Digital Consumer Identification in the Supply Chain

Abstract

A method is provided that includes collecting first-party data from a client device associated with a consumer and storing the first-party data in a database. The method also includes comparing the first-party data with second-party data in the database to identify a consumer profile associated with the first-party data and the second-party data, and serving a digital advertisement to the client device based on the consumer profile in the database and the first-party data. A system is also provided that implements the above method.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This disclosure claims priority under the rules and regulations of the WIPO Patent Cooperation Treaty to U.S. Provisional Patent Application No. 62 / 803,860, entitled "DIGITAL CONSUMER IDENTIFICATION IN A SUPPLY CHAIN," to Wassim Samir CHAAR et al., filed February 11, 2019, the contents of which are incorporated by reference herein in their entirety for all purposes.

[0002] The present disclosure generally relates to a system and procedure for identifying consumers using various first and second party data sets mixed and combined with existing household and consumer identification graphs stored in a server database (e.g., for marketing purposes). More specifically, the embodiments disclosed herein relate to identifying consumers using probabilistic techniques involving consumer interest data, geographic location data, demographic data, personality data, media consumption data, iAB-supplied classification data, digital signals, and the like, which helps to create a robust profile of the consumer over time. The consumer profile can ultimately be used to cluster and target consumers with digital advertising campaigns. [Background technology]

[0003] Current digital advertising and marketing techniques typically use fragmented information collected from consumers who subscribe to a particular network service, which can result in missed marketing opportunities or even wasted advertising resources when targeted to the wrong targets. Summary of the Invention

[0004] In one embodiment, a computer-implemented method is provided that includes collecting first party data from a client device associated with a consumer and storing the first party data in a database. The computer-implemented method also includes comparing the first party data to second party data in the database to identify a consumer profile associated with the first party data and the second party data, and serving a digital advertisement to the client device based on the consumer profile in the database and the first party data.

[0005] In a second embodiment, a computer-implemented method is disclosed that includes requesting a digital value-added certificate from a client device to a first server hosting a consumer network, the client device being associated with a consumer who has joined the consumer network. The computer-implemented method also includes providing first party data from the client device to the first server. The first party data is correlated with second party data in a database accessible to the first server, and at least one of the first party data or the second party data includes geographic location information indicating the presence of the consumer within a predetermined radius from the retail store. The first server is configured to identify a consumer profile associated with the first party data and the second party data, and generate a digital value-added certificate for the consumer based on a degree to which the geographic location information corresponds to the consumer profile. The computer-implemented method also includes receiving the digital value-added certificate at the client device, and authenticating the digital value-added certificate at a point of sale in the retail store.

[0006] In yet another embodiment, a system is disclosed that includes a memory circuit that stores instructions and one or more processors. The one or more processors are configured to execute at least one instruction that causes the system to collect first party data from a client device associated with a consumer and store the first party data in a database. The one or more processors also execute instructions to compare the first party data with second party data in the database, identify a consumer profile associated with the first party data and the second party data, and provide a digital advertisement to the client device based on the consumer profile in the database and the first party data. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 illustrates a system configured for digital consumer identification in a supply chain according to some embodiments. [Diagram 2] FIG. 2 illustrates an example architecture suitable for implementing the system in FIG. 1, according to some embodiments. [Diagram 3] FIG. 1 illustrates a table of a database that cross-correlates multiple columns including frequent shopper card ID with second party provided data according to some embodiments. [Figure 4] FIG. 1 illustrates a graph correlating multiple household identifiers and digital identifiers in a database according to some embodiments. [Diagram 5] 1 is a flowchart including steps in a method for identifying consumers using first party owned and second party provided data sets according to some embodiments. [Figure 6] 1 is a flowchart including steps in a method for providing digital advertisements to a consumer responsive to the consumer's identification in a database, according to some embodiments. [Figure 7] 1 is a flow chart including steps in a method of requesting a digital value-added certificate from a server hosting a consumer network according to some embodiments. [Figure 8] 8 is a block diagram illustrating an exemplary computer system on which the client and network devices of FIG. 1 and the methods of FIGS. 5-7 may be implemented, according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] In the figures, elements labeled with the same or similar reference numbers may have similar functionality and characteristics, unless otherwise stated.

[0009] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details. In other instances, well-known structures and techniques have not been shown in detail so as not to obscure the present disclosure.

[0010] The system provided herein is configured to collect various first and second party data sets such as consumer interest data, geographic location data, demographic data, personality data, media consumption data, iAB supply classification data, digital signals, and the like, store this information in a database, perform various data science techniques and procedures to probabilistically cluster and resolve unknown consumer identities from within existing household and consumer identity graphs, and provide the ability to target the resolved consumer identity profiles in the digital space. In some embodiments, the system disclosed herein may operate in a situation where the consumer is located inside a retail store or at a point of sale (POS) of a retail store.

[0011] The embodiments disclosed herein provide a solution to a problem that arises in the area of ​​computer technology of developing advertising models directed to targeted payloads across large populations of consumers. The tools disclosed herein provide the technical capability to accurately identify consumers or groups of consumers from digital information collected by multiple parties in a centralized database.

[0012] The subject systems and methods provide several advantages, including the display of correlated table and graph data. The proposed solutions can reduce processing time to find correlations and identify consumer patterns and networks, thus further improving the capabilities of the computer itself. Additionally, the subject systems reduce the strain on computer processors, network hardware and resources, and / or device power to perform correlations and update models across one or more components of the system, providing improvements that can be realized in tangible ways.

[0013] 1 illustrates a system 100 configured to identify a consumer 101 using geographic location data and other digital identification data collected over a network 150. The system 100 includes a server 130A, a server 130B (collectively referred to hereinafter as "servers 130"), a database 152, and client devices 110-1, 110-2, and 110-3 (collectively referred to hereinafter as "client devices 110"). In some embodiments, the client devices 110 include internet-connectable devices such as a mobile device 110-1, a tablet device 110-2, a home computer 110-3, and other appliances such as a television set, and any other sensors and devices via the network 150. The client devices 110, the server 130, and the database 152 may be communicatively coupled to each other wirelessly (e.g., cellular networks, WiFi, BlueTooth, and the like) or through wired channels (e.g., telephone lines, Ethernet, and the like). Client device 110, server 130, and database 152 may each include at least a memory circuit and one or more processor circuits. The memory circuit may include instructions that, when executed by the processor circuit, cause the device, servers A and B, and the database to perform at least some steps in the methods disclosed herein.

[0014] The client device 110 may include one or more applications 122, such as application programming interfaces (APIs) or software development kits (SDKs), hosted by any of the servers 130. Without limitation, multiple servers 130 may host multiple APIs or SDKs installed on the device to provide different network services to the consumer 101. For example, server 130B may be a network publishing host that provides multimedia content (e.g., news and entertainment, services, and the like) to the consumer 101 for downstream. In some embodiments, server 130B may host mapping and other geolocation services to the consumer (e.g., applications 122-1 and 122-2 through the GPS device 112 of smartphone 110-1). Similarly, client device 110-3 may be a desktop computer running application 122-3 (API) and / or application 122-4 (SDK). More generally, the server 130 may provide any network service to the consumer 101, including retail shopping, consumer brands, services, information, entertainment, travel, business, and the like.

[0015] The consumer 101 may have a frequent shopper card 115 including a frequent shopper card ID 119-12 with membership to a network service hosted by server A. In some embodiments, the client device 110 or application 122 may include personally identifiable information (PII), such as an advertising digital identifier 119-1, a device identifier 119-2, an IP address 119-3, or any other ID naming scheme that is PII-safe following guidelines established by iAB. Hereinafter, the advertising digital identifier 119-1, the device identifier 119-2, or the IP address 119-3 are collectively referred to as "digital identifiers 119." In some embodiments, the consumer 101 may be located within a retail store (e.g., a particular section, aisle, or kiosk of the store), or even at a point of sale (POS) of the retail store, and the server 130 may be able to obtain the digital identifier 119 for use in the methods disclosed herein, and the like.

