Patent Publication Number: US-10783560-B2

Title: Systems and techniques for configuring profile merge rules prior to determining segments of recipients of electronic communication

Description:
TECHNICAL FIELD 
     This disclosure relates generally to computer-implemented methods and systems and more particularly relates to configuring electronic communications. 
     BACKGROUND 
     Individuals and other entities commonly have multiple electronic devices and electronic accounts. For example, an individual may have a desktop computer, a laptop, a tablet, a cell phone, and a work computer. The individual may also have several bank accounts, several accounts for online retailers, several e-mail accounts, etc., and access the various accounts using the multiple electronic devices. It is desirable to be able to collect information about entities and use the information to provide targeted electronic communication to the entities. For example, it is desirable to identify that an individual is interested in baseball based on content accessed on the individual&#39;s devices and accounts. A tracking entity (which can include entities such as advertisers, marketers, and other agencies) can track actions taken by one or more tracked entities. Tracked entities can include entities such as organizations, end users/consumers, and devices used by the end users/consumers. A data management platform (DMP) can facilitate the collection of the data and apply segment rules on the collected data, allowing a tracking entity to identify the tracked entities that fall within a certain set of selected criteria. Tracked entities can be segmented to identify a group of entities (i.e., a segment) that are interested in baseball based on the tracked actions of those entities satisfying segment criteria. All tracked entities in the baseball-interest segment can then be targeted with an advertisement, for example, related to an upcoming baseball event. 
     While the large scale collection and aggregation of data across multiple entities and devices using electronic networks can greatly enhance the distribution and targeting of electronic communication, the electronic nature of the data collection creates hurdles unique to the network environment in which they are used. For example, DMPs ingest data from different data sources keyed off of different identifiers (e.g., data from particular devices, data from activity on particular user accounts, etc.). These different identifiers are tied to a particular user identifier (i.e. mapping data of many sources to a single identifier). However, one significant problem that arises due to the unique nature of electronic data collection is that the tracking entity (e.g., the advertiser, marketer, agency, etc.) has little or no control over how this mapping information is used to merge data from different sources. This can lead to the mixing of data from multiple tracked entities, which can dilute the accuracy of the desired segment. 
     Treating each device and each user account as its own entity does not accurately reflect a tracked entity because individuals and other tracked entities are typically associated with multiple devices and accounts. For example, a tracked entity may log on to an authenticated profile and access a tracking entity website using both a cellular phone and a tablet computer. Prior solutions of collecting data describing characteristics and activities of the user account as well as each device separately provides a fractured profile on the tracked entity. Therefore, prior solutions of segmenting a tracked entity based on behaviors expressed in an individual device profile, account profile, etc. provides a tracking entity an incomplete view for segmentation. 
     SUMMARY 
     Systems and methods disclosed herein generate a segment of tracked entities for targeting electronic communications from a collection of profiles for the tracked entities. The collection of profiles can include a collection of authentication-based profiles and device-based profiles for the tracked entities. An authentication-based profile refers to a collection of attributes and metadata collected for a particular authenticated user account. A device-based profile refers to a collection of attributes and metadata collected for a particular device, regardless of the users of the device have logged in to authenticated profiles. One embodiment involves presenting options for merging the authentication-based profiles and the device-based profiles for the tracked entities. Upon receiving a selection of the options, systems and methods create a collection of profiles by merging at least some of the authentication-based profiles and device-based profiles into a merged profile. Rules defining a segment can be applied to the merged profile, generating a targeted segment of tracked entities. 
     These illustrative embodiments are mentioned not to limit or define the disclosure, but to provide examples to aid understanding thereof. Additional embodiments are discussed in the Detailed Description, and further description is provided there. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
       These and other features, embodiments, and advantages of the present disclosure are better understood when the following Detailed Description is read with reference to the accompanying drawings. 
         FIG. 1  is a block diagram depicting an example of a system for merging authentication-based profiles with device-based profiles and generating a segment of tracked entities from the collection of profiles. 
         FIG. 2  is a diagram depicting an example of a user interface for presenting options for merging authentication-based profiles and device-based profiles. 
         FIG. 3  is a diagram depicting an example of a user interface for presenting options for entering segment rules. 
         FIG. 4  is a flowchart illustrating an exemplary method for generating a segment of tracked entities from the collection of profiles. 
