Systems and methods for providing people-based prospective buyer planning

Abstract

A system and method for targeted advertising to specific consumers is disclosed. The system may include a memory storing instructions and at least one processor, the at least one processor configured to execute the instructions to receive consumer data from a client device over a network, identify a plurality of clients provided consumers from the consumer data, obtain a plurality of unique consumer identifiers corresponding to the plurality of clients provided consumers, and identify at least one first duplicate unique consumer identifier by matching at least one of the plurality of clients provided consumers with at least one ad publisher provided consumer provided by a first ad publisher device having a highest priority among the plurality of ad publisher devices.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application is a continuation-in-part of U.S. Application No. 16 / 214,769, filed December 10, 2018, and a continuation-in-part of U.S. Application No. 15 / 786,551, filed October 17, 2017 (now U.S. Patent No. 10,181,136), which claims priority to U.S. Provisional Patent Application No. 62 / 409,374, filed October 17, 2016, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure generally relates to computerized systems and methods for people-based advertising audience (hereinafter "advertising audience" is described as "potential buyers") planning and providing targeted advertising. [Background technology]

[0003] Vendors can target specific consumers within a consumer population to address the needs of a personalized market. For example, a vendor can offer customized promotions to certain potential customers. The content of such promotions (e.g., advertisements) can be uniquely tailored to different consumers. While personalizing promotional content for electronic distribution can potentially increase revenue, it also has several drawbacks. For example, marketing efforts to address the needs of a single customer can be burdensome, time-consuming, impractical due to its scalability, and expensive.

[0004] Consumer needs and desires may overlap with other needs and desires. Marketing efforts based on dividing potential consumer buyers into discrete categories that represent specific, distinct characteristics can be useful. For example, clustering (grouping) based on selected behavioral data, demographic data, and product preferences can increase efficiency and reduce costs. However, these traditional categorical segmentations (dividing consumers into distinct groups to facilitate marketing efforts) can deprive marketing efforts of the benefits of category marketing. For example, two consumers of the same age may receive the same advertisements because they are similarly categorized based on age. However, these consumers may be in different stages of life and therefore may have different goals or values. This can result in one consumer in a category enthusiastically purchasing the advertised product while the other consumer is vehemently opposed to purchasing the product. Segmenting these two consumers based solely on a single criterion (e.g., age) can be inefficient and ineffective.

[0005] Traditional segmentation techniques can also raise privacy and security concerns. For example, traditional systems typically identify consumers using identifiers or information, including personally identifiable information (e.g., name, email address, phone number, etc.). Traditional systems also typically exchange these identifiers over communications networks. This can result in data breaches or losses that could expose consumers' personally identifiable information to attackers or other unauthorized users. Furthermore, attackers (e.g., hackers) can use personally identifiable information obtained from one attack against the same or additional consumers in a subsequent attack (e.g., using phishing, social engineering, etc.).

[0006] Although traditional advertising platforms allow advertising clients to supply their own consumer data, they are incompatible with or do not support client-specific segmentation. As a result, advertising clients cannot define their own segments (distinct groups of consumers for facilitating sales activities). Furthermore, in traditional advertising platforms, when an advertising client wants to publish a list of potential buyers from a set of consumer data, the platform selects potential buyers based on a comparison of the set of consumer data with consumer data provided by an ad publisher to determine the list of potential buyers from the set of consumer data. However, when the client wants to publish the remainder of the set of consumer data, the platform compares the entire set of consumer data with consumer data provided by a second ad publisher without excluding the lists of potential buyers that have already been published. This causes the publishing system to operate inefficiently.

[0007] Therefore, there is a need for improved methods of providing people-based prospective buyer planning and targeted advertising. Summary of the Invention

[0008] One aspect of the present disclosure is directed to a computer-implemented system for targeted advertising to specific consumers. The system may include a memory storing instructions and at least one processor, the at least one processor being configured to execute the instructions to receive consumer data from client devices over a network, identify a plurality of clients-provided consumers from the consumer data, obtain a plurality of unique consumer identifiers corresponding to the plurality of clients-provided consumers, and identify at least one first duplicate unique consumer identifier by matching at least one consumer of the plurality of clients-provided consumers with at least one consumer provided by an ad publisher provided by a first ad publisher device having a highest priority among the plurality of ad publisher devices.

[0009] Another aspect of the present disclosure is directed to a computer-implemented method for targeted advertising to specific consumers, the computer-implemented method including receiving consumer data from a client device over a network, identifying a plurality of client-provided consumers from the consumer data, obtaining a plurality of unique consumer identifiers corresponding to the plurality of client-provided consumers, and identifying at least one first duplicate unique consumer identifier by matching at least one consumer of the plurality of client-provided consumers with at least one consumer provided by an ad publisher provided by a first ad publisher device having a highest priority among the plurality of ad publisher devices.

[0010] Yet another aspect of the present disclosure is directed to a non-transitory computer-readable medium storing instructions executable by a processor to perform a method for targeted advertising to specific consumers, the method including receiving consumer data from a client device over a network, identifying a plurality of client-provided consumers from the consumer data, obtaining a plurality of unique consumer identifiers corresponding to the plurality of client-provided consumers, and identifying at least one first duplicate unique consumer identifier by matching at least one consumer of the plurality of client-provided consumers with at least one consumer provided by an ad publisher provided by a first ad publisher device having a highest priority among the plurality of ad publisher devices.

[0011] Other systems, methods, and computer-readable media are also contemplated herein. [Brief explanation of the drawings]

[0012] [Figure 1]FIG. 1 is a schematic block diagram illustrating an example embodiment of a system for targeted advertising to specific consumers consistent with disclosed embodiments. [Figure 2] 1 is a diagram of an example target prospective buyer survey interface consistent with the disclosed embodiments; [Figure 3] 1 is a diagram of an example performance report consistent with disclosed embodiments. [Figure 4] 1 is a flowchart illustrating an example method for targeted advertising to specific consumers consistent with disclosed embodiments. [Figure 5] 1 is an example table showing consumer records flagged to indicate the registration of the consumer records in classes or segments within the consumer data, consistent with disclosed embodiments; [Figure 6] FIG. 2 is a schematic diagram illustrating multiple data provided by corresponding multiple ad publisher devices and assigned priorities of the data, consistent with disclosed embodiments. [Figure 7A] FIG. 1 is a schematic diagram illustrating a first verification test of a waterfall verification test, consistent with disclosed embodiments. [Figure 7B] FIG. 10 is a schematic diagram illustrating a second verification test of the waterfall verification test, consistent with disclosed embodiments. [Figure 8] FIG. 1 is a flow diagram illustrating an example method for waterfall verification testing consistent with disclosed embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0013] The following detailed description refers to the accompanying drawings. Whenever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar parts. While several example embodiments are described herein, modifications, adaptations, or other implementations are possible. For example, substitutions, additions, or modifications may be made to the components and steps illustrated in the drawings, and the example methods described herein may be modified by substituting, reordering, or removing steps from the disclosed methods, or by adding steps to the disclosed methods. Therefore, the following detailed description is not limited to the disclosed embodiments and examples, and the appropriate scope of the invention is defined by the appended claims.

