” (e.g. “division status” equals (=) “not processed”) as thread I600 may have marked.

FIG. 7D depicts an exemplary process flow of a division analysis process (e.g. a “Thread N”606). A first task may be to identify segment combinations that have “poor” or “low” response rates but are not worth simply deleting, for example, segment combinations with low Var (variation lift) results but, a response volume that is greater than a minimum. Such segment combinations may become candidates for division or other alteration.

Instep971,thread606 may begin a loop to check each segment combination record in the buyer report table (640). In one embodiment,thread606 may check instep972 the status of each record to see, for example, if its “division status” field indicates “not processed”.Thread606 may next attempt to identify from the user response data, if there are any segment combinations that can be marked as “candidates” for division.

For a segment combination record that was “not processed”,thread606 may locate or compute a result variable for that segment combination, such as for example the “Var” value (representing lift variation). The user response data may provide a user response rate value indicative the responsiveness of the users to the advertisement, and a user volume value indicative of the number of users exposed to the advertisement by segment combination.Thread606 may identify from the user response data, at least one segment combinations as a “candidate” for division by, for example, identifying segment combinations having achieved a user response rate that is lower than a pre-determined value and a user volume value that is higher than a pre-determined threshold amount.

If, instep974, that value is below a threshold,thread606 proceeds instep975 to check a second variable, such as response volume (e.g. as discussed in the previous section). In one embodiment, if the response volume is above a threshold, thenthread606 may mark this segment combination (e.g. in step976) as a “candidate” for division/alteration. If records to do not meet the tests (in this case the var and response tests),thread606 may move past those records, marking them, for example as “processed” (see step981). In addition to checking the “var” and “response” variables, other variables could be examined. Records passing the twotests974 and975 (or other tests) may be candidates for division or other alteration.

In one embodiment,thread606 may then analyze a candidate record instep976 to determine if that particular segment combination is part of any other larger segment definition, such as whether the segment combination in question is a sub group of another segment. Segments combinations which are part of other, larger segment combinations, in one embodiment may be skipped, for example, because they will be handled as part of the analysis for the “parent” of the group.Thread606 may make such a check by going over the different parts of each group and verifying if overlapping parameters are included (e.g. same parameters, value sub-ranges, etc.). If the segment combination is a sub-combination of another segment,thread606 may terminate further processing of that segment combination (e.g. marking it as “processed” in step981).

If the segment is not a sub-group of another segment,thread606 may, instep977, analyze the elements of the segment combination in question to determine if, for example, the definitions for the segments that make up the segment combination include segment parameters that would allow the segment combination to be divided.Thread606 may determine if the segment combination comprises additional segment parameters having values capable of dividing a segment combination parameter currently used to identify user computers in the segment combination. If the segment can be divided,thread606 may determine if it is possible to divide the segment into two or more sub segments in order to allow later re structure of this segment. One situation where it may not be possible to divide the segment might be where the segment is a “raw segment” with single value (for example, Gender=female). In thiscase thread606 may do nothing with this record. In other cases,thread606 determines additional parameters for dividing. The additional segment parameters may be determined from parameters existing in a segment definition associated with a segment comprising the segment combination.

Instep978,thread606 may collect all segments that make up the segment combination and mark them as “processed”. For example, a segment combination may have the following segments in the behavioral segment database:

For example: (where Px is behavioral segment)

- (Sa) Segment A is combined from: P1 (1 . . . 6), P2(7 . . . 8), P3(1 . . . 30)
- (Sb) Segment B is combined from: P1(1 . . . 4), P2(7), P3(1 . . . 30)
- (Sc) Segment C is combined from P1(5 . . . 6), P2(8), P30
- (C and B are included in A)

Instep979,thread606 may make a determination as to whether new segment records using the sub-segments can be created.Thread606 may create, for example, new segment parameters from dividing minimum and maximum range values associated with a parameter then currently used.Thread606 may, instep979, compare, for example, the “var” gaps (variation lift, response ratio for content) between the sub-segment members (for example, if Var(Vc) is 5%, Var(Va) is 12%, and Var(Vb) is 16% the system can detect that Vc should not be part of the group Va) If the “var” gaps are greater than (>) a threshold amount (e.g. a “max_allowed_internal_gap” value) If the gap is large, for example,thread606 can determine that the different parts of the segment do not behave the same. thenthread606 may insert two new segment combination records onto the division/alteration table655. Following the exemplary structure for the division/alteration table655 set forth above, record entries for the divided records may include (referring to the sample above) Vc as separate group and different definition of Va that does not include Vc inside.

