Patent Publication Number: US-2012047022-A1

Title: Providing Individualized Advertisement Based on Collaboratively Collected User Information

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 61/375,994, filed Aug. 23, 2010, which is incorporated by reference herein. 
    
    
     FIELD OF DISCLOSURE 
     The present disclosure relates generally to electronic commerce (e-commerce or ecommerce), and more particularly to providing customized advertisements through a collaborative advertising system. 
     BACKGROUND 
     Description of the Art 
     Selling goods and services through websites on the Internet has become commonplace. Merchants operating websites that offer goods and services for sale often place advertisements on other websites (web portals) in order to inform potential customers about their offerings, and to direct users to web pages where purchases can be made. 
     To make the advertisements displayed on a web portal more effective, it is desirable to tailor the advertisements based on information about the users viewing the advertisements. Information such as previous purchases made, items browsed on a merchant&#39;s website, advertisements clicked, etc., can be used to infer a user&#39;s interests in goods and services. This information can be used to provide better targeted advertisements that are tuned to a user&#39;s particular needs. 
     Unfortunately, a single merchant or web portal typically only has access to information about its own users, i.e., customers that have visited its websites. In addition, a single merchant or web portal can only capture user data concerning user actions on its own website. With such a small amount of data on users, a single merchant or web portal will not have much information about individual users, and it cannot effectively infer much, if anything, about the purchasing interests for that user. 
     Thus, there is lacking, inter alia, a way to collect user information from users of different merchant websites and different web portals and to collaboratively use the collected user information to serve targeted advertisements for any merchant on any web portal. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The disclosed embodiments have other advantages and features which will be more readily apparent from the detailed description, the appended claims, and the accompanying figures (or drawings). A brief introduction of the figures is below. 
         FIG. 1  is a high-level block diagram of a collaborative advertising system operating in a networked environment, according to one embodiment of the present disclosure. 
         FIG. 2  is a high-level block diagram illustrating an example computer. 
         FIG. 3  is a high-level block diagram illustrating a detailed view of modules within the collaborative advertising system according to one embodiment. 
         FIG. 4A  and  FIG. 4B  are flow charts illustrating the operation of the collaborative advertising system according to one embodiment. 
         FIG. 5  is a ladder diagram illustrating a process for serving a targeted advertisement to a user according to one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The Figures (FIGS.) and the following description relate to preferred embodiments by way of illustration only. It should be noted that from the following discussion, alternative embodiments of the structures and methods disclosed herein will be readily recognized as viable alternatives that may be employed without departing from the principles of what is claimed. 
     Reference will now be made in detail to several embodiments, examples of which are illustrated in the accompanying figures. It is noted that wherever practicable similar or like reference numbers may be used in the figures and may indicate similar or like functionality. The figures depict embodiments of the disclosed system (or method) for purposes of illustration only. One skilled in the art will readily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles described herein. 
     Configuration Overview 
     A system (and process) is configured to provide targeted advertisements to a user client device, operated by a user, through a collaborative advertising computer system. In one example embodiment, a system establishes a user data system including a plurality of behavioral profiles, where each behavioral profile is associated with a unique user identifier. The system receives from a merchant computing system, a recent user activity report for the user client device, and receiving from a web portal computing system a request for a targeted advertisement for the user client device. The system also receives a unique user identifier from the user client device, and retrieves a behavioral profile associated with the user client device from the user data system, using the received unique user identifier. The system generates an instantaneous purchasing profile for the user client device, where the instantaneous purchasing profile is generated using the recent user activity report and the retrieved behavioral profile. The system further provisions a targeted advertisement to the user client device based on the instantaneous purchasing profile. 