[0014] Embodiments of the present disclosure are directed to systems and methods configured to provide targeted advertisements to specific consumers. For example, a client device (e.g., an advertiser or ad publisher system) can provide consumer data to an advertising agency over a network. The consumer data can include, for example, personally identifiable information (e.g., name, email address, phone number, street address, Social Security number, etc.) and non-personally identifiable information (e.g., device identifier, demographic data, segments, model scores, etc.). The advertising agency can process the consumer data and assign unique consumer identifiers to consumers identified in the consumer data. In some embodiments, the unique consumer identifiers may not include personally identifiable information. The advertising agency can then generate a target pool of potential buyers (hereinafter referred to as a "targeted potential buyer group") for its client based on the unique consumer identifiers. Using unique consumer identifiers, as in certain embodiments of the present disclosure, can assist in improving the efficiency of generating the target potential buyer group. Furthermore, using such unique consumer identifiers, as in certain embodiments of the present disclosure, can enhance data security, fidelity, and accuracy.

[0015] Referring to Figure 1, a schematic block diagram illustrating an example embodiment of a system for targeted advertising is shown. As illustrated in Figure 1, the system 100 can include one or more data sources 102, a data processor 104, a target buyer candidate generator 106, an application interface 108, and a data analyzer 110.

[0016] Data sources 102 may include consumer data 102A provided by one or more advertisers, consumer data 102B provided by one or more ad publishers, consumer data 102C provided by one or more third-party data providers, or consumer data 102D provided by one or more advertising agencies (e.g., agencies that provide targeted advertising services to advertisers and ad publishers). In some embodiments, the data in one or more data sources 102 may be provided or stored as text files, binary files, database records, or various other types of computer-readable data formats.

[0017] In some embodiments, advertisers, ad publishers, third-party data providers, and ad agencies may utilize various types of computing devices to communicate with each other, including, for example, servers, desktop computers, notebook computers, mobile devices, tablets, smartphones, wearable devices such as smart watches, smart bracelets, smart glasses, etc., or any other device capable of communicating with a wired or wireless network.

[0018] In some embodiments, advertiser-provided consumer data 102A, ad publisher-provided consumer data 102B, third-party data provider-provided consumer data 102C, and ad agency-provided consumer data 102D can be stored on physically or logically separate data storage devices to mitigate data intermingling. For example, advertiser-provided consumer data 102A can be stored on a first data storage device that is physically or logically separate from a second data storage device used to store ad publisher-provided consumer data 102B. Similarly, third-party data provider-provided consumer data 102C can be stored on a third data storage device that is physically or logically separate from a fourth data storage device used to store ad agency-provided consumer data 102D. In some embodiments, consumer data 102A provided by different advertisers can be stored on physically or logically separate data storage devices. Similarly, consumer data 102B provided by different ad publishers and consumer data 102C provided by different third-party data providers can be stored on physically or logically separate data storage devices. Such data storage may be implemented using any volatile or non-volatile memory including, for example, magnetic, semiconductor, tape, optical, removable, non-removable, or any other type of storage device or computer readable medium.

[0019] The data processor 104 can serve as an entry point for consumer data received from various data sources 102A, 102B, 102C, or 102D. The data processor 104 can include one or more dedicated processing units, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or various other types of processors or processing units coupled with non-transitory processor-readable memory configured to store processor-executable code. When the processor-executable code is executed by the data processor 104, the data processor 104 can execute instructions in response to various types of input signals received over wired or wireless networks.

[0020] In some embodiments, the data processor 104 can be configured to recognize personally identifiable information included in the consumer data 102 (e.g., name, email address, telephone number, street address, or social security number, etc.). The data processor 104 can be configured to recognize personally identifiable information based on labels associated with data fields included in the consumer data 102 (e.g., data fields included in the consumer data 102 can be labeled "Name," "Email Address," or "Phone Number"). Additionally or alternatively, the data processor 104 can be configured to recognize personally identifiable information based on the format of the data presented to it (e.g., a string of 10 digits can be recognized as a telephone number, while a string having an "@" symbol can be recognized as an email address). It should be understood that the data processor 104 can be configured to recognize personally identifiable information included in the consumer data 102 using various other techniques without departing from the scope and spirit of the present disclosure. The data processor 104 may then utilize a data separation processor 126 (which may be implemented as a component of the data processor 104) to separate personally identifiable information (PII) included in the consumer data 102 from non-personally identifiable information (non-PII) included in the consumer data 102 (e.g., device identifiers, demographic data, segments, or model scores).

[0021] In some embodiments, PII included in consumer data 102 can be processed separately from non-PII included in consumer data 102. For example, as illustrated in FIG. 1, PII included in consumer data 102 can be processed by consumer identification processor 114 (which can be implemented as a component of data processor 104). Consumer identification processor 114 can be configured to recognize one or more consumers identified in consumer data 102 based on, for example, name, email address, telephone number, street address, or Social Security number. In some embodiments, if an advertising agency has access to consumer database 102D, consumer identification processor 114 can recognize consumers by comparing consumer data 102A provided by advertisers (or consumer data 102B provided by ad publishers) with consumer data 102D.

[0022] In some embodiments, the consumer identification processor 114 may implement various types of data formatting, filtering, validation, parsing, standardization, normalization, or correction techniques to process the consumer data 102. In these embodiments, the consumer identification processor 114 may also utilize various types of deterministic or probabilistic processing techniques to facilitate the consumer recognition process. Suitable deterministic or probabilistic processing techniques may include, but are not limited to, accounting for variations in name spelling (e.g., spelling "Robert" as "Rob" or "Bob" as "Bobby"), variations in address representations (e.g., "Road" or "Rd," the presence or absence of apartment unit numbers, variations in the spelling of cities, etc.), correcting for common email address errors (e.g., misspelled or out-of-order characters in domain names, etc.), and guessing telephone area codes based on city and state.

[0023] The consumer identification processor 114 may assign a unique consumer identifier to one or more consumers recognized (e.g., by the consumer identification processor 114) in the consumer data 102. In some embodiments, the unique consumer identifier assigned by the consumer identification processor 114 may not include any personally identifiable information. In other words, the unique consumer identifier assigned by the consumer identification processor 114 is a pseudonymous identifier.

