Patent Publication Number: US-2012047014-A1

Title: Method and system for using email receipts for targeted advertising

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The present invention relates to data mining and online advertising. 
     2. Background 
     Advertisers and other entities in the online world are interested in assessing what products and services consumers are purchasing. Various techniques are currently used to make such an assessment. For example, the web pages that are viewed by users or the search queries that are entered by users into search engines may be tracked and analyzed to infer the purchasing habits of the users. However, such an analysis is subject to interpretation because the actual purchases are not known. In another technique, “conversions” may be measured to make such an assessment. A conversion may occur when a user interacts with an online advertisement in a manner desired by the advertiser. For example, a conversion may occur when the user selects (e.g., clicks on) the advertisement to cause an action to occur, such as displaying further information about the advertised item and providing an interface that the user may use to purchase the item (e.g., by displaying the advertiser&#39;s website, etc.). In another example, a conversion may occur when the user actually purchases the advertised item. By tracking conversions, advertisers can better assess the effectiveness of their advertisements, and can better learn what products and services are being purchased by consumers. 
     Currently, to determine when such a conversion occurs, a pixel or beacon is present that is associated with an online advertisement. The pixel or beacon is activated when a user interacts with the advertisement in a predetermined manner to indicate that a conversion has occurred. However, this technique has disadvantages, including being labor-intensive, limiting the potential for granular targeting, and not operating at scale. As such, further techniques are desired for measuring conversions and/or for otherwise assessing what products and services that consumers are purchasing. 
     BRIEF SUMMARY OF THE INVENTION 
     Various approaches are described herein for, among other things, classifying users based on email associated with the users. For instance, email that is stored in an email store (e.g., in email mailboxes of the users) may be analyzed to classify the users. Information included in the emails may be extracted, and the users may be classified into one or more user categories according to the extracted information. The user categories may be used in various ways, including being used to determine purchasing habits of the users, to provide conversion information, and/or may be used in further ways. 
     The extracted information may be analyzed in a manner so as to protect the personal information of the users according to any applicable privacy rules or regulations. For instance, anonymous email targeting may be implemented. In such an implementation, any user personal information contained in the email may be disassociated (e.g., deleted, not extracted from the email, maintained separately, etc.) from other information extracted from the email. In another implementation, personalized email targeting may be implemented. In such an implementation, a user may be enabled to opt-in or opt-out of having the user&#39;s personal information associated with the other information extracted from the emails. A default setting may be used to “disassociate-by-default” user personal information from the other information extracted from the emails. 
     Any number of types of emails may be analyzed to classify users in any number of ways. For instance, in one example method implementation, a plurality of emails may be stored in an email store. A plurality of commercial emails stored in the email store may be determined The commercial emails may be parsed to extract commercial information included in the commercial emails. The commercial information may be parsed to generate user classification data. 
     In example system implementation, a user classifier is provided. The user classifier includes a commercial email determiner, a commercial email parser, and a commercial information processor. The commercial email determiner is configured to determine a plurality of commercial emails in an email store. The commercial email parser is configured to parse the commercial emails to extract commercial information included in the commercial emails. The commercial information processor is configured to process the commercial information to generate user classification data. 
     Furthermore, the commercial information processor may include a commercial information analyzer and a user classification data generator. The commercial information analyzer is configured to analyze the commercial information to categorize one or more users in one or more categories. The user classification data generator is configured to generate user classification data that indicates the one or more users categorized in the one or more categories. 
     In one example aspect, the commercial information analyzer is configured to analyze the commercial information to categorize each of the users in one or more of a shopping category, a frequent shopper category, a spending level category, a time-based purchaser category, a purchase time category, a purchase frequency category, an average purchase amount category, a purchaser demographics category, a similar purchasing characteristics category, an exclusion category, and a correlated purchases category. 
     Generated user classification data may be used in various ways. For instance, in one implementation, user profiles may be generated based on the user classification data. In another implementation, online advertisements may be selected for display based on the generated user classification data. 
     Computer program products are also described herein that enable classification of users using information extracted from email, and that enable further embodiments as described herein. 
     Further features and advantages of the disclosed technologies, as well as the structure and operation of various embodiments, are described in detail below with reference to the accompanying drawings. It is noted that the invention is not limited to the specific embodiments described herein. Such embodiments are presented herein for illustrative purposes only. Additional embodiments will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments of the present invention and, together with the description, further serve to explain the principles involved and to enable a person skilled in the relevant art(s) to make and use the disclosed technologies. 
         FIG. 1  shows a block diagram of a data communication system, according to an example embodiment. 
         FIG. 2  shows a block diagram of an email server, according to an example embodiment. 
         FIG. 3  shows a block diagram of an example of the data communication system of  FIG. 1 , according to an embodiment. 
         FIG. 4  shows a flowchart for classifying users, according to an example embodiment. 
         FIG. 5  shows a block diagram of a user classifier, according to an example embodiment. 
         FIG. 6  shows a block diagram of commercial email determiner, according to an example embodiment. 
         FIG. 7  shows a block diagram of commercial email parser, according to an example embodiment. 
         FIG. 8  shows a block diagram of a commercial information processor, according to an example embodiment. 
         FIG. 9  shows a flowchart for processing commercial information to classify users, according to an example embodiment. 
         FIG. 10  shows a block diagram of commercial information analyzer, according to an example embodiment. 
         FIG. 11  shows a block diagram of a user profile generator, according to an example embodiment. 
         FIG. 12  shows a process for selecting online advertisements, according to an example embodiment. 
         FIG. 13  shows a block diagram of an example advertisement network, according to an embodiment. 
         FIG. 14  is a block diagram of a computer in which embodiments may be implemented. 
     
    
    
     The features and advantages of the disclosed technologies will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number. 
     DETAILED DESCRIPTION OF THE INVENTION 
     I. Introduction 
     The following detailed description refers to the accompanying drawings that illustrate exemplary embodiments of the present invention. However, the scope of the present invention is not limited to these embodiments, but is instead defined by the appended claims. Thus, embodiments beyond those shown in the accompanying drawings, such as modified versions of the illustrated embodiments, may nevertheless be encompassed by the present invention. 
     References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” or the like, indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the art to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Techniques for using emails to determine conversions and to classify users for various purposes are described herein. Email that is stored in an email store may be analyzed to classify the users. Information included in the emails may be extracted, to provide conversion information, and to enable the users to be classified into one or more user categories according to the extracted information. 
     For instance, in one embodiment, commercial email receipts are used as conversions for personal and/or anonymous targeting for online advertising. Machine reading of email header and/or email body content may be performed to determine what users are purchasing. The purchasing determination may be used as conversion-based data within online advertising targeting models according to anonymous targeting and/or personalized targeting techniques. Anonymous targeting may be performed to use one or more user&#39;s commercial email habits to reason about the habits of other users. In such case, there is no association of a user back to the original email recipient. According to personalized targeting, an association between users and their email receipts may be maintained. 