[0016] Database 152 may include a PII secure database and may store information 125 about consumer 101. In some embodiments, information 125 may include digital identifiers 119 and other consumer information collected by any of applications 122. Database 152 may include one or more databases accessible to at least one of servers 130 through network 150. In some embodiments, server 130 may include its own database coupled to the network through a firewall configured to provide privacy and security for the information.

[0017] In some embodiments, but not limited to, the GPS device 112 may provide latitude and longitude coordinates of the client device 110-1 to the API 122-1 or SDK 122-2 executing in memory. Either or both of the servers 130 may collect the geographic location of the mobile device 110-1, including other technographic information specific to the device (IP address, device operating system, "user-agent", and the like), information about the application 122 or website the consumer 101 is running, and other PII-secure contextual information. The server 130 may also store the digital identifier 119 and other information 125 in the database 152 based on consent and agreement by the consumer 101.

[0018] FIG. 2 illustrates an exemplary architecture 200 suitable for implementing the system in FIG. 1, according to some embodiments. A client device 210 is communicatively coupled to a server 230 via a network 150. The server 230 may also access a database 252 via the network 150. In some embodiments, the database 252 may be part of the server 230. Although the architecture 200 illustrates only one server 230 and one client device 210, in some embodiments, multiple client devices may be coupled to multiple servers through the network 150, of which the client device 210 and the server 230 are only examples. The client device 210 may include a personal computer, a portable or mobile computer (e.g., a laptop), a cell phone, a smartphone, a palm device, or any other device, such as a printer, a smart printer, or a display (e.g., the client device 110). The client device 210 may also include or be communicatively coupled to one or more peripheral devices, such as an input device 214 and an output device 216. In some embodiments, the input devices 214 include any of a touch screen, a stylus, a mouse, a keyboard, or a microphone. Similarly, the output devices 216 may include a display, speakers, and the like.

[0019] The client device 210 and the server 230 may include a processor 212-1 and memory 220-1 (client device 210), and a processor 212-2 and memory 220-2 (server 230), hereinafter collectively referred to as "processor 212" and "memory 220." A communication module 218-1 of the client device 210 and a communication module 218-2 of the server 230 enable each of these devices to interface with the network 150 (hereinafter collectively referred to as "communication module 218"). The communication module 218 may include radio frequency circuitry and an antenna configured to transmit and receive radio frequency signals, such as Bluetooth, near field coupling, Wi-Fi, and the like. In that regard, the communication module 218 may enable one or more client devices as the client device 210 and one or more servers as the server 230 to communicate directly with each other, separate from the network 150.

[0020] The database 252 may include an identification table 255. In some embodiments, the identification table 255 may include a correlation table that associates the digital identifier 219 with multiple devices 210 and consumer purchase histories of multiple consumers. In some embodiments, the table 355 is a correlation table between multiple digital data sources. The database 252 may also include information from retail stores and brand manufactured products.

[0021] The memory 220-1 may include an application 222 (e.g., APIs, SDKs, and the like, see application 122) including instructions that, when executed by the processor 212-1, cause the client device 210 to at least partially perform some of the methods disclosed herein. In some embodiments, the application 222 may be installed and hosted by the server 230 based on a consumer's permission. Similarly, the memory 220-2 may include a marketing engine 240 and a digital identification engine 246 having instructions that, when executed by the processor 212-2, cause the server 230 to at least partially perform some of the methods according to the present disclosure. The client device 210 may also include a digital identifier 219 associated with the device and / or a digital identification number (e.g., digital identifier 119) of the application 222.

[0022] The marketing engine 240 performs marketing analysis and devises advertising campaign strategies and promotional offers to consumers in light of business rules, advertising rules, and consumer profiles. In some embodiments, the marketing engine 240 includes management tools 242 and advertising tools 244. In some embodiments, the management tools 242 include analysis algorithms based on business rules. In some embodiments, the advertising tools 244 include algorithms based on campaign rules, campaign management, campaign optimization, campaign analysis, and handling of campaign errors. In some embodiments, the marketing engine 240 includes identity resolution algorithms and is configured to perform identity mapping and onboarding of new subscribers. In some embodiments, the marketing engine 240 includes installing and updating digital platform adapters and interfaces of the client device 210, including the application 222 and its associated devices (e.g., the GPS device 112).

[0023] The digital identification engine 246 may include a correlation tool 248 and a database management tool 249. In some embodiments, the correlation tool 248 includes techniques and procedures in the field of data science to probabilistically identify consumers for purposes of targeting appropriate and attractive advertising media based on the digital identifiers 219 and the identification table 255. The database management tool 249 may create and update the identification table 255 and the digital identifiers 219 from multiple client devices 210 compiled into the digital information 225.

[0024] In some embodiments, the correlation tool 248 and the database management tool 249 may collect and aggregate data (e.g., first party data, second party data, third party data, and the like) from one or more servers 230. In some embodiments, at least one of the first party data or the second party data includes a digital identifier 219, and the correlation tool 248 compares the first party data to the second party data to determine the likelihood that the client device 210 is being used by a consumer associated with the consumer profile in the database 252. In some embodiments, the correlation tool 248 correlates at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website running on the client device (e.g., in the application 222). In some embodiments, the correlation tool 248 pairs the first party data and the second party data within a predetermined radius of the geographic location information associated with the client device 210. In some embodiments, the correlation tool 248 associates the consumer profile with a group of consumers having a household identifier. In some embodiments, the correlation tool 248 identifies the second party data based at least in part on the first party data using a model trained with data stored in the database. In some embodiments, the first party data includes mobility data of the client device, and the correlation tool 248 infers the second party data using a behavioral model based on the mobility data. In some embodiments, the digital identification engine 246 receives digital advertisements and purchasing data associated with the consumer and updates a model that compares the first party data to the second party data based on the purchasing data. The model may include neural network models, machine learning models, artificial intelligence models, and any type of multiple linear regression, non-linear, or linear models. In some embodiments, the correlation tool 248 updates a consumer profile based on the first party data and the second party data. In some embodiments, the correlation tool 248 identifies a correlation between the consumer and the consumer profile and identifies a confidence level of the correlation.In some embodiments, the correlation tool 248 receives third party data associated with consumers and updates the database with the third party data to improve the accuracy with which consumer profiles are associated with consumers.

[0025] 3 illustrates a database table 355 that cross-correlates multiple columns including loyalty card IDs with second party provided data (e.g., identification table 255 and database 252) according to some embodiments. Table 355 includes a database that cross-correlates a household ID (HHID) column 311 with a loyalty card ID (LCID) column 312 and a digital ID (DID) column 313 with various first and second party source data according to some embodiments. Without limitation, location information (e.g., provided by a GPS device, see GPS device 112) may be included in a latitude column 314, a longitude column 315, and an IP address column 316. Table 355 may further include consumer information that may be provided from other data sources, including PII secure consumer information that may be obtained (e.g., purchased or licensed) from a third party server. Consumer information may be included in "Personality Traits" column 317, "Interests" column 318, and "iAB Categories" column 319. In some embodiments, the information in columns 317-319 may be provided by a social networking server to which the consumer may subscribe, or by machine learning, artificial intelligence algorithms operating using other digital data available to the consumer (e.g., any of columns 314-316). Table 355 may be stored in a database and used by a digital identification engine (e.g., digital identification engine 246) to establish correlations between different columns of table 355 and consumer identifications.