     Overview of a Collaborative Advertising System 
       FIG. 1  is a high-level block diagram of an example embodiment of a collaborative advertising system  103  operating in a networked environment  100 . One or more merchant computing systems  101   a ,  101   b , etc. (generally  101 ), a collaborative advertising computing system  103 , one or more web portal computing systems  104   a ,  104   b , etc. (generally  104 ), and one or more user client devices  105   a ,  105   b , etc. (generally  105 ) communicate via a network  106 . In one embodiment, the systems  101 ,  103 ,  104  and client  105  are remote and independent of each other. As illustrated in  FIG. 1  the networked environment  100  includes only a limited number of each entity, but the description herein will correspond to one of each entity for ease of understanding. However, it is understood the principle as disclosed herein would apply to a plurality of devices. 
     The network  106  is the Internet or another system of interconnected computer networks that use standard communications technologies and/or protocols to facilitate data transmission. Thus, the network  106  can include links using technologies such as Ethernet, 802.11, worldwide interoperability for microwave access (WiMAX),  3 G, digital subscriber line (DSL), asynchronous transfer mode (ATM), InfiniBand, PCI Express Advanced Switching, etc. Similarly, the networking protocols used on the network  106  can include multiprotocol label switching (MPLS), the transmission control protocol/Internet protocol (TCP/IP), the User Datagram Protocol (UDP), the hypertext transport protocol (HTTP), the simple mail transfer protocol (SMTP), the file transfer protocol (FTP), etc. The data exchanged over the network  106  can be represented using technologies and/or formats including the hypertext markup language (HTML) and the extensible markup language (XML). In addition, all or some of links can be encrypted using conventional encryption technologies such as secure sockets layer (SSL), transport layer security (TLS), virtual private networks (VPNs), Internet Protocol security (IPsec), etc. In another embodiment, the entities can use custom and/or dedicated data communications technologies instead of, or in addition to, the ones described above. 
     The merchant computing system  101  is used by a merchant to communicate with a user client device  105  operated by a human consumer. In one embodiment the merchant computing system  101  is a web server configured to send web pages to the user client device  105 , for example, a computer server running the APACHE web server software, or other equivalent web server software. The merchant computing system may also be a virtual computing instance running in a data center, for example, a virtual computing instance running in AMAZON WEB SERVICES (AWS). The merchant computing system  101  accepts connections from user client devices and sends content—for example web pages—to the user client devices. By interacting with the merchant computing system  101 , using the user client device  105 , a consumer is able to browse the products and services offered by the merchant. The merchant computing system  101  also accepts orders for products and services from the user client device  105 . An example of a merchant computing system  101  is a shopping website such as AMAZON.COM, or an auction website such as EBAY.COM. 
     The merchant computing system  101  is configured (e.g., programmed and/or functionally structured) to receive connections from user client devices that have been directed to it from advertisements placed on web pages hosted by web portal computing systems  104 . When the merchant computing system  101  receives a connection from a user client device  105 , through an advertisement, the merchant computing system  101  may provide a web page, containing information about a product or service, to the user client device  105 . The provided web page may also allow the consumer operating the user client device  105  to place an order for a product or service. 
     The merchant computing system  101  may also provide a data stream to the collaborative advertising computing system  103 , containing information and content related to the products and services that the merchant computing system  101  is offering for sale. The collaborative advertising computing system  103  may use this information and content to generate advertisements for the products and services offered for sale by the merchant computing system  101 . 
     The merchant computing system  101  sends user activity reports to the collaborative advertising computing system  103 . These user activity reports contain information about the browsing (e.g., viewing and interacting) and purchasing activities of the user client device  105 , observed by the merchant computing system  101 . The merchant computing system  101  reports the observed information along with other user client device information such as the network address (e.g., Internet Protocol (IP) address) to the collaborative advertising computing system  103 . In addition, the merchant computing system  101  may enable the collaborative advertising computing system  103  to place a web browser cookie (called the “user identification cookie”) in the web browser executing on the user client device  105 . This user identification cookie contains a unique user identifier, which is useful in identifying a given user client device across multiple sessions and multiple merchant computing systems and web portal computing systems. The merchant computing system  101  may also allow the web portal computing systems  104  and other merchant computing systems  101  to place web browser cookies (called “collaborative cookies”) in the web browser executing on the user client device  105 . These collaborative cookies are used by web portal computing systems  104  and other merchant computing systems  101  to determine that a specific user client device  105  is tracked by the collaborative advertising computing system  103 . 