     Embodiments provide numerous advantages over conventional approaches. For example, embodiments provide a clean determinant of a user conversion as compared to search query or browsing behavior. An email receipt for a purchased item transmitted from a commercial entity to a user provides a clear indication that the user purchased the item, and thus provides a clear indication of a conversion. Embodiments simplify techniques for determining a conversion. For example, pixels or beacons do not need to be setup and/or managed, as in conventional techniques. Furthermore, embodiments enable additional data to be provided that is not typically available with pixel-based conversion. Examples of such data include a value of a conversion, an identification of a product within the conversion, a time left on a contract if a subscription-based conversion has occurred, and further data. 
     Example embodiments are described in further detail in the following subsections. 
     II. Example Systems and Methods for Classifying Users Based on Emails 
     In embodiments, emails delivered between entities may be analyzed to determine conversions and to classify one or both of the entities. Such embodiments may be implemented in various environments. For example,  FIG. 1  shows a block diagram of a data communication system  100 , according to an example embodiment. As shown in  FIG. 1 , system  100  includes a first user device  102 , an email server  104 , a second user device  106 , and a network  108 . System  100  is described as follows to illustrate email delivery between entities. 
     First and second user devices  102  and  106  may each be any type of device that enables a user to send and receive email, including a desktop computer (e.g., a personal computer), a mobile computer or computing device (e.g., a Palm® device, a RIM Blackberry® device, a personal digital assistant (PDA), a laptop computer, a notebook computer, etc.), a smart phone, or other type of computing device. Email server  104  may include one or more servers, which may be any type of computing device described herein or otherwise known that facilities delivery of emails. User device  102 , user device  106 , and email server  104  are communicatively coupled by network  108 . Network  108  may include one or more communication links, communication networks, and/or communication devices (e.g., routers, switches, hubs, etc.). For instance, network  108  may be a PAN (personal area network), a LAN (local area network), a WAN (wide area network), or a combination of networks, such as the Internet. First, second, and third communication links  116 ,  118 , and  120 , which respectively couple user device  102 , email server  104 , and user device  106  to network  108 , may include any number of communication links, including wired and/or wireless links, such as IEEE 802.11 wireless LAN (WLAN) wireless links, Worldwide Interoperability for Microwave Access (Wi-MAX) links, cellular network links, wireless personal area network (PAN) links (e.g., Bluetooth™ links), Ethernet links, USB links, etc. 
     System  100  is configured to enable devices such as user devices  102  and  106  to communicate with each other via email. For example, as shown in  FIG. 1 , user device  102  includes an email client  110 , and user device  106  includes an email client  114 . Email clients  110  and  114  operate to manage user email at user devices  102  and  106 , respectively. For instance, email clients  110  and  114  may be mail user agents (MUAs) or other types of email clients. A first user at user device  102  may interact with an email user interface of email client  110  to generate an email addressed to a second user, or the email may be generated automatically at user device  102 . The generated email is transmitted from user device  102  as email  122  in a first communication signal according to any suitable protocol (e.g., using the Simple Mail Transfer Protocol (SMTP) or other communication protocol). Network  108  is configured to deliver email  122  to an email mailbox of the second user. As shown in  FIG. 1 , email  122  may be transmitted through first communication link  116 , network  108 , and second communication link  118  to email server  104 . 
     Email server  104  receives email  122  in the transmitted first communication signal. Email server  104  may be a mail transfer agent (MTA) or other type of email server configured to transfer email between user devices. As shown in  FIG. 1 , email server  104  includes an email store  124 . Email store  124  stores email for a plurality of users. For example, as shown in  FIG. 1 , email store  124  may store user emails in email mailboxes  112 , such as email mailboxes  112   a - 112   n,  with each email mailbox  112  being associated with a particular user. Thus, email  122  may be stored in email store  124  in an email mailbox  112  associated with the second user, such as email mailbox  112   a.    
     The second user may desire to check email mailbox  112   a  for any received emails. For instance, at second user device  106 , the second user may interact with a user interface of email client  114  to initiate a download of email from email mailbox  112   a,  or email client  114  may initiate the email download automatically (e.g., using a Post Office Protocol (POP) or other communication protocol). As shown in  FIG. 1 , any emails stored in email mailbox  112   a,  including email  122 , may be transmitted to user device  106 . Email  122  is transmitted in a second communication signal from email server  104 . The second communication signal is transmitted through second communication link  118 , network  108 , and third communication link  120 , and is received by user device  106 . The second user may subsequently read email  122  at user device  106 . 
       FIG. 1  is provided for illustrative purposes, and is not intended to be limiting. In embodiments, emails may be transmitted between user devices in alternative ways. For instance, in another embodiment, the first user may log into email server  104  to generate email  122  at email server  104 . Additionally or alternatively, the second user may log into email server  104  to access and read email  122  in email mailbox  112   a.    
     In an embodiment, email stored in email store  124  may be read and analyzed to extract information that may be used to determine conversions and to classify users. For instance,  FIG. 2  shows a block diagram of an email server  200 , according to an example embodiment. As shown in  FIG. 2 , email server  200  includes email store  124 , which includes email mailboxes  112   a - 112   n.  Furthermore, as shown in  FIG. 2 , email server  200  includes a user classifier  202 . As shown in  FIG. 2 , user classifier  202  receives emails  204  from email mailboxes  112   a - 112   n  in email store  124 . Emails  204  may include emails from all of email mailboxes  112   a - 112   n,  or may include emails from selected email mailboxes  112   a - 112   n  (e.g., according to user opt-in/opt-out selections, etc.). User classifier  202  is configured to extract information from emails  204 , and to classify the users associated with emails  204  (e.g., the targeted email recipients) according to the extracted information. As shown in  FIG. 202 , user classifier  202  generates user classification data  206 . User classification data  206  may include one or more categories of users. User classification data  206  may be used in various ways. For example, in an embodiment, user classification data  206  may be used to select advertisements to be displayed to users. 
     Note in an embodiment, as shown in  FIG. 2 , user classifier  202  may be included in an email server, such as email server  200 . In another embodiment, user classifier  202  may be located in a computer system that is separate from an email server. In embodiments, email store  124  may store email for any number of users, including thousands or millions of users, and may include any number of email mailboxes  112 . By having access to large numbers of emails associated with a large number of users, user classifier  202  is enabled to generate user classification data  206  that leverages a large amount of knowledge extracted from the emails. By utilizing a large amount of knowledge, an amount of “noise” can be reduced when inferring information for a single user or for a single type of commercial mail message in a “wisdom of the crowds” type of approach. 
     In embodiments, user classifier  202  may be configured to classify the users in a personalized manner or in an anonymous manner. For instance, in an example of personalized user classification, users may be enabled to opt-in to a user classification system to allow their personal information to be associated with one or more generated user categories. Alternatively, users may be enabled to opt-out of having their personal information associated with the one or more of the generated user categories, and/or their personal information may not be associated with the one or more generated user categories by default. User classifier  202  may be configured to protect personal information of users according to any applicable privacy rules or regulations. 
     User classifier  202  may be configured to classify users according to any type of information obtainable from emails  204 . Examples of such information include commercial information, social information, hobby-related information, etc. Example embodiments for classifying users according to commercial information are described in the following subsections. Such embodiments are not intended to be limiting, and in further embodiments, users may be classified according to other types of information extracted from email. 