Accompanying linking data may also be written to the divided/altered combinations members list657.

If, instep977,thread606 determines that the segment combination does not have further segment parameters (e.g. it cannot be divided into original parts or vertically)thread606 instep980 may create additional segment parameters. Assume, for example, behavioral segment X has a poor results (e.g. it has a low Var value but enough of response that it could be considered a “candidate” for division). If a segment definition for age (used in this segment combination) holds the range of 20-30, thenthread606 may insert two new segment combination records onto the division/alteration table655 with, for example:


	Segment parameter A: 20-24 (for new record 1) and
	Segment parameter B: 25-30 (for new record 2).

Wherethread606 either uses existing parameters from the segment definition orthread606 creates new parameter,thread606 may add additional segment parameters to at least one of the segments comprising the candidate segment combination, wherein the additional segment parameters divide a parameter currently used within the segment combination to identify users to the segment combination. As, for example, the age range with parameters20-30 can be split in to two ranges 20-24 and 25-30 those new ranges will be added to the combination and stored, for example on the divided/altered combinations table655. A division process (e.g. Thread V,608) may use those additional ranges to divide the segment. One embodiment may divide a parameter currently used within a segment combination by splitting the range of the parameter into two sub ranges for example to associate the computers of the users to the segment combination.

For each of the additions of segment records in

steps

979 and980, “Thread IV” (606) may create a report showing that possible divisions could be made for the segment combinations in question. Also, in another embodiment, if a segment combination that had been performing poorly was a unified combination,thread606 may alternatively break the unified combination into its constituent approved combinations. For example, where a “candidate” segment combination is a combination of two previously identified segment combinations and a division may be arranged to split the “candidate” segment combination into the two previously identified combinations, rather than adding segment parameters. Other possible methods and techniques of breaking up or altering a segment combination are also possible.

Where a segment combination has been processed as a candidate for division (e.g. in

steps

978,979 and980),thread606 may then mark record for the combination as “processed” instep981 and the loop, instep982 to locate another segment combination to process.

Thread V: Division Analysis

In one embodiment, Thread V (608) may compare the entries in the divided/altered combinations table (e.g.655), with the current behavioral segments and may update the behavioral segment combinations (and definitions) in the behavioral segments table (e.g.610) based on the comparisons.FIG. 7E depicts one exemplary process flow for analyzing segment division/alteration possibilities (e.g., a “Thread V”608).

Instep985,thread608 may begin a process, e.g. a loop, to examine each of the records in the divided/altered combinations table655. Asthread608 processes, thread606 (e.g. Thread IV) also processes. Theexchange system10 may receive further user response data within a pre-determined time period and thread606 (Thread IV) may determine a second time based on the received further user response data that a segment combination, already is a “candidate”, should, again, be considered a candidate for division.

If instep987,thread608 determines that the combination in question had been made a “candidate” for division (e.g. a change by division was “requested”) more than once (or more than a predetermined number of times) within X days (or a predetermined time period), thenthread608 may, instep989, make changes in the behavioral segment definition table610 for segment combination in question and also for its definitions, if needed. For example, a “candidate” segment combination may be divided afterthread608 determines the segment combination has been found bythread606 to be a candidate for division in a pre-determined period more than a pre-determined number of times. For example, the two new segment combinations will be used and the “old” segment will be taken out of use (and e.g. archived/or deleted after a time period).