         FIG. 5  is a block diagram depicting example hardware implementations for the components described in  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     Computer-implemented systems and methods are disclosed for customizing the merging of profile data from individuals and other entities prior to segmenting the entities for targeting purposes. As noted above, each tracked entity can be associated with one or more device-based profiles (e.g., a profile for a particular electronic device) as well as one or more authentication-based profiles (e.g., a profile associated with a particular user account with a tracking entity). An individual that is a customer at a bank, for example, has an authentication-based online profile with the bank (the tracking entity). The individual (the tracked entity) accesses the online account via multiple electronic devices, such as a computer, tablet device, or cellular phone. Each electronic device is associated with a respective device-based profile. Due to the unique nature of large scale electronic data collection, tracking entities have little or no control over how the multiple profiles associated with tracked entities are mapped to a comprehensive identity. Embodiments disclosed herein allow a tracking entity to select options specifying criteria for how the authentication-based profiles should be merged with the device-based profiles prior to segmentation and targeting of electronic communication. 
     For example, a tracking entity that is a financial services marketer may need to ensure that any targeting of financial services is based on a single user&#39;s data and not data of multiple users that may be using a user device. Thus, the tracking entity selects options specifying that a known authenticated profile (e.g., a single user&#39;s authenticated information) is used for identifying the market segment but no device level data is used, as the device level data may include anonymous information on other users. In another example, the tracking entity selects options specifying that no device level data should be merged due to financial regulations or other limitations. As another example, a tracking entity is a retailer collecting data on multiple users in a single household that log in and purchase products off of the retailer&#39;s website. The tracking entity wants to use authenticated user level data (e.g., data from each authenticated user in the household) in combination with anonymous data collected across devices in the household. This allows the tracking entity to target offers based on the data from the individual users and data collected on the household level across devices. 
     After selecting the options specifying criteria for how authentication-based profiles should be merged with device-based profiles, the tracking entity can then apply segment rules on the collection of merged profiles for identifying a specific market segment. The increased flexibility of a tracking entity being able to specify how tracked entity data is merged prior to segmentation is a significant advantage over prior systems that provided no such flexibility. 
     As used herein, the term “tracking entity” is used to refer to organizations, individuals, and devices that track and collect data pertaining to characteristics and interactions (e.g., “traits” as defined below) of tracked entities. Examples of tracking entities include advertisers, retailers, web marketers, financial institutions, and other entities interested in collecting information on end user behavior. 
     As used herein, the term “tracked entity” is used to refer to organizations, individuals, and devices that perform activities that are tracked by tracking entities. 
     As used herein, the term “trait” is used to refer to a feature or characteristic of an organization, individual, device or of an activity performed by an organization, individual, or device that is collected or derived by a tracking entity. Examples of traits include (but are not limited to) user interactions that generated data; a name, address, an age or other demographic information, or other information about a user whose interactions generated the data; an internet protocol address of a device used to access a third-party website; a navigation history (clicks, hyperlink selections, etc.) of a browser used to access the third-party website; a purchase history of a user whose interactions generated the data; and the types of content (e.g., a video, an advertisement, etc.) presented by the third-party website and with which users interacted to generate the collected data. 
     As used herein, the term “segment” is used to refer to a list of individuals, devices, and other entities (tracked entities) that qualify for a set of rules defining a particular collection of one or more traits. The set of rules defining a particular collection of traits can be referred to as “segment rules.” Thus, the collection of traits in a segment relate to a set of users (tracked entities) who share common attributes. Examples of a segment include (but are not limited to) a collection of traits related to tracked entities who viewed a particular advertisement on a particular webpage over a specified time range, a collection of traits related to tracked entities who viewed articles on a subject between a specified minimum amount of time and a specified maximum amount of time, a collection of traits related to tracked entities who purchased a particular product, a collection of traits related to tracked entities who purchased an item after viewing a product page on a particular website, and a collection of traits related to tracked entities that do not exhibit a particular characteristic (e.g., a collection of users who have a collection of traits for viewing a product and do not have any of a collection of traits that exhibit a particular characteristic such as buying the product). Traits may be combined in a segment using any suitable combination of Boolean operators (AND, OR, or NOT). Data describing a segment is stored using any suitable storage object (e.g., a database of the various traits for the segment). 
     As used herein, the term “authentication-based profile” is used to refer to a collection of trait data associated with an authenticated account for a particular tracked entity. The authentication-based profile can include trait information unique to the particular tracked entity. For example, an authentication-based profile can include the collection of trait data gathered while a tracked entity has logged in to a user account for an online service, the tracked entity being authenticated by entering a user name and passcode, exchanging a public/private encryption key pair, or other means of electronic authentication. 
     As used herein, the term “device-based profile” is used to refer to a collection of trait data associated with an electronic device. A device-based profile can include trait information gathered for all anonymous users of the electronic device. For example, a tracking entity may gather trait information received from an electronic device (e.g., a tablet computer) that is used by multiple tracked entities that may or may not have logged in to an authenticated profile. The device-based profile includes all trait data gathered that is associated with the device even considering non-authenticated users. 
