A system and method for providing people-based buyer candidate planning.

Abstract

Systems and methods for targeted advertising to specific consumers will be disclosed. [Solution] The system may include a memory for storing instructions and at least one processor, the at least one processor configured to receive consumer data from a client device via a network, identify a plurality of consumers provided by the client from the consumer data, obtain a plurality of unique consumer identifiers corresponding to the plurality of consumers provided by the client, and execute instructions to identify 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 advertiser, provided by a first advertiser device having the highest priority among a plurality of advertiser devices.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application is a continuation - in - part application No. 16 / 214,769 filed on December 10, 2018, which is a continuation application No. 15 / 786,551 of the U.S. filed on October 17, 2017 (currently U.S. Patent No. 10,181,136), and claims the priority of the U.S. provisional patent application No. 62 / 409,374 filed on October 17, 2016, the entire contents of which are hereby incorporated by reference.

[0002] The present disclosure relates generally to computerized systems and methods for providing human - based advertising targeting (hereinafter, "advertising targets" are described as "purchase candidates") planning and targeted advertising.

Background Art

[0003] Vendors can target specific consumers within a consumer population to address market needs according to individual preferences. For example, a vendor can provide customized sales promotions to a certain potential customer. The content of such sales promotions (e.g., advertisements) can be uniquely tailored to different consumers. Personalizing sales promotion content for electronic distribution may lead to increased revenue, but there are also some drawbacks. For example, sales activities for addressing the needs of a single customer can be too burdensome, time - consuming, infeasible due to scalability, and can be expensive.

[0004] Consumer needs and desires may overlap with other needs and desires. Sales activities based on dividing promising consumer buyers into separate 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 segmentation methods (dividing consumers into separate groups to facilitate sales activities) may deprive salespeople of the benefits of category-based sales. For example, two consumers of the same age may receive the same advertisement because they are similarly categorized based on age. However, these consumers may be in different life situations and therefore may have different goals or values. This could result in one consumer in the category eagerly purchasing the advertised product, while the other is vehemently opposed to purchasing it. Segmenting these two consumers based on a single criterion (e.g., age) alone may 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 that include personally identifiable information (e.g., name, email address, phone number, etc.). Furthermore, traditional systems typically exchange these identifiers over communication networks. This can result in data breaches or loss, potentially exposing consumers' personally identifiable information to attackers or other malicious users. Additionally, attackers (e.g., hackers) can use personally identifiable information obtained from one attack against the same or additional consumers in subsequent attacks (e.g., using phishing, social engineering, etc.).

[0006] Traditional advertising platforms allow advertising clients to supply their own consumer data, but this data is incompatible with or does not support the client's own segmentation. Therefore, advertising clients cannot define their own segments (different groups of consumers to promote 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 the advertiser. However, when the client wants to publish the rest of the set of consumer data, the platform compares the entire set of consumer data with consumer data provided by a second advertiser, without excluding the list of potential buyers already published. This causes the publishing system to operate inefficiently.

[0007] Therefore, there is a demand for improved methods that provide people-based buyer candidate planning and targeted advertising. [Overview of the Initiative]

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

[0009] Other aspects of this disclosure are directed toward computer-implemented methods for targeted advertising to specific consumers. The computer-implemented methods may include 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 the 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 advertiser, provided by a first advertiser device having the highest priority among a plurality of advertiser devices.

[0010] Further aspects of this disclosure are directed to a non-temporary computer-readable medium storing processor-executable instructions for performing a method for targeted advertising to specific consumers. The method may include 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 the 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 advertiser, provided by a first advertiser device having the highest priority among a plurality of advertiser devices.

[0011] Other systems, methods, and computer-readable media are also considered here. [Brief explanation of the drawing]

[0012] [Figure 1]This schematic block diagram illustrates an example embodiment of a system for targeted advertising to specific consumers, consistent with the disclosed embodiments. [Figure 2] This is a diagram of an example target buyer candidate search interface, consistent with the disclosed embodiments. [Figure 3] These are example figures and tables from the results report that are consistent with the disclosed embodiments. [Figure 4] This flowchart illustrates an example method for targeted advertising to specific consumers, consistent with the disclosed embodiments. [Figure 5] This table is an example showing flagged consumer records that indicate the registration of consumer records in a class or segment within consumer data, consistent with the disclosed embodiments. [Figure 6] This schematic diagram illustrates the multiple data points and assigned priority of the data provided by the corresponding multiple ad-publisher devices, consistent with the disclosed embodiments. [Figure 7] Figure 7A is a schematic diagram illustrating a first matching test of a waterfall matching test, consistent with the disclosed embodiments. Figure 7B is a schematic diagram illustrating a second matching test of a waterfall matching test, consistent with the disclosed embodiments. [Figure 8] This flowchart illustrates an example method for waterfall matching testing, consistent with the disclosed embodiments. [Modes for carrying out the invention]

[0013] The following detailed description refers to the accompanying drawings. Where possible, the same reference numerals are used in the drawings and the following description to refer to the same or similar parts. While several exemplary 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 exemplary methods described herein may be modified by substituting, rearranging, removing, or adding steps to the disclosed methods. Accordingly, the following detailed description is not limited to the disclosed embodiments and examples, and the appropriate scope of the invention is defined by the accompanying claims.