[0024] In some embodiments, pseudonymous identifiers assigned by the consumer identification processor 114 can uniquely identify a particular consumer at a particular street address. For example, a distinct identifier can be assigned to each particular address, and similarly, a distinct identifier can be assigned to each consumer name. Unique pairs of address and consumer identifier can then be assigned and exchanged as proxies for the underlying PII data records without exposing the PII data in subsequent components. Such pseudonymous identifiers, by definition, do not contain information that personally identifies the consumer and therefore can provide anonymity compared to identifiers based on PII. Pseudonymous identifiers can also provide improved security, fidelity, and accuracy compared to identifiers based on web cookies, device identifiers, or Internet Protocol (IP) addresses (which typically have multiple consumers associated with the same identifier, generating noise and reducing data fidelity). In some embodiments, the consumer identification processor 114 can maintain a cross-reference 122 between the pseudonymous identifiers and identifiers originally used by the client (e.g., advertiser or ad publisher). This cross-reference 122 may be stored in one or more non-transitory processor-readable memories accessible to the consumer identification processor 114 (and the data processor 104 generally).

[0025] The pseudonymous identifiers assigned by the consumer identification processor 114 can then be aggregated with the non-PII contained in the consumer data 102 to generate pseudonymous consumer data 116. Note that the pseudonymous consumer data 116 can in turn include pseudonymously identifiable information that can be utilized to generate target potential buyer groups for clients without disclosing any personally identifiable information about the consumer.

[0026] In some embodiments, the target potential buyer group is generated using a target potential buyer generator 106. The target potential buyer generator 106 may include one or more dedicated processing units, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or various types of processors or processing units coupled with non-transitory processor-readable memory configured to store processor-executable code. When the processor-executable code is executed by the target potential buyer generator 106, the target potential buyer generator 106 can execute instructions to generate the target potential buyer group. In some embodiments, the target potential buyer generator 106 is configured to process only pseudonymous consumer data 116. Utilizing the pseudonymous consumer data 116 in this manner can help improve the efficiency of the target potential buyer generator 106.

[0027] For example, assume that an advertiser wishes to run targeted advertisements on a platform operated by an ad publisher. It may be of interest to both parties to utilize the target candidate generator 106 to generate a group of potential buyers for the targeted advertisements. To do so, the advertiser and ad publisher may choose to provide their corresponding consumer populations (i.e., consumer data) 102A and 102B to the target candidate generator 106. The advertiser-provided consumer data 102A and the ad publisher-provided consumer data 102B may first be processed by a data processor 104, which may remove personally identifiable information from the provided data to generate pseudonymous consumer data 116, as described above. The target candidate generator 106 may then obtain a list 118 of consumers common to both the advertiser-provided consumer data and the ad publisher-provided consumer data. This list of consumers 118 can be obtained very efficiently by matching pseudonymous identifiers associated with consumer data provided by advertisers with pseudonymous identifiers associated with consumer data provided by ad publishers after they have been processed by data processor 104.

[0028] In some embodiments, a list 118 of consumers common to both the advertiser-provided consumer data and the ad publisher-provided consumer data can be readily identified as a group of target potential buyers. Alternatively, the list 118 of consumers can be thought of as a reference population, which can be expanded using one or more similar potential buyer models 120. For example, the target potential buyer generator 106 can analyze non-personally identifiable information (e.g., demographic data, segments, model scores, etc.) associated with consumers identified in the list 118 of consumers to obtain one or more top-level attributes describing such consumers. The top-level attributes thus identified can then be used to assist in identifying additional consumers provided by a third-party data provider (e.g., data derived from consumer data 102C) or additional consumers provided by an advertising agency (e.g., data derived from consumer data 102D).

[0029] In another example, the advertiser may choose to ask the target purchaser candidate generator 106 to process the advertiser-provided consumer data 102A without having to consider any of the consumer data provided by the ad publisher. The advertiser-provided consumer data 102A may be processed by the data processor 104, which may generate the pseudonymous consumer data 116 as described above. The target purchaser candidate generator 106 may then analyze the pseudonymous consumer data 116 generated based on the advertiser-provided consumer data 102A to identify one or more top-level attributes that describe the advertiser-provided consumer data 102A. The top-level attributes thus identified may then be used to assist in identifying additional consumers provided by a third-party data provider (e.g., data derived from consumer data 102C) or additional consumers provided by an advertising agency (e.g., data derived from consumer data 102D).

[0030] It should be understood that the above-described target buyer generation techniques are presented by way of example and are not intended to be limiting. It should be understood that the particular implementation of the target buyer generation process may vary from the examples presented above without departing from the scope and spirit of the present disclosure.

[0031] In some embodiments, once the target potential buyer group is generated, the target potential buyer generator 106 can deliver the target potential buyer group to advertisers (e.g., over a network) for review and approval. FIG. 2 illustrates an example review interface. In this example, the target potential buyer group is generated based on consumer data recorded in an electronic consumer database 102D provided by an advertising agency. The electronic consumer database 102D, in one embodiment, contains millions of records about consumers, each with over 1,000 attributes, including, but not limited to, email addresses, phone records, vehicle records, IP addresses, mortgage information, lifestyle / behavioral data, demographic data, transaction association data, life event data (e.g., new movers, new homeowners, new parents, credit threshold violations by the three major U.S. credit reporting agencies (Equifax, Experian, TransUnion)), wealth metrics, credit statistics, automobile data and automobile statistics, real estate data, social media handles / flags, social influence, other syndicated research data, and the like. Other embodiments of the electronic consumer database 102D are also possible.

[0032] An advertiser could utilize the example survey interface shown in FIG. 2 to confirm or modify the target potential buyer group. For example, the example survey interface could include a visual representation 204 of the target potential buyer group. The visual representation 204 could include one or more diagrams depicting the composition of the target potential buyer group. For example, the visual representation 204 could show composition with respect to education level, gender, marital status, etc. The visual representation 204 could also include composition with respect to age group, occupation, etc. The visual representation 204 could further show the estimated reach (number of potential buyers who may see the ad) of the ad if the advertiser approves the proposed target potential buyer group (and, if available, the actual reach based on historical / recorded data).

[0033] The example survey interface also includes a control panel 202 configured to receive control input from the advertiser. For example, if the advertiser chooses not to target a particular age group 206, the advertiser can select the age group 206 (e.g., by clicking on the age group 206 using a computer mouse) and click a "Remove Potential Purchasers" button in the control panel 202 to remove the particular age group 206 from the target potential purchaser group. Any modifications made by the advertiser can be communicated over the network to the target potential purchaser generator 106, which can adjust the target potential purchaser group accordingly. Alternatively, if the advertiser is satisfied with the target potential purchaser group, the advertiser can choose to confirm / approve the target potential purchaser group by clicking a "Confirm" button in the control panel 202.

[0034] It should be understood that the example survey interface shown in Figure 2 is presented by way of example only and is not intended to be limiting. Once the advertiser has confirmed / approved the target group of potential buyers, the application interface 108 may distribute the target group of potential buyers to one or more ad publishers upon receiving the advertiser's approval.