     In one embodiment, the merchant computing system  101  includes in its web pages a segment of JAVASCRIPT code designed to cause the web browser executing on the user client device  105  to visit the web domain hosted by the collaborative advertising computing system  103 , and thereby enables the collaborative advertising computing system to place the user identification cookie in the web browser executing on the user client device  105 . The collaborative advertising computing system  103 , in turn, may provide a segment of JAVASCRIPT code to the web browser executing on the user client device  105 ; this JAVASCRIPT code causes the user client device  105  web browser to visit the web domains of participating web portal computing systems and merchant computing systems  101 , thereby enabling these participating computing systems to place their collaborative cookies in the user client device&#39;s web browser. 
     The web portal system  104  is used by an internet content provider to publish content on the network  106 ; the content may include one or more advertisements. The content may be web pages where the advertisements take the form of banner ads, pop-ups, and/or pop-unders. In one embodiment, the advertisements are hosted on the collaborative advertising computing system  103 , and the web portal system  104  provides embedded links to the advertisements in its web pages. When the user client device  105  displays the web pages (e.g., through a browser application or applet), the links cause the advertisement content to be downloaded from the collaborative advertising computing system  103 . 
     When sending content, such as a web page, to a user client device  105 , the web portal computing system  104  may determine that the user client device  105  is one that is tracked by the collaborative advertising computing system  103 . When this determination is made, the web portal computing system  104  may request that the collaborative advertising computing system  103  provide an advertisement targeted at the user client device  105 , based on that user client device&#39;s past browsing and purchasing history as recorded in the collaborative advertising computing system  103 . The web portal computing system  104  may provide additional information to the collaborative advertising system  103 , in order to improve the targeting of the advertisement. The additional information may include the subject of the web page that the user client device  105  is displaying. 
     In order to make the determination that the user client device  105  is one that is tracked by the collaborative advertising computing system  103 , the web portal computing system  104  may check for a “collaborative cookie” that was previously placed by that web portal computing system  104  in the web browser of the user client device  105 . In addition, the web portal computing system  104  may place a segment of JAVASCRIPT code in its web page designed to cause the user client device web browser to visit the collaborative advertising computing system  103 ; this enables the collaborative advertising computing system  103  to retrieve the data in that user client device&#39;s user identifier cookie. 
     Although the web portal computing system  104  and the merchant computing system  101  are shown as distinct entities in  FIG. 1 , in some embodiments the same computing system may incorporate both a web portal computing system and a merchant computing system. For example, some shopping websites themselves host advertisements for their own products and for other merchants&#39; products. 
     In one embodiment, the user client device  105  is an electronic device used by a human consumer to browse content on the web portal computing system  104 , and to shop for products and services on the merchant computing system  101 . The user client device  105  may be, for example, a desktop, laptop, or tablet computer, a mobile telephone, a set-top box, a dedicated electronic reader, or other form of electronic device with processing capability, and includes a web browser for viewing content received from the network  106 . The consumer uses the user client device  105  to view and interact with the advertisements provided by the collaborative advertising computing system  103 . The advertisements provided by the collaborative advertising computing system  103  may be presented on the user client device  105  as part of a web page provided by the web portal computing system  104 . For example, the consumer may view a web page received from the web portal computing system  104  on the user client device  105 , where the web page has a banner advertisement provided by the collaborative advertising computing system  103 . By interacting with the advertisement provided by the collaborative advertising computing system  103 , the user client device  105  may be directed to a web page hosted by the merchant computing system  101 . Using the web page received from the merchant computing system  101 , the consumer operating the user client device  105  may then place an order for a product or service offered by the merchant computing system  101 . 