In changing the segment combination,thread608 may create at least one new segment combination by dividing the candidate segment combination according to the additional segment parameters. To create a new, “divided” segment combination,thread608 may replace the currently used parameter (e.g. age 20-30) with one of the additional, added parameters (e.g. age 20-25).

In one embodiment,thread608 may, for example, de-activate (e.g. mark as inactive) the “candidate” segment combination and replace it with combinations. If segment parameters were created (rather than found as previously existing),thread608 may add these to the segment definitions. Instep990 thread V (608) loops to step985 and processes the next record in the divided/altered combinations table655 (e.g., in the example above, assuming Va existed, it will be ‘marked as not used’ and Vb and Vb will be defined).

Using Optimized Segments/Tagging Policy

When the behavioral segments definition table610 has been updated with optimized segments, theexchange system10 will use these optimized segment definitions to “tag” users, e.g. through the “User Session I”process202, described inFIGS. 2 and 4A. In one embodiment, theexchange system10 operating as a server connected to a network, such as the internet may “tag” by writing, saving, or otherwise storing an identifier of the new segment combination on computers of a plurality of new computer users at the time each new computer user access a Web site of adata publisher24 to identify the computers of the new computer users with the new combination. Such tagging, for example, may identify the user as being identified, connected to or associated with one of the new, optimized segment combinations. Tagging (e.g. through cookies) will make the user's optimized segment identification available to a serving system (e.g. the servingsystem118 of the RTB bidder18). For example, with the new, optimized segment identification, at least one of the new computer users, using their computer, may access the Website of amedia publisher22 and by virtue of that access receives at their computer an advertisement targeted to the new segment.

The servingsystem118, e.g. of anRTB bidder18, may then receive content requests from users who have been “tagged” with cookies that identify them for theRTB bidder18 as belonging to an optimized segment.

Many serving systems (e.g.118) may use an advertising serving system to execute media optimization and decide which creatives to send to users for each advertisement exposure or “advertising call”.

Embodiments of theexchange system10 can be integrated with a serving system (e.g.118) in a way that enables the serving system's optimization engine to use the behavioral data as one of the measured variables. In one embodiment, and as described above, the behavioral segment data is written in the form of cookies (e.g. generated by the usercookie update module322 of theexchange system10 using segment information stored in thedatabase370 within memory307 (FIG. 3) and provided to the browser programs of the users (e.g.52,FIG. 4A) where the cookies are stored in the memory of the computer of the user (see57 and51,FIG. 4A). When the user accesses a Web site of amedia publisher24, the cookie information is read by the optimization engine (e.g. within the advertising serving system220) of the serving system118 (e.g. when thebrowser program52 transfers the cookie information in a access request for themedia publisher24 Web site).

With the data written in a cookies format, the optimization engine of the serving system treats each behavioral segment as a discrete value for the behavioral measured variable. Based on the segment values the optimization engine uses the performance of each creative on each behavioral segment for the creative choosing algorithm. The servingsystem118 may then run the creative or other advertisement using the updated, optimized segment information found in the user cookies. As the segment is “tuned”, more and more users, for example, may be identified to a segment with a high advertising response rate. This high response rate may be detected by the serving system engines and advertising can be targeted then, but the serving system to users having the exchange system segment identifiers.

As the serving system runs advertisements to users carrying theexchange system10 segment combinations, theexchange system10, in one embodiment keeps receiving data (e.g. summaries or direct data of the user activities). This additional data may be used for further optimizing segment combinations, for example, following the procedure of the learning/iteration test runs. Based on these additional learning/iteration tests, the quality level value of a qualified segment can be updated. Additionally, the group of users identified with the segment can become larger.

In using the optimized segments, e.g. to “tag” users and to perform optimization runs, theexchange system10 and theRTB bidder18 may perform operations according to a “tagging policy”. As described above, theexchange system10 “tags” users for it clients, e.g. theRTB bidder18 during on-going User Session I202 processes (seeFIG. 2) The client, e.g. theRTB bidder18 also uses the tags to place advertisements. A tagging policy may be implemented to coordinate between the actions, e.g. to preserve stability of the segments. One tagging policy “rule” may be to not to use a new, optimized segment, (for example to “tag” users with the optimized segment), for a time period, e.g., 24 hours after the optimization. Other “rules” may be implemented.