[0035] In some embodiments, because the target potential purchaser generator 106 is configured to process only pseudonymous consumer data 116, the target potential purchaser group generated by the target potential purchaser generator 106 may not include certain identifiers required by ad publishers. Accordingly, it should be noted that in some embodiments, ad publishers may request that the target potential purchaser group be transformed according to an ad publisher-specific transformation protocol such that the target potential purchaser group delivered to them includes the identifiers required by the ad publisher.

[0036] In some embodiments, the data processor 104 can be configured to act as a controlled exit point for transforming / modifying pseudonymous identifiers, if necessary, based on the ad publisher's specifications. More specifically, in some embodiments, the data processor 104 can utilize the cross-reference dataset 122 stored earlier in the pseudonymous identifier generation process (described above) to assist in transforming the pseudonymous identifiers included in the target potential purchaser group. For example, if the ad publisher uses web cookies or device identifiers to identify its target potential purchasers, the data processor 104 can use the reference data stored in the cross-reference dataset 122 to transform the pseudonymous identifiers included in the target potential purchaser group into web cookies or device identifiers. Similarly, if the ad publisher uses encrypted email to identify its target potential purchasers, the data processor 104 can use the reference data stored in the cross-reference dataset 122 to transform the pseudonymous identifiers included in the target potential purchaser group into encrypted email. The application interface 108 can then provide the target potential purchaser group with the transformed identifiers to the ad publisher for executing an advertising campaign.

[0037] It should be understood that the above conversion is not always required. In some embodiments, for example, an ad publisher may partner with an advertising agency and therefore have shared access to the pseudonymous identifiers. In such embodiments, the application interface 108 may provide the target buyer group directly to the ad publisher without conversion, and the ad publisher may use the pseudonymous identifiers to identify consumers in the target buyer group and execute targeted advertising campaigns.

[0038] In some embodiments, performance data associated with ad campaigns can be collected and analyzed by the system 100. For example, some ad publishers can provide log-level details associated with their ad campaigns. The log-level details can include information about the advertiser, ad publisher, ad campaign, potential buyer, date, time, and location where the ad appeared, along with the ad impression and click count associated with the ad campaign. The system 100 can use the data analyzer 110 to collect the log-level details in a storage area 124 (commonly referred to as a staging area or data landing zone). The data analyzer 110 can then use the log-level details collected in the storage area 124 to facilitate data analysis.

[0039] For example, the data analyzer 110 can use the log-level details collected in the storage area 124 to determine performance metrics, including, but not limited to, ad impressions, click-through rates, completion rates, completion rates, engagement times, engagement rates, etc. The data analyzer 110 can then provide reports 128 containing the performance metrics to the advertiser or ad publisher to evaluate the effectiveness of the advertising campaign. In some embodiments, the data analyzer 110 can present the performance metrics to the advertising agency, advertiser, or ad publisher through an interactive user interface (e.g., a web page or a mobile device application). Alternatively, or additionally, the data analyzer 110 can present the performance metrics to the advertising agency, advertiser, or ad publisher as periodic reports. In some embodiments, the presentation of the performance metrics (through an interactive user interface or through periodic reports) can include textual or graphical representations, such as those shown in FIG. 3.

[0040] It should be noted that FIG. 3 is merely a simplified example depicting an example format for presenting performance metrics. For example, panel 302 may provide a user with a list of ad publishers involved in a particular advertising campaign. In an interactive user interface, a user may select one of the ad publishers from panel 302, and display area 304 may display performance metrics associated with the selected ad publisher. Display area 304 may display the performance metrics in a variety of formats, including line graphs, pie charts, bar graphs, or textual descriptions. In some embodiments, performance metrics may be aggregated, while the aggregated performance metrics may be further analyzed against segment and demographic attributes made available in pseudonymous consumer data 116 to provide additional accuracy and depth of knowledge.

[0041] Referring now to Figure 4, a flow diagram illustrating an example method 400 for targeted advertising to a particular consumer is shown, consistent with disclosed embodiments. While the example method 400 is described herein as a series of steps, it should be understood that the order of the steps may vary in other implementations. In particular, the steps may be performed in any order or in parallel. It should be understood that each step of the method 400 may be performed by one or more processors, computers, servers, controllers, etc.

[0042] In some embodiments, method 400 can be performed by system 100 (as shown in FIG. 1). In step 402, method 400 can include receiving client-provided data from a client device by system 100 over a network. The client can be an advertiser or an ad publisher. The client can provide its customer base (i.e., its consumer data) to system 100. The consumer data can include non-personally identifiable information (non-PII) about the consumer and Both may include personally identifiable information (PII). Consumer data may also include client-assigned identifiers.

[0043] In step 404, method 400 can include identifying one or more consumers identified in the client-provided data. Consumers can be identified by matching the client-provided data with consumer data recorded in an electronic consumer database. In some embodiments, the electronic consumer database can contain millions of records about consumers, each with over 1,000 attributes, including, but not limited to, email addresses, phone records, vehicle records, IP addresses, mortgage information, lifestyle / behavioral data, demographic data, transaction association data, life event data (e.g., new movers, new homeowners, new parents, credit threshold violations by the three major U.S. credit reporting agencies (Equifax, Experian, TransUnion)), asset metrics, credit statistics, vehicle data and vehicle statistics, real estate data, social media handles / flags, social influence, other syndicated research data, etc. It should be understood that the electronic consumer database can be expanded to include consumers based in other regions.

[0044] In step 406, method 400 may assign a unique consumer identifier to the consumer identified in the client-provided data. In some embodiments, the unique consumer identifier assigned to the consumer does not include any personally identifiable information originally contained in the client-provided data. In other words, the unique consumer identifier assigned in this manner is a pseudonymous identifier. In some embodiments, a cross-reference is maintained between the pseudonymous identifier and the client-assigned identifier originally provided by the client. This cross-reference can be used to later assist in converting the pseudonymous identifier to a client-assigned identifier if such conversion is requested by the client.

[0045] At step 408, method 400 can include generating a target potential buyer group. As described above with respect to FIG. 1 , system 100 can generate the target potential buyer group using consumer data provided by the advertiser alone or in conjunction with consumer data provided by one or more ad publishers, in conjunction with one or more advertising agencies, or one or more third-party data providers. Note that the target potential buyer group generation process is based on pseudonymous identifiers. In other words, in some embodiments, step 408 does not directly compare consumer data provided by the advertiser with consumer data provided by the ad publisher. Instead, in those embodiments, step 408 can be configured to generate the target potential buyer group by matching pseudonymous identifiers associated with consumer data provided by the advertiser with pseudonymous identifiers associated with consumer data provided by the ad publisher.

[0046] In step 410, method 400 can include system 100 delivering the target buyer group to a client device over a network to facilitate targeted advertising to specific consumers. Step 410 can deliver the target buyer group to an advertiser for review and approval. The advertiser can request changes to the target buyer group, if necessary. Otherwise, the advertiser can approve the target buyer group, and the advertiser can proceed with purchasing targeted advertising.