[0014] Embodiments of this disclosure are directed toward systems and methods configured to provide targeted advertising to specific consumers. For example, a client device (e.g., an advertiser or ad publisher system) can provide consumer data to an advertising agency via a network. Consumer data may include, for example, personally identifiable information (e.g., name, email address, telephone number, street address, social security number, etc.) and non-personally identifiable information (e.g., device identifier, demographic data, segment, model score, 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 group of target buyer candidates (hereinafter referred to as the "Target Buyer Candidate Group") for the client based on the unique consumer identifiers. As in some embodiments of this disclosure, the use of unique consumer identifiers can help improve the efficiency of Target Buyer Candidate Group generation. Furthermore, as in some embodiments of this disclosure, the use of such unique consumer identifiers can enhance the security, fidelity, and accuracy of the data.

[0015] Referring to Figure 1, a schematic block diagram is shown illustrating an embodiment of a system as an example for targeted advertising. As illustrated in Figure 1, the system 100 may 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] The data source 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 (for example, 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 advertising agencies can utilize various types of computing devices to communicate with one another. Such computing devices may include, for example, servers, desktop computers, notebook computers, mobile devices, tablets, smartphones, wearable devices such as smartwatches, smart bracelets, smart glasses, or any other devices capable of communicating with wired or wireless networks.

[0018] In some embodiments, consumer data 102A provided by advertisers, consumer data 102B provided by ad issuers, consumer data 102C provided by third-party data providers, and consumer data 102D provided by advertising agencies can be stored in physically or logically separate data storage devices to reduce data mixing. For example, consumer data 102A provided by advertisers can be stored in a first data storage device that is physically or logically separate from a second data storage device used to store consumer data 102B provided by ad issuers. Similarly, consumer data 102C provided by third-party data providers can be stored in a third data storage device that is physically or logically separate from a fourth data storage device used to store consumer data 102D provided by advertising agencies. In some embodiments, consumer data 102A provided by different advertisers can be stored in physically or logically separate data storage devices. Similarly, consumer data 102B provided by different ad issuers and consumer data 102C provided by different third-party data providers can be stored in physically or logically separate data storage devices. Such data storage devices can 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 function as an entry point for consumer data received from various data sources 102A, 102B, 102C, or 102D. The data processor 104 may 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-temporary processor-readable memory configured to store processor-executable code. When 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 via a wired or wireless network.

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

[0021] In some embodiments, the PII included in consumer data 102 can be processed separately from the non-PII included in consumer data 102. For example, as illustrated in FIG. 1, the PII included in consumer data 102 can be processed by a consumer identification processor 114 (which can be implemented as a component of data processor 104). The 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, phone number, street address (number), or social security number. In some embodiments, if an advertising agency has access to consumer database 102D, the consumer identification processor 114 can recognize consumers by comparing consumer data 102A (or consumer data 102B provided by an advertising publisher) provided by an advertiser with consumer data 102D.

[0022] In some embodiments, the consumer identification processor 114 can implement various types of data formatting, filtering, verification, parsing, standardization, normalization, or correction techniques to process consumer data 102. In these embodiments, the consumer identification processor 114 can also utilize various types of deterministic or probabilistic processing techniques to facilitate the consumer recognition process. Suitable deterministic or probabilistic processing techniques include, but are not limited to, accounting for variations in name spellings (e.g., spelling “Robert” as “Rob” or “Bob” as “Bobby”), variations in address representations (e.g., “Road” or “Rd”, presence or absence of apartment unit numbers, variations in spellings for cities, etc.), correcting common email address errors (e.g., spelling mistakes or transposed characters in the domain name), and inferring phone area codes based on cities and states.

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

[0024] In some embodiments, the pseudonymous identifiers assigned by the consumer identification processor 114 can uniquely identify a particular consumer at a particular street address (number). For example, a separate identifier can be assigned to each particular address, and similarly, a separate identifier can be assigned to each consumer name. Then, without exposing the PII data to subsequent components, a unique pair of address and consumer identifier can be assigned and exchanged as a proxy for the underlying PII data record. Such pseudonymous identifiers, by definition, do not contain information that identifies the individual consumer, and thus can provide anonymity compared to PII-based identifiers. 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, generate noise, and reduce data fidelity). In some embodiments, the consumer identification processor 114 can maintain a cross-reference 122 between the pseudonymous identifier and the identifier originally used by a client (e.g., an advertiser or advertising publisher). This cross-reference 122 can be stored in one or more non-transitory processor-readable memories accessible to the consumer identification processor 114 (and generally the data processor 104).

[0025] The pseudonym identifier assigned by the consumer identification processor 114 can then be integrated with non-PII information included in the consumer data 102 to generate pseudonym consumer data 116. Note that the pseudonym consumer data 116 can then contain pseudonym identifiable information that can be used to generate target buyer candidate groups for a client without disclosing any personally identifiable information about the consumer.

[0026] In some embodiments, target buyer candidate groups are generated using a target buyer candidate generator 106. The target buyer candidate 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-temporary processor-readable memory configured to store processor-executable code. When the processor-executable code is executed by the target buyer candidate generator 106, the target buyer candidate generator 106 can execute instructions to generate target buyer candidate groups. In some embodiments, the target buyer candidate generator 106 is configured to process only pseudonymous consumer data 116. Utilizing pseudonymous consumer data 116 in this manner can help improve the efficiency of the target buyer candidate generator 106.

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

[0028] In some embodiments, a list 118 of consumers common to both consumer data provided by advertisers and consumer data provided by ad publishers can be readily identified as a target buyer candidate group. Alternatively, the list of consumers 118 can be considered a baseline population, which can be extended using one or more similar buyer candidate models 120. For example, the target buyer candidate generator 106 can analyze non-personally identifiable information (e.g., demographic data, segments, model scores, etc.) associated with consumers identified in the list of consumers 118 to obtain one or more top-level attributes describing such consumers. These top-level attributes can then be used to help identify additional consumers provided by third-party data providers (e.g., data derived from consumer data 102C) or additional consumers provided by advertising agencies (e.g., data derived from consumer data 102D).