[0026] In an embodiment according to the present disclosure, table 355 is created or stored in a database coupled to a server (e.g., database 252 and server 230). The database collects information (e.g., information 125 and 225) from various devices and first and second party data sources, correlates the data sets, and refines and creates new consumer household, frequent customer, and digital ID associations in any order. Correlation may include, for example, pairing entries in HHID column 311 with entries in LCID column 312 and entries in DID column 313 based on the same (or similar) entries in latitude column 314 and longitude column 315. For example, row 5 of table 355 shows a consumer logging into an account with LCID_5 (LCID column 312) on server A using a mobile device with DID_5 (DID column 313). The mobile device may further report a geographic location that matched HHID_2 (latitude: 37.301390, longitude: -122.043210). In such a case, the correlation between the HHID_2, LCID_5, and DID_5 values ​​may be strong: a user of a mobile device with DID_5 is also likely to have frequent flyer card LCID_5 and reside within household HHID_2. Thus, rows 1-5 and 7 of table 355 may be associated with a deterministic match 320.

[0027] Note that, as expected, multiple consumers may be associated with a given entry in HHID column 311. For example, a household with HHID_2 includes at least two individuals with LCID_4 and LCID_5 in LCID column 312. Each individual is using a different mobile device with DID_4 and DID_5, respectively (see DID column 313, ll. 4 and 5). A geographic location with latitude: 37.301390 and longitude: -122.043210 is likely the home location of HHID_2. Furthermore, a household with HHID_1 includes at least three individuals with LCID_1, LCID_2, and LCID_3. Each individual is using a different mobile device with DID_1, DID_2, and DID_3, respectively. The geographic location with latitude: 37.900921 and longitude: -108.525893 may be the home location of HHID_1, or the location of a retail store, or any other location associated with a mobile device with DID_3. Note that in the particular case of HHID_1, the exact location of HHID_1 may not be corroborated by DID_1, DID_2 (e.g., no data in latitude column 314 or longitude column 315 for the first two rows of table 355). For example, server A may not have had access to the geographic locations of mobile devices DID_1 and DID_2, but may still be able to correlate all to HHID-1 based on other information. The other information may include purchasing patterns with each frequent flyer card (based on the entries in LCID column 312): items, retailer locations, and purchase times over a long sampling period.

[0028] Table 355 may also include entries with weaker correlations, where LCID values ​​may be missing or not yet established, and probabilistic matching 340 may be used to link, connect, and strengthen the correlation from one ID set to another (e.g., any one of columns 311-319). This may be the case when a server (e.g., server 130A) accesses data and information of consumers who are not frequent flyer card network subscribers. Thus, in household HHID_5, consumers with mobile devices DID_8, DID_9, and DID_10 may be associated with geographic locations (41.878113, -87.629799), (41.893353, -87.681200), and (41.875972, -87.669708), respectively (see rows 8-10 of table 355). These geographic locations, although different, are very close to each other (see data block 342A), which likely indicates either the home location of HHID_5 or the location of a store or mall where different household members go shopping (e.g., at the same or overlapping times). However, in some embodiments, the difference in the geographic locations of the household members of HHID_5 (which in this particular case could be several square kilometers) may reduce the certainty that these DIDs should be associated with HHID_5. Another probabilistic match may link two consumers with mobile devices DID_11 and DID_12 with household HHID_6, with IP address 216.3.128.12 (see rows 11-12 of table 355, block 342B). For example, server A may have detected both DID_11 and DID_12 logging into the network from IP address 216.3.128.12 in the middle of the night. Additionally, based on other information (collected by Server A from Server B), both consumers of HHID_6 may be listed as health enthusiasts with an interest in cycling (e.g., through purchase history, usual locations in parks or outdoors, typical speeds of movement at those locations, and the like).

[0029] In addition, table 355 may indicate that household HHID_3 may be linked with mobile device DID_6 (see row 6 of table 355). However, while the geographic location (40.712776, -74.005974) and IP address 73.15.66.31 are established, the absence of a frequent flyer card backing up the data (e.g., no data in LCID column 312) or the lack of additional household members weakens the certainty that the correlation shown in table 355 is correct. In some embodiments, the existence of a correlation between the HHID, LCID, and DID values ​​indicates a strong deterministic match 320 between HHID_4, LCID_6, and DID_7, even in the absence of any additional consumer data other than the geographic location, or IP address, or iAB category (iAB12) that may be determined from the frequent flyer card account associated with LCID_6.

[0030] 4 illustrates a graph 400 correlating a plurality of household identifiers 411-1, 411-2, 411-3, 411-4, and 411-5 (collectively referred to below as “household identifiers 411”) and digital identifiers 419-1, 419-2, 419-3, 419-4, 419-5, 419-6, 419-7, 419-8, 419-9, 419-10, 419-11, and 419-12 (collectively referred to below as “digital identifiers 419”) of a database, according to some embodiments. Graph 400 includes a mapping correlation between a plurality of household identifiers 411 and digital identifiers 419 of a database (e.g., databases 152 and 252), according to some embodiments. In some embodiments, the graph 400 may be obtained by a digital identification engine of a server from identification tables stored in a database (e.g., digital identification engine 246, server 130, server 230, database 152, database 252, identification table 255, and table 355). Thus, the graph 400 associates DID_1, DID_2, and DID_3 with HHID_1 through strong deterministic links 410-1. Similarly, DID_4 and DID_5 are associated with HHID_2 through strong deterministic links 410-2, and DID_7 is associated with HHID_4 through strong deterministic links 410-3. The graph 400 also shows DID_8, DID_9, and DID_10 associated with HHID_5 through probabilistic links 430-1 (with high confidence, as shown by the similar geographic locations of the three mobile devices DID_8-10 (see table 355)). DID_11 and DID_12 are also associated with HHID_2 through probabilistic link 430-2 (with high confidence because the IP addresses associated with both devices are the same, and both consumers have the same personality traits and appear to share the same iAB categories).

[0031] Graph 400 also shows a non-deterministic link 420 between DID 419-6 and HHID 411-3 due to the lack of information regarding the frequent shopper card (see table 355). Note that in the case of HHID 411-3, even if a geographic location is established in addition to an IP address, the absence of a second mobile device or consumer that correlates with the same household somewhat reduces the certainty that the link between HHID 411-3 and DID 419-6 is accurate.

[0032] FIG. 5 is a flow chart including steps in a method 500 for identifying consumers using a data set owned by a first party and provided by a second party, according to some embodiments. In some embodiments, one or more of the steps in the method 500 may be performed by one or more of the devices and components shown in FIG. 1 and FIG. 2. For example, in some embodiments, one or more of the steps in the method 500 may be performed by a client device or a server including a processor executing instructions stored in a memory circuit (e.g., client devices 110 and 210, servers 130 and 230, and processor 212, and memory 220, see FIG. 1, FIG. 2). In some embodiments, the memory may include a marketing engine having management and advertising tools, and a digital identification engine having correlation and database management tools (e.g., marketing engine 240, management tools 242, advertising tools 244, digital identification engine 246, correlation tools 248, and database management tools 249, see FIG. 2). In some embodiments, the server may also be communicatively coupled via a network to a database that stores digital information organized in the form of an identification table or any other table, and in the form of a graph (e.g., databases 152 and 252, digital information 225, identification table 255, table 355, graph 400, and network 150, see Figs. 1, 2, 3, and 4). In some embodiments, to communicate with each other and with the network, the client device and the server may use input devices, output devices, and communication modules to perform steps according to method 500 (e.g., input device 214, output device 216, and communication module 218, see Fig. 2). Furthermore, in some embodiments, a method according to the present disclosure may include at least one of the steps in method 500 being performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

[0033] Step 502 includes the APIs and SDKs collecting information from devices providing technographic and digital device data including data sets provided by second parties providing consumer interest data, geographic location data, demographic data, personality data, media consumption data, iAB-supplied classification data, digital signals, and the like. In some embodiments, step 502 includes supplementing an existing consumer identity map database with the collected data described above.

[0034] Step 504 includes storing the collected location information in a database. In some embodiments, step 504 is performed by an application programming interface installed on the device, and step 504 includes accessing the application programming interface with a second application programming interface installed on the device referenced in FIG. 1. In some non-limiting embodiments, step 504 includes storing an identifier of the device ID in the database, and comparing the information provided by the second party to the consumer profile in the database includes matching the identifier of the device with a consumer household identifier in the database.