     The collaborative advertising computing system  103  receives information about the browsing and purchasing activities of user client devices  105 —called user activity reports—from the merchant computing systems  101 , and uses this information to provide targeted advertisements to the user client devices  105 . In one embodiment these targeted advertisements are generated at the request of the web portal system  104 , and are displayed on the user client device  105  as part of a web page provided by the web portal computing system  104 . 
     The collaborative advertising computing system  103  collects user activity reports that contain browsing and purchasing information about the user client device  105 , from the merchant computing system  101 , and stores this information in the user data store  120 . Using the information in the user data store  120 , the collaborative advertising computing system  103  generates or updates a behavioral profile for the user client device  105  and stores the behavioral profile in the user data system  124 . The behavior profile of the user client device  105  contains data which captures the historic purchases, browsing, and preferences of the consumer using the user client device  105 . 
     The collaborative advertising computing system  103  may also receive data streams from the merchant computing system  101 , where the data streams contain information and content about the products and services offered by the merchant computing system  101 . This information and content is stored in the merchant data store  121 . The collaborative advertising computing system  103  uses the information in the merchant data store  121  to generate advertisements for the user client device  105 . 
     The collaborative advertising computing system  103  receives information from the web portal computing system  104  such as the subject matter and the position and size of advertising spaces on the web pages provided to the user client device  105 . The collaborative advertising system  103  may store this information in the portal data store  122 . This information is used when generating advertisements targeted for the user client device  105 . 
     The collaborative advertising computing system  103  may also receive other information from the web portal computing system  104 , such as user activity reports from the web portal concerning browsing activities of the user client device  105  on the web pages provided by the web portal computing system  104 . These user activity reports may be stored in the user data store  120 . This information may also be used to generate or update behavioral profiles stored in the user data system  124 . 
     To generate an advertisement targeted at the user client device  105 , the collaborative advertising computing system  103  retrieves the behavior profile in the user data system  124  that is associated with the user client device  105 . Based on the behavior profile and the most recent received user activity reports for the user client device  105 , the collaborative advertising computing system  103  generates an instantaneous purchasing profile for the user client device  105 . The instantaneous purchasing profile is used to predict the entity, e.g., the product, category, brand, or merchant, that the consumer using user client device  105  would be most interested in. Using this information, the collaborative advertising computing system  103  retrieves content and information from the merchant data store  121  in order to construct an advertisement for this entity. 
     By way of example, the behavior profile of the user client device  105  and the most recent user activity reports concerning the user client device  105  may generate an instantaneous purchasing profile that indicates that the consumer using the user client device  105  is highly likely to buy NIKE running shoes. The collaborative advertising computing system  103  will then fetch content and information from the merchant data store  121  to generate an advertisement banner for NIKE running shoes, which links to a merchant computing system  101  (e.g., to a shopping website of a merchant) that offers the shoe for sale. This advertisement banner will be displayed as part of a web site (webpage provided by web portal computing system  104 ) displayed on user client device  105 . 
     Because the collaborative advertising computing system  103  obtains user activity reports and other information from multiple different merchant computing systems  101  and multiple different web portal computing systems  104 , the collaborative advertising computing system  103  has more information about a specific customer (who is using a user client device  105 ) than any other single merchant computing system or web portal computing system. In addition, the collaborative advertising system  103  may collect additional information from other sources such as social networking websites and product review websites. With all of this information the collaborative advertising computing system  103  has the most complete picture of a given consumer and can make the most accurately targeted advertisements for that consumer&#39;s user client device  105 . 