     A. Example Systems and Methods for Classifying Users According to Commercial Emails 
     In embodiments, emails delivered between entities may be analyzed to determine characteristics of the entities that may be used to classify one or both of the entities. Such embodiments may be implemented in various environments. For example, system  100  of  FIG. 1  may be configured in various ways to enable classification of users. For instance,  FIG. 3  shows a block diagram of a data communication system  300 , according to an example embodiment. Data communication system  300  is configured to classify users according to commercial information. In the example of system  300  in  FIG. 3 , commercial emails, such as email purchase receipts/confirmations and/or other emails from vendors, that are addressed to users are processed by user classifier  202  to classify the users. System  300  is described for purposes of illustration, and in further embodiments, users may be classified in other ways, according to other types of emails, as would be apparent to persons skilled in the relevant art(s) from the teachings herein. Such additional user classification embodiments are within the scope and spirit of the present invention. 
     Data communication system  300  of  FIG. 3  is similar to system  100  of  FIG. 1 , with email server  200  of  FIG. 2  being included in system  300  (in place of email sever  104  of  FIG. 1 ). As shown in  FIG. 3 , email server  200  includes email store  124  (which includes email mailboxes  112   a - 112   n ) and user classifier  202 . Furthermore, system  300  includes a commercial entity device  302  as an example of user device  102  of  FIG. 1 . Commercial entity device  302  includes an email client  310  configured to manage email. A user of user device  106  may purchase an item (e.g., a product or service) from a vendor associated with commercial entity device  302 . For example, the user of user device  106  may interact with a website (e.g., a web service) of commercial entity device  302  to purchase the item from the vendor, may communicate with the vendor by phone to purchase the item, or may purchase the item in any other manner. As a result of the purchase transaction, commercial entity device  302  transmits a receipt email  322 , which is an email that confirms the purchase transaction, and may indicate attributes of the transaction, such as the item purchased, a purchase price, the date of purchase, an estimated delivery date (if applicable), etc. 
     Email  322  is transmitted from commercial entity device  302  in a first communication signal according to any suitable protocol. Network  108  delivers email  322  to an email mailbox of the second user. As shown in  FIG. 1 , email  322  may be transmitted through first communication link  116 , network  108 , and second communication link  118  to email server  200 . Email server  200  receives email  322 , and email store  124  may store email  322  in email mailbox  112   a  associated with the user of user device  106 . 
     The user of user device  106  may check email mailbox  112   a  (e.g., by interacting with a user interface of email client  114 ). Email  322  may be read by the user by logging into email server  200 , or email  322  may transmitted in a second communication signal from email server  200  to user device  106  to be read by the user. The second communication signal is transmitted through second communication link  118 , network  108 , and third communication link  120 , and is received by user device  106 . The user of user device  106  is thereby enabled to view receipt email  322 . 
     In this manner, commercial emails, such as receipt email  322  may be collected in email mailboxes  112  in email store  124 . Commercial emails indicate that an item purchase transaction has occurred, and may be an email receipt/confirmation of the transaction, for example. User classifier  202  may be configured to analyze the commercial emails of email store  124  to classify users to generate user classification data  206  ( FIG. 2 ). In embodiments, user classifier  202  performs user classification based on commercial emails in various ways. For instance,  FIG. 4  shows a flowchart  400  for classifying users, according to an example embodiment. In an embodiment, user classifier  202  may operate according to flowchart  400 . Furthermore,  FIG. 5  shows a block diagram of a user classifier  500 , according to an example embodiment. User classifier  500  is an example of user classifier  202  that is configured to classify users according to commercial information extracted from email. As shown in  FIG. 5 , user classifier  500  includes a commercial email determiner  502 , a commercial email parser  504 , a commercial information processor  506 , and a conversion indicator  512 . Flowchart  400  is described with respect to user classifier  500  for illustrative purposes. Further structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following description of flowchart  400  and user classifier  500 . 
     Flowchart  400  begins with step  402 . In step  402 , a plurality of commercial emails in an email store is determined For example, as shown in  FIG. 5 , commercial email determiner  502  receives emails  204 . Commercial email determiner  502  is configured to analyze emails  204  to determine any commercial emails included therein, which are output by commercial email determiner  502  as commercial emails  508 . 
     Commercial emails are emails that provide an indication of commercial interest. For example, in one embodiment, commercial emails include email receipts to persons that indicate that an item purchase transaction has occurred, and that may be an email receipt/confirmation of the transaction. In another embodiment, commercial emails may further include emails to persons from commercial entities, whether or not the emails specifically indicate that an item purchase transaction has occurred. For example, if a person is on an email list of a car dealership, the person may likely have an interest in purchasing a car, or may have purchased a car recently. As such, an email to the person from the car dealership provides an indication of a commercial interest (i.e., the person&#39;s likely interest in cars). In another example, a person who receives email from a store probably shops at the store, and as such, the email from the store is an indication of a commercial interest. In still another example, a person who receives an email from a financial services company probably has an account at the financial services company, and as such, the email from the financial services company is an indication of a commercial interest. 
     Commercial email determiner  502  may be configured to determine commercial emails in emails  204  in any manner, including by analyzing the contents of each email of emails  204 . For instance, commercial email determiner  502  may be trained on a set of commercial email receipts to determine aspects of commercial emails to search for when analyzing emails. In another example, commercial email determiner  502  may classify particular websites/domains as commercial or non-commercial, and may determine whether an email is commercial or non-commercial depending on the source website/domain. Commercial email determiner  502  may additionally and/or alternatively use further techniques to determine commercial emails, in embodiments. 
     For instance,  FIG. 6  shows a block diagram of commercial email determiner  502 , according to an example embodiment. As shown in  FIG. 6 , commercial email determiner  502  includes an email header analyzer  602  and an email body analyzer  604 . Email header analyzer  602  is configured to analyze the header portions of each email included in emails  204  for commercial email indications to determine commercial emails. Email body analyzer  604  is configured to analyze the body portions of each email included in emails  204  for commercial email indications to determine commercial emails. In embodiments, either one or both of email header analyzer  602  and email body analyzer  604  may be present in commercial email determiner  502 . 
     For example, email header analyzer  602  may analyze one or more fields of an email header, including one or more of the “From:” field, the “To:” field, the “Subject:” field, the “Date:” field, and/or any other email header field. For instance, the “From:” field may include an email address of the email sender (e.g., an email address associated with commercial entity device  302  of  FIG. 3 ). Email header analyzer  602  may be configured to determine the sending domain name of the email address in the “From:” field, and to compare the determined sending domain name with a predetermined list of domain names previously determined to be associated with commercial entities/vendors. For example, an email address indicated in the “From:” field of an email may be auto-confirm@amazon.com, which has the domain name of “amazon.com.” The predetermined list of domain names maintained by email header analyzer  602  may include the domain name “amazon.com,” indicating that emails received from amazon.com are commercial emails. As such, email header analyzer  602  may compare the received domain name of amazon.com to the predetermined list of domain names to find a match, and thereby indicate the email as a commercial email received from a commercial entity. In such case, the email may be included in commercial emails  508  by email header analyzer  602 . 
     In another example, an email address indicated in the “From:” field of an email may be josephsmith@hotmail.com, which has the domain name of “hotmail.com.” The predetermined list of domain names maintained by email header analyzer  602  may not include the domain name “hotmail.com,” indicating that emails received from hotmail.com are not commercial emails. As such, email header analyzer  602  may not find a match for hotmail.com in the predetermined list, and may thereby indicate the email as not being a commercial email, received from a non-commercial entity. In such case, the email may not be included in commercial emails  508  by email header analyzer  602 . 