[0047] In some embodiments, method 400 may include step 412 configured to convert the pseudonymous identifier used to generate the target potential buyer group into an identifier recognized by the ad publisher. This conversion may be facilitated using the cross-referencing described above. In some embodiments, step 412 may convert the pseudonymous identifier into a web cookie-based identifier, a device identifier, or an encrypted email-based identifier. It should be understood that step 412 may convert the pseudonymous identifier into other types of client-assigned identifiers without departing from the spirit and scope of this disclosure.

[0048] In some embodiments, method 400 may also include step 414 configured to provide performance analytics for the targeted ads. For example, some ad publishers may provide log-level details associated with their ad campaigns. The log-level details may include information about the advertiser, ad publisher, ad campaign, potential buyer, date, time, and location where the ad appeared, along with ad impressions and click counts associated with the ad campaign. Step 414 may collect the log-level details and use the collected log-level details to provide data analytics, as described above.

[0049] 1 , in some embodiments, advertiser-provided consumer data 102A, ad publisher-provided consumer data 102B, third-party-provided consumer data 102C, and ad agency-provided consumer data 102D may include consumer records with at least one flag indicating the consumer record's enrollment in a class or segment within the consumer data. In some embodiments, the at least one flagged consumer record may be flagged with a binary digit "0" or "1." In other embodiments, the at least one flagged consumer record may be flagged with a "yes" or "no." In other embodiments, the at least one flagged consumer record may be flagged with a "true" or "false."

[0050] FIG. 5 is an example table 500 showing flagged consumer records, indicating the registration of consumer records in a class or segment within consumer data, consistent with disclosed embodiments. For example, in table 500, the class is a high lifetime value class, and at least one flagged consumer record is flagged to indicate that the consumer is a high lifetime value consumer and is not a recent purchaser. As shown in FIG. 5, table 500 includes a left-most column indicating consumers using personally identifiable information, such as an email address (xyz@yahoo.com), a name (John Doe), a phone number ((202)123-4567), etc. However, the consumer indication is not so limited and may be other personally identifiable information, such as the consumer's street address or Social Security number, or non-personally identifiable information, such as the consumer's device identifier, demographic data, segment, or model score. Table 500 also includes a center column containing a flag (true or false) indicating whether the consumer listed in the left-most column is a high lifetime value consumer. For example, a consumer identified by an email address (xyz@yahoo.com) and a consumer identified by a phone number ((202)123-4567) is a high customer lifetime value consumer, while a consumer identified by a name (John Doe) is not a high customer lifetime value consumer. Table 500 also includes a right-most column that contains a flag (true or false) indicating whether the consumer listed in the left-most column is a recent purchaser. For example, a consumer identified by an email address (xyz@yahoo.com) and a consumer identified by a phone number ((202)123-4567) is not a recent purchaser, while a consumer identified by a name (John Doe) is a recent purchaser.

[0051] In some embodiments, the system 100 (e.g., the data processor 104 or the target buyer candidate generator 106) can apply a set of modeling techniques and automated methods to identify one or more consumers whose data profiles are statistically similar to the data profiles of a seed set of consumers. For example, the system 100 can identify one or more consumers whose data profiles are statistically similar to the data profiles of a seed set of consumers. The seed set of consumers can be provided by the advertiser's consumer base.

[0052] In some embodiments, system 100 (e.g., data processor 104 or target potential buyer generator 106) can segment or sub-segment consumers into one or more subsets, each of which includes one or more statistically similar consumers. For example, when developing a potential buyer group of “car enthusiasts,” system 100 can divide consumers into effectively identical populations, which can then be tested to see if they prefer a particular type of offer, message, or creative treatment, or alternative option. The statistical similarity of two consumers can be determined based on the consumers’ event statistics, such as how many times a consumer has purchased the same type of car, how many times a consumer clicked on the same advertisement, how many times a consumer skipped the same advertisement, or how many times a consumer inquired about the same car dealer. The degree of similarity can also be quantitatively determined based on the collection and analysis of statistical data. In this manner, an automatic segmentation function can be implemented to divide a given potential buyer population into statistically similar subsets, resulting in increased efficiency.

[0053] In some embodiments, the system 100 (e.g., the data processor 104 or the target potential buyer generator 106) can generate a unique potential buyer list record in which potential buyers are uniquely collected, selected, compiled, and shared in a value-added manner (hereinafter referred to as "curated") based on the advertiser's specifications. The system 100 can further generate identity keys (also referred to as "identification keys") for the potential buyers in the unique potential buyer list and send the generated identity keys to the second advertising platform and / or programmatic partner. In this way, the second advertising platform or programmatic partner can recognize the characteristics of the list of potential buyers without performing detailed analysis on the consumer data, resulting in increased efficiency.

[0054] Referring to FIG. 1 , in some embodiments, ad publisher-provided consumer data 102B may include data provided by multiple ad publisher devices. System 100, e.g., data processor 104 or target buyer candidate generator 106, may assign priorities to the consumer data provided by the multiple ad publisher devices based, for example, on the importance or relevance of the consumer data. FIG. 6 is a schematic diagram illustrating multiple pieces of data provided by corresponding multiple ad publisher devices and the assigned priorities of the data consistent with disclosed embodiments. As an example, FIG. 6 shows consumer data 102B provided by 50 different ad publisher devices, with the priorities of the data listed in descending order. For example, consumer data 601 provided by a first ad publisher device has a first priority (priority 1), consumer data 602 provided by a second ad publisher device has a second priority (priority 2), and consumer data provided by a 50th ad publisher device has a 50th priority (priority 50).

[0055] In some embodiments, consumer data provided by multiple different ad publisher devices can be stored on physically or logically separate data stores to reduce data intermingling. For example, consumer data 601 provided by a first ad publisher device can be stored on a first data store that is physically or logically separate from a second data store used to store consumer data 602 provided by a second ad publisher device and a fiftieth data store used to store consumer data 650 provided by a fiftieth ad publisher device. Figure 6 shows consumer data provided by fifty different ad publisher devices. However, the number of ad publishers is not so limited and can be any number less than fifty or any number greater than fifty.

[0056] In some embodiments, when identifying potential target purchasers using consumer data provided by multiple different ad publishers, such as that shown in FIG. 6, the potential target purchaser generator 106 can utilize a waterfall matching test, such as that described with respect to FIGS. 7A, 7B, and 8. In this test, the potential target purchaser generator 106 can receive consumer data from advertiser devices over a network and identify multiple consumers provided by the advertiser from the consumer data. For example, as discussed above, the potential target purchaser generator 106 can identify multiple consumers provided by the advertiser by comparing the consumer data received from the advertiser devices with consumer data recorded in an electronic consumer database of the system 100. The potential target purchaser generator 106 can obtain multiple unique consumer identifiers corresponding to the multiple consumers provided by the advertiser. The multiple unique consumer identifiers may not include personally identifiable information. The potential target purchaser generator 106 can then identify at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the advertiser with at least one consumer provided by the ad publisher that is provided by a first ad publisher device having the highest priority among the plurality of ad publisher devices.