     As used herein, the term “segmentation” is used to refer to the computer-implemented process of determining a segment. 
     Referring now to the drawings,  FIG. 1  is a block diagram depicting a computing environment in which a computing system  100  can track data received from computing devices  120   a - c  and target electronic communications to particular segments of tracked entities  124   a - d . The computing system  100  includes features for configuring options specifying how different authentication-based profiles and device-based profiles of the tracked entities  124   a - d  should be merged prior to defining a segment. As described above, individuals and other tracked entities can have different authentication-based profiles and different device-based profiles because tracked entities frequently use multiple electronic devices and share electronic devices when accessing online electronic content.  FIG. 1  reflects this by depicting tracked entities  124   a  as using multiple computing devices  120   a - c . Similarly, in this example, tracked entity  124   b  uses multiple computing device  120   a ,  120   c . Tracked entities also often share electronic devices, such as when multiple users in a household have individual accounts and share a common tablet computer or personal computer.  FIG. 1  depicts this with tracked entities  124   a - d  all sharing computing device  120   c.    
     The computing system  100  tracks and collects traits and data from tracked entities  124   a - d  as the tracked entities  124   a - d  interact with one or more online services  116   a - b . Online services  116   a - b  provides applications, data, and other functions that are accessed by computing devices  120   a - c  via a network  115 . The network  115  includes the Internet and also include local intranets or other suitable networks. Examples of the online services  116   a - b  include, but are not limited to, social media websites, websites for purchasing products or services, customer facing websites for financial institutions, etc. The computing devices  120   a - c  execute respective user applications  122   a - c  that are used to access the online services  116   a - b . Examples of the user applications  122   a - c  include, but are not limited to, web browsers for accessing websites provided by the online services, applications specific to the online services, etc. Electronic communications between computing devices  120   a - c  and online services  116   a - b  are tracked and traits collected by computing system  100 . In some embodiments, the computing system  100  includes its own online service (not shown) through the use of a web server, media server, or other content server and track tracked entity  124   a - d  interactions with the computing system  100  online service and collect traits based on those interactions. 
     Each tracked entity  124   a - d  can be associated with one or more authentication-based profiles for each of the online services  116   a - b  accessed by the tracking entities  124   a - d . For example, if online service  116   a  is a financial services website, each tracked entities  124   a - d  has a separate user account (authentication-based profile) with online service  116   a . Similarly, each computing device  120   a - c  can be associated with a unique device-based profile identifying the computing device. The computing system  100  stores, in a database in memory (not shown), identifiers identifying the authentication-based profiles and device-based profiles. 
     To track and collect traits and data from tracked entities  124   a - d , merge collected data from the different profiles of the tracked entities  124   a - d , determine the required segments, and provide targeted electronic communications to the determined segments, the computing system  100  includes computer-readable memory storing instructions for executing a tracking engine  102 , a merging engine  103 , a segmenting engine  104 , and a targeting engine  106 . In some embodiments, one or more of the tracking engine  102 , merging engine  103 , segmenting engine  104 , and targeting engine  106  are included in one or more computer server systems separate from the computing system  100  and communicating with the computing system  100  via network  115  or other network. 
     The tracking engine  102  collects traits from tracked entities  124   a - d  as well as from computing devices  120   a - c  used by the tracked entities  124   a - d . The computing system  100  includes a database in memory that stores tracked traits of each tracked entity  124   a - d  and computing device  120   a - c . For each trait associated with a tracked entity  124   a - d  authentication-based profile, the tracking engine  102  stores information and details regarding the collected trait in a database and associate the trait information with the appropriate authentication-based profile identifier. Similarly, for each trait associated with computing device  120   a - c , the tracking engine  102  stores information an details regarding the collected trait in a database and associate the trait information with the appropriate device-based profile identifier. The tracking engine  102  provides the collected data and profile information to merging engine  103 . 
     Based on inputs received by the computing system  100  selecting profile merge configurations, merging engine  103  merges authentication-based and device-based profiles for computing devices  120   a - c  and tracked entities  124   a - d . In one example, inputs selecting profile-merge configuration options specify that authentication-based profiles should be merged with device-based profiles. In this example, trait information collected for the authentication-based profile for tracked entity  124   a  and trait information collected for the authentication-based profile for tracked entity  124   b  is merged with trait information collected for the device-based profile of computing device  120   a . This configuration option is useful, for example, for a tracking entity that is an online streaming video provider that intends to target product recommendations based on overall household device activity as well as individual user account activity. 