     In another example, email body analyzer  604  may analyze the contents of the body of an email to find email body attributes that may indicate the email as being transmitted from a commercial entity. For instance, email body analyzer  604  may search the contents of the body of the email for one or more words such as “order,” “grand total,” shipping,” “billing,” and/or “purchase” that indicate a commercial transaction has taken place, for a commercial entity/vendor name in a predetermined list of commercial entity/vendor names, for monetary amounts (e.g., indicated by a dollar sign or other monetary denomination), a predetermined signature block for a commercial entity/vendor, and/or other indication that the email is a commercial email. 
     For example, an email may include the following information in the email body: 
                                ***********************************************************       BILLING AND SHIPPING INFORMATION       ***********************************************************       E-mail Address: joesmith@yahoo.com       Billing and Shipping Address:       Joseph Smith       5100 Main St.       Toledo, OH 43601       United States       ***********************************************************       ORDER DETAILS       ***********************************************************       Shipping estimate for these items: November 17, 2009       1 “Call of Duty: Modern Warfare 2”       Video Game; $59.99       Sold by: Amazon.com, LLC                    
Email body analyzer  604  may detect one or more of the words “billing,” “order,” “shipping,” or “sold,” may detect the vendor name “Amazon.com,” and/or may detect the monetary amount of “$59.99” to determine that the example email is a commercial email. Email body analyzer  604  may be configured to indicate the email as a commercial email by detecting a predetermined number and/or combination of such email body attributes.
 
     The embodiment of commercial email determiner  502  shown in  FIG. 6  is provided for purposes of illustration, and in other embodiments, commercial email determiner  502  may be configured in alternative ways to determine commercial emails. 
     As shown in  FIG. 5 , conversion indicator  512  receives commercial emails  508 . When present, conversion indicator  512  is configured to generate conversions  514  to indicate conversions that have occurred based on commercial emails  508 . For example, conversion indicator  512  may indicate a total number of conversions in conversions  514  based on the total number of commercial emails in commercial emails  508 . In another embodiment, conversion indicator  512  may analyze each commercial email in commercial emails  508  to categorize each commercial email as a particular type of conversion, including categorizing conversions by vendor, by product type, etc. Conversion indicator  512  may be configured to count commercial emails as conversions for particular advertising campaigns. For instance, in an embodiment, conversion indicator  512  may be configured to count commercial emails associated with a particular vendor that are received after initiation of a related advertising campaign as conversions for that advertising campaign. 
     Referring back to  FIG. 4 , in step  404 , the commercial emails are parsed to extract commercial information. For example, as shown in  FIG. 5 , commercial email parser  504  receives commercial emails  508 . Commercial email parser  504  is configured to parse the emails included in commercial emails  508  for commercial information that may be used to classify users. As shown in  FIG. 5 , commercial email parser  504  outputs commercial information  510 . 
     Commercial email parser  504  may be configured to parse commercial emails  508  in any manner to extract any type and amount of commercial information contained therein, as desired for the particular application. For instance, in embodiments, commercial email parser  504  may be manually configured and/or may be trained on example emails to extract purchased product information and/or any other commercially relevant information. 
     For example, commercial email parser  504  may contain a header parser configured to extract one or more fields of each email header, including one or more of the “From:” field, the “To:” field, the “Subject:” field, the “Date:” field, and/or any other email header field, and/or may include a body parser configured to extract information from the email body, such as one or more of a billing address, a shipping address, one or more item names, one or more item types, a purchase price for each item and a grand total (if more than one item is purchased), a commercial entity/vendor name, a shipping date, a lease expiration date, and/or any other information from the email body. 
     For instance, in one example commercial email, commercial email parser  504  may extract from the email header auto-confirm@amazon.com as the vendor email address from the “From:” header field, “Joseph Smith” as the user name from the “To:” header field, “Dec. 11, 2009” as the purchase date from the “Date:” header field. In this example, from the email body, commercial email parser  504  may extract “Call of Duty: Modern Warfare  2 ” as the item name, “video game” as the item type, “$59.99” as the item purchase price, “Toledo, Ohio 43601” as the shipping and billing city/state/zip code, and “Amazon.com” as the vendor name. 
     Commercial information  510  may include commercial information in any format. For example, commercial information  510  may be formed as a table, an array, a spreadsheet, or any other data structure. Commercial information  510  may include the extracted commercial information for each email organized together, may include the commercial information extracted for each user organized together (e.g., extracted commercial information for one or more emails addressed to the user being organized together), or may include extracted commercial information organized in any other manner. 
     Furthermore, in an embodiment, commercial email parser  504  may receive additional commercially-relevant information to include in commercial information  510  that may subsequently be used to classify users. For example, in an embodiment, commercial email parser  504  may receive one or more of browsing history data associated with users, searching data (e.g., queries entered into a search engine) associated with users, purchase records received by users from offline retailers, and/or further information. Such information may be received from any associated sources, including user accounts, a search engine, and/or other sources, and may be filtered according to any applicable privacy settings. 
     As described above, in embodiments, user classifier  202  may be configured to classify users in a personalized manner or in an anonymous manner. For instance, in an embodiment, email server  200  or other computing device may provide a user interface for users to configure privacy settings for accessing emails in their email mailboxes. For instance, a user may be enabled to interact with the user interface to opt-out of user classification, such that emails in their email mailbox are not analyzed by commercial email determiner  502 , and thus are not included in commercial emails  508 . The user may be enabled to opt-in to user classification such that emails in their email mailbox are analyzed by commercial email determiner  502 , and thus may be included in commercial emails  508 . In such case, the user may be enabled to select personalized targeting or anonymous targeting by further interacting with the user interface. According to personalized targeting, the user allows their personal information to be extracted by commercial email parser  504 . According to anonymous targeting, the user does not allow their personal information to be extracted by commercial email parser  504 . 
     For instance,  FIG. 7  shows a block diagram of commercial email parser  504 , according to an example embodiment. As shown in  FIG. 7 , commercial email parser  504  includes a privacy module  702 . Privacy module  702  may receive a targeting selection  704  for each user having emails stored in email store  124 . Targeting selection  704  may be selected by the user, and indicates whether the user selects personalized targeting or anonymous targeting. If targeting selection  704  indicates that the user selected personalized targeting, privacy module  702  is configured to enable commercial email parser  504  to extract personal information from commercial emails of the user. If targeting selection  704  indicates that the user selected anonymous targeting, privacy module  702  is configured to disable commercial email parser  504  from extracting personal information from commercial emails of the user. In such case, an anonymous label (e.g., user  1 , user  2 , etc.) may be associated with the extracted commercial information rather than the user&#39;s name or other personal information. 
     Referring back to  FIG. 4 , in step  406 , the commercial information is processed to generate user classification data. For example, as shown in  FIG. 5 , commercial information processor  506  receives commercial information  510 . Commercial information processor  506  is configured to process the commercial information in commercial information  510  to generate user classification data that indicates user categories. As shown in  FIG. 5 , commercial information processor  506  outputs user classification data  206 . 