The operator for theRTB bidder18 system or theexchange system10 operator may analyze user responses generated from the advertisements placed, to review the “tagging policy” that may be in place. The operator for theRTB bidder18 system or the operator for theexchange system10 may make changes if needed to the tagging policy being used for a particular advertising campaign. Changes by the operator of theRTB bidder18 system may be communicated to theexchange system10. Theexchange system10 operator may also recommend changes to the tagging policy.

In reviewing and determining a tagging policy, various aspects may be taken into consideration. Theexchange system10 in one embodiment may be set to not change existing used segments. Thus, if a segment has been actually used in placing an advertisement for an advertising campaign, that segment might not be changed. This results from the observation that there is time period between writing, saving, or otherwise storing the segment information and the usage/utilization which in some embodiments must be backwards compatible. In such a case, the exchange system may not update the segment with any new combinations, but may however, keep the new combinations (e.g. for further analysis and combining) for a defined period (which may be defined in the system parameters). With such a policy the new segments can be created but set, for example, to “not active” for user tagging.

As a second consideration, the process of reviewing and determining a tagging policy may document the nature of the segments used with a given “life time period”. It is known that users may receive “cookies” having a certain “lifetime” or “expiration” date. One tagging policy might be to set the life time or expiration date for the cookies used to “tag” users, e.g. in the User Session I202 processing. After that time period, the segment may be removed according to policy from the behavioral definition table610 and advertising will not be placed according to this segment. From time to time also conversion of segments may be made to improve the efficiency of targeting. Running parameters may also be calculated by the exchange system based10 on optimized data values and quality level values for each of the qualified segments.

Virtual and Non-Virtual Segments

As stated above, segments or groups (such as segment combinations) can in some embodiments also be provided to theRTB bidder18, such as by anexchange system18, with meaningful content for manual processing or as a “virtual” segment. When the segments are provided as virtual segments, theRTB bidder18 knows only, for example, the identity of the segment and certain data such as its conversion ratio When “real” segments (and segment combinations) with meaningful content are provided, theRTB bidder18 understands what parameters are making up the segments and theRTB bidder18 can examine and further test that parameter data. “Virtual” segment combinations may appear as follows to a serving system (e.g. for an RTB bidder18):


	Exchange System	Time	Location

	Seg
1	Morning	east
	Seg
2	Noon	mid
	Seg
3	evening	west
	Seg
4	night

“Real” or full data segment combinations may appear as follows to a serving system (e.g. for an RTB bidder18):


	Exchange System	Time	Location

	Seg
1 Age 0-20	Morning	east
	Seg
2 Age 21-40	Noon	mid
	Seg
3 Age 41-60	evening	west
	Seg
4 Age 61-120	night

Virtual segments provide an advantage at times, because they can be monitored by automated processes, such as that of by embodiments of the present invention and updated automatically without intervention by a serving system, thus allowing an organization such as an RTB bidding organization to more fully automate its advertising placement process through optimized segments. In other instances, the operator of the serving system (e.g.118) may wish to have descriptive data concerning the segments.

Management of Data Seller/Publishers: Auto Registration

The management of data publishers (data sellers), e.g.24,FIG. 1, can also be incorporated in embodiments of the present invention. Such management of data publishers (data sellers) may be executed by a sellers/publishers management unit, which is depicted inFIG. 9 at999.