[0035] Step 506 includes performing a set of analyses and procedures from the field of data science to probabilistically categorize and link disparate identities and identify households and devices in an identity map database. In some embodiments, but not limited to, the consumer profile includes PII-secure first party data such as the consumer's purchase history at retailers, technographic data, and data sets provided by second parties providing consumer interest data, geographic location data, demographic data, personality data, media consumption data, iAB supply category classification data, digital signals, and the like. In some embodiments, but not limited to, step 506 includes correlating transactions in the consumer packaged goods space with various location data, consumer interest data, technographic data, demographic data, personality data, media consumption data, and the like.

[0036] Step 508 includes targeting digital advertisements, promotions, offers, and digital media to the consumer in the marketing and advertising space based on the newly identified identity federation. In some embodiments, the first party acquired data set, such as location information from the device, includes location data within a retail store, the location associated with a segment of consumer packaged goods including consumer transaction data collected in a PII-secure manner. Thus, in some embodiments, step 508 includes correlating the consumer profile with the segment of consumer packaged goods. In some embodiments, step 508 includes providing a value-added certificate to purchase the item at the retail store. In some embodiments, step 508 includes sending a media file including the promotional information for display on the mobile device and other devices. In some embodiments, step 508 includes updating the consumer profile in the database with information from the first and second party data sets. In some embodiments, storing the location information in the database includes categorizing users of client devices (e.g., client devices 110 or 210) into consumer categories, and step 508 includes providing advertisements associated with the consumer categories for display across devices in the form of mobile, desktop, connected TV, and the like.

[0037] FIG. 6 is a flow chart including steps in a method 600 of providing digital advertisements to consumers in response to the consumer's identification in a database, according to some embodiments. In some embodiments, one or more of the steps in the method 600 may be performed by one or more of the devices and components shown in FIG. 1 and FIG. 2. For example, in some embodiments, one or more of the steps in the method 600 may be performed by a client device or a server including a processor executing instructions stored in a memory circuit (e.g., client devices 110 and 210, servers 130 and 230, and processor 212, and memory 220, see FIG. 1, FIG. 2). In some embodiments, the memory may include a marketing engine having management tools and advertising tools, and a digital identification engine having correlation tools and database management tools (e.g., marketing engine 240, management tools 242, advertising tools 244, digital identification engine 246, correlation tools 248, and database management tools 249, see FIG. 2). In some embodiments, the server may also be communicatively coupled via a network to a database that stores digital information organized in the form of an identification table or any other table, and in the form of a graph (e.g., databases 152 and 252, digital information 225, identification table 255, table 355, graph 400, and network 150, see Figs. 1, 2, 3, and 4). In some embodiments, to communicate with each other and with the network, the client device and the server may perform steps according to method 600 using input devices, output devices, and communication modules (e.g., input device 214, output device 216, and communication module 218, see Fig. 2). Furthermore, in some embodiments, a method according to the present disclosure may include at least one of the steps in method 600 being performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

[0038] Step 602 includes collecting first party data from a client device associated with the consumer. In some embodiments, at least one of the first party data or the second party data includes geographic location information indicative of the consumer's presence within a predetermined radius from a retail store, and step 602 includes determining a likelihood that the geographic location information corresponds to a consumer profile. In some embodiments, at least one of the first party data or the second party data includes frequent shopper identification data of the consumer of a consumer retail service network, and step 602 includes determining a likelihood that the frequent shopper identification data, the consumer profile, and a device code associated with the client device correspond to the same consumer. In some embodiments, at least one of the first party data or the second party data includes a device code associated with the client device, and step 602 includes determining a likelihood that the client device is being used by a consumer associated with the consumer profile. In some embodiments, step 602 includes correlating at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device. In some embodiments, step 602 includes pairing the first party data and the second party data within a predetermined radius of the geographic location information associated with the client device. In some embodiments, step 602 includes associating a consumer profile with a group of consumers that have a household identifier.

[0039] Step 604 includes storing the first party data in a database. In some embodiments, step 604 includes storing the first party data in a first database and retrieving the second party data from a second database.

[0040] Step 606 includes comparing the first party data to the second party data in the database and identifying a consumer profile associated with the first party data and the second party data. In some embodiments, step 606 includes identifying the second party data based at least in part on the first party data using a model trained with data stored in the database. In some embodiments, the first party data includes mobility data of the client device and step 606 includes inferring the second party data using a behavioral model based on the mobility data. In some embodiments, step 606 includes receiving purchase data associated with the digital advertisement and the consumer and updating a model that compares the first party data to the second party data based on the purchase data. In some embodiments, step 606 includes updating the consumer profile based on the first party data and the second party data. In some embodiments, step 606 includes identifying a correlation between the consumer and the consumer profile and identifying a confidence level of the correlation. In some embodiments, step 606 includes receiving third party data associated with the consumer and updating the database with the third party data to improve the accuracy with which the consumer profile is associated with the consumer. In some embodiments, the client device is associated with a group of consumers, and step 606 includes selecting a consumer from the group of consumers based on the first party data and the second party data and identifying a consumer profile.

[0041] Step 608 includes providing the digital advertisement to the client device based on the consumer profile in the database and the first party data. In some embodiments, step 608 includes providing a value-added certificate to the consumer, the value-added certificate being associated with a product for sale at the retail store identified according to the first party data. In some embodiments, step 608 includes receiving a request from the client device to access the consumer network and receive the value-added certificate along with the digital advertisement. In some embodiments, step 608 includes modifying a correlation between the first party data and the second party data in the database based on a purchase at the retail store, the purchase being based on the digital advertisement. In some embodiments, step 608 includes collecting the second party data from a second server that provides network services to the client device. In some embodiments, step 608 includes providing print instructions to a printer in the retail store identified according to the first party data, the print instructions including instructions to print the consumer's value-added certificate.

[0042] FIG. 7 is a flow chart including steps in a method 700 of requesting a digital value-added certificate from a server hosting a consumer network, according to some embodiments. In some embodiments, one or more of the steps in the method 700 may be performed by one or more of the devices and components shown in FIG. 1 and FIG. 2. For example, in some embodiments, one or more of the steps in the method 700 may be performed by a client device or a server including a processor executing instructions stored in a memory circuit (e.g., client devices 110 and 210, servers 130 and 230, and processor 212, and memory 220, see FIG. 1, FIG. 2). In some embodiments, the memory may include a marketing engine having management and advertising tools, and a digital identification engine having correlation and database management tools (e.g., marketing engine 240, management tools 242, advertising tools 244, digital identification engine 246, correlation tools 248, and database management tools 249, see FIG. 2). In some embodiments, the server may also be communicatively coupled via a network to a database that stores the identification table or any other table and digital information organized in the form of a graph (e.g., databases 152 and 252, digital information 225, identification table 255, table 355, graph 400, and network 150, see Figs. 1, 2, 3, and 4). In some embodiments, to communicate with each other and with the network, the client device and the server may use input devices, output devices, and communication modules to perform steps according to method 700 (e.g., input device 214, output device 216, and communication module 218, see Fig. 2). Furthermore, in some embodiments, a method according to the present disclosure may include at least one of the steps in method 700 being performed in a different order, simultaneously, quasi-simultaneously, or overlapping in time.

[0043] Step 702 includes requesting a digital value-added certificate from a client device to a first server hosting a consumer network, the client device being associated with a consumer who has subscribed to the consumer network.