     Commercial information processor  506  may be configured to process commercial information  510  in any manner, as desired for the particular application. For instance,  FIG. 8  shows a block diagram of commercial information processor  506 , according to an example embodiment. As shown in  FIG. 8 , commercial information processor  506  includes a commercial information analyzer  802  and a user classification data generator  804 . Commercial information processor  506  of  FIG. 8  is described with respect to  FIG. 9 .  FIG. 9  shows a flowchart  900  for processing commercial information to classify users, according to an example embodiment. In an embodiment, commercial information processor  506  of  FIG. 8  may operate according to flowchart  900 . Further structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the following description of flowchart  900  and commercial information processor  506  of  FIG. 8 . 
     Flowchart  900  begins with step  902 . In step  902 , the commercial information is analyzed to categorize one or more users into one or more categories. For example, as shown in  FIG. 8 , commercial information analyzer  802  receives commercial information  510 . Commercial information analyzer  802  is configured to analyze commercial information  510  to categorize users into one or more categories. As shown in  FIG. 8 , commercial information analyzer  8802  generates category information  806 , which indicates one or more categories and the one or more users categorized into each category. 
     For instance, with regard to personalized targeting, commercial information analyzer  802  may categorize each user identified in a “To:” field (the targeted email recipients) of a commercial email into one or more categories. In an anonymous targeting embodiment, commercial information  510  may include an anonymous user label (e.g., user  1 , user  2 , etc.) associated with the commercial information extracted by commercial email parser  504  for each user. Commercial information analyzer  802  may be configured to categorize the commercial information associated with each anonymous user label into one or more categories. 
     For example, with regard to personalized targeting, the user “Joseph Smith” may be categorized by commercial information analyzer  802  into a “video game user” category due to the above described example commercial email receipt for the purchase of the item “Call of Duty: Modern Warfare  2 ” of item type “video game.” If anonymous targeting is used (e.g., the user Joseph Smith elected anonymous targeting for his user profile and/or anonymous targeting is used by default), the anonymous user label “user  3587 ” (or other anonymous user label) for “Joseph Smith” may be categorized into the “video game user” category as described above. As a result, category information  806  may include an indication that Joseph Smith or “user  3587 ” is included in the “video game” category. 
     Any number and type of categories for users may be indicated in category information  806 . Further examples of the categorization of users by commercial information analyzer  802  are described in the next section provided further below. 
     In step  904 , user classification data is generated that indicates the one or more users categorized in the one or more categories. For instance, as shown in  FIG. 8 , user classification data generator  804  receives commercial information  806  and category information  806 . Classification data generator  804  is configured to combine and/or organize commercial information  510  and category information  806  to generate user classification data  206 . User classification data  206  includes commercial information  510  and indicates the one or more categories and the one or more users categorized into each category of category information  206 . User classification data  206  may be formed as a table, an array, a spreadsheet, or any other data structure. 
     The embodiment of commercial email processor  506  shown in  FIG. 8  is provided for purposes of illustration, and in other embodiments, commercial email processor  506  may be configured in alternative ways to analyze commercial information to generate user classification data. 
     The following subsection describes examples of categories for user classification. 
     B. Examples Categories for User Classification 
     As described above, commercial information analyzer  802  is configured to analyze commercial information  510  to categorize users into one or more categories (e.g., according to step  902  in  FIG. 9 ). As shown in  FIG. 8 , commercial information analyzer  802  generates category information  806 , which indicates one or more categories and the one or more users categorized into each category. In embodiments, commercial information analyzer  802  may be configured to classify users into any number of categories. For instance,  FIG. 10  shows a block diagram of commercial information analyzer  802 , according to an example embodiment. As shown in  FIG. 10 , commercial information analyzer  802  includes a shopping categorizer  1002 , a frequent shopper categorizer  1004 , a spending level categorizer  1006 , a time-based purchaser categorizer  1008 , an ad exclusion categorizer  1010 , a purchase time categorizer  1012 , a purchase frequency categorizer  1014 , an average purchase amount categorizer  1016 , a purchaser demographics categorizer  1018 , a correlated purchase categorizer  1020 , and a similar purchasing characteristics categorizer  1022 . In embodiments, commercial information analyzer  802  may include any one or more of these categorizers shown in  FIG. 10 , as well as additional and/or alternative categorizers. The categorizers of  FIG. 10  are described as follows. 
     Shopping categorizer  1002  is configured to analyze commercial information  510  to determine one or more users that purchased one or more items in a shopping category, and to indicate the users determined to be included in the shopping category in category information  806 . For example, shopping categorizer  1002  may be configured to create shopper segments/categories based on types of purchases, as indicated by commercial information  510 . For instance, commercial information  510  may indicate that a particular user received one or more email receipts from clothing retailers (e.g., the Gap, Ann Taylor, Banana Republic, etc.) for purchased clothing items, and therefore may categorize the user in a clothing shopper category. In the example described above, commercial information  510  may indicate that a particular user received one or more email receipts for purchased video games, and therefore may categorize the user in a video game shopper category. Commercial information  510  may indicate that a particular user received one or more email receipts from electronics retailers (e.g., Buy.com, Amazon, Fry&#39;s, etc.) for electronics items, and therefore may categorized the user in an electronics shopper category. Commercial information  510  may indicate that a particular user received one or more email receipts from a book retailer for book items, and therefore may categorize the user in a book reader category. Shopping categorizer  1002  may be configured to categorize users in any type and number of shopping categories. User classification data generator  804  is configured to generate user classification data  206  to indicate the users included in the shopping categories. 
     Frequent shopper categorizer  1004  is configured to analyze commercial information  510  to determine a number of commercial emails received by a user in a predetermined period of time. If a user receives at least a threshold number of commercial emails within the predetermined period of time, frequent shopper categorizer  1004  is configured to indicate the user to be included in a frequent shopper category. Frequent shopper categorizer  1004  may categorize users in a single frequent shopper category, or may categorize users across several frequent shopper categories. Examples of such frequent shopper categories include categories defined according to shopping frequency (e.g., based on the number of commercial emails received in the predetermined time period), according to product area (e.g. leisure travel, sports equipment, ticket buys), and/or according to further criteria. User classification data generator  804  is configured to generate user classification data  206  to indicate the users categorized in the one or more frequent shopper categories. 
     Spending level categorizer  1006  is configured to analyze commercial information  510  to determine an amount of money spent by a user in an item purchase indicated in a commercial email addressed to the user. If a user spends more money on an item purchase than a threshold amount, spending level categorizer  1006  may categorize the user in a high spender category. Spending level categorizer  1006  may categorize users in a single spending level category, or may categorize users across several spending level categories, which may be further categorized according to other factors, such as product area (e.g. leisure travel, sports equipment, ticket buys), number of items purchased (e.g., a single item or multiple items), etc. User classification data generator  804  is configured to generate user classification data  206  to indicate the users categorized in the one or more spending level categories. 
     Time-based purchaser categorizer  1008  is configured to analyze commercial information  510  to determine an expiration time indication indicated in a commercial email addressed to a user for an item, such as a leased or rented item. If an expiration time is indicated for the item, time-based purchaser categorizer  1008  may categorize the user in a time-based purchaser category based on the determined expiration time. For instance, a user may receive an email receipt for a car lease, and the email receipt may indicate the car lease is a three month lease. In such an example, the user may be categorized in a three-month car purchaser category because the user may likely be in the market for a new car or another car lease in three months (car manufacturers and/or dealers may be interested in receiving this the contents of this category to direct advertising to such users). User classification data generator  804  is configured to generate user classification data  206  to indicate the user in the time-based purchaser category. 