     Hardware Environment 
     The entities shown in  FIG. 1  are implemented using one or more computing devices with processing capability.  FIG. 2  is a high-level block diagram illustrating an example computing device, e.g., computer  200 . The computer  200  includes at least one processor  202  coupled to a chipset  204 . The chipset  204  includes a memory controller hub  220  and an input/output (I/O) controller hub  222 . A memory  206  and a graphics adapter  212  are coupled to the memory controller hub  220 , and a display  218  is coupled to the graphics adapter  212 . A storage device  208 , keyboard  210 , pointing device  214 , and network adapter  216  are coupled to the I/O controller hub  222 . Other embodiments of the computer  200  have different architectures. 
     The storage device  208  is a non-transitory computer-readable storage medium such as a hard drive, compact disk read-only memory (CD-ROM), DVD, or a solid-state memory device. The memory  206  holds instructions and data used by the processor  202 . The pointing device  214  is a mouse, track ball, or other type of pointing device, and is used in combination with the keyboard  210  to input data into the computer  200 . The graphics adapter  212  displays images and other information on the display  218 . The network adapter  216  couples the computer  200  to one or more computer networks. 
     The computer  200  is adapted to execute computer program modules for providing functionality described herein. As used herein, the term “module” refers to computer program logic used to provide the specified functionality. Thus, a module can be implemented in hardware, firmware, and/or software. In one embodiment, program modules are stored on the storage device  208 , loaded into the memory  206 , and executed by the processor  202 . 
     The types of computers  200  used by the entities of  FIG. 1  can vary depending upon the embodiment and the processing power required by the entity. For example, the collaborative advertising computing system  103  might comprise multiple blade servers working together to provide the functionality described herein. As another example, the user client  105  might comprise a smartphone with limited processing power. The computers  200  can lack some of the components described above, such as keyboards  210 , graphics adapters  212 , and displays  218 . In addition, the collaborative advertising computing system  103  can run in a single computer  200  or multiple computers  200  communicating with each other through a network such as in a server farm. 
     Collaborative Advertising Computing System 
       FIG. 3  is a high level block diagram showing an example embodiment of components of a collaborative advertising computing system  103 . The collaborative advertising computing system  103  comprises a web portal data system  311 , a merchant data system  312 , an advertisement generation system  313 , a collaboration system  314 , a user data system  124 , and a data store  301 . The data store  301  includes the user data store  120 , the merchant data store  121 , and the web portal data store  122 . 
     The merchant data system  312  receives data, through the network  106 , from the merchant computing systems  101 . Although  FIG. 1  shows only two merchant computing system  101   a  and  101   b , in practice there may be hundreds or thousands of such merchant computing systems communicating with the merchant data system  312 . The merchant data system  312  receives user activity reports from the merchant computing systems  101 . These user activity reports contain browsing and purchasing activities of a user through that user&#39;s user client device  105 . The user activity reports are recorded by the merchant computing systems  101 . The user activity reports may be stored in the user data store  120 . The merchant data system  312  may also receive product and service data feeds from the merchant computing systems  101 . These product and service data feeds provide information and content describing the products and services offered for sale by the merchant computing systems  101 . The information includes details like price, category, brand, inventory, etc. The content includes things like product thumbnails, product photos, descriptions, product ratings etc. The information and content received through the feeds is stored in the merchant data store  121 . 
     The user data system  124  is used by the collaborative advertising computing system  103  to determine the purchasing intentions of users that are using the one or more user client devices  105 . The user data system  124  utilizes the information in the user data store  120 , e.g., the information received from merchant computing systems  101  in the user activity reports, to generate behavior profiles for the one or more user client devices  105 . A behavior profile contains the browsing (e.g., viewing and/or interacting) and purchasing history of a particular user through that user&#39;s user client device  105 . The viewing and interacting history includes the user&#39;s views of, and interactions with advertisements. The behavior profile is identified by a unique user identifier. As the collaborative advertising computing system  103  receives user activity reports, the user data system  124  updates the existing behavior profiles. 