[0057] 7A is a schematic diagram illustrating a first matching test of a waterfall matching test consistent with disclosed embodiments. As shown in FIG. 7A, the target potential purchaser generator 106 can match multiple consumers provided by advertisers (represented by circles on the left side of FIG. 7A) with consumers provided by ad publisher 1 having the highest priority, and obtain a target potential purchaser group 1 common to both the consumer data provided by the advertisers and the consumer data provided by the ad publishers. The target potential purchaser group 1 can be obtained by matching pseudonymous identifiers associated with the consumer data provided by the advertisers with pseudonymous identifiers associated with the consumer data provided by ad publisher 1 after they are processed by the data processor 104.

[0058] After the first matching test, the target candidate purchaser generator 106 can determine whether the number of unmatched consumers among the plurality of consumers provided by the advertiser is greater than a threshold number. For example, in FIG. 7A , the portion of consumers provided by the advertiser, excluding target candidate purchaser group 1, shows unmatched consumers. The threshold number can be a number predetermined by the system 100. If the number of unmatched consumers is greater than the threshold number, the target candidate purchaser generator 106 can identify at least one second duplicate unique consumer identifier by matching at least one of the unmatched consumers with at least one consumer provided by an ad publisher provided by a second ad publisher device of the plurality of ad publisher devices. The second ad publisher device has the second highest priority among the plurality of ad publisher devices.

[0059] 7B is a schematic diagram illustrating a second matching test of the waterfall matching test consistent with disclosed embodiments. As shown in FIG. 7B, the target potential purchaser generator 106 can match the unmatched consumers provided by the advertiser (represented by the circle on the left side of FIG. 7B) with the consumers provided by Ad Publisher 2, which has the second highest priority, to obtain a target potential purchaser group 2 common to both the unmatched consumer data provided by the advertiser and the consumer data provided by Ad Publisher 2. The target potential purchaser group 2 can be obtained by matching the pseudonymous identifiers associated with the unmatched consumer data provided by the advertiser with the pseudonymous identifiers associated with the consumer data provided by Ad Publisher 2 after they are processed by the data processor 104.

[0060] After the second matching, the target candidate purchaser generator 106 can again determine whether the number of unmatched consumers (consumers provided by the advertiser excluding both target candidate purchaser group 1 and target candidate purchaser group 2) is greater than a threshold number. If the number of unmatched consumers is greater than the threshold number, the target candidate purchaser generator 106 can perform a third matching test. The target candidate purchaser generator 106 can iteratively match the remaining consumers among the plurality of consumers provided by the advertiser with consumers provided by the ad issuer with the highest priority among the unmatched ad issuers (ad issuers whose consumer data was not compared with the consumer data provided by the advertiser) until the number of remaining consumers is less than the threshold number. The target candidate purchaser generator 106 can select an ad issuer device from the plurality of ad issuer devices based on descending order of priority of the plurality of ad issuer devices. For example, the target candidate purchaser generator 106 can select an ad issuer device based on the table in FIG. 6.

[0061] In some embodiments, after each matching test, the target purchase candidate generator 106 can further identify one or more consumers whose data profile is statistically similar to the data profile of the seed set of consumers. In some embodiments, after each matching, the target purchase candidate generator 106 can further segment or sub-segment the consumers into one or more subsets, each of the one or more subsets including one or more statistically similar consumers.

[0062] In some embodiments, after each matching test, the target potential buyer generator 106 can generate a unique potential buyer list record in which the potential buyers are curated based on the advertiser's specifications, and can further generate identity keys for the potential buyers in the unique potential buyer list and send the generated identity keys to the second advertising platform and / or programmatic partner. For example, after the first matching, the target potential buyer generator 106 can generate a unique potential buyer list record using target potential buyer group 1 ( FIG. 7A ) in which the potential buyers are curated based on the advertiser's specifications. The target potential buyer generator 106 can also generate identity keys for the potential buyers in the unique potential buyer list and send the generated identity keys to the advertising platform and / or programmatic partner.

[0063] In some embodiments, after each match, the target potential purchaser generator 106 can receive the advertiser's approval to purchase the media advertisements, and can deliver the group of target potential purchasers obtained from the match to the corresponding ad issuer device upon receiving the advertiser's approval. For example, after a first match, the target potential purchaser generator 106 can receive the advertiser's approval to purchase the media advertisements, and can deliver target potential purchaser group 1 to the first ad issuer device upon receiving the advertiser's approval. Prior to delivering target potential purchaser group 1 to the first ad issuer device, the target potential purchaser generator 106 can transform at least one first unique consumer identifier included in target potential purchaser group 1 according to a transformation protocol specified by the ad issuer. For example, the target potential purchaser generator 106 may convert at least one first unique consumer identifier included in the target potential purchaser group 1 into at least one of a web cookie-based identifier, a television-based identifier, an encrypted email-based identifier, or a device identifier prior to delivery of the target potential purchaser group 1 to the first ad publisher device.

[0064] In some embodiments, after each match, the target potential purchaser generator 106 can generate a target potential purchaser group and deliver the target potential purchaser group to the advertiser device. For example, after a first match, the target potential purchaser generator 106 can generate target potential purchaser group 1 and deliver target potential purchaser group 1 (FIG. 7A) to the advertiser device to facilitate targeted advertising to the specific consumer. Similarly, after a second match, the target potential purchaser generator 106 can generate target potential purchaser group 2 (FIG. 7B) and deliver target potential purchaser group 2 to the advertiser device to facilitate targeted advertising to the specific consumer.

[0065] 8 is a flow diagram illustrating an example method 800 for waterfall matching testing consistent with disclosed embodiments. It should be understood that each step of method 800 can be performed by one or more processors, computers, servers, controllers, or the like. In some embodiments, method 800 can be performed by system 100, for example, by target buyer candidate generator 106 as shown in FIG. 1.

[0066] In step 802, method 800 may include matching the unmatched advertiser-provided consumers with ad publisher-provided consumers provided by the ad publisher with the highest priority among the unmatched ad publishers. For example, for a first matching test, the unmatched consumers are the original advertiser-provided consumers, e.g., as shown by the left circle in FIG. 7A. For any subsequent matching test, the unmatched consumers are the portion of the original advertiser-provided consumers after removing all matching consumers in the previous matching test, e.g., the unmatched advertiser-provided consumers, as shown by the left circle in FIG. 7B.

[0067] At step 804, method 800 may include generating a group of target potential purchasers based on overlap of unique consumer identifiers. For example, the group of target potential purchasers may be generated by obtaining a group of consumers common to both unmatched consumer data provided by the advertiser and consumer data provided by the ad publisher. As discussed above, the target potential purchasers may be obtained by matching pseudonymous identifiers associated with consumer data provided by the advertiser with pseudonymous identifiers associated with consumer data provided by the ad publisher.