     Ad exclusion categorizer  1010  is configured to analyze commercial information  510  to determine an indication in a commercial email addressed to a user that the user owns an item of an item type, such as an indication that the user purchased the item (in an email receipt). If a determination is made that the user purchased the item, ad exclusion categorizer  1010  is configured to include the user in an exclusion category associated with the item type based on the determined indication (e.g., negative targeting of items). For instance, commercial information  510  may indicate that a user received an email receipt for a monthly mortgage payment. As a result, ad exclusion categorizer  1010  may determine that the user has a mortgage. Based on the user having a mortgage, ad exclusion categorizer  1010  may include the user in a mortgage ad exclusion category so that the user is not shown advertisements from mortgage lenders (e.g., Lending Tree, etc.). By excluding users from receiving advertisements for items that the users own/have purchased, user satisfaction may be increased through better ad relevance. User classification data generator  804  is configured to generate user classification data  206  to indicate that the user is included in the exclusion category. 
     In embodiments, commercial information analyzer  802  may be configured to analyze commercial information  510  to categorize users according to various factors that may be used to provide insight into a prospective audience for an advertisement campaign. For instance, purchase time categorizer  1012  is configured to analyze commercial information  510  to categorize users into a purchase time category according to a time of day, a day of week, a day of month, or other date/time indication, that users purchase their category of product. For example, purchase time categorizer  1012  may determine the date/time indication from the “Date:” field and/or other information extracted from commercial emails. Purchase frequency categorizer  1014  is configured to analyze commercial information  510  to categorize users into a purchase frequency category according to a frequency of item purchases. For example, purchase frequency categorizer  1014  may determine a purchase frequency for a user from a number of commercial emails that indicate periodic item purchases by the user. Average purchase amount categorizer  1016  is configured to analyze commercial information  510  to categorize users into an average purchase amount category according to an average amount spent by users on items in a particular product category. For example, average purchase amount categorizer  1016  may determine an average amount spent by a user by determining purchase amounts for items in a particular product category from email receipts of the user, and averaging the purchase amounts. Purchaser demographics categorizer  1018  is configured to analyze commercial information  510  to categorize users into one or more purchaser demographics categories. For instance, purchaser demographics categorizer  1018  may determine demographics of users that purchase products in a particular category, when present in commercial information  510 , such as a sex of a user (male or female), age of a user, language of a user, geographic location of a user, etc., and may categorize the users in corresponding demographics categories. User classification data generator  804  is configured to generate user classification data  206  to indicate one or more users categorized in the purchase time category, the purchase frequency category, the average purchase amount category, and/or one or more purchaser demographics category. 
     Correlated purchase categorizer  1020  is configured to determine a plurality of purchases made by a user by analyzing commercial information  510 . For example, correlated purchase categorizer  1020  may analyze a plurality of purchases made by the user, as indicated by commercial information extracted from a plurality of commercial emails addressed to the user. Correlated purchase categorizer  1020  may determine that the plurality of purchases are correlated (e.g., based on types of products that together create a holistic collection), and if so, include the user in a correlated purchases category. For instance, correlated purchases categorizer  1020  may determine that commercial information  510  indicates that a user purchased items from Diapers.com, Toy R&#39;Us and Babies R&#39;US (e.g., within a predetermined time period). In such case, correlated purchases categorizer  1020  may determine the user to be a parent and/or a mother (if gender of the user is female), and may include the user in a parents category and/or mom&#39;s category. User classification data generator  804  is configured to generate user classification data  206  to indicate the user in the correlated purchases category. 
     Similar purchasing characteristics categorizer  1022  is configured to analyze commercial information  510  to determine similar purchases made by a plurality of users (e.g., “lookalike targeting”). For instance, similar purchasing characteristics categorizer  1022  may determine that a plurality of users have performed at least one of having purchased a same item, purchased a same type of item, purchased an item from a same vendor, or spent a similar amount of money on a purchase. Based on the determination, similar purchasing characteristics categorizer  1022  may determine the plurality of users to have similar purchasing characteristics, and may include the users together in a similar purchasing characteristics category. For instance, in an embodiment, an advertiser may desire to determine a set of users that have particular characteristics. In an embodiment, the advertiser may provide a set of characteristics that the advertiser desires of the users. The advertiser may list the characteristics directly, or in other form, such as in the form of a set of conversions (e.g., a set of email receipts) associated with a set of users. Similar purchasing characteristics categorizer  1022  may be configured to determine users having characteristics matching the provided characteristics (e.g., matching the list of characteristics, matching the set of email receipts, etc.) to be grouped with the set of users. User classification data generator  804  is configured to generate user classification data  206  to indicate the matching users together in the similar purchasing characteristics category. 
     The next subsection describes example uses for user classification data. 
     C. Examples Applications for User Classification Data 
     User classification data (e.g., user classification data  206 ) generated from commercial email according to embodiments of the present invention may be used in various applications. User classification can be used to target users, personally or anonymously, while they are browsing and/or logged into websites, including email sites or non-email sites. For example, user classification data generated from commercial email according to anonymous targeting techniques can be used to validate other conversion-based models (e.g., such as BT and search-based models such as search re-targeting), to train models that use conversion data such as behavioral targeting, to determine the likelihood of individuals with the same context (e.g., within a common email address book) making similar item purchases, to determine the likely sequencing and regularity of item purchases, and/or for other applications. 
     User classification data generated from commercial email according to personalized targeting techniques can be used in various ways, including being used to build user profiles representative of user purchase behavior. For instance,  FIG. 11  shows a block diagram of a user profile generator  1102 , according to an example embodiment. As shown in  FIG. 11 , user profile generator  1102  receives user classification data  206 . User profile generator  1102  is configured to generate user profiles  1104  from user classification data  206 . For example, in an embodiment, user profile generator  1102  may generate a user profile for each user that elected to opt-in to personalized targeting. User profile generator  1102  may generate a user profile for each user that indicates each category in which the user was categorized (e.g., by commercial information analyzer  802 ). For example, user profile generator  1102  may generate a user profile for Joseph Smith that indicates Joseph to be categorized in a video game shopper category, an infrequent shopper category, a low spender category, three-month car purchaser category a motorcycle ad exclusion category, a Monday purchaser category, a weekly purchaser category, a $50-$100 purchase amount category, a male category, a Midwestern geographic category, a parents category, etc. 
     Such user profiles may be used to improve online advertisement targeting, by using commercial email data as conversion data. Example for this type of personalized targeting include: targeting premium shoppers (by purchase volume or spend), creating granular individual user segments (e.g. book readers, mortgage owners, online shoe buyers), determining upsell users who have already purchased products at the time they are reading their email, determining correlations between multiple purchases and the types of users who make similar purchases, targeting users based on where they are within the subscription period for regular purchases (e.g. person with a car lease only has two month before the lease terminates), targeting users with complementary products to ones already purchased, and avoiding targeting users based on items already purchased to improve their advertising experience. 