In another embodiment the sellers/publishers management unit999 provides an auto registration facility for data publishers24 (data sellers) to connect to theexchange system10 automatically. In an embodiment with such a feature, the sellers/publishers management unit999 provides auto registration based on the following operational phases:

- Registration—when a data publisher24 (data seller) opens an account, the data publisher24 (data seller) can receive a user name and password. The data publisher24 (data seller) may further enter data about the data publisher24 (data seller)'s Web properties. In one embodiment, examples of such data points include:


a. Name of his web property;
b. Type of his web property (web site, web gadget, SW program etc);
c. If its a site - all the relevant URLs;
d. Known characteristics of his users (age groups, ethnicity, . . .);
e. Text language/s for every URL; and
f. Vertical relevance of each URL

In the registration phase the data publisher24 (data seller) also enters its contact data, cooperate data and the data needed for monthly payments (payment method, bank account etc).

- Tans Generation—after finalizing its registration, the data publisher24 (data seller) enters into or uses a UI that enables that seller to generate data publisher24 (data seller) tags (or Seller/Publisher Tag) for each of its URLs. After implementing those tags the data publisher24 (data seller) needs to enter a tags verification UI, part of the sellers/publishers management unit995, where data publisher24 (data seller) follows a set of instructions that enables the sellers/publishers management unit995 to verify that each of the tags works properly. A data publisher24 (data seller) tag is not activated before such verification finishes successfully.
- URL Qualification—Each of the URLs that have a verified Seller/Publisher Tag is set by the sellers/publishers management unit995 to be a tested URL. In such an embodiment, all the calls of each such tested URL are logged by the sellers/publishers management unit995 once they are designated to be a tested URL, but their data is not used, in this embodiment, for optimization and segmentation. The sellers/publishers management unit995 may first verify, based on a set of URL measurements, that the URL functions “normally” before setting it as an Active URL. As a result of the URL Measurements sellers/publishers management unit995 sets a URL qualification grade. To facilitate this process, the exchange operator in one embodiment defines the following data about the URL qualification grade:


	Min grade to automatically set a URL as Active URL;
	Min grade to set a notification to the Exchange Operator
	to check and decide is to set the URL as Active URL;
	Grade that below it a warning is set for the URL; or
	Grade that below it the URL is set a Fraud URL.

The URL Measurements are based on a variety of check points that similar to the fraud measurements. see above discussion of fraud detection for a definition of those measurements and method of setting URL qualification grade.

- Active Run—Once the URL qualification tests are passed successfully the URL is set to be an active URL. From that time forward all the past and future creative events are used for the optimization and segmentation procedures. In one embodiment, the exchange operator can define for a specific URL a creative event that took place before a certain date/time that can be ignored by the optimization and segmentation procedures. As described above, the on-going fraud detection process takes place though-out the activity of each data publisher24 (data seller) as their sites remain active URLs.

Management of Data Seller/Publishers: Payments/Billing

At the end of every month the sellers/publishers management unit995, in another embodiment, calculates the due income to each data publisher24 (data seller). This income is based on the payment terms that were defined for that data publisher24 (data seller). Examples of such payment terms are, for example:

- Rev Share of X % from the income on every impression that took place to a user based on that user's visit to that Data Seller's web property. If such impression took place based on more than one Data Seller, the income of that impression is divided equally between all the Data Sellers;
- Min Guaranteed Income for every 1000 unique users that called one of the seller's Seller Tags.

In one embodiment, the sellers/publishers management unit995 sets default payment terms for each data publisher24 (data seller). Those terms can be modified, for example, by an exchange operator. The exchange operator can define a criteria for a manual verification of a data publisher24 (data seller), and after approving such the data publisher24 (data seller), the exchange operator can define a new set of values for notification, for example, of when to conduct the next manual verification and vise versa.

In such an embodiment, the income division between the data publishers24 (data sellers) is based on revenue share combined from the income generated by the segments and the relative contribution of the seller to this segment in a mater of volume (number of users generated by this seller) and the segment relative part in the segment structure. For example, if virtual segment123 is combined from 4 raw segments, the exchange system will count the ratio of generated users in the last defined period (a week for example) generated by each raw segment and calculate the portion/affect of the specific seller in the revenue generation. based on this calculation the seller will get his share (percentage from the income generate to the exchange system)

At the end of each calendar month the sellers/publishers management unit995 calculates the total due income per data publisher24 (data seller), and offers that data in report. In addition, the sellers/publishers management unit995 logs the pay side data to save all the events income that are associated to each data publisher24 (data seller). At the end of each calendar month the sellers/publishers management unit995 calculates the total due debt of data publisher24 (data seller) and offers that data in report.