     The user data system  124  may also contain models, formulas, and rules used to determine a purchasing intention score for a product, category, brand, merchant, or any other entity. The purchasing intention score is a measure of the likelihood that the user operating the user client device  105  will be interested in purchasing something associated with that entity. For example, a high purchasing intention score for the brand ADIDAS indicates that the user operating the user client device is likely to purchase some item associated with that brand. The purchasing intention score is calculated using both the behavioral profile and the recent user activity reports for the user client device  105 . The difference between a “recent” and “old” user activity report is not black and white, and the relevance of an activity report to the determination of purchasing intention can be based on a formula or a rule as opposed to all or nothing. In addition, the influence of old activity reports is still reflected in the behavioral profile for the user client device, since the behavioral profile is updated based on the user activity reports. 
     The purchasing intention score may be calculated or recalculated whenever a user activity takes place. A user activity can be any interaction with a website. Examples of user activities are viewing a product, clicking an advertisement link, and navigating to a new page. Each user activity may be reported to the collaborative advertising computing system  103  in a user activity report. These reported user activities can cause a change in the purchasing intention scores for the user client device  105 . Additionally, each time an advertisement is presented to the user through the user client device  105 , or when the user interacts with an advertisement displayed on the user client device  105 , the purchasing intention score may be updated. However, the update of the purchasing intention score need not simply be based on the user activity that triggered the update; other parameters, such as time duration between events, and the relationship between the current event and prior events reported for the user client device  105  and other user client devices with similar behavioral profiles may also be taken into consideration when calculating a new purchasing intention score. 
     Using the purchasing intention scores, the user data system  124  is able to generate an instantaneous purchasing profile for any known user client device, given the unique user identifier stored in the user client device. The instantaneous purchasing profile for a user client device consists of a number of purchasing intention scores and the associated products, categories, brands, merchants, and other entities for those scores. For a given user client device, choosing the entity with the highest purchasing intention score gives the entity that the user has the most predicted “interest” in purchasing. The user operating the user client device is said to have an “expressed purchasing intention” towards that entity. Purchasing intention scores can also be generated for entities chained together, such as a purchasing intention score for a particular brand, of a particular product, from a particular merchant. For example, a purchasing intention score can be generated for a ROLEX/Watch/From AMAZON.COM. Often the expressed purchasing intention for a user will be for such a chain of entities because users generally want to buy a particular brand of a particular product, such as a SONY television, LEVI&#39;S jeans, etc. 
     The web portal data system  311  receives information and requests from the web portal computing systems  104 . Although  FIG. 1  shows only two web portal computing systems  104   a  and  104   b , in practice there may be hundreds or thousands of such web portal computing systems communicating with the web portal data system  311 . The web portal data system  311  receives information from the web portal computing systems  104  regarding the subject matter of web pages sent to the user client devices  105 , and the position and size of advertising space available on the web pages sent to the user client devices  105 . The web portal data system  311  also receives requests from web portal computing systems for advertisements targeted at specific user client devices  105 . The data received by the web portal data system  311  may be stored in the web portal data store  122 , for later retrieval. 
     The advertisement generation system  313  generates targeted advertisements, for specific user client devices  105 , based on requests from web portal computing systems  104 , using the information in the user data system  124 , merchant data store  121 , user data store  120 , and web portal data store  122 . In order to generate a targeted advertisement for a user client device  105 , the advertising generation system  313  first retrieves the unique user identifier stored in the user identifier cookie in the web browser executing on the user client device  105 . The user identifier may be retrieved through the execution of a JAVASCRIPT segment embedded in a web page displayed in the web browser of the user client device  105 . 
     Using the unique user identifier the advertising generation system  313  requests the instantaneous purchasing profile for the user client device  105  from the user data system  124 . Based on the instantaneous purchasing profile, the advertising generation system  313  determines the entity (e.g., the product, category, merchant and/or brand) for which the user has expressed a purchasing intension. The advertisement generation system  313  can also be configured to obtain dimensions of a space available for the advertisement from either the web portal data store  122 , or directly from the web portal computing system  104 . Based on the purchasing intention, the advertising generation system  313  retrieves product information and content from the merchant data store  121 , or directly from the data feed of the merchant computing system, and based on this content and information generates an advertisement. The advertisement is sent to the user client device  105 , where it is displayed along with content from the web portal computing system. The advertisement, when activated, directs the user client device  105  to a web page for a participating merchant computing system. 