     User classification data, including user profiles, may be used in online advertising embodiments in various ways. For instance, an advertisement selector may perform a step  1202  shown in  FIG. 12 , according to an embodiment. In step  1202 , an online advertisement is selected for display based at least on the generated user classification data.  FIG. 13  shows a block diagram of an example advertisement (“advertisement”) network  1300 , according to an embodiment. As shown in  FIG. 13 , network  1300  includes an advertisement selector  1310 . Advertisement selector  1310  may perform step  1202  of  FIG. 12 , in an embodiment. Advertisement network  1300  operates to serve advertisements provided by advertisers, such as display advertisements or other types of advertisements, to publisher sites (e.g., Web sites). When such sites are accessed by users of the network, the advertisements are displayed to the users. Advertisement network  1300  is an example display advertisement network provided for purposes of illustration. Advertisement selection using user classification data generated as described herein may also be used for advertisement selection in alternative environments. Advertisement network  1300  is described as follows. 
     As shown in  FIG. 13 , advertisement network  1300  includes a plurality of user devices  1302   a - 1302   m,  a plurality of publisher servers  1304   a - 1304   n,  an advertisement serving system  1306 , and at least one advertiser system  1308 . Communication among user devices  1302   a - 1302   m,  publisher servers  1304   a - 1304   n,  advertisement serving system  1306 , and advertiser system  1308  is carried out over one or more networks using well-known network communication protocols. Example networks include a personal area network (PAN), a local area network (LAN), a wide-area network (WAN), a combination of networks such as the Internet, etc. 
     User devices  1302   a - 1302   m  are capable of communicating with any one or more of publisher servers  1304   a - 1304   n  in network  1300 . For example, each of user devices  1302   a - 1302   m  may include a web browser that enables a user who owns (or otherwise has access to) the user system to access sites (e.g., websites) that are hosted by publisher servers  1304   a - 1304   n.  Each of user devices  1302   a - 1302   m  is shown in  FIG. 13  to be communicatively coupled to publisher  1  server(s)  1304   a  to access a site published by publisher  1 . Persons skilled in the relevant art(s) will recognize that each of user devices  1302   a - 1302   m  is capable of connecting to any of publisher servers  1304   a - 1304   n  for accessing the sites hosted thereon. 
     Publisher servers  1304   a - 1304   n  are capable of communicating with user devices  1302   a - 1302   m  in network  1300 . Each of publisher servers  1304   a - 1304   n  is configured to host a site (e.g., a website) published by a corresponding publisher  1 -N so that such site is accessible to users of network  1300  via user devices  1302   a - 1302   m.  Each of publisher servers  1304   a - 1304   n  is further configured to serve advertisement(s) to users of network  1300  when those users access a website that is hosted by the respective publisher server. 
     User devices  1302   a - 1302   m  may each be any type of electronic device configured with web browsing functionality (or other suitable network communication functionality), including a desktop computer (e.g., a personal computer, etc.), a mobile computing device (e.g., a personal digital assistant (PDA), a laptop computer, a notebook computer, a tablet computer (e.g., an Apple iPad™), a netbook, etc.), a mobile phone (e.g., a cell phone, a smart phone, etc.), or a mobile email device. 
     Advertisement serving system  1306  may receive advertisements from advertiser system  1308  and/or other sources. Advertisement serving system  1306  is configured to serve the advertisements to publisher servers  1304   a - 1304   n  when the sites hosted by servers  1304   a - 1304   n  are accessed by users, thereby facilitating the delivery of advertisements to the users. Advertisement serving system  1306  may be implemented in various ways, including in the form of one or more computing systems, such as one or more servers. 
     As shown in  FIG. 13 , advertisement serving system  1306  includes an advertisement selector  1310 . Advertisement selector  1310  is configured to select the advertisements to be served to publisher servers  1304   a - 1304   n,  generating an advertisement selection  1314  that indicates the selected advertisement. As shown in  FIG. 13 , advertisement selector  1310  receives user classification data  206  and an advertisement request  1312 . Advertisement request  1312  may be received from a publisher server  1304 , for example. Advertisement request  1312  may indicate user information, including an identity of a user at a user device  1302  displaying a web page that requests an advertisement from a publisher server  1304 , and/or further contextual information (e.g., an IP address for the user device, etc.). In response to advertisement request  1312 , advertisement selector  1310  is configured to select an advertisement (e.g., from a pool of advertisements) based at least on user classification data  206 . Although shown in  FIG. 13  as receiving user classification data  206 , advertisement selector  1310  may receive user profiles  1104  ( FIG. 11 ), and may use user profiles  1104  to select an advertisement. In an embodiment, advertisement selector  1310  may be configured to match the advertisement request with user classification data  206  to select an advertisement. For instance, advertisement selector  1310  may determine a user profile of user profiles  1104  for the user identified in request  1312 , and may select an advertisement based on the user profile, such as by matching categories that include the user with advertisement criteria. 
     For instance, Joseph Smith may have a user profile of user profiles  1104  that indicates Joseph to be categorized in a video game shopper category, a low spender category, a $50-$100 purchase amount category, and a Toledo, Ohio geographic category. Advertisement selector  1310  may match this user profile with an advertisement having attributes matching the categories of the user profile, such as an advertisement for a video game store located in Toledo, Ohio. Advertisement selection  1314  may indicate the selected advertisement. Advertisement serving system  1306  may serve the advertisement indicated by advertisement selection  1314  to the requesting publisher server  1304  or directly to the user device  1302  to be displayed to the user. 
     Advertisement selector  1310  may be configured to select advertisements in any manner, including by matching user classification data with advertisements as described above, by selecting upsell advertisements, by selecting advertisements for sequential sales, and/or by selecting advertisements in other ways. 
     For example, in an embodiment, advertisement selector  1310  may be configured to select an online advertisement for display to a user as an advertisement for an item selected as an upsell of another item indicated to have been previously purchased by the user. For instance, an email receipt may indicate a user to be a Netflix subscriber to the lowest tier plan. In an embodiment, advertisements for higher tier plans may be served to users that are categorized in a Netflix subscriber lower tier plan category. In an embodiment, if a subsequent email receipt indicates that a higher tier plan was purchased, the upsell opportunity may be closed out. 
     In another embodiment, advertisement selector  1310  may be configured to select the online advertisement for display to a user as an advertisement for a sequential sale item to an item indicated to have been previously purchased by the user. For instance, people who purchase trips to ski resorts often subsequently purchase ski equipment. User classification data  206  may include a segment or category of ski resort travelers (e.g., based on email receipts transmitted within last week). In an embodiment, advertisements for ski equipment may be served to users that are categorized in the ski trip travelers category. In another embodiment, information indicating the users included in the ski resort travelers category may be sold to ski equipment retailers for use by the ski equipment retailer. In an embodiment, if a subsequent email receipt indicates that the ski equipment was purchased, the sequential sale opportunity may be closed out. 