[0044] Step 704 includes providing the first party data from the client device to the first server. In some embodiments, the first party data is correlated with second party data in a database accessible to the first server. In some embodiments, at least one of the first party data or the second party data includes geographic location information indicative of a presence of the consumer within a predetermined radius from the retail store. In some embodiments, the first server is configured to identify a consumer profile associated with the first party data and the second party data and generate a digital value-added certificate for the consumer based on a degree to which the geographic location information corresponds to the consumer profile. In some embodiments, step 704 includes providing at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device. In some embodiments, step 704 further includes allowing the first server to pair the first party data and the second party data within a predetermined radius of the geographic location information associated with the client device. In some embodiments, step 704 includes selecting a consumer profile from a group of consumers having a household identifier, the group of consumers being provided by the first server based on the correlation of the first party data and the second party data. In some embodiments, step 704 includes permitting the first server to identify second party data based at least in part on the first party data using a model trained using data stored in the database. In some embodiments, step 704 includes providing mobility data of the client device to the first server, the first server configured to infer the second party data using a behavioral model based on the mobility data. In some embodiments, step 704 includes permitting the first server to update a consumer profile based on the first party data and the second party data. In some embodiments, step 704 includes permitting the first server to identify a correlation between the consumer and the consumer profile and identifying a confidence level of the correlation.In some embodiments, step 704 includes authorizing the second server to provide second party data to the first server. In some embodiments, step 704 includes authorizing the first server to update the database with the second party data to improve the accuracy with which the consumer profile is associated with the consumer. In some embodiments, step 704 includes authorizing the first server to modify a correlation between the first party data and the second party data in the database based on a purchase at a retail store associated with the digital value-added certificate. In some embodiments, the first party data includes information obtained by the first server from an application installed on the client device, and step 704 includes authorizing the first server to obtain the information. In some embodiments, step 704 includes accessing a second server hosting a social network and authorizing the second server to provide second party data to the first server, the second party data including data associated with the consumer's social network activity. In some embodiments, step 704 includes authorizing the first server to obtain the second party data from a second database.

[0045] Step 706 includes receiving, at the client device, the digital value-added certificate.

[0046] Step 708 includes authenticating the digital value-added certificate at a point of sale in the retail store. In some embodiments, step 708 includes authenticating the digital value-added certificate to a first server. In some embodiments, at least one of the first party data or the second party data includes frequent shopper identification data of a consumer of a consumer retail service network, and step 708 includes authenticating the frequent shopper identification data to the first server with the client device. In some embodiments, at least one of the first party data or the second party data includes a device code associated with the client device, and step 708 includes authenticating to the first server that the client device is being used by a consumer associated with the consumer profile. In some embodiments, step 708 includes providing to the first server purchase data associated with the digital value-added certificate, the first server configured to update a model comparing the first party data to the second party data based on the purchase data. In some embodiments, the digital value-added certificate is associated with a product for sale at the retail store, and step 708 includes requesting a second digital value-added certificate for a second product for sale at the retail store. In some embodiments, step 708 includes providing printing instructions to a printer within the retail store, the printing instructions including instructions to print a digital value-added certificate.

[0047] Hardware Overview Figure 8 is a block diagram illustrating an example computer system 800 on which the client and network devices of Figure 1 and the methods of Figures 5 through 7 may be implemented. In certain aspects, the computer system 800 may be implemented using hardware, or a combination of software and hardware, in a dedicated network device or integrated into another entity or distributed across multiple entities.

[0048] A computer system 800 (e.g., client device 110 and server 130) includes a bus 808 or other communication mechanism for communicating information, and a processor 802 (e.g., processor 212) coupled to the bus 808 for processing information. By way of example, computer system 800 may be implemented with one or more processors 802. Processor 802 may be a general purpose microprocessor, a microcontroller, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a state machine, gate logic, discrete hardware components, or any other suitable entity capable of performing calculations or other manipulations of information.

[0049] In addition to hardware, computer system 800 may include code that creates an execution environment for the computer programs in question, e.g., code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of these, stored in included memory 804 (e.g., memory 220), such as Random Access Memory (RAM), Flash memory, Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable PROM (EPROM), registers, a hard disk, a removable disk, a CD-ROM, a DVD, or any other suitable storage device coupled to bus 808 for storing information and instructions to be executed by processor 802. Processor 802 and memory 804 may be supplemented by, or incorporated within, special purpose logic circuitry.

[0050] The instructions may be stored in memory 804 and encoded on a computer-readable medium for execution by or to control the operation of one or more computer program consumer products, e.g., computer system 800, and may be embodied in one or more modules of computer program instructions according to any method known to those skilled in the art, including computer languages ​​such as, but not limited to, data-oriented languages ​​(e.g., SQL, dBase), system languages ​​(e.g., C, Objective-C, C++, Assembly), architecture languages ​​(e.g., Java, .NET), and application languages ​​(e.g., PHP, Ruby, Perl, Python). The instructions may also be implemented in a computer language, such as an array language, an aspect-oriented language, an assembly language, an authoring language, a command line interface language, a compiled language, a concurrent language, a brace language, a dataflow language, a data structuring language, a declarative language, an esoteric language, an extensible language, a fourth generation language, a functional language, an interaction language, an interpreted language, an iterative language, a list-based language, a miniature language, a logic-based language, a machine language, a macro language, a metaprogramming language, a multi-paradigm language, numerical analysis, a non-English based language, an object-oriented class-based language, an object-oriented prototype-based language, an offside rule language, a procedural language, a self-reflective language, a rule-based language, a scripting language, a stack-based language, a synchronization language, a syntax processing language, a visual language, a wirth language, and an xml-based language. The memory 804 may also be used for storing temporary variables or other intermediate information during execution of instructions to be executed by the processor 802.

[0051] The computer programs described herein do not necessarily correspond to files in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple cooperating files (e.g., files that store one or more modules, subprograms, or portions of code). A computer program can be deployed to run on one computer, or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network. The processes and logic flows described herein can be implemented by one or more programmable processors executing one or more computer programs that perform functions by operating on input data and generating output.

[0052] The computer system 800 further includes a data storage device 806, such as a magnetic or optical disk, coupled to the bus 808 for storing information and instructions. The computer system 800 can be coupled to various devices via an input / output module 810. The input / output module 810 can be any input / output module. An exemplary input / output module 810 includes a data port, such as a USB port. The input / output module 810 is configured to connect to a communication module 812. An exemplary communication module 812 (e.g., communication modules 218 and 238) includes a network interface card, such as an Ethernet card and a modem. In certain embodiments, the input / output module 810 is configured to connect to multiple devices, such as an input device 814 (e.g., input device 114) and / or an output device 816 (e.g., output device 116). An exemplary input device 814 includes a keyboard and a pointing device, such as a mouse or a trackball, by which a consumer can provide input to the computer system 800. Other types of input devices 814, such as tactile, visual, audio, or brain-computer interface devices, can also be used to provide interaction with the consumer. For example, feedback provided to the consumer can be any form of sensory feedback, e.g., visual, audio, or tactile feedback, and input from the consumer can be received in any form, including acoustic, voice, tactile, or brainwave input. Exemplary output devices 816 include display devices, such as liquid crystal display (LCD) monitors, that display information to the consumer.

[0053] According to one aspect of the disclosure, the client device 110 and the server 130 can be implemented using the computer system 800 in response to the processor 802 executing one or more sequences of one or more instructions contained in the memory 804. Such instructions may be read into the memory 804 from another machine-readable medium, such as the data storage device 806. Execution of the sequences of instructions contained in the main memory 804 causes the processor 802 to perform the process steps described herein. Also, one or more processors in a multi-processing arrangement may be utilized to execute the sequences of instructions contained in the memory 804. In alternative aspects, hardwired circuitry may be used in place of or in combination with software instructions to implement various aspects of the disclosure. Thus, aspects of the disclosure are not limited to any specific combination of hardware circuitry and software.

[0054] Various aspects of the subject matter described herein can be implemented in a computing system that includes back-end components, e.g., data network devices, or middleware components, e.g., application network devices, or front-end components, e.g., client computers having a graphical consumer interface or web browser through which a consumer can interact with an implementation of the subject matter described herein, or any combination of one or more of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. The communication network (e.g., network 150) can include, for example, any one or more of a LAN, a WAN, the Internet, and the like. Furthermore, the communication network can include, for example, any one or more of the following network topologies, including, but not limited to, a bus network, a star network, a ring network, a mesh network, a star-bus network, a tree-like or hierarchical network, or the like. The communication module can be, for example, a modem or an Ethernet card.