[0029] In other examples, the advertiser may choose to ask the target buyer candidate generator 106 to process the consumer data 102A provided by the advertiser without having to consider any of the consumer data provided by the ad issuer. The consumer data 102A provided by the advertiser can be processed by the data processor 104, which can generate pseudonymous consumer data 116 as described above. The target buyer candidate generator 106 can then parse the pseudonymous consumer data 116 generated based on the consumer data 102A provided by the advertiser in order to identify one or more top-level attributes describing the consumer data 102A provided by the advertiser. The top-level attributes thus identified can then be used to help identify additional consumers provided by third-party data providers (e.g., data derived from consumer data 102C) or additional consumers provided by advertising agencies (e.g., data derived from consumer data 102D).

[0030] It should be understood that the target buyer candidate generation technology described above is presented as an example and is not intended to be restrictive. It should also be understood that specific implementations of the target buyer candidate generation process may differ from the examples presented above, without deviating from the scope and spirit of this disclosure.

[0031] In some embodiments, once a target buyer candidate group is generated, the target buyer candidate generator 106 can distribute the target buyer candidate group to advertisers for investigation and approval (e.g., via a network). Figure 2 shows an example investigation interface. In this example, the target buyer candidate group is generated based on consumer data recorded in an electronic consumer database 102D provided by an advertising agency. In one embodiment, the electronic consumer database 102D contains millions of records about consumers, each record having over 1,000 attributes, including, but not limited to, email addresses, phone records, vehicle records, IP addresses, mortgage information, lifestyle / behavior data, demographic data, transactional collaboration data, life event data (e.g., recently moved, newly homeowner, newly parent, credit threshold violation by the three major US credit reporting agencies (Equifax, Experian, TransUnion), asset indicators, credit statistics, vehicle data and vehicle statistics, real estate data, social media handles / flags, social influence, and other syndicated research data. Other embodiments of the electronic consumer database 102D are also possible.

[0032] Advertisers may use the example survey interface shown in Figure 2 to review or modify their target buyer candidate groups. For example, the example survey interface may include a visual representation 204 of the target buyer candidate group. Visual representation 204 may include one or more figures showing the composition of the target buyer candidate group. For example, visual representation 204 may show composition regarding education level, gender, marital status, etc. Visual representation 204 may also include composition regarding age group, occupation, etc. Visual representation 204 may further show the estimated reach of the ad (the number of potential buyers who might see the ad) (and, if available, the actual reach based on historical / recorded data) if the advertiser approves the presented target buyer candidate group.

[0033] An example survey interface may also include a control panel 202 configured to receive control input from advertisers. For example, if an advertiser chooses not to target a particular age group 206, the advertiser can select the age group 206 (for example, by clicking on the age group 206 using a computer mouse) and remove that particular age group 206 from the target target group by clicking the "Remove Target Buyer" button in the control panel 202. The modifications made by the advertiser can be sent via network communication to the target target generator 106, which can then adjust the target group accordingly. On the other hand, if the advertiser is satisfied with the target group, the advertiser can choose to confirm / approve the target group by clicking the "Confirm" button in the control panel 202.

[0034] It should be understood that the example survey interface shown in Figure 2 is presented as an example only and is not intended to be restrictive. Once the advertiser confirms / approves the target buyer candidate group, the application interface 108 can distribute the target buyer candidate group to one or more advertisers upon receiving the advertiser's approval.

[0035] In some embodiments, the target buyer candidate generator 106 is configured to process only pseudonymous consumer data 116, so the target buyer candidate groups generated by the target buyer candidate generator 106 may not include certain identifiers required by the advertiser. Therefore, it should be noted that in some embodiments, the advertiser may request that the target buyer candidate groups delivered to them be transformed according to an advertiser-specific transformation protocol so that they include identifiers required by the advertiser.

[0036] In some embodiments, the data processor 104 can be configured to function as a controlled exit point for converting / modifying pseudonym identifiers based on the advertiser's specifications, if necessary. More specifically, in some embodiments, the data processor 104 can utilize a cross-reference dataset 122 stored earlier in the pseudonym identifier generation process (described above) to assist in converting pseudonym identifiers included in target buyer candidate groups. For example, if the advertiser uses web cookies or device identifiers to identify its target buyer candidates, the data processor 104 can use the reference data stored in the cross-reference dataset 122 to convert the pseudonym identifiers included in target buyer candidate groups to web cookies or device identifiers. Similarly, if the advertiser uses encrypted email to identify its target buyer candidates, the data processor 104 can use the reference data stored in the cross-reference dataset 122 to convert the pseudonym identifiers included in target buyer candidate groups to encrypted email. The application interface 108 can then provide the target buyer candidate groups with the converted identifiers to the advertiser for running an advertising campaign.

[0037] It should be understood that the above conversion is not always required. In some embodiments, for example, an advertiser may partner with an advertising agency and thus have shared access to the pseudonym identifier. In such embodiments, the application interface 108 may provide the target buyer candidate group directly to the advertiser without conversion, and the advertiser may use the pseudonym identifier to identify consumers within the target buyer candidate group and run targeted advertising campaigns.