     In another example, inputs selecting profile merge configuration options specify that traits collected a current device profile should be merged with traits collected on a last known authentication-based profile. This configuration option is useful, for example, for a tracking entity that intends to send targeted electronic communications to the last known authenticated user of a computing device  102  (i.e. merge trait information regarding the last known authentication-based profile with trait information for a particular computing device  102 ). Other options for merging profiles via the merging engine  103  are possible and additional examples are discussed below. 
     The merging engine  103  provides the merged profile information to segmenting engine  104 . Based on input into the computing system  100  specifying segment rules, the segmenting engine  104  applies the segment rules to the merged profiles received from merging engine  103  to generate the desired target segment. In an example, a segment rule is defined to identify a set of users that have received a particular electronic communication a minimum number of times (e.g., ten times). The segmenting engine  104  applies this segment rule to the merged profiles received from merging engine  103  to output a segment that includes the merged profiles satisfying the segment rule. 
     For example, the computing system  100  receives as input profile merge rules specifying that all device-based profiles for a particular authentication-based profile should be merged. This is useful, for example, in a situation where a tracking entity intends to target electronic communications to authenticated users that access online content using multiple devices while taking into account data and traits collected on each of the user&#39;s devices. In this example, traits associated with authentication-based profile for tracked user  124   a  will be merged with traits associated with device-based profiles for computing devices  120   a - c . Similarly, traits associated with authentication-based profile for tracked user  124   b  will be merged with traits associated with device-based profiles for computing devices  124   a ,  124   c . Continuing the segment rule example from above, the computing system  100  also receives as input a segment rule defining a segment as users that have received a particular electronic communication a minimum number of times (e.g., receiving a particular communication 10 times). The segmenting engine  104  will apply the segment rule to the merged profile, providing as output the tracked entities  124   a - d  that have received the particular electronic communication a minimum number of times across all computing devices  120   a - c  used by the tracked entity. For example, if tracked user  124   a  received the electronic communication on computing device  120   a  six times, received the electronic communication on computing device  120   b  three times, and received the electronic communication on computing device  120   c  four times, the merged profiles indicate that tracked user  124   a  received the electronic communication thirteen times. The segmenting engine  104  will thus include tracked entity  124   a  as a member of the segment in this example. 
     The segmenting engine  104  provides the generated segment to targeting engine  106 . The targeting engine  106  provides electronic communications to the tracked entities  124   a - d  and computing devices  120   a - c  that fall within the segment identified by segmenting engine  104 . Continuing the example above, for recipients in a segment that received a first electronic communication ten times, the targeting engine  106  is configured to provide a second electronic communication. For recipients outside the segment (recipients that have not received the first electronic communication ten times), the targeting engine  106  is configured to provide the first electronic communication. Embodiments described herein thus allow a tracking entity using the computing system  100  to define how authentication-based profiles and device-based profiles should be merged and provide targeted electronic communications to members of the merged profiles that satisfy defined segment rules. 
     To define how authentication-based profiles and device-based profiles should be merged and to define segment rules, the computing system  100  is communicatively coupled to a display device  108  with a user interface  110 . The user interface  110  presents options specifying how authentication-based and device-based profiles should be merged. The user interface  110 , in this example, also presents prompts requesting segment rules. The user interface  110  includes any suitable electronic input device for receiving inputs, such as a touchscreen interface, a computer display communicatively coupled with mouse and keyboard inputs, or other suitable input devices. Inputs defining profile merge options and segment rules are received via user interface  110  and provided as inputs to merging engine  103  and segmenting engine  104 . 
       FIG. 2 , for example, is a diagram depicting an example interface  200  for receiving inputs specifying profile merge options. Interface  200  include a section for basing information about the profile merge option, including a dialog box  202  for the profile merge rule name and a dialog box  204  for a description of the profile merge option. A radio button  212  presents an option specifying whether the profile merge rule should be set as default. By receiving an input selecting radio button  212 , merging engine  103  applies the settings in interface  200  as default settings for new segments, but does not change any existing defaults for existing segments. 