[0055] Computer system 800 may include client and network devices. The client and network devices are generally remote from each other and typically interact through a communications network. The relationship of the client and network devices arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. Computer system 800 may be, for example, but not limited to, a desktop computer, a laptop computer, or a tablet computer. Computer system 800 may also be incorporated in another device, for example, but not limited to, a mobile phone, a PDA, a mobile audio player, a Global Positioning System (GPS) receiver, a video game console, and / or a television set-top box.

[0056] The term "machine-readable storage medium" or "computer-readable medium" as used herein refers to any medium or media that participates in providing instructions to the processor 802 for execution. Such media may take many forms, including but not limited to non-volatile media, volatile media, and transmission media. Non-volatile media include, for example, optical or magnetic disks, such as the data storage device 806. Volatile media include dynamic memory, such as the memory 804. Transmission media include coaxial cables, copper wire, and optical fibers, including the wires that form the bus 808. Common forms of machine-readable media include, for example, a floppy disk, a flexible disk, a hard disk, a magnetic tape, any other magnetic medium, a CD-ROM, a DVD, any other optical medium, a punched card, a paper tape, any other physical medium with a pattern of holes, a RAM, a PROM, an EPROM, a flash EPROM, any other memory chip or cartridge, or any other medium from which a computer can read. The machine-readable storage medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition affecting a machine-readable propagated signal, or a combination of one or more of them.

[0057] In one aspect, a method may be an operation, an instruction, or a function, or vice versa. In one aspect, a claim may be modified to include some or all of the words (e.g., instructions, actions, functions, or components), one or more words, one or more sentences, one or more expressions, one or more paragraphs, and / or one or more claims recited in one or more other claims.

[0058] To illustrate the interchangeability of hardware and software, various illustrative blocks, modules, components, methods, operations, instructions, algorithms, etc. have been described generally in terms of their functionality. Whether such functionality is implemented as hardware, software, or a combination of hardware and software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application.

[0059] As used herein, the phrase "at least one of" with the term "and" or "or" preceding a series of items and separating any of the items modifies the list as a whole, and not each member (e.g., each item) of the list. The phrase "at least one of" does not require the selection of at least one item; rather, the phrase allows for a meaning including at least one of any of the items, and / or at least one of any combination of the items, and / or at least one of each of the items. By way of example, the phrase "at least one of A, B, and C" or "at least one of A, B, or C" refers, respectively, to only A, only B, or only C, any combination of A, B, and C, and / or at least one of each of A, B, and C.

[0060] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment described herein as "exemplary" should not necessarily be construed as preferred or advantageous over other embodiments. Phrases such as one aspect, this aspect, another aspect, some aspects, one or more aspects, one implementation, this implementation, another implementation, some implementations, one or more implementations, one embodiment, this embodiment, another embodiment, some embodiments, one or more embodiments, one configuration, this configuration, another configuration, some configurations, one or more configurations, the subject technology, the disclosure, the present disclosure, and other variations thereof and the like are for convenience and do not imply that the disclosure associated with such phrases is essential to the subject technology or that such disclosures apply to all configurations of the subject technology. Disclosure associated with such phrases may apply to all configurations, or to one or more configurations. Disclosure associated with such phrases may provide one or more examples. A phrase such as one aspect or some aspects may refer to one or more aspects and vice versa, and this is equally true for the other above mentioned phrases.

[0061] Reference to an element in the singular is not intended to mean "one and only one" unless otherwise specified, but rather "one or more." Masculine pronouns (e.g., his) include feminine and neuter (e.g., her and its) and vice versa. The term "some" refers to one or more. Underlined and / or italicized headings and subheadings are used merely for convenience and do not limit the subject art and are not to be referenced in connection with interpreting the description of the subject art. Relative terms such as first and second and the like may be used to distinguish one entity or action from another and do not necessarily require or imply any actual such relationship or order between such entities or actions. All structural and functional equivalents of the elements of the various configurations described throughout this disclosure that are known or that later become known to those skilled in the art are expressly incorporated herein by reference and are intended to be encompassed by the technology of the present subject matter. Moreover, nothing disclosed herein is intended to be made available to the public, regardless of whether such disclosure is expressly set forth in the above description. No claim element is to be construed under the provisions of 35 U.S.C. 112, sixth paragraph, unless the element is expressly recited using the phrase "means for" or, in the case of a method claim, the element is recited using the phrase "step for."

[0062] Enumeration of embodiments Embodiments disclosed herein include the following.

[0063] I. A computer-implemented method that includes collecting first party data from a client device associated with a consumer and storing the first party data in a database. The computer-implemented method also includes comparing the first party data to second party data in the database to identify a consumer profile associated with the first party data and the second party data, and serving a digital advertisement to the client device based on the consumer profile in the database and the first party data.

[0064] II. A computer-implemented method including requesting a digital value-added certificate from a client device to a first server hosting a consumer network, the client device being associated with a consumer who has subscribed to the consumer network. The computer-implemented method also includes providing first party data from the client device to the first server. The first party data is correlated with second party data in a database accessible to the first server, and at least one of the first party data or the second party data includes geographic location information indicating a presence of the consumer within a predetermined radius of the retail store. The first server is configured to identify a consumer profile associated with the first party data and the second party data, and generate a digital value-added certificate for the consumer based on a likelihood that the geographic location information corresponds to the consumer profile. The computer-implemented method also includes receiving the digital value-added certificate at the client device, and authenticating the digital value-added certificate at a point of sale in the retail store.

[0065] III. A system including a memory storing instructions and a processor. The processor is configured to execute at least one instruction to cause the system to collect first party data from a client device associated with a consumer and store the first party data in a database. The one or more processors also execute instructions to compare the first party data with second party data in the database, identify a consumer profile associated with the first party data and the second party data, and provide a digital advertisement to the client device based on the consumer profile in the database and the first party data.

[0066] In addition to embodiments I, II, and III, embodiments in accordance with the present disclosure may include any one or more of the following elements in any combination.

[0067] Element 1, wherein at least one of the first party data or the second party data includes geographic location information indicative of a presence of a consumer within a predetermined radius of a retail store, and comparing the first party data with the second party data includes determining a likelihood that the geographic location information corresponds to a consumer profile. Element 2, wherein at least one of the first party data or the second party data includes frequent shopper identification data of a consumer of a consumer retail service network, and comparing the first party data with the second party data includes determining a likelihood that the frequent shopper identification data, the consumer profile, and a device code associated with the client device correspond to the same consumer. Element 3, wherein at least one of the first party data or the second party data includes a device code associated with the client device, and comparing the first party data with the second party data includes determining a likelihood that the client device is being used by a consumer associated with the consumer profile. Element 4, wherein comparing the first party data with the second party data includes correlating at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device. Element 5, where comparing the first party data to the second party data includes pairing first party data and second party data within a predetermined radius of geographic location information associated with the client device, element 6, where comparing the first party data to the second party data in the database includes associating a consumer profile with a group of consumers having a household identifier, element 7, where element 7 further includes identifying the second party data based on at least a portion of the first party data using a model trained with data stored in the database, element 8, where the first party data includes mobility data of the client device and further includes inferring the second party data using a behavioral model based on the mobility data.Element 9, further comprising receiving purchase data associated with the digital advertisement and the consumer and updating a model that compares the first party data to the second party data based on the purchase data. Element 10, further comprising updating a consumer profile based on the first party data and the second party data. Element 11, wherein comparing the first party data to the second party data comprises identifying a correlation between the consumer and the consumer profile and identifying a confidence level of the correlation. Element 12, further comprising receiving third party data associated with the consumer and updating a database with the third party data to improve the likelihood that the consumer profile is associated with the consumer. Element 13, wherein providing the digital advertisement comprises providing a value-added certificate to the consumer, the value-added certificate being associated with a product for sale at a retail store identified according to the first party data. Element 14, further comprising receiving a request from a client device to access a consumer network and receive the value-added certificate along with the digital advertisement. Element 15, further comprising modifying a correlation between the first party data and the second party data in the database based on a purchase at the retail store, the purchase being based on the digital advertisement. Element 16, the client device is associated with a group of consumers, and further comprising selecting a consumer from the group of consumers based on the first party data and the second party data to identify a consumer profile. Element 17, the client device is associated with a group of consumers, and further comprising collecting second party data from a second server that provides network services to the client device. Element 18, the client device storing the first party data in a database comprises storing the first party data in a first database and retrieving the second party data from a second database. Element 19, the client device providing the digital advertisement based on the consumer profile and the first party data comprises providing printing instructions to a printer in the retail store identified according to the first party data, the printing instructions comprising printing instructions for the consumer's value-added certificate.