Management of Data Seller/Publishers: Fraud Detection

As part of an active run (the optimized run described above), the sellers/publishers management unit995 may monitor (e.g. constantly or continually) the sellers table (650,FIG. 7) within thedatabase370 to identify fraud and meaningful changes in the quality of the data provided by each of the data publishers24 (data sellers). Fraud is common and in the case of anexchange system10, data publishers24 (data sellers) are motivated to conduct fraudulent activity and thus increase their income.

In the embodiment employing fraud management, the sellers/publishers management unit995 conducts a series of measurements to identify fraud indication. Based on the results of those measurements, the sellers/publishers management unit995 sets a fraud suspicion grade value for each data publisher24 (data seller). In one embodiment, the fraud suspicion grade holds is a value between 0 to 100 where, in such an embodiment, 0 means there is a minimum fraud suspicious for that data publisher24 (data seller) and100 means there is a maximum level of fraud suspicious for the data publisher24 (data seller).

The sellers/publishers management unit995 runs a variety of fraud measurements to set the fraud suspicion grade. Examples of some such fraud measurements include:

- Rapid Volume Change—measuring changes in the volume of calls from the specific data publisher24 (data seller).
- Pages Ratio Change—measuring the level of change in the ratio of the call from the various pages or media properties of the specific data publisher24 (data seller).
- URL Definitions—specific data publisher24 (data seller) can be defined to be limited based on URL text criteria such as only “.com” URL or a certain root URL such as—“www.url.com”.
- IP Duplication—measures the amount of duplicated IPs that the calls of the data publisher24 (data seller) are coming from.
- Market Comparables—measures the ratios between all available data points as compared to the market standards. Examples of such data points—ratios between the various OSs, browsers types and browsers versions, US states.
- Manual Check Points—the exchange operator can need a set of check-point to be monitored by the sellers/publisher management unit995. Those can be, for example:
  - Maximum call volume from any of the various pages or media properties of the specific data publisher24 (data seller).
  - Maximum ratios between pairs of the various pages or media properties of the specific data publisher24 (data seller). For example maximum percentage (%) of users that move from the landing page to any of the inner pages.

In one embodiment, the sellers/publishers management unit995 may store or hold a table with data about the values for each fraud measurement with the table holding the parameters about each fraud measurement such as for example the following:


	1. AFraud Measurements Weight
	2. First range of values and the correlating Fraud Suspicion
	Grade for the fraud measurement values.
	3. Second range of values and the correlating Fraud Suspicion
	Grade for the fraud measurement values.
	4. . . .
	5. Last range of values and the correlating Fraud Suspicion
	Grade for the fraud measurement values.

Those values may be set for example manually by the exchange operator via dedicated Ills for the sellers/publishers management unit995.

In one embodiment, the sellers/publishers management unit995 performs fraud detection on an on-going basis. The sellers/publishers management unit995 decides when to re-measure a specific data publisher24 (data seller), based on the following criteria:

- Sensitivity Level—manually set by the system operator;
- Current Fraud Suspicion Grade—what is the current fraud suspicion grade of that data publisher24 (data seller) where the measurement frequency increases with the increase of the fraud suspicion grade;
- Historical Fraud Suspicion Grade—weight average fraud suspicion grade of that data publisher24 (data seller) were older fraud suspicion grades are given less weight;
- Amount of new data—how many new creatives events were logged for this data publisher24 (data seller) since the last time the measurements run for it;
- Revenue Level—what is the daily income level of that data publisher24 (data seller); and
- Age—how many days of activity is recorded in the sellers table630 about this data publisher24 (data seller). Measurements are run more often to new sellers than to old ones.