[0038] In some embodiments, the system 100 can collect and analyze performance data associated with advertising campaigns. For example, several advertisers can provide log-level details associated with their advertising campaigns. Log-level details may include information about the advertiser, advertiser, advertising campaign, potential buyers, date, time, and location where the ad appeared, along with the impression the ad made and click count associated with the advertising campaign. The system 100 can utilize a data analyzer 110 to collect log-level details in a storage area 124 (typically 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 log-level details collected in the storage area 124 to determine performance metrics, including, but not limited to, the impression an ad makes, click-through rate, completion rate, achievement rate, engagement time, and engagement rate. The data analyzer 110 can then provide the advertiser or publisher with a report 128 containing the performance metrics 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 publisher through an interactive user interface (e.g., a web page or mobile device application). Alternatively or additionally, the data analyzer 110 can present the performance metrics to the advertising agency, advertiser, or publisher as periodic reports. In some embodiments, the presentation of performance metrics (through an interactive user interface or through periodic reports) can include textual or graphical representations, as shown in Figure 3.

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

[0041] Referring here to Figure 4, a flowchart is shown illustrating Method 400 as an example of targeted advertising to specific consumers, consistent with the disclosed embodiments. While Method 400 as an example is described here as a series of steps, it should be understood that the order of the steps may vary in other implementations. In particular, the steps can be performed in any order or in parallel. It should be understood that each step of Method 400 can 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 Figure 1). In step 402, Method 400 may include System 100 receiving data provided by a client from a client device via a network. The client may be an advertiser or an ad publisher. The client may provide System 100 with its customer base (i.e., its consumer data). The consumer data includes non-personally identifiable information (non-PII) about the consumer and Both may contain personally identifiable information (PII). Consumer data may also contain identifiers assigned by the client.

[0043] In step 404, method 400 may include identifying one or more consumers identified in the data provided by the client. Consumers can be identified by matching the data provided by the client with consumer data recorded in an electronic consumer database. In some embodiments, the electronic consumer database may contain millions of records about a consumer, each record having more than 1,000 attributes, including, but not limited to, email addresses, phone records, vehicle records, IP addresses, mortgage information, lifestyle / behavioral data, demographic data, transactional collaboration data, life event data (e.g., new mover, new homeowner, new parent, credit threshold violation by the three major US credit reporting agencies (Equifax, Experian, TransUnion), asset indicators, credit statistics, vehicle data and vehicle statistics, real estate data, social media handles / flags, social influence, and other syndicated research data. It should be understood that the electronic consumer database can be extended to include consumers based in other domains.

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

[0045] In step 408, method 400 may include generating target buyer candidate groups. As described above with respect to Figure 1, system 100 can generate target buyer candidate groups using only consumer data provided by advertisers, or together with consumer data provided by one or more advertisers, one or more third-party data providers, in conjunction with one or more advertising agencies. Note that the target buyer candidate group generation process is based on pseudonymous identifiers. In other words, in some embodiments, step 408 does not directly compare consumer data provided by advertisers with consumer data provided by advertisers. Rather, in those embodiments, step 408 may be configured to generate target buyer candidate groups by matching pseudonymous identifiers associated with consumer data provided by advertisers with pseudonymous identifiers associated with consumer data provided by advertisers.

[0046] In step 410, method 400 may include the distribution of target buyer candidate groups to client devices via the network by system 100 in order to facilitate targeted advertising to specific consumers. Step 410 may distribute the target buyer candidate groups to advertisers for investigation and approval. Advertisers may request changes to the target buyer candidate groups if necessary. Otherwise, advertisers may approve the target buyer candidate groups and commence purchasing targeted advertising.

[0047] In some embodiments, Method 400 may include a step 412 configured to convert pseudonymous identifiers used to generate target buyer candidate groups into identifiers recognized by the advertiser. This conversion can be facilitated using the aforementioned cross-referencing. In some embodiments, step 412 may convert the pseudonymous identifiers into identifiers based on web cookies, device identifiers, or encrypted email identifiers. It should be understood that step 412 may convert the pseudonymous identifiers into identifiers assigned by other types of clients without departing from the spirit and scope of this disclosure.

[0048] In some embodiments, Method 400 may also include step 414 configured to provide targeted advertising performance analysis. For example, some advertisers may provide log-level details associated with their advertising campaigns. Log-level details may include information about the advertiser, advertiser, advertising campaign, potential buyers, date, time, and location where the ad appeared, along with the impression the ad made and click counts associated with the advertising campaign. Step 414 may collect the log-level details and use the collected log-level details to provide data analysis, as described above.

[0049] Referring to Figure 1, in some embodiments, consumer data 102A provided by advertisers, consumer data 102B provided by ad publishers, consumer data 102C provided by third parties, and consumer data 102D provided by advertising agencies may include consumer records that are flagged with at least one flag indicating the registration of a consumer record in a class or segment within the consumer data. In some embodiments, consumer records that are flagged with at least one flag may be flagged with the binary digit "0" or "1". In other embodiments, consumer records that are flagged with at least one flag may be flagged with "yes" or "no". In other embodiments, consumer records that are flagged with at least one flag may be flagged with "true" or "false".

[0050] Figure 5 shows Table 500 as an example of flagged consumer records, illustrating the registration of consumer records in a class or segment within consumer data, consistent with the disclosed embodiments. For example, in Table 500, a class is a high customer lifetime value class, and at least one flagged consumer record is flagged to indicate that the consumer is a high customer lifetime value consumer and is not a recent purchaser. As shown in Figure 5, Table 500 includes a leftmost column showing consumers using personally identifiable information, such as an email address (xyz@yahoo.com), name (John Doe), and telephone number ((202)123-4567). However, what identifies a consumer is not limited in this way and may be other personally identifiable information, such as a consumer's street address (house number) or social security number, or non-personally identifiable information, such as a consumer's device identifier, demographic data, segment, or model score. Table 500 includes a middle column containing a flag (true or false) indicating whether the consumer listed in the leftmost column is a high customer lifetime value consumer. For example, consumers identified by their email address (xyz@yahoo.com) and telephone number ((202)123-4567) are consumers with high customer lifetime value, while consumers identified by their name (John Doe) are not. Table 500 also includes a right-hand column containing a flag (true or false) indicating whether the consumers listed in the left-hand column are recent buyers or not. For example, consumers identified by their email address (xyz@yahoo.com) and telephone number ((202)123-4567) are not recent buyers, while consumers identified by their name (John Doe) are recent buyers.