     Details regarding the profile merge rule are presented via window  214 . For example, interface  200  presents authentication-based profile merge options  216   a - c  and device-based profile merge options  218   a - c . While  FIG. 2  depicts authentication-based profile merge options  216   a - c  and device-based profile merge options  218   a - c  as presented via radio buttons, other interface elements for presenting the authentication-based profile merge options  216   a - c  are possible. Authentication-based profile merge options  216   a - c  include options such as no authenticated profile  216   a , current authenticated profile  216   b , and last authenticated profile  216   c . The no authenticated profile option  216   a  specifies that no authentication-based profile should be used when merging with device-based profiles (e.g., when a tracking entity intends to target devices with anonymous user information such as users accessing online services who do not have authenticated accounts with the online service/tracking entity). The current authenticated profile option  216   b  specifies that current authentication-based profiles should be used when merging with device-based profiles (e.g., when a tracking entity intends to target tracked entities  124   a - d  such as users with online accounts accessing online services while logged in with those accounts). The last authenticated profile option  216   c  specifies that the last known authentication-based profile should be used for merging with device-based profiles. For example, a selection specifying the last authenticated profile option  216   c  can be received when targeting a last known user without forcing authentication. 
     Turning to the device-based profile merge options, the no device profile option  218   a  specifies that no device-based profiles should be used when merging with authentication-based profiles. The current device profile option  218   b  specifies that the device level traits and gathered activity should be merged with the selected authentication-based profiles. The profile merge device graph  218   c  specifies that gathered traits and device data from the last ‘X’ number of devices that a tracked entity has authenticated on should be used for merging, where ‘X’ is any number configurable via the interface  200 . Alternatively, ‘X’ number of devices may be pre-configured. For example, receiving an input selecting the profile merge device graph  218   c  may specify that gathered traits and device data from the last  3  devices that have been authenticated should be used for merging. The profile merge device graph option  218   c  can be received as a selection when targeting a user that accesses online services using multiple devices. 
     In addition to the above, other authentication-based profile merge options and device-based profile merge options are also possible. For example, a household device graph option (not shown) can also be presented on interface  200 . The household device graph option can be received as a selection when a tracking entity intends to target users that utilize a particular grouping of devices (e.g., tracking data for all computing devices  102   a - c  within a household). A household device option can group and merge device-based profiles based on, for example, a common Internet Protocol address, a common geographic area, or other common traits. Another device-based profile merge option can include a user device graph option (not shown), which is similar to the profile merge device graph  218   c  in that multiple data points for traits and device data are used for merging. Unlike the profile merge device graph  218   c , however, the user device graph option gathers a broader set of data points. Data points used for merging using the user device graph option include IP addresses, location data, user behavior, and other probabilistic data points. Thus, the user device graph option executes an algorithm that links devices together to approximate a tracked entity without requiring explicit authentication. For example, the user device graph option correlates like IP addresses and other inputs between devices to determine a set of devices that are used by a tracked entity. The user device graph option uses all devices determined as relating to a tracked entity for segmentation. 
     The interface  200  can also present a window  220  presenting authenticated profile options  230   a - c . The authenticated profile options  230   a - c  presented in window  220  provide further granular control over which authenticated sources to use for merging. For example, a tracking entity may manage multiple authentication sources. An example of this is a tracking entity that operates multiple websites with different authenticated logins for tracked entities. A tracking entity that is an entertainment producer may operate one website that for a first entertainment network and a second website for a second entertainment network. Tracked entities may have authenticated profiles and log into one or both of the websites. The tracking entity can select which website to use as the source data by selecting an authenticated profile option  230   b  indicating the first authentication data source or authenticated profile option  230   c  indicating the second authentication data source. The Customer Relationship Management (CRM)  230   a  option indicates that all traits and device data gathered from tracking entities should be used for merging. The authenticated profile options  230   a - c  may be customized for the tracking entity. For example, authenticated profile option  230   b  (indicating authentication source  1 ) would specify the name of the first website in window  220 , and authenticated profile option  230   c  would specify the name of the second website in window  220 . 
     The selections for the authenticated profile options  230   a - c  work in conjunction with the selections for authentication-based profile merge options  216   a - c . If the current authenticated profile  216   b  and authenticated profile option  230   b  (indicating source  1 ) are received as selections, merging engine  103  uses collected trait information from the current tracked entity that are authenticated on the first authenticated source of the tracking entity, even if the tracked entity is logged on and authenticated with multiple sources (e.g., multiple websites) of the tracking entity. If the last authenticated profile  216   c  and authenticated profile option  230   b  are received as selections, merging engine  103  uses collected trait information from the last known authentication on the first source of the tracking entity. 
     In some aspects, tracking entities may need to ensure that no personably identifiable information is used in determining segments. For this scenario, interface  200  presents window  222  with an option for data export controls. By receiving a selection for the radio box in window  222 , the merging engine  103  will filter out personally identifiable information from tracked entities  124   a - d  before providing the merged profiles to the segmenting engine  104 . Examples of personally identifiable information include the name, social security number, or other identifiable traits of a tracked entity. 