     The collaboration system  314  allows merchants operating merchant computing systems  101 , and operators of web portal computing systems  104  to sign up for participation in the collaborative advertising system. The collaboration system  314  also receives notification from merchant computing systems  101  when advertisements lead to purchases. The collaboration system  314  can receive payments from the merchants for the referrals, for the purchases, or based on other terms. The collaboration system  314  may also send payments to web portal systems  104  based on advertisements placed on the web portal systems&#39; web pages. 
     Example Operation of Collaborative Advertising Computing System 
       FIG. 4A  illustrates the process for establishing the data in the user data system  124 . The collaborative advertising computing system receives  400  user activity reports about one or more user client devices, from one or more merchant computing systems  101 . The user activity reports contain the purchasing and browsing activities of the user client devices. The most recent user activity reports are the most relevant for the purpose of determining a user&#39;s purchasing intentions. The collaborative advertising computing system  103  provides  401  unique user identifiers to each user client device. The unique user identifiers may be stored on the user client devices  105  as cookies through web browsers running on the user client devices. The collaborative advertising computing system  103  generates  402  a behavioral profile for each user client device  105  based on the user activity reports received for that user client device. Each behavioral profile is associated with the unique user identifier of its user client device. 
       FIG. 4B  illustrates the process for generating targeted advertisements for a user client device based on a behavior profile and recent user activity reports. The collaborative advertising system  103  receives  403  one or more recent user activity report from merchant computing systems. The collaborative advertising computing system  103  receives  404  a request for a targeted advertisement for a specific user client device, from a web portal computing system  104 . The unique user identifier of the user client device is retrieved  405  from the user identifier cookie on the user client device  105 . The behavioral profile associated with the unique user identifier is retrieved  406  from the user data system  124 . This is the behavioral profile for the user client device  105 . The collaborative advertising computing system  103  generates  407  an instantaneous purchasing profile for the user client device  105  based on the retrieved behavioral profile and the recent user activity reports. The collaborative advertising computing system  103  generates  408  a targeted advertisement based on the instantaneous purchasing profile, and provides the generated advertisement to the user client device  105 . 
       FIG. 5  is a ladder diagram illustrating one embodiment of an example process for a participating merchant computing system  101  to serve a targeted advertisement to a potential customer (using a user client device) in an environment with several user client devices  105  and several merchant computing systems  101 . As shown, a first user using a user client device (“user  1 ” in  FIG. 5 ) visits a first participating merchant computing system (“merchant  1 ” in  FIG. 5 ) to perform some user activities such as browsing or purchasing an item or a service. The first participating merchant computing system reports the user activities to the collaborative advertising computing system  103  (“Collaborative System” in  FIG. 5 ) in a user activity report (“Activity Report” in  FIG. 5 ), and enables the collaborative advertising computing system  103  to place a user identifier cookie (“UID Cookie” in  FIG. 5 ) in the first user client device&#39;s browser, where the user identifier cookie contains a unique user identifier for the user client device. 
     The collaborative advertising computing system  103  stores the user activity report in the user data store  120 , and updates (or creates) a behavioral profile associated with the unique user identifier in the user data system  124 . The collaborative advertising computing system  103  also notifies a participating web portal computing system (“portal  1 ” in  FIG. 5 ) about the first user client device, and enables the web portal computing system to place its own collaborative cookie (“Coll. Cookie” in  FIG. 5 ) in the first user client device&#39;s browser. 