     III. Example Computer Implementations 
     User classifier  202 , user classifier  500 , commercial email determiner  502 , commercial email parser  504 , commercial information processor  506 , conversion indicator  512 , email header analyzer  602 , email body analyzer  604 , privacy module  702 , commercial information analyzer  802 , user classification data generator  804 , shopping categorizer  1002 , frequent shopper categorizer  1004 , spending level categorizer  1006 , time-based purchaser categorizer  1008 , ad exclusion categorizer  1010 , purchase time categorizer  1012 , purchase frequency categorizer  1014 , average purchase amount categorizer  1016 , purchaser demographics categorizer  1018 , correlated purchase categorizer  1020 , similar purchasing characteristics categorizer  1022 , user profile generator  1102 , and advertisement selector  1310  may be implemented in hardware, software, firmware, or any combination thereof. For example, user classifier  202 , user classifier  500 , commercial email determiner  502 , commercial email parser  504 , commercial information processor  506 , conversion indicator  512 , email header analyzer  602 , email body analyzer  604 , privacy module  702 , commercial information analyzer  802 , user classification data generator  804 , shopping categorizer  1002 , frequent shopper categorizer  1004 , spending level categorizer  1006 , time-based purchaser categorizer  1008 , ad exclusion categorizer  1010 , purchase time categorizer  1012 , purchase frequency categorizer  1014 , average purchase amount categorizer  1016 , purchaser demographics categorizer  1018 , correlated purchase categorizer  1020 , similar purchasing characteristics categorizer  1022 , user profile generator  1102 , and/or advertisement selector  1310  may be implemented as computer program code configured to be executed in one or more processors. Alternatively, user classifier  202 , user classifier  500 , commercial email determiner  502 , commercial email parser  504 , commercial information processor  506 , conversion indicator  512 , email header analyzer  602 , email body analyzer  604 , privacy module  702 , commercial information analyzer  802 , user classification data generator  804 , shopping categorizer  1002 , frequent shopper categorizer  1004 , spending level categorizer  1006 , time-based purchaser categorizer  1008 , ad exclusion categorizer  1010 , purchase time categorizer  1012 , purchase frequency categorizer  1014 , average purchase amount categorizer  1016 , purchaser demographics categorizer  1018 , correlated purchase categorizer  1020 , similar purchasing characteristics categorizer  1022 , user profile generator  1102 , and/or advertisement selector  1310  may be implemented as hardware logic/electrical circuitry. 
     The embodiments described herein, including systems, methods/processes, and/or apparatuses, may be implemented using well known servers/computers, such as a computer  1400  shown in  FIG. 14 . For example, user device  102 , email server  104 , user device  106 , email server  200 , commercial entity device  302 , user devices  1302   a - 1302   m,  publisher servers  1304   a - 1304   n,  advertisement serving system  1306 , and/or advertiser system  1308  can be implemented using one or more computers  1400 . 
     Computer  1400  can be any commercially available and well known computer capable of performing the functions described herein, such as computers available from International Business Machines, Apple, Sun, HP, Dell, Cray, etc. Computer  1400  may be any type of computer, including a desktop computer, a server, etc. 
     Computer  1400  includes one or more processors (also called central processing units, or CPUs), such as a processor  1404 . Processor  1404  is connected to a communication infrastructure  1402 , such as a communication bus. In some embodiments, processor  1404  can simultaneously operate multiple computing threads. 
     Computer  1400  also includes a primary or main memory  1406 , such as random access memory (RAM). Main memory  1406  has stored therein control logic  1428 A (computer software), and data. 
     Computer  1400  also includes one or more secondary storage devices  1410 . Secondary storage devices  1410  include, for example, a hard disk drive  1412  and/or a removable storage device or drive  1414 , as well as other types of storage devices, such as memory cards and memory sticks. For instance, computer  1400  may include an industry standard interface, such a universal serial bus (USB) interface for interfacing with devices such as a memory stick. Removable storage drive  1414  represents a floppy disk drive, a magnetic tape drive, a compact disk drive, an optical storage device, tape backup, etc. 
     Removable storage drive  1414  interacts with a removable storage unit  1416 . Removable storage unit  1416  includes a computer useable or readable storage medium  1424  having stored therein computer software  1428 B (control logic) and/or data. Removable storage unit  1416  represents a floppy disk, magnetic tape, compact disk, DVD, optical storage disk, or any other computer data storage device. Removable storage drive  1414  reads from and/or writes to removable storage unit  1416  in a well known manner. 
     Computer  1400  also includes input/output/display devices  1422 , such as monitors, keyboards, pointing devices, etc. 
     Computer  1400  further includes a communication or network interface  1418 . Communication interface  1418  enables the computer  1400  to communicate with remote devices. For example, communication interface  1418  allows computer  1400  to communicate over communication networks or mediums  1442  (representing a form of a computer useable or readable medium), such as LANs, WANs, the Internet, etc. Network interface  1418  may interface with remote sites or networks via wired or wireless connections. 
     Control logic  1428 C may be transmitted to and from computer  1400  via the communication medium  1442 . 
     Any apparatus or manufacture comprising a computer useable or readable medium having control logic (software) stored therein is referred to herein as a computer program product or program storage device. This includes, but is not limited to, computer  1400 , main memory  1406 , secondary storage devices  1410 , and removable storage unit  1416 . Such computer program products, having control logic stored therein that, when executed by one or more data processing devices, cause such data processing devices to operate as described herein, represent embodiments of the invention. 
     Devices in which embodiments may be implemented may include storage, such as storage drives, memory devices, and further types of computer-readable media. Examples of such computer-readable storage media include a hard disk, a removable magnetic disk, a removable optical disk, flash memory cards, digital video disks, random access memories (RAMs), read only memories (ROM), and the like. As used herein, the terms “computer program medium” and “computer-readable medium” are used to generally refer to the hard disk associated with a hard disk drive, a removable magnetic disk, a removable optical disk (e.g., CDROMs, DVDs, etc.), zip disks, tapes, magnetic storage devices, MEMS (micro-electromechanical systems) storage, nanotechnology-based storage devices, as well as other media such as flash memory cards, digital video discs, RAM devices, ROM devices, and the like. Such computer-readable storage media may store program modules that include computer program logic for user classifier  202 , user classifier  500 , commercial email determiner  502 , commercial email parser  504 , commercial information processor  506 , conversion indicator  512 , email header analyzer  602 , email body analyzer  604 , privacy module  702 , commercial information analyzer  802 , user classification data generator  804 , shopping categorizer  1002 , frequent shopper categorizer  1004 , spending level categorizer  1006 , time-based purchaser categorizer  1008 , ad exclusion categorizer  1010 , purchase time categorizer  1012 , purchase frequency categorizer  1014 , average purchase amount categorizer  1016 , purchaser demographics categorizer  1018 , correlated purchase categorizer  1020 , similar purchasing characteristics categorizer  1022 , user profile generator  1102 , advertisement selector  1310 , flowchart  400 , flowchart  900 , and/or step  1202  (including any one or more steps of flowcharts  400  and  900 ), and/or further embodiments of the present invention described herein. Embodiments of the invention are directed to computer program products comprising such logic (e.g., in the form of program code or software) stored on any computer useable medium. Such program code, when executed in one or more processors, causes a device to operate as described herein. 
     The invention can work with software, hardware, and/or operating system implementations other than those described herein. Any software, hardware, and operating system implementations suitable for performing the functions described herein can be used. 
     IV. Conclusion 
     While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art(s) that various changes in form and details can be made therein without departing from the spirit and scope of the invention. Thus, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.