     Options selecting one or more options in interface  200  are received via user interface  110  in order to define various profile merge configurations. Different profile merge configurations provide a tracking entity significant flexibility for targeting specific segments of tracked entities at various levels of granularity. For example, inputs selecting current authenticated profile option  216   b  and no device profile option  218   a  can be received in combination for one profile merge configuration. This allows a tracking entity to target tracked entities  124   a - d  based on single user authenticated data only while not taking into account anonymous device level data or data gathered from other users. An example use case is a financial services marketer (e.g., tracking entity) that needs to ensure onsite targeting is only using a single user&#39;s data for segmentation and not multiple user&#39;s data to comply with regulations. 
     In another example, inputs selecting current authenticated profile option  216   b  or  216   c , multiple authenticated profile options  230   a - c  (i.e. multiple data sources), and no device profile option  218   a  can be received in combination for a profile merge configuration. This allows a tracking entity to target tracked entities  124   a - d  based on single user authenticated data from multiple sources. An example use case is an online media streaming service provider (e.g., a tracking entity) that needs to target individual authenticated users of the streaming service within a household to provide customized media recommendations and also wants to include household level recommendations. The selection for no device profile  218   a  indicates that anonymous device data will not be included in the recommendations. The authenticated profile options  230   a - c  specify the authenticated profiles/data sources to use for data collection and segmentation. If inputs selecting multiple authenticated profile options  230   a - c  are selected, multiple data authenticated sources are merged. 
     In another example, inputs selecting no authenticated profile option  216   a  and current device profile option  218   b  are received in another profile merge configuration. This allows a tracking entity to only use device-level activity to target the device users. For example, a consumer product company may collect trait information from computing devices  120   a - c  regardless of the specific user (i.e. all anonymous device-level activity will be gathered and merged). 
     In another example, inputs selecting no authenticated profile option  216   a  and user device graph option are received as another profile merge configuration. In this example, the tracking entity can be a credit card company that is prospecting to obtain new credit card members. As the target audience does not yet have authenticated profiles, the profile merge device graph option  218   c  is not possible. Receiving a selection of no authenticated profile option  216   a  and user device graph option enables targeting of a user across devices with consistent messaging and the use of frequency caps. 
     In another example, inputs selecting no authenticated profile option  216   a  and a household device graph option are received as another profile merge configuration. This allows a tracking entity to target computing devices  120   a - c  across a household or other common location without considering traits from tracked entities  124   a - d . This is useful, for example, for a tracking entity that is a travel booking company offering sales (via electronic communications) on select vacation packages. Since the target audience does not currently have authentication-based profiles with the travel booking company, the travel booking company targets the computing devices  120   a - c  in the household, thus reaching all anonymous users with consistent messaging. 
     In another example, inputs selecting the last authenticated profile option  216   c  with current device profile  218   b  are received as another profile merge configuration. This allows a tracking entity to target the last known authenticated user of a device. This is useful, for example, for a tracking entity has authenticated access for its online service, has information indicating that a tracked entity  124   a  has previously logged on to the online service via an authentication-based profile, but has not currently logged while accessing the online service via the computing device  120   a.    
     In another example, inputs selecting the current authenticated profile  216   b  or last authenticated profile  216   c , current device profile  218   b , and authenticated profile options  230   a  (indicating CRM) and  230   b  (indicating to merge one of multiple profile sources) are received as another profile merge configuration. This allows a tracking entity to target a tracked entity  124  based on known household and user activity across devices across devices in order to deliver targeted communications based on the household, user, and anonymous activity on the currently authenticated device. 
     In another example, inputs selecting the current authenticated profile option  216   b  and a user device graph profile option are received as another profile merge configuration. This allows a tracking entity to target tracked entities based on both authenticated data of a tracked entity  124   a  collected across computing devices  120   a - c  in combination with anonymous data collected across computing devices  120   a - c . This can be useful, for example, for a tracking entity that intends to target offers based on the users across all tracked devices. 
     In another example, inputs selecting the current authenticated profile  216   b  or the last authenticated profile  216   c , multiple data sources via authenticated profile options  230   a  (indicating CRM) and  230   b  (indicating to merge one of multiple profile sources), a current device profile  218   b , and a household level device graph selection (not shown) are received as another profile merge configuration. This allows a tracking entity to target authenticated user level data and authenticated household level data across devices in combination with anonymous data collected across devices in the household. This allows the tracking entity to target communications based on the complete cross device activity for the tracked entities in the household. 