[0051] In some embodiments, the system 100 (e.g., a data processor 104 or a 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 those of a consumer seed set. For example, the system 100 can identify one or more consumers whose data profiles are statistically similar to those of a consumer seed set. The consumer seed set can be provided by the advertiser's consumer base.

[0052] In some embodiments, system 100 (e.g., data processor 104 or target buyer candidate generator 106) can segment or sub-segment consumers into one or more subsets, each of which contains one or more statistically similar consumers. For example, when developing a buyer candidate group of "car enthusiasts," system 100 can divide consumers into a population that is effectively identical and can be tested to see if that population prefers a particular type of offer, message, or creative treatment or alternative option. The statistical similarity between two consumers can be determined based on consumer event statistics, such as how many times a consumer has purchased the same type of car, how many times a consumer has clicked on the same advertisement, how many times a consumer has skipped the same advertisement, or how many times a consumer has visited the same car dealership. The degree of similarity can also be determined quantitatively based on the collection and analysis of statistical data. In this way, an automated segmentation function is introduced to divide a given buyer candidate population into statistically similar subsets, resulting in increased efficiency.

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

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

[0055] In some embodiments, consumer data provided by multiple different ad-publisher devices can be stored in physically or logically separate data storage devices to reduce data mixing. For example, consumer data 601 provided by a first ad-publisher device can be stored in a first data storage device that is physically or logically separate from a second data storage device used to store consumer data 602 provided by a second ad-publisher device, and from a 50th data storage device used to store consumer data 650 provided by a 50th ad-publisher device. Figure 6 shows consumer data provided by 50 different ad-publisher devices. However, the number of ad-publishers is not limited in this way and may be any number less than 50 or any number greater than 50.

[0056] In some embodiments, when identifying target buyer candidates using consumer data provided by multiple different advertisers, as shown in Figure 6, the target buyer candidate generator 106 can utilize a waterfall matching test, as described with respect to Figures 7A, 7B, and 8. In this test, the target buyer candidate generator 106 can receive consumer data from advertiser equipment via a network and identify multiple consumers provided by advertisers from the consumer data. For example, as discussed above, the target buyer candidate generator 106 can identify multiple consumers provided by advertisers by comparing the consumer data received from advertiser equipment with consumer data recorded in the electronic consumer database of system 100. The target buyer candidate generator 106 can obtain multiple unique consumer identifiers corresponding to multiple consumers provided by advertisers. The multiple unique consumer identifiers do not have to contain personally identifiable information. The target buyer candidate generator 106 can identify at least one first duplicate unique consumer identifier by matching at least one of the multiple consumers provided by the advertiser with at least one consumer provided by the advertiser, which is provided by the first advertiser device having the highest priority among the multiple advertiser devices.

[0057] Figure 7A is a schematic diagram illustrating a first matching test of a waterfall matching test, consistent with the disclosed embodiments. As shown in Figure 7A, the target buyer candidate generator 106 can match multiple consumers provided by advertisers (indicated by the circles on the left side of Figure 7A) with consumers provided by advertiser 1, which has the highest priority, and can obtain a target buyer candidate group 1 common to both the consumer data provided by advertisers and the consumer data provided by advertisers. The target buyer candidate group 1 can be obtained by matching the pseudonym identifier associated with the consumer data provided by advertisers with the pseudonym identifier associated with the consumer data provided by advertiser 1, after they have been processed by the data processor 104.

[0058] After the first matching test, the target buyer candidate generator 106 can determine whether the number of consumers that are not matched among the multiple consumers provided by the advertiser is greater than a threshold number. For example, in Figure 7A, the portion of consumers provided by the advertiser, excluding target buyer candidate group 1, represents consumers that are not matched. The threshold number may be a number predetermined by the system 100. If the number of consumers that are not matched is greater than the threshold number, the target buyer candidate generator 106 can identify at least one second duplicate unique consumer identifier by matching at least one of the consumers that are not matched with at least one consumer provided by the advertiser, which is provided by the second advertiser device of the multiple advertiser devices. The second advertiser device has the second highest priority among the multiple advertiser devices.

[0059] Figure 7B is a schematic diagram illustrating a second matching test of the waterfall matching test, consistent with the disclosed embodiments. As shown in Figure 7B, the target buyer candidate generator 106 can match unmatched consumers provided by the advertiser (indicated by the left circle in Figure 7B) with consumers provided by the second-highest priority advertiser 2, and can obtain a target buyer candidate group 2 common to both the unmatched consumer data provided by the advertiser and the consumer data provided by the advertiser 2. The target buyer candidate group 2 can be obtained by matching the pseudonym identifier associated with the unmatched consumer data provided by the advertiser with the pseudonym identifier associated with the consumer data provided by the advertiser 2, after they have been processed by the data processor 104.

[0060] After the second matching, the target buyer candidate generator 106 can again determine whether the number of consumers that are not matched (consumers provided by advertisers excluding both target buyer candidate group 1 and target buyer candidate group 2) is greater than a threshold number. If the number of consumers that are not matched is greater than a threshold number, the target buyer candidate generator 106 can perform a third matching test. The target buyer candidate generator 106 can repeat the process of matching the remaining consumers among the multiple consumers provided by advertisers with consumers provided by the advertiser with the highest priority advertiser among the non-matched advertisers (advertisers whose consumer data was not compared with the consumer data provided by advertisers) until the number of remaining consumers is less than a threshold number. The target buyer candidate generator 106 can select an advertiser from among multiple advertiser devices based on the descending priority of the multiple advertiser devices. For example, the target buyer candidate generator 106 can select an advertiser device based on the table in Figure 6.