[0068] Element 20, where at least one of the first party data or the second party data includes geographic location information indicating a presence of a consumer within a predetermined radius of a retail store, and to compare the first party data to the second party data, the one or more processors execute instructions to determine a likelihood that the geographic location information corresponds to a consumer profile, element 21, where at least one of the first party data or the second party data includes frequent shopper identification data of a consumer of a consumer retail service network, and to compare the first party data to the second party data, the one or more processors execute instructions to determine a likelihood that the frequent shopper identification data, the consumer profile, and a device code associated with a client device correspond to the same consumer, element 21, where at least one of the first party data or the second party data includes a device code associated with a client device, and to compare the first party data to the second party data, the one or more processors execute instructions to determine a likelihood that the client device is being used by a consumer associated with the consumer profile, element 22. Element 23, where to compare the first party data with the second party data, the one or more processors execute instructions to correlate at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device. Element 24, where to compare the first party data with the second party data, the one or more processors execute instructions to pair first party data and second party data within a predetermined radius of geographic location information associated with the client device, element 24. Element 25, where to compare the first party data with the second party data in the database, the one or more processors execute instructions to associate a consumer profile with a group of consumers having a household identifier, element 25. Element 26, where the one or more processors further execute instructions to identify the second party data based at least in part on the first party data using a model trained with data stored in the database.Element 27, where the first party data includes mobility data of the client device, and the one or more processors further execute instructions for inferring second party data using a behavioral model based on the mobility data. Element 28, where the one or more processors further execute instructions for receiving purchase data associated with the digital advertisement and the consumer, and updating a model for comparing the first party data to the second party data based on the purchase data. Element 29, where the one or more processors further execute instructions for updating a consumer profile based on the first party data and the second party data. Element 30, where to compare the first party data to the second party data, the one or more processors execute instructions for identifying a correlation between the consumer and the consumer profile and identifying a confidence level of the correlation. Element 31, where the one or more processors further execute instructions for receiving third party data associated with the consumer, and updating a database with the third party data to improve the certainty that the consumer profile is associated with the consumer. Element 32, where to provide the digital advertisement, the one or more processors execute instructions for providing a value-added certificate to the consumer, the value-added certificate being associated with a product for sale at a retail store identified according to the first party data. Element 33, where the one or more processors further execute instructions for receiving a request from a client device to access a consumer network and receive a value-added certificate along with the digital advertisement, element 33. Element 34, where the one or more processors further execute instructions for modifying a correlation between the first party data and the second party data in the database based on a purchase at the retail store, the purchase based on the digital advertisement, element 34. Element 35, where the client device is associated with a group of consumers, and the one or more processors further execute instructions for selecting a consumer from the group of consumers based on the first party data and the second party data and identifying a consumer profile, element 35.Element 36, where the one or more processors execute instructions to collect second party data from a second server that provides network services to the client device; element 37, where the one or more processors execute instructions to store the first party data in a first database and retrieve the second party data from a second database to store the first party data in a database; element 38, where the one or more processors execute instructions to provide printing instructions to a printer in a retail store identified according to the first party data to provide digital advertisements based on the consumer profile and the first party data, the printing instructions including instructions to print a value-added certificate for the consumer;

[0069] Element 39, further comprising authenticating the digital value-added certificate to the first server; Element 40, wherein at least one of the first party data or the second party data includes frequent shopper identification data of the consumer of the consumer retail service network, and further comprising authenticating the frequent shopper identification data to the first server with the client device; Element 41, wherein at least one of the first party data or the second party data includes a device code associated with the client device, and further comprising authenticating to the first server that the client device is being used by the consumer associated with the consumer profile; Element 42, wherein providing the first party data includes providing at least one of an Internet Protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device; Element 43, further comprising allowing the first server to pair the first party data and the second party data within a predetermined radius of the geographic location information associated with the client device; Element 44, further comprising selecting a consumer profile from a group of consumers having a household identifier, the group of consumers being provided by the first server based on the correlation of the first party data and the second party data, element 44. Element 45, further comprising allowing the first server to identify second party data based on at least a portion of the first party data using a model trained using data stored in the database, element 45. Element 46, wherein providing the first party data includes providing mobility data of the client device to the first server, the first server being configured to infer the second party data using a behavioral model based on the mobility data, element 46. Element 47, further comprising providing purchase data associated with the digital value-added certificate to the first server, the first server being configured to update the model that compares the first party data to the second party data based on the purchase data, element 47.Element 48, further comprising authorizing the first server to update the consumer profile based on the first and second party data. Element 49, further comprising authorizing the first server to identify a correlation between the consumer and the consumer profile and further comprising identifying a confidence level of the correlation. Element 50, further comprising authorizing the second server to provide the second party data to the first server. Element 51, further comprising the digital value-added certificate being associated with a product sold at the retail store and requesting a second digital value-added certificate for a second product sold at the retail store. Element 52, further comprising authorizing the first server to update the database with the second party data to improve the certainty that the consumer profile is associated with the consumer. Element 53, further comprising authorizing the first server to modify the correlation between the first and second party data in the database based on a purchase at the retail store associated with the digital value-added certificate. Element 54, the first party data includes information obtained by the first server from an application installed on the client device, further comprising authorizing the first server to obtain the information; element 55, the first party data includes information obtained by the first server from an application installed on the client device, further comprising authorizing the first server to access a second server hosting a social network and provide second party data to the first server, the second party data includes data associated with the consumer's social network activity, element 55; element 56, the first server obtains the second party data from a second database, element 56; and element 57, the second party data includes information obtained by the first server from an application installed on the client device, further comprising authorizing the first server to obtain the second party data from a second database, element 57.

[0070] Although this specification includes many specifics, these should not be interpreted as limitations on the scope of what can be described, but rather as descriptions of specific implementations of the subject matter. Some features described in the context of separate embodiments herein can also be implemented in a single embodiment in combination. Conversely, various features described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Furthermore, although features may be described above as functioning in a particular combination, and even initially described as such, one or more features from the described combination may, in some cases, be deleted from the combination, and the described combination may relate to a subcombination or a variation of a subcombination.

[0071] Although the subject matter herein has been described with respect to certain aspects, other aspects may be implemented and are within the scope of the appended claims. For example, although operations are shown in a particular order in the figures, this should not be understood as requiring such operations to be performed in the particular order or sequence shown, or that all of the illustrated operations be performed, to achieve desired results. Actions recited in the claims may be performed in a different order and still achieve desired results. As an example, the processes depicted in the accompanying figures do not necessarily require the particular order or sequence shown to achieve desired results. In certain circumstances, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system components in the above-described aspects should not be understood as requiring such separation in all aspects, and it should be understood that the program components and systems described above may generally be integrated together in a single software product or packaged into multiple software products.