     In one embodiment, along with receiving inputs specifying profile merge options, the computing system  100  also receives inputs specifying segment rules via user interface  110 .  FIG. 3  is a diagram depicting an example interface  300  for presenting queries regarding segment rules. Interface  300  includes, for example, a dialog prompt  302  for receiving as input the name of a segment and a dialog box  304  for receiving as input the description of the segment. Further, interface  300  includes a dialog prompt  306  for receiving as input the segment rule(s). Segment rules are specified as, for example, specific traits desired for the segment. In some embodiments, multiple segment rules are specified via logical operators (e.g., AND, OR, NOT, etc). For example, to identify a segment of tracked entities of individuals over 25 years of age and have accessed sporting-related online services, the requested traits are received in dialog prompt  306  with an AND operator. Interface  300  also includes dialog prompt  308  for selecting one of the merge rules that were defined via interface  200 . Merge rules may be presented in dialog prompt  308  via a drop box that lists the merge rules by the names specified in dialog prompt  202  (shown in  FIG. 2 ). Receiving an input selecting a merge rule  308  applies the selected merge rule to the segment. For example, a profile merge rule can specify that traits gathered under a loyalty authentication-based profile should be merged with a device profile. In this example, the segment rules defined in interface  300  are applied to the collection of profiles defined by the loyalty authentication-based profile and device profile. 
       FIG. 4  is a flowchart depicting an example process  400  for merging authentication-based profiles with device-based profiles. The process  400  may be performed by one or more of the components listed in  FIG. 1  or by any other suitable component or in any suitable computing and/or communication environment. 
     The process  400  involves presenting options for merging profiles of tracked entities, as shown in block  410 . For example, a computing device presents interface  200  via user interface  110 . As discussed above, interface  200  presents options for merging authentication-based profiles via authentication-based profile merge options  216   a - c  and device-based profiles via device-based profile merge options  218   a - c.    
     The process  400  further involves receiving a selection of options specifying criteria for merging authentication-based profiles with device-based profiles, as shown in block  420 . For example, computing system  100  receives inputs via user interface  110 , the inputs indicating a selection of options specifying the criteria for merging profiles. In one embodiment, options for merging authentication-based profiles and device-based profiles are selected via radio check boxes. Details of the various profile merge configurations and use cases are discussed above with respect to  FIG. 2 . The selection of options specifying the criteria for merging profiles are provided to merging engine  103  executing in memory of computing system  100 . 
     The process  400  further involves creating a collection of profiles by merging at least some of the profiles into merged profiles, as shown in block  430 . As discussed above, authentication-based profiles for each tracked entity  124   a - d  and device-based profiles for computing devices  120   a - c  are identified and stored as identifiers in a memory of computing system  100 . The computing system  100  also stores, in a database in the memory, information on the collected traits associated with each of the of the authentication-based profile identifiers and device-based profile identifiers. The merging engine  103  merges the authentication-based profiles and device-based profiles specified by the received selection of options by merging the collected trait information associated with the selected profile identifiers and generating a collection of merged trait information. 
     The process  400  further involves generating a segment of tracked entities from the collection of profiles by applying segment rules to the collection of profiles. For example, the collection of profiles generated in step  430  is provided to segmenting engine  104 . Segmenting engine  104  can also receive, via user interface  110 , one or more segment rules defining the intended segment. Segmenting engine  104  applies the received segment rules to the collection of profiles to generate a segment of tracked entities. In one example, segment rules indicate that the desired target segment includes individuals over 30 years of age who spend a minimum amount of time on a streaming video website. The segmenting engine  104  evaluates the segment rules against the collection of profiles to determine the tracked entities within the collection of profiles that are over 30 years of age and spend a minimum amount of time on a streaming video website. 
     The process further includes transmitting, to the segment of tracked entities, an electronic communication uniquely tailored for the segment of tracked entities, as shown in block  450 . For example, targeting engine  106  transmits an electronic communication to tracked entities  124   a - d  that satisfied the segment rule(s). The electronic communication is uniquely tailored to the segment of tracked entities. For example, a tracking entity may intend to provide a certain electronic advertisement specifically to tracked entities that have purchased a car during the last year. In this example, segmenting engine  104  identifies the tracked entities that satisfy the segment—those that have purchased a car in the last year. Targeting engine  106  transmits the unique advertisement to the identified segment. 
     Any suitable computing system or group of computing systems can be used to implement the computing system  100  and computing devices  120   a - c . For example,  FIG. 5  is a block diagram depicting examples of implementations of such components. The computing system  100  can include a processor  1002  that is communicatively coupled to a memory  1004  and that executes computer-executable program code and/or accesses information stored in the memory  1004 . The processor  1002  may comprise a microprocessor, an application-specific integrated circuit (“ASIC”), a state machine, or other processing device. The processor  1002  can include one processing device or more than one processing device. Such a processor can include or may be in communication with a computer-readable medium storing instructions that, when executed by the processor  1002 , cause the processor to perform the operations described herein. 