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

[0062] In some embodiments, after each matching test, the Target Buyer Candidate Generator 106 can generate a specific Buyer Candidate List record in which Buyer Candidates are curated based on the advertiser's specifications, and can further generate identity keys for the Buyer Candidates in the Specific Buyer Candidate List, and can send the generated identity keys to the second advertising platform and / or programmatic partner. For example, after the first matching, the Target Buyer Candidate Generator 106 can generate a specific Buyer Candidate List record using Target Buyer Candidate Group 1 (Figure 7A) in which Buyer Candidates are curated based on the advertiser's specifications. The Target Buyer Candidate Generator 106 can also generate identity keys for the Buyer Candidates in the Specific Buyer Candidate List, and can send the generated identity keys to the advertising platform and / or programmatic partner.

[0063] In some embodiments, after each match, the target buyer candidate generator 106 can receive advertiser approval for the purchase of media advertisements and, upon advertiser approval, can deliver the group of target buyer candidates obtained from the match to the corresponding ad issuer device. For example, after a first match, the target buyer candidate generator 106 can receive advertiser approval for the purchase of media advertisements and, upon advertiser approval, can deliver the target buyer candidate group 1 to the first ad issuer device. Before delivering the target buyer candidate group 1 to the first ad issuer device, the target buyer candidate generator 106 can transform at least one first unique consumer identifier included in the target buyer candidate group 1 according to a transformation protocol specified by the ad issuer. For example, the target buyer candidate generator 106 can convert at least one first unique consumer identifier included in the target buyer candidate 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 before delivering the target buyer candidate group 1 to the first advertiser device.

[0064] In some embodiments, after each match, the target buyer candidate generator 106 can generate a target buyer candidate group and distribute the target buyer candidate group to the advertiser's device. For example, after the first match, the target buyer candidate generator 106 can generate target buyer candidate group 1 and distribute target buyer candidate group 1 (Figure 7A) to the advertiser's device to promote targeted advertising to specific consumers. Similarly, after the second match, the target buyer candidate generator 106 can generate target buyer candidate group 2 (Figure 7B) and distribute target buyer candidate group 2 to the advertiser's device to promote targeted advertising to specific consumers.

[0065] Figure 8 is a flowchart showing Method 800 as an example for a waterfall matching test, consistent with the disclosed embodiments. It should be understood that each step of Method 800 can be performed by one or more processors, computers, servers, or controllers. In some embodiments, Method 800 can be performed by System 100, for example, by the Target Buyer Candidate Generator 106 as shown in Figure 1.

[0066] In step 802, method 800 may include matching unmatched consumers provided by the advertiser with consumers provided by the advertiser that have the highest priority among the unmatched advertisers. For example, for the first matching test, the unmatched consumers are the original consumers provided by the advertiser, for example, as shown by the left circle in Figure 7A. For any subsequent matching test, the unmatched consumers are the portion of the original consumers provided by the advertiser after all matching consumers from the previous matching test have been removed, for example, the unmatched consumers provided by the advertiser, as shown by the left circle in Figure 7B.

[0067] In step 804, method 800 may include generating target buyer candidate groups based on overlaps in specific consumer identifiers. For example, target buyer candidate groups can be generated by obtaining groups of consumers common to both unmatched consumer data provided by advertisers and consumer data provided by advertisers. As discussed above, target buyer candidate groups can be obtained by matching pseudonym identifiers associated with consumer data provided by advertisers with pseudonym identifiers associated with consumer data provided by advertisers.

[0068] In step 806, method 800 may include distributing the target buyer candidate groups to client devices. For example, the generated target buyer candidate groups may be distributed to advertiser devices to facilitate targeted advertising to specific consumers.

[0069] In step 808, method 800 may include determining whether the number of remaining consumers among those offered by the advertiser is greater than a threshold number. If the number of remaining consumers among those offered by the advertiser is greater than a threshold number, method 800 returns to step 802 and repeats steps 802, 804, 806, and 808 until the number of remaining consumers among those offered by the advertiser no longer exceeds a threshold number.

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

[0071] While this disclosure has been shown and described with reference to its specific embodiments, it will be understood that this disclosure can be practiced in other environments without modification. The above description is presented for illustrative purposes only. It is not exhaustive and is not limited to the exact form or embodiment disclosed. Modifications and adaptations will be apparent to a person skilled in the art, considering the specifications and from the practice of the disclosed embodiments. In addition, while the embodiments of the disclosed embodiments are described as being stored in memory, a person skilled in the art will recognize that these embodiments can also be stored in secondary storage devices, such as hard disks, CD-ROMs, or other forms of RAM or ROM, USB media, DVDs, Blu-rays, or other optical media, or other types of computer-readable media.

[0072] Computer programs based on the descriptions and methods disclosed herein are within the skill of an experienced developer. Various programs or program modules can be created using any technique known to a person skilled in this art, or can be designed in relation to existing software. For example, a program section or program module can be designed in or using .Net Framework, .Net Compact Framework (and related languages ​​such as Visual Basic, C, etc.), Java®, C++, Objective-C, HTML, HTML / AJAX combinations, XML, or HTML including Java applets.