     Subsequently, a second user client device (“user  2 ” in  FIG. 5 ) visits a second participating merchant computing system (“merchant  2 ” in  FIG. 5 ) to perform some user activities such as browsing and purchasing an item or a service. As described above, the second participating merchant computing system reports the observed user activities to the collaborative advertising computing system  103 . The collaborative advertising computing system  103  places a user identifier cookie, containing a unique user identifier, in the second user client device&#39;s web browser, and the participating web portal computing system places a collaborative cookie in the second user client device&#39;s web browser. The collaborative advertising computing system  103  stores the user activity report in the user data store  120 , and updates (or creates) a behavioral profile associated with the unique user identifier in the user data system  124 . At this point, the first user client device has not interacted with the second merchant computing system, and the second user client device has not interacted with the first merchant computing system. 
     The first user client device subsequently connects to the web portal computing system and performs some browsing activities. The web portal computing system detects the presence of its collaborative cookie in the first user client device&#39;s web browser and thus determines that the first user client device is known to the collaborative advertising computing system  103 . As a result, the web portal computing system notifies the collaborative advertising computing system  103  of the connection by the first user client device, and transmits the observed user activities along with advertisement placement information to the collaborative advertising computing system. The collaborative advertising computing system  103  identifies the first user client device based on the unique user identifier in the user identifier cookie residing in the first user client device&#39;s web browser. The system  103  retrieves the behavioral profile associated with that user identifier from the user data system  124  and the recent user activity report received from the first merchant computing system stored in the user data store  120 . 
     The collaborative advertising computing system  103  generates an instantaneous purchasing profile for the first user client device based on the retrieved information. The collaborative advertising computing system  103  also determines that the second merchant computing system is the most likely entity to close a sale with the user operating the first user client device. The collaborative advertising computing system  103  generates an advertisement for the second merchant computing system matching this inferred user intent, and serves the generated advertisement to the first user client device through the content web page displayed in the web browser of the first user client device. Because it is likely that the advertisement matches the intent of the user operating the first user client device, the user clicks on (or otherwise activates) the advertisement and is redirected to the second merchant computing system. 
     Thus by collecting customer information from many participating sources and domains, the collaborative advertising system beneficially builds a large database of customer information for customers of many participating sources. The collaborative advertising computing system  103  can infer a customer&#39;s intent based on information collected for that customer and generate an advertisement that is likely to lead to a purchase by that customer. 
     The embodiments disclosed herein beneficially aggregate information about users&#39; purchasing and browsing habits to provide advertisements on the user client devices  105  that help those users find the products and services that they are most likely to purchase. Example embodiments disclosed herein also beneficially provide merchant websites (merchant computing systems  101 ) with high quality user traffic, through advertising links, where the visiting users are highly likely to make a purchase. Further, the disclosed embodiments are able to generate tangible advertisements and electronically deliver them to specific user client devices based on prior electronic browsing and purchasing experiences of the users. 
     Additional Considerations 
     As used herein any reference to “one embodiment” or “an embodiment” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment. 
     Some embodiments may be described using the expression “coupled” and “connected” along with their derivatives. It should be understood that these terms are not intended as synonyms for each other. For example, some embodiments may be described using the term “connected” to indicate that two or more elements are in direct physical or electrical contact with each other. In another example, some embodiments may be described using the term “coupled” to indicate that two or more elements are in direct physical or electrical contact. The term “coupled,” however, may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. The embodiments are not limited in this context. 
     As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having” or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). 
     In addition, use of the “a” or “an” are employed to describe elements and components of the embodiments herein. This is done merely for convenience and to give a general sense of the disclosure. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise. 
     Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for a system and a process for collaboratively collecting customer information and providing individualized advertisements. Thus, while particular embodiments and applications have been illustrated and described, it is to be understood that the present invention is not limited to the precise construction and components disclosed herein and that various modifications, changes and variations which will be apparent to those skilled in the art may be made in the arrangement, operation and details of the method and apparatus disclosed herein.