     The memory  1004  can include any suitable non-transitory computer-readable medium. The computer-readable medium can include any electronic, optical, magnetic, or other storage device capable of providing a processor with computer-readable instructions or other program code. Non-limiting examples of a computer-readable medium include a magnetic disk, memory chip, ROM, RAM, an ASIC, a configured processor, optical storage, magnetic tape or other magnetic storage, or any other medium from which a computer processor can read instructions. The instructions may include processor-specific instructions generated by a compiler and/or an interpreter from code written in any suitable computer-programming language, including, for example, C, C++, C #, Visual Basic, Java, Python, Perl, JavaScript, and ActionScript. 
     The computing system  100  may also comprise a number of external or internal devices such as input or output devices. For example, the computing system  100  is shown with an input/output (“I/O”) interface  1008  that can receive input from input devices or provide output to output devices. Display device  108 , for example, can be communicatively coupled to I/O interface  1008 . A bus  1006  can also be included in the computing system  100 . The bus  1006  can communicatively couple one or more components of the computing system  100 . 
     The computing system  100  can execute program code that configures the processor  1002  to perform one or more of the operations described above. The program code can include one or more of the tracking engine  102 , merging engine  103 , segmenting engine  104 , or targeting engine  106 . The program code may be resident in the memory  1004  or any suitable computer-readable medium and may be executed by the processor  1002  or any other suitable processor. In some embodiments, the modules can be resident in the memory  1004 , as depicted in  FIG. 5 . In additional or alternative embodiments, one or more modules can be resident in a memory that is accessible via a data network, such as a memory accessible to a cloud service. 
     The computing system  100  can also include at least one network interface device  1010 . The network interface device  1010  can include any device or group of devices suitable for establishing a wired or wireless data connection to one or more data networks  115 . Non-limiting examples of the network interface device  1010  include an Ethernet network adapter, a modem, and/or the like. The computing system  100  can transmit messages as electronic or optical signals via the network interface device  1010 . 
     The computing devices  120   a - c  can similarly each include a processor that is communicatively coupled to a memory and that executes computer-executable program code and/or accesses information stored in the memory and otherwise include similar computing components as described with respect to computing system  100 . 
     Numerous specific details are set forth herein to provide a thorough understanding of the claimed subject matter. However, those skilled in the art will understand that the claimed subject matter may be practiced without these specific details. In other instances, methods, apparatuses, or systems that would be known by one of ordinary skill have not been described in detail so as not to obscure claimed subject matter. 
     Unless specifically stated otherwise, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” and “identifying” or the like refer to actions or processes of a computing device, such as one or more computers or a similar electronic computing device or devices, that manipulate or transform data represented as physical electronic or magnetic quantities within memories, registers, or other information storage devices, transmission devices, or display devices of the computing platform. 
     The system or systems discussed herein are not limited to any particular hardware architecture or configuration. A computing device can include any suitable arrangement of components that provides a result conditioned on one or more inputs. Suitable computing devices include multipurpose microprocessor-based computer systems accessing stored software that programs or configures the computing system from a general purpose computing apparatus to a specialized computing apparatus implementing one or more embodiments of the present subject matter. Any suitable programming, scripting, or other type of language or combinations of languages may be used to implement the teachings contained herein in software to be used in programming or configuring a computing device. 
     Embodiments of the methods disclosed herein may be performed in the operation of such computing devices. The order of the blocks presented in the examples above can be varied—for example, blocks can be re-ordered, combined, and/or broken into sub-blocks. Certain blocks or processes can be performed in parallel. 
     The use of “adapted to” or “configured to” herein is meant as open and inclusive language that does not foreclose devices adapted to or configured to perform additional tasks or steps. Additionally, the use of “based on” is meant to be open and inclusive, in that a process, step, calculation, or other action “based on” one or more recited conditions or values may, in practice, be based on additional conditions or values beyond those recited. Headings, lists, and numbering included herein are for ease of explanation only and are not meant to be limiting. 
     While the present subject matter has been described in detail with respect to specific embodiments thereof, it will be appreciated that those skilled in the art, upon attaining an understanding of the foregoing, may readily produce alterations to, variations of, and equivalents to such embodiments. Accordingly, it should be understood that the present disclosure has been presented for purposes of example rather than limitation, and does not preclude inclusion of such modifications, variations, and/or additions to the present subject matter as would be readily apparent to one of ordinary skill in the art.