[0068] At step 806, the method 800 may include delivering the target group of potential buyers to a client device. For example, the generated target group of potential buyers may be delivered to an advertiser device to facilitate targeted advertising to specific consumers.

[0069] In step 808, the method 800 may include determining whether the number of remaining consumers among the consumers provided by the advertiser is greater than a threshold number. If the number of remaining consumers among the consumers provided by the advertiser is greater than the threshold number, the method 800 returns to step 802 and repeats steps 802, 804, 806, and 808 until the number of remaining consumers among the consumers provided by the advertiser does not exceed the threshold number.

[0070] At step 810, the method 800 may include terminating the waterfall matching test if the number of remaining consumers among the consumers provided by the client device does not exceed a threshold number.

[0071] While the present disclosure has been shown and described with reference to specific embodiments thereof, it will be understood that the present disclosure can be practiced in other environments without modification. The above description is presented for illustrative purposes. It is not exhaustive and is not limited to the precise form or embodiment disclosed. Modifications and adaptations will be apparent to those skilled in the art from consideration of the specification and practice of the disclosed embodiments. Additionally, while aspects of the disclosed embodiments are described as being stored in memory, those skilled in the art will recognize that these aspects can also be stored on other types of computer-readable media, such as secondary storage devices, e.g., hard disks, CD-ROMs, or other forms of RAM or ROM, USB media, DVDs, Blu-rays, or other optically driven media, etc.

[0072] Computer programs based on the descriptions and disclosed methods are within the skill of an experienced developer. The various programs or program modules can be created using any of the techniques known to those skilled in the art, or can be designed in conjunction with existing software. For example, program sections or program modules can be designed in or with the .Net Framework, the .Net Compact Framework (and related languages ​​such as Visual Basic, C, etc.), Java, C++, Objective-C, HTML, a combination of HTML / AJAX, XML, or HTML containing Java applets.

[0073] Furthermore, while example embodiments are described herein, the scope of any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., combinations of aspects across various embodiments), adaptations, and / or alterations will be recognized by those skilled in the art based on this disclosure. Any limitations in the claims should be construed broadly based on the language employed in the claims and not limited to the examples described herein and during prosecution of the application. The examples should be construed as non-exclusive. Furthermore, the steps of the disclosed methods can be modified in any manner, including by changing the order of steps and / or inserting or deleting steps. Accordingly, it is intended that the specification and examples be considered exemplary only, with a true scope and spirit being indicated by the following claims, along with their full scope of equivalents. The invention disclosed in this specification includes the following aspects. <Aspect 1> 1. A computer-implemented system for targeted advertising to specific consumers, comprising: a memory storing instructions; at least one processor, wherein the at least one processor: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by said client; The system is configured to execute the instructions to identify at least one first duplicate unique consumer identifier by matching at least one of a plurality of consumers provided by the client with at least one consumer provided by an advertisement publisher provided by a first advertisement publisher device having the highest priority among a plurality of advertisement publisher devices. <Aspect 2> 2. The system of claim 1, wherein the consumer data received from the client device comprises at least one flagged consumer record indicating enrollment of the consumer record in a class or segment within the consumer data. <Aspect 3> 3. The system of claim 2, wherein the at least one flagged consumer record is flagged with a binary digit "0" or "1." <Aspect 4> 3. The system of embodiment 2, wherein the at least one flagged consumer record is flagged as "yes" or "no." <Aspect 5> 3. The system of embodiment 2, wherein the at least one flagged consumer record is flagged as "true" or "false." <Aspect 6> 3. The system of claim 2, wherein the class is a high lifetime value consumer class, and wherein the at least one flagged consumer record is flagged to indicate that the consumer is a high lifetime value consumer and is not a recent purchaser. <Aspect 7> 2. The system of aspect 1, wherein the at least one processor is further configured to execute the instructions to identify one or more consumers whose data profile is statistically similar to a data profile of a seed set of consumers. <Aspect 8> 8. The system of embodiment 7, wherein the consumer seed set is provided by the client's consumer base. <Aspect 9> 2. The system of aspect 1, wherein the at least one processor is further configured to execute the instructions to segment the consumers provided by the client into one or more subsets, each subset including one or more statistically similar consumers. <Aspect 10> The at least one processor: generating a first group of target potential purchasers based on the at least one first overlapping unique consumer identifier; 2. The system of embodiment 1, further configured to execute the instructions to deliver the first group of target potential purchasers to the client to facilitate targeted advertising to specific consumers. <Aspect 11> The at least one processor: determining whether a number of non-matching consumers among a plurality of consumers provided by the client is greater than a threshold number; If the number of unmatched consumers is greater than the threshold number, further configured to execute the instructions to identify at least one second duplicate unique consumer identifier by matching at least one of the unmatched consumers with at least one consumer provided by an ad publisher provided by a second ad publisher device of the plurality of ad publisher devices; 11. The system of aspect 10, wherein the second advertisement publisher device has a second highest priority among the plurality of advertisement publisher devices. <Aspect 12> The at least one processor: further configured to execute the instructions to repeat matching remaining consumers among the plurality of consumers provided by the client with consumers provided by an ad publisher having a highest priority among unmatched ad publishers until the number of remaining consumers is less than the threshold number; 12. The system of claim 11, wherein the at least one processor is configured to select an advertisement publisher device from among the plurality of advertisement publisher devices based on a descending order of priority of the plurality of advertisement publisher devices. <Aspect 13> The at least one processor: generating a second group of target potential purchasers based on the at least one second overlapping unique consumer identifier; 12. The system of claim 11, further configured to execute the instructions to deliver the second group of target potential purchasers to the client device to facilitate targeted advertising to specific consumers. <Aspect 14> The at least one processor: generating a unique prospective buyer list record in which prospective buyers are curated based on the advertiser's specifications; 2. The system of embodiment 1, further configured to execute the instructions to generate identity keys for the potential buyers in the unique potential buyer list and send the generated identity keys to an advertising platform or a programmatic partner. <Aspect 15> The at least one processor: receive advertiser approval for media advertising purchases; 11. The system of aspect 10, further configured to execute the instructions to deliver the first group of target potential buyers to the first ad publisher device upon receiving approval from the advertiser. <Aspect 16> 16. The system of claim 15, wherein the at least one processor is further configured to execute the instructions to convert the at least one unique consumer identifier included in the first group of target potential buyers according to a conversion protocol specified by an ad publisher prior to delivery of the group of target potential buyers to the first ad publisher device. <Aspect 17> 17. The system of claim 16, wherein the at least one processor is further configured to convert the at least one first unique consumer identifier included in the first group of target potential buyers to at least one of a web cookie-based identifier, a television-based identifier, an encrypted email-based identifier, or a device identifier prior to delivery of the first group of target potential buyers to the first ad publisher device. <Aspect 18> 1. A computer-implemented method for targeted advertising to specific consumers, comprising: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by the client; and identifying at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the client with at least one consumer provided by an advertisement publisher provided by a first advertisement publisher device having the highest priority among the plurality of advertisement publisher devices. <Aspect 19> 1. A non-transitory computer-readable medium storing instructions executable by a processor to perform a method for targeted advertising to a particular consumer, the method comprising: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by the client; and identifying at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the client with at least one consumer provided by an ad publisher provided by a first ad publisher device having the highest priority among the plurality of ad publisher devices.