[0073] Furthermore, while embodiments are described herein as examples, 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. Limitations in the claims should be interpreted broadly based on the language used in the claims and not limited to the examples described herein and during the performance of the application. The examples should be interpreted as non-exclusive. Furthermore, the steps of the disclosed methods can be modified in any way, including by changing the order of the steps and / or by inserting or deleting steps. Thus, the specification and examples are intended to be considered illustrative only, and the true scope and spirit are shown by the entire scope of the claims below and their equivalents. The inventions disclosed herein include the following embodiments: <Aspect 1> A computer-based system for targeted advertising to specific consumers, The memory that stores the instructions, A system comprising at least one processor, wherein the at least one processor is The system receives consumer data from client devices via the network. From the aforementioned consumer data, multiple consumers provided by the client are identified, Obtain multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, A system characterized in that it is configured to execute the command 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 advertiser, provided by the first advertiser device having the highest priority among a plurality of advertiser devices. <Aspect 2> The system according to embodiment 1, characterized in that the consumer data received from the client device comprises at least one flagged consumer record indicating the registration of a consumer record in a class or segment within the consumer data. <Aspect 3> The system of embodiment 2, characterized in that at least one flagged consumer record is flagged with the binary digit "0" or "1". <Aspect 4> The system according to embodiment 2, characterized in that at least one flagged consumer record is flagged as either "yes" or "no". <Aspect 5> The system according to embodiment 2, characterized in that at least one flagged consumer record is flagged as either "true" or "false". <Aspect 6> The system according to embodiment 2, characterized in that the class is a consumer class with high customer lifetime value, and at least one flagged consumer record is flagged to indicate that the consumer is a consumer with high customer lifetime value and is not a recent purchaser. <Aspect 7> The system according to embodiment 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 the data profile of a seed set of consumers. <Aspect 8> The system according to embodiment 7, characterized in that the consumer seed set is provided by the client's consumer base. <Pattern 9> The system according to embodiment 1, wherein the at least one processor is further configured to execute instructions to segment the consumers provided by the client into one or more subsets, each containing one or more statistically similar consumers. <Aspect 10> The aforementioned at least one processor is Based on the aforementioned at least one first uniquely duplicated consumer identifier, a first target buyer candidate group is generated. The system according to embodiment 1, further configured to execute the instructions to deliver the first target group of potential buyers to the client in order to promote targeted advertising to specific consumers. <Aspect 11> The aforementioned at least one processor is Determine whether the number of consumers that are not matched among the multiple consumers provided by the aforementioned client is greater than a threshold number. If the number of consumers that are not matched is greater than the threshold number, the system is further configured to execute the instruction to identify at least one second duplicate unique consumer identifier by matching at least one of the consumers that are not matched with at least one consumer provided by an advertiser, provided by the second advertiser device of the plurality of advertiser devices. The system according to embodiment 10, characterized in that the second advertising issuer device has the second highest priority among the plurality of advertising issuer devices. <Aspect 12> The aforementioned at least one processor is The command is further configured to execute the instruction to repeat matching the remaining consumers among the multiple consumers provided by the client with consumers provided by the advertiser having the highest priority among the non-matching advertisers, until the number of the remaining consumers falls below the threshold number. The system according to embodiment 11, characterized in that the at least one processor is configured to select an advertising issuing device from among the plurality of advertising issuing devices based on the descending priority of the plurality of advertising issuing devices. <Aspect 13> The aforementioned at least one processor is A second group of target buyer candidates is generated based on the aforementioned at least one second unique consumer identifier. The system of embodiment 11, further configured to execute the instructions to deliver the second target group of potential buyers to the client device in order to promote targeted advertising to specific consumers. <Aspect 14> The aforementioned at least one processor is Generate a unique list of potential buyers curated based on the advertiser's specifications. The system according to embodiment 1, further configured to generate an identity key for the buyer candidate in the specific buyer candidate list, and to execute the instruction to send the generated identity key to an advertising platform or programmatic partner. <Aspect 15> The aforementioned at least one processor is After receiving approval from the advertiser for the purchase of media advertising, The system according to embodiment 10, further configured to execute the command to distribute the first target buyer candidate group to the first ad issuer device upon receiving approval from the advertiser. <Aspect 16> The system of embodiment 15, further characterized in that the at least one processor is configured to execute the instructions to convert the at least one unique consumer identifier included in the first target buyer candidate group in accordance with a conversion protocol identified by the advertiser, before the target buyer candidate group is delivered to the first advertiser device. <Aspect 17> The system of embodiment 16, further configured to convert the at least one processor included in the first target buyer candidate group into at least one of a web cookie-based identifier, a television-based identifier, an encrypted email-based identifier, or a device identifier before the first target buyer candidate group is delivered to the first ad issuer device. <Aspect 18> A computer-aided method for targeted advertising to specific consumers, Receiving consumer data from client devices via the network, From the aforementioned consumer data, identify multiple consumers provided by the client, Obtaining multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, A method characterized by identifying 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 advertiser, provided by the first advertiser device having the highest priority among a plurality of advertiser devices. <Aspect 19> A non-temporary computer-readable medium storing processor-executable instructions for performing a method for targeted advertising to specific consumers, wherein the method is: Receiving consumer data from client devices via the network, From the aforementioned consumer data, identify multiple consumers provided by the client, Obtaining multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, A non-temporary computer-readable medium characterized by identifying 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 advertiser, provided by the first advertiser device having the highest priority among a plurality of advertiser devices.