After the set of test is run, the fraud suspicion grade is updated. In one embodiment, the exchange system operator defines, for example, the following data points in regards to the updated fraud suspicion grade:


	Fraud Suspicion Grade to issue a warning;
	Fraud Suspicion Grade to issue a severe warning;
	Fraud Suspicion Grade to issue a severe warning and ignore

	all data from the data publisher 24 (data seller) until
	approval of the system operator.

In one embodiment, the exchange system operator can also run reports about fraud suspicion grade such as: 1) the fraud suspicion grade history for a specific data publisher24 (data seller); or 2) Values of each of the measurements in the current or past measurement sessions.

Additional Embodiments

Referring toFIG. 1 it also can be seen that embodiments of the system and method of the present invention can be used to provide and update segment data not only toRTB bidders18, but also to other entities that are concerned with media advertising and advertisement space purchase. For example, instead of providing segments to theRTB bidders18, an embodiment of the present invention can be arranged to provide combination or segment data

The embodiments of the system and method of the present invention presented herein also have application beyond that of Internet advertising. The teachings of the present invention could also be applied to systems for purchase of advertising spaces such as print advertisements, target mail marketing and other such marketing types, or to other systems, providing messages or measuring feedback, beyond messaging. Further, beyond marketing, the teachings of the present invention can be applied to other systems, providing messages or measuring feedback, beyond messaging.

EXPLANATION OF TERMS

Advertisement or Creative—may be a single advertising unit such as an Internet banner advertisement with a specific picture or short movie that promotes a product or service. For purposes of this patent application, advertisements also include other types of advertising or “creatives”, other displays provided by a serving system to a user or other computing elements by which a serving system presents or obtains data.

Serving System—may be one or more web servers that serve creatives or other advertisements to Web sites based on definitions set by a system operator. A serving system may be operated by or on behalf of an advertiser, an advertising agency or an RTB bidder, for example.

System Operator—may be a person that operates a serving system, for example configuring it and using it to run advertising campaigns.

User Data—may be data either “live” user data or data in summary form on a users' response to an advertisement. User data in summary form may show the conversion ration for the users of a given segment.

Behavioral Segment or Group—may be a group of users that can be clustered together based on one or more behavioral parameter. For example a behavioral segment can be all the users that have visited site-1 or site-2 in weekends

Quality Level—may define how well the users of a specific segment responded to a specific creative or other advertisement.

Qualified Segment(s)—may define segments that have a Quality Level that as computed for a given advertisement or campaign exceeds a given level.

Exchange System—may be a system that runs on a stand-alone grid of one or more servers and implements a full data exchange by connecting, for example:

- Data Buyers—may be entities that serve advertisements on the internet via serving systems. Those entities use segment data as provided by the exchange system to improve media monetization when they place advertisements. A data buyer, include all organizations running or using a serving system to place advertisements and could be, for example, an advertiser, an advertising agency or a RTB bidder; and
- Data Sellers—may be entities that have access to or otherwise collect user data that has value to the Data Buyers. In embodiments, data sellers use the exchange system to generate an income from their data. An example of a data seller can be an automobile research sites. Such a site offers data buyers data for targeting advertisements to users that are interested in specific types of new cars (SUVs, sport cars, etc).

Exchange Operator—may be a person that operates an exchange system.

Buyer Tag—may be a browser cookie stored under the buyer name on the computers of users (clients who access the Web sites of data publishers (data sellers). This information is stored by the exchange system while the user accesses the data publisher (data seller) site. With a buyer tag, for example, data concerning the users' segment affiliation user may be passed to a serving system, such as a serving system of an RTB bidder.

User ID (Identification)—may be a unique ID that is given to every user for the purpose of attributing all relevant to that user. To assure that the user privacy is kept, the User ID may not include any personal identifiable information, e.g., is anonymous.