Claims

1. 1. A computer-implemented system for targeted advertising to specific consumers, comprising: a memory storing instructions; at least one processor, wherein the at least one processor: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by said client; identifying at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the client with at least one consumer provided by an ad publisher provided by a first ad publisher device of the plurality of ad publisher devices, the first ad publisher device having a highest priority among the plurality of ad publisher devices, the highest priority being assigned based on importance or relevance of a plurality of data provided by the plurality of ad publisher devices; generating a first group of target potential purchasers based on the at least one first overlapping unique consumer identifier; determining whether a number of unmatched consumers of a plurality of consumers provided by the client is greater than a threshold number; configured to execute the instructions to identify at least one second duplicate unique consumer identifier by matching at least one of the unmatched consumers with at least one consumer provided by an ad publisher provided by a second ad publisher device of the plurality of ad publisher devices based on a determination that the number of unmatched consumers is greater than the threshold number; The second advertisement publisher device has a second highest priority among the plurality of advertisement publisher devices.

2. 10. The system of claim 1, wherein the consumer data received from the client device comprises at least one flagged consumer record indicating enrollment of the consumer record in a class or segment within the consumer data.

3. 3. The system of claim 2, wherein the at least one flagged consumer record is flagged with a binary digit "0" or "1."

4. The system of claim 2 , wherein the at least one flagged consumer record is flagged as either "yes" or "no."

5. The system of claim 2 , wherein the at least one flagged consumer record is flagged as either "true" or "false."

6. 3. The system of claim 2, wherein the class is a high lifetime value consumer class, and the at least one flagged consumer record is flagged to indicate that the consumer is a high lifetime value consumer and is not a recent purchaser.

7. 10. The system of claim 1, wherein the at least one processor is further configured to execute the instructions to identify one or more consumers whose data profile is statistically similar to a data profile of a seed set of consumers.

8. The system of claim 7 , wherein the consumer seed set is provided by the client's consumer base.

9. 10. The system of claim 1, wherein the at least one processor is further configured to execute the instructions to segment the consumers provided by the clients into one or more subsets, each subset including one or more statistically similar consumers.

10. 10. The system of claim 1, wherein the at least one processor is further configured to execute the instructions to deliver the first group of target potential purchasers to the client device to facilitate targeted advertising to specific consumers.

11. The at least one processor receiving an advertiser approval from the advertiser device; 11. The system of claim 10, further configured to execute the instructions to deliver the first group of target potential purchasers to the first ad publisher device upon receiving the advertiser's approval.

12. 12. The system of claim 11, wherein the at least one processor is further configured to execute the instructions to transform at least one first unique consumer identifier included in the first group of target potential buyers according to a transformation protocol specified by an ad publisher prior to delivery of the first group of target potential buyers to the first ad publisher device.

13. 13. The system of claim 12, wherein the at least one processor is further configured to convert the at least one first unique consumer identifier included in the first group of target potential buyers to at least one of a web cookie-based identifier, a television-based identifier, an encrypted email-based identifier, or a device identifier prior to delivery of the first group of target potential buyers to the first ad publisher device.

14. The at least one processor is further configured to execute the instructions to repeat matching remaining consumers among the plurality of consumers provided by the client with consumers provided by an ad publisher having a highest priority among unmatched ad publishers until the number of remaining consumers is less than the threshold number; 2. The system of claim 1, wherein the at least one processor is configured to select an advertisement publisher device from among the plurality of advertisement publisher devices based on a descending order of priority of the plurality of advertisement publisher devices.

15. The at least one processor generating a second group of target potential purchasers based on the at least one second overlapping unique consumer identifier; 10. The system of claim 1, further configured to execute the instructions to deliver the second group of target potential purchasers to the client device to facilitate targeted advertising to specific consumers.

16. The at least one processor generating a record of a unique potential buyer list in which potential buyers are curated based on the advertiser's specifications; 10. The system of claim 1, further configured to execute the instructions to generate identity keys for the potential buyers in the unique potential buyer list and send the generated identity keys to an advertising platform or a programmatic partner.

17. The at least one processor assigning priorities to the plurality of advertisement publisher devices based on the importance or relevance of the plurality of data provided by the plurality of advertisement publisher devices; The system of claim 1 , further configured to execute the instructions to list the plurality of advertisement publisher devices in descending order of priority.

18. 1. A computer-implemented method for targeted advertising to specific consumers, comprising: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by the client; identifying at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the client with at least one consumer provided by an ad publisher provided by a first ad publisher device of the plurality of ad publisher devices, the first ad publisher device having a highest priority among the plurality of ad publisher devices, the highest priority being assigned based on importance or relevance of a plurality of data provided by the plurality of ad publisher devices; generating a first group of target potential purchasers based on the at least one first overlapping unique consumer identifier; determining whether a number of unmatched consumers of a plurality of consumers provided by the client is greater than a threshold number; based on determining that the number of unmatched consumers is greater than the threshold number, identifying at least one second duplicate unique consumer identifier by matching at least one of the unmatched consumers with at least one consumer provided by an ad publisher provided by a second ad publisher device of the plurality of ad publisher devices; The method, wherein the second advertisement publisher device has a second highest priority among the plurality of advertisement publisher devices.

19. 1. A non-transitory computer-readable medium storing instructions executable by a processor to perform a method for targeted advertising to a particular consumer, the method comprising: receiving consumer data from a client device over a network; identifying a plurality of consumers provided by the client from the consumer data; obtaining a plurality of unique consumer identifiers corresponding to a plurality of consumers provided by the client; identifying at least one first duplicate unique consumer identifier by matching at least one of the plurality of consumers provided by the client with at least one consumer provided by an ad publisher provided by a first ad publisher device of the plurality of ad publisher devices, the first ad publisher device having a highest priority among the plurality of ad publisher devices, the highest priority being assigned based on importance or relevance of a plurality of data provided by the plurality of ad publisher devices; generating a first group of target potential purchasers based on the at least one first overlapping unique consumer identifier; determining whether a number of unmatched consumers of a plurality of consumers provided by the client is greater than a threshold number; based on determining that the number of unmatched consumers is greater than the threshold number, identifying at least one second duplicate unique consumer identifier by matching at least one of the unmatched consumers with at least one consumer provided by an ad publisher provided by a second ad publisher device of the plurality of ad publisher devices; The second advertisement publisher device has a second highest priority among the plurality of advertisement publisher devices.

Images