Claims

1. A computer-based system for targeted advertising to specific consumers, The memory that stores the instructions, A system comprising at least one processor, wherein the at least one processor is The system receives consumer data from client devices via the network. From the aforementioned consumer data, multiple consumers provided by the client are identified, Obtain multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, By matching at least one of the multiple consumers provided by the client with at least one consumer provided by an advertiser, provided by the first advertiser device of the multiple advertiser devices, at least one first duplicate unique consumer identifier is identified, the first advertiser device having the highest priority among the multiple advertiser devices, and the highest priority being assigned based on the importance or relevance of the multiple data provided by the multiple advertiser devices, Based on the aforementioned at least one first uniquely duplicated consumer identifier, a first target buyer candidate group is generated. Determine whether the number of consumers that are not matched among the multiple consumers provided by the aforementioned client is greater than a threshold number. Based on the determination that the number of consumers that are not matched is greater than the threshold number, the system is configured to execute the command to identify at least one second duplicate unique consumer identifier by matching at least one of the consumers that are not matched with at least one consumer provided by an advertiser, provided by the second advertiser device of the plurality of advertiser devices. The second advertising issuer device is a system having the second highest priority among the plurality of advertising issuer devices.

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

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

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

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

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

7. The system according to 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 the data profiles of a seed set of consumers.

8. The system according to claim 7, wherein the consumer seed set is provided by the client consumer base.

9. The system according to claim 1, wherein the at least one processor is further configured to execute instructions to segment consumers provided by a client into one or more subsets, each containing one or more statistically similar consumers.

10. The system according to claim 1, wherein the at least one processor is further configured to execute the instructions to deliver the first target buyer candidate group to the client device in order to facilitate targeted advertising to specific consumers.

11. The aforementioned at least one processor is After receiving advertiser approval from the advertiser device, The system according to claim 10, further configured to execute the command to distribute the first target buyer candidate group to the first ad issuer device upon receiving approval from the advertiser.

12. The system according to claim 11, wherein the at least one processor is further configured to execute the instructions to convert at least one first specific consumer identifier included in the first target buyer candidate group in accordance with a conversion protocol identified by the advertiser, prior to the distribution of the target buyer candidate group to the first advertiser device.

13. The system according to 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 target buyer candidate group into at least one of a web cookie-based identifier, a television-based identifier, an encrypted email-based identifier, or a device identifier before the distribution of the first target buyer candidate group to the first ad publisher device.

14. The at least one processor is further configured to execute the instruction to match the remaining consumers among the plurality of consumers provided by the client with consumers provided by the advertiser having the highest priority among the non-matching advertisers, until the number of the remaining consumers is less than the threshold number. The system according to claim 1, wherein the at least one processor is configured to select an advertising issuing device from among the plurality of advertising issuing devices based on the descending order of priority of the plurality of advertising issuing devices.

15. The aforementioned at least one processor is A second group of target buyer candidates is generated based on the aforementioned at least one second uniquely duplicated consumer identifier. The system according to claim 1, further configured to execute the instructions to deliver the second target group of potential buyers to the client device in order to promote targeted advertising to specific consumers.

16. The aforementioned at least one processor is Generate a record of a specific list of potential buyers, curated based on the advertiser's specifications. The system according to claim 1, further configured to generate an identity key for the buyer candidate in the specific buyer candidate list, and to execute the instruction to send the generated identity key to an advertising platform or programmatic partner.

17. The aforementioned at least one processor is Based on the importance or relevance of the multiple data provided by the multiple ad-publishing devices, a priority is assigned to the multiple ad-publishing devices. The system according to claim 1, further configured to execute the command in order to list the priority of the plurality of ad issuer devices in descending order.

18. A computer-aided method for targeted advertising to specific consumers, Receiving consumer data from client devices via the network, From the aforementioned consumer data, identify multiple consumers provided by the client, Obtaining multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, Identifying at least one first duplicate unique consumer identifier by matching at least one of the multiple consumers provided by the client with at least one consumer provided by an advertiser, provided by the first advertiser device of the multiple advertiser devices, wherein the first advertiser device has the highest priority among the multiple advertiser devices, and the highest priority is assigned based on the importance or relevance of the multiple data provided by the multiple advertiser devices. Based on the aforementioned at least one first uniquely duplicated consumer identifier, a first group of target buyer candidates is generated. To determine whether the number of consumers that are not matched among the multiple consumers provided by the aforementioned client is greater than a threshold number, Based on the determination that the number of consumers who are not matched is greater than the threshold number, the system identifies at least one second duplicate unique consumer identifier by matching at least one of the consumers who are not matched with at least one consumer provided by an advertiser, provided by the second advertiser device of the plurality of advertiser devices. The method wherein the second ad issuer device has the second highest priority among the plurality of ad issuer devices.

19. A non-temporary computer-readable medium storing processor-executable instructions for performing a method for targeted advertising to specific consumers, wherein the method is: Receiving consumer data from client devices via the network, From the aforementioned consumer data, identify multiple consumers provided by the client, Obtaining multiple unique consumer identifiers corresponding to multiple consumers provided by the aforementioned client, Identifying at least one first duplicate unique consumer identifier by matching at least one of the multiple consumers provided by the client with at least one consumer provided by an advertiser, provided by the first advertiser device of the multiple advertiser devices, wherein the first advertiser device has the highest priority among the multiple advertiser devices, and the highest priority is assigned based on the importance or relevance of the multiple data provided by the multiple advertiser devices. Based on the aforementioned at least one first uniquely duplicated consumer identifier, a first group of target buyer candidates is generated. To determine whether the number of consumers that are not matched among the multiple consumers provided by the aforementioned client is greater than a threshold number, Based on the determination that the number of consumers who are not matched is greater than the threshold number, the system identifies at least one second duplicate unique consumer identifier by matching at least one of the consumers who are not matched with at least one consumer provided by an advertiser, provided by the second advertiser device of the plurality of advertiser devices. The second advertising issuer device is a non-temporary computer-readable medium having the second highest priority among the plurality of advertising issuer devices.

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