[0072] The title, background, brief description of the drawings, abstract, and drawings are incorporated herein in the disclosure and are provided as illustrative examples of the disclosure, not as limiting descriptions. They are presented with the understanding that they will not be used to limit the scope or meaning of the claims. In addition, the detailed description shows that the description provides illustrative examples, and that various features have been grouped together in various implementations for the purpose of streamlining the disclosure. The method of disclosure should not be interpreted as reflecting an intention that the described subject matter requires more features than are expressly recited in each claim. Rather, as the claims reflect, inventive subject matter lies in fewer than all features of a single disclosed structure or operation. The claims are incorporated herein in the detailed description, with each claim standing on its own as a separately described subject matter.

[0073] The subject technology is set forth according to various aspects, for example as set forth in the following claims. Various examples of aspects of the subject technology are set forth in the claims. These are provided as examples and not as limitations on the subject technology. The claims are not intended to be limited to the aspects described herein, but rather should be accorded full scope in accordance with the claims as literal, and should encompass all legal equivalents. Nonetheless, none of the claims are intended to encompass subject matter that does not satisfy the requirements of applicable patent law, and the claims should not be so interpreted.

Claims

1. collecting first party data from a client device associated with a consumer, the first party data including a device code associated with the client device and geographic location information indicative of a presence of the consumer within a predetermined radius of a retail store; storing the first party data in a database; comparing the first party data with the second party data in the database to identify consumer profiles for consumer clustering, the consumer profiles being associated with the first party data and the second party data; serving digital advertisements to the client devices based on the consumer profile and the first party data; providing said consumer with a value-added certificate associated with a product for sale at said retail store; The computer-implemented method includes:

2. The computer-implemented method of claim 1 , wherein the second party data includes geographic location information indicating the presence of the consumer within a predetermined radius of a retail store.

3. 3. The computer-implemented method of claim 1 or 2, wherein at least one of the first party data or the second party data comprises frequent shopper identification data of the consumer of a consumer retail services network.

4. The computer-implemented method of claim 1 , wherein the second party data includes a device code associated with the client device.

5. 5. The computer-implemented method of claim 1, wherein comparing the first party data to the second party data includes correlating at least one of an internet protocol address, an operating system version of the client device, a user agent, an application, or a website executing on the client device.

6. 6. The computer-implemented method of claim 1, wherein comparing the first party data to the second party data includes pairing the first party data and the second party data within a predetermined radius of geographic location information associated with the client device.

7. 7. The computer-implemented method of claim 1, wherein comparing the first party data to the second party data includes associating the consumer profile with a group of consumers having a household identifier.

8. 8. The computer-implemented method of claim 1 , further comprising identifying the second party data based at least in part on the first party data using a model trained using data stored in the database.

9. 9. The computer-implemented method of claim 1, wherein the first party data includes mobility data of the client device, and further comprising inferring the second party data using a behavioral model based on the mobility data.

10. 10. The computer-implemented method of claim 1, further comprising receiving purchasing data associated with the digital advertisement and the consumer, and updating a model that compares the first party data to the second party data based on the purchasing data.

11. The computer-implemented method of claim 1 , further comprising updating the consumer profile based on the first party data and the second party data.

12. 12. The computer-implemented method of claim 1, wherein comparing the first party data with the second party data includes identifying a correlation between the consumer and the consumer profile and identifying a confidence level of the correlation.

13. 13. The computer-implemented method of claim 1, further comprising receiving third party data associated with the consumer and updating the database with the third party data to improve the accuracy with which the consumer profile is associated with the consumer.

14. 14. The computer-implemented method of claim 1, further comprising receiving a request from the client device to access a consumer network and receive a value-added certificate along with the digital advertisement.

15. 15. The computer-implemented method of claim 1 , further comprising modifying a correlation between the first-party data and the second-party data in the database based on a purchase at a retail store, the purchase being based on the digital advertisement.

16. 16. The computer-implemented method of claim 1, wherein the client device is associated with a group of consumers, and further comprising selecting a consumer from the group of consumers based on the first party data and the second party data and identifying the consumer profile.

17. 17. The computer-implemented method of claim 1, further comprising: collecting the second party data from a second server that provides a network service to the client device.

18. 18. The computer-implemented method of claim 1, wherein storing the first party data in the database comprises storing the first party data in a first database and retrieving the second party data from a second database.

19. 19. The computer-implemented method of claim 1, wherein providing the digital advertisement based on the consumer profile and the first party data includes providing printing instructions to a printer in a retail store identified according to the first party data, the printing instructions including instructions to print a value-added certificate for the consumer.

20. a memory circuit for storing instructions; and one or more processors configured to execute at least one instruction, The at least one instruction may include: collecting first party data from a client device associated with a consumer, the first party data including a device code associated with the client device and geographic location information indicative of a presence of the consumer within a predetermined radius of a retail store; storing the first party data in a database; comparing the first party data with the second party data in the database to identify consumer profiles for consumer clustering, the consumer profiles being associated with the first party data and the second party data; serving digital advertisements to the client devices based on the consumer profile and the first party data; providing said consumer with a value-added certificate associated with a product for sale at said retail store; A system that performs the above.

21. 21. The system of claim 20, wherein the second party data includes geographic location information indicating a presence of the consumer within a predetermined radius of a retail store.

22. 22. The system of claim 20 or 21, wherein at least one of the first party data or the second party data includes frequent shopper identification data of the consumer of a consumer retail services network.

23. 23. The system of claim 20, wherein the second party data includes a device code associated with the client device.

24. 24. The system of claim 20, wherein the one or more processors execute instructions to correlate at least one of an internet protocol address, an operating system version of the client device, a user agent, an application, or a website running on the client device when comparing the first party data to the second party data.

25. 25. The system of claim 20, wherein when comparing the first party data to the second party data, the one or more processors execute instructions to pair the first party data and the second party data within a predetermined radius of geographic location information associated with the client device.

26. 26. The system of any one of claims 20 to 25, wherein when comparing the first party data to the second party data, the one or more processors execute instructions to associate the consumer profile with a group of consumers having a household identifier.

27. 27. The system of claim 20, wherein the one or more processors further execute instructions to identify the second party data based at least in part on the first party data using a model trained using data stored in the database.

28. 28. The system of claim 20, wherein the first party data includes mobility data of the client device, and the one or more processors further execute instructions for inferring the second party data using a behavioral model based on the mobility data.

29. 29. The system of claim 20, wherein the one or more processors further execute instructions to receive purchasing data associated with the digital advertisement and the consumer, and update a model that compares the first party data to the second party data based on the purchasing data.

30. 30. The system of any one of claims 20 to 29, wherein the one or more processors further execute instructions to update the consumer profile based on the first party data and the second party data.

31. 31. The system of any one of claims 20 to 30, wherein when comparing the first party data to the second party data, the one or more processors execute instructions to identify a correlation between the consumer and the consumer profile and identify a confidence level of the correlation.

32. 32. The system of any one of claims 20 to 31, wherein the one or more processors further execute instructions to receive third party data associated with the consumer and update the database with the third party data to improve the accuracy with which the consumer profile is associated with the consumer.

33. 33. The system of claim 20, wherein the one or more processors further execute instructions to receive a request from the client device to access a consumer network and receive a value-added certificate along with the digital advertisement.

34. 34. The system of claim 20, wherein the one or more processors further execute instructions to modify a correlation between the first party data and the second party data in the database based on a purchase at a retail store, the purchase being based on the digital advertisement.

35. 35. The system of any one of claims 20 to 34, wherein the client device is associated with a group of consumers, and the one or more processors further execute instructions to select a consumer from the group of consumers based on the first party data and the second party data and identify the consumer profile.

36. 36. The system of claim 20, wherein the one or more processors execute instructions to collect the second party data from a second server that provides a network service to the client device.

37. 37. The system of any one of claims 20 to 36, wherein when storing the first party data in the database, the one or more processors execute instructions to store the first party data in a first database and retrieve the second party data from a second database.

38. 38. The system of any one of claims 20 to 37, wherein when providing the digital advertisement based on the consumer profile and the first party data, the one or more processors execute instructions to provide printing instructions to a printer in a retail store identified according to the first party data, the printing instructions including instructions to print a value-added certificate for the consumer.

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