User Parameter—may be a specific data point about a specific user that relates to his or her Web activity, computer, location etc. A non-exclusive list of examples of user parameters are:

- Demographic data such as age and gender;
- Visited site category such as travel or finance;
- Visited site or site section such as cars.com or cars.com/SUVs;
- Activity day-type (weekday, weekend . . . ) or time-type (morning, noon . . . );
- OS such as MS or Apple.

Parameter Value—may be a value that is set for a certain user parameter. For example for the user parameter “OS”, the parameter value can be “MS” or “Apple”.

Creative Event or User Event—may be a data point about a specific creative or other advertisement that can be, for example:

Impression (or Exposure)—may define a physical exposure of the creative to an end user on a certain site of other web property;

Action—An action may describe any action that a user that saw the creative executed on the site. Examples of Actions include, for example, a “click” by a user (e.g. using a mouse), filling a web form, completing an e-commerce transaction etc.

Buyer's Record—may be user response data passed from the serving system (e.g. through a summary statistical report), for example to an exchange system, which may provide data concerning one or more user response events or actions associated to a specific creative or other advertisement. For example, a buyer's record holds data reported by a data buyer (such as an RTB bidder) which for example is stored in the buyer record table (for “qualified combinations” used by thread I . . . II and IV).

Buyer Report Table—may be a sorted data-base table that holds the user response data sent from a data buyer such as an RTB bidder of an exchange system. Segment -- as used herein may be a data structure, e.g. on a computer, which holds attributes or parameters used to characterize groups of people, e.g. a sub-set or portion of a larger group, Each member that was identified as being part of the segment holds an identifier to this segment. The data structure used for forming segment combinations may not hold any particular reference to individual members, but may hold aggregate information, such as, for example, how many members (percentage of users) belong to this group.

Segment Definition—as used herein may be a data structure, e.g. on a computer, which sets attributes and parameters (or sub-segments) that identify the nature, properties or meanings of the segment (for example—“frequency of reading news”, “Origin country”, “gender”).

Segment Combination—may be segment definitions (or segments) combined together in data structures, e.g. on a computer. A “segment combination” may hold the identifications or descriptions of people (e.g. computer users) that belong to more than one segment. For example a segment for people who “read news every 3 hours or more” and a segment for people who “like fictional books” can be combined into a segment combination (The relation in combination can be “and” or “or”). Segment combinations can also be referred to herein as segments.

Sub Segment—as used herein may be a data structure (e.g. stored on a computer) that identifies or describes a sub group, part of the group that belongs to a certain segment. For example—“people who get updated with news with frequency of 2 days or more”).

Behavioral segment—segment whose definition deals with people's behavior and not objective criteria (for example, “tendency to read news”, “common working hours” comparing to “age” or “origin country” or “gender”).

Behavioral Segment—as used herein may be a segment definition (herein a data structure, implemented on a computer), having definitional attributes that pertain to or describe subjective aspects of people's behavior such as for example, “tendency to read news”, “common working hours” when comparing such aspects to other aspects such as “age” or “origin country” or “gender”.

Behavioral Segment Combinations—may be a behavioral segment combined into further data structures (e.g. on a computer) with either non-behavioral segments or other behavioral segments.

Segmentation—may be the on-going process of updating the definitions of the various segments (for example, behavioral segments).

Segmentation Engine or Segment Planner—may be the systems or processes that execute the segmentation process.

Optimized Event—may be which of the creative events that is desired to be optimized. If, for example, the optimized event is clicks, then the exchange system can look for the behavioral segments that generate the given minimum CTR level and direct the creative to those segments.

Qualified Combination—may be a sub-set of records from the buyers report table that was found as suitable for processing (for example—the minimum number of users that were exposed to the content was larger than “XXX”).

Predictive Lift—may be the average response rate that a qualified combination or other segment has, divided by a factor such as, for example, global average response rate.

CTR—may be the percentage of those users clicking on a link out of the total number who viewed the link or text ad. A CTR level may be a percentage used as a reference value.

CONCLUSION

Each of the various embodiments discussed herein may be combined, and features or each of the embodiments herein may be combined or used in various combinations. While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.