Analyze second-party digital marketing data

Abstract

Disclosed herein are system, method, and computer program product embodiments for analyzing second-party advertising data. Embodiments operate by determining a set of dimensions that a source uses to aggregate data for an advertising campaign. The embodiment creates a sub-unit advertising campaign based at least in part on the advertising campaign, the determined set of dimensions, and the dimensions of interest. The embodiment receives measurement data associated with the execution of the sub-unit advertising campaign. The embodiment then analyzes the measurement data.

Description

Background Art

[0001] Digital marketing has changed how companies and organizations build and develop relationships with customers and individuals. Digital marketing involves creating and executing advertising campaigns through digital platforms. As part of this process, digital marketers (or advertisers) may be interested in measuring the success of their digital advertising campaigns. Digital marketers may be interested in tracking a number of user engagements, such as page impressions, clicks, website clicks, adds to carts, and actual purchases. Digital marketers can use this information to determine the efficiency of their advertising campaigns and make better decisions for their campaigns in the future.

[0002] Traditionally, digital marketers use advertising campaigns to attract potential consumers to their online stores. Digital marketers may rely on different types of data to measure the success of their advertising campaigns. For example, digital marketers may collect data about customer behaviors, actions, or interests directly on their own websites. This data is often referred to as first-party data because digital marketers collect the data directly on their own websites. Because digital marketers collect the data directly on their own websites, the data often contains very detailed information about their customers.

[0003] Digital marketers may also receive data about customer behaviors, actions, or interests from external sources, such as data aggregators. This data is often referred to as third-party data. Unlike first-party data, third-party data may be collected at entities that are not controlled by the digital marketer, such as third-party websites. In addition to online data, third-party data may also include offline data, such as offline traffic and sales. Third-party data is generally available to any interested digital marketer. However, this third-party data typically has less information than first-party data. For example, third-party data typically does not have individual customer-level data (e.g., details about website interactions or repeat sales), which may help improve advertising campaign performance. The lack of detailed information in third-party data is often due to the data being provided in aggregated form. Digital marketers may not find third-party data very useful because third-party data does not provide unique insights about their specific customers and third-party data can be widely used by their competitors.

[0004] Recently, digital marketers may rely on second-party data to measure the success of their digital advertising campaigns. Second-party data can be data collected at a source that is not controlled by the digital marketer, but that may be provided to the digital marketer by that source. In other words, the digital marketer may receive second-party data directly from the source. For example, the digital marketer may negotiate with a source that is willing to share its customer data directly with the digital marketer. Unlike third-party data, second-party data is not typically sold to any interested digital marketer. Also, second-party data can often be more granular than third-party data.

[0005] Digital marketers may rely on second-party data because it can offer several advantages over third-party data. First, competitors may not have access to second-party data. Second, second-party data can be more granular than third-party data.

[0006] However, second parties may not be able to provide digital marketers with comprehensive visibility into who their customers are. This is due to two reasons. First, digital marketers may receive incomplete second-party data. Second, digital marketers may receive second-party data in an aggregated form. Both of these issues are typically because the source cannot provide the underlying raw data due to privacy or legal restrictions, or because the source has a competing reason for not providing the underlying raw data.

[0007] Without complete, granular second-party data, digital marketers may not be able to effectively measure the effectiveness of their advertising campaigns. Therefore, digital marketers need a way to use second-party data to form a larger, more complete picture of their customers. In other words, digital marketers need a way to get as close to first-party data as possible when first-party data is not available or accessible to digital marketers. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings are incorporated herein and form a part of this specification.

[0009] Figure 1 is a functional block diagram of an exemplary operating environment in which embodiments of the present disclosure may be implemented.

[0010] Figure 2 is a block diagram of a system for analyzing second-party measurement data according to an exemplary embodiment.

[0011] Figure 3 is a block diagram of a system for processing second party measurement data from a subunit advertising campaign according to an exemplary embodiment.

[0012] Figure 4 is a flow diagram of a process for analyzing an advertising campaign according to an exemplary embodiment.

[0013] Figure 5 is an exemplary computer system that can be used to implement various embodiments.

[0014] In the drawings, like reference numbers generally refer to the same or similar elements. In addition, generally, the leftmost digit(s) of a reference number identifies the drawing in which the reference number first appears. DETAILED DESCRIPTION

[0015] In the following description, many specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. However, it will be apparent to those skilled in the art that embodiments including structures, systems and methods can be practiced without these specific details. The descriptions and representations herein are common means for experienced persons or technicians in the art to most effectively convey the essence of their work to other technicians in the art. In other cases, well-known methods, procedures, components and circuits are not described in detail to avoid unnecessarily obscuring aspects of the present disclosure.

[0016] References to "one embodiment", "embodiment", "exemplary embodiment", etc. in the specification indicate that the described embodiment may include certain features, structures or characteristics, but each embodiment may not necessarily include the certain features, structures or characteristics. Moreover, such phrases do not necessarily refer to the same embodiment. In addition, when a certain feature, structure or characteristic is described in conjunction with an embodiment, whether or not explicitly described, it is considered that a person skilled in the art can implement such feature, structure or characteristic in conjunction with other embodiments.

[0017] 1. Overview

[0018] Provided herein are system, device, apparatus, method and / or computer program product embodiments and / or combinations and sub-combinations thereof for analyzing second-party digital marketing data. Specifically, embodiments relate to combining second-party data with other data (e.g., other second-party data or third-party data) to approximate first-party data. Before describing further details of these embodiments, a brief overview of digital marketing is provided, including what digital advertising campaigns are, how advertising messages (or "ads") are targeted to users, and how the success of digital marketing campaigns is measured.

[0019] 1.1 Digital Advertising Campaigns

[0020] At a high level, marketing involves connecting advertisers with their audiences. This may involve creating and executing various advertising campaigns through a series of advertisements that share a single idea or theme of the company or organization with the audience. Advertising is often referred to as "creative" because it is usually the result of creative inspiration or skill.

[0021] In traditional marketing, a creative can be a newspaper ad, a television ad, a radio ad, a billboard, or any other type of creative as will be understood by one of ordinary skill in the art. In digital marketing, a creative can be a static creative, an animated creative, or a rich media creative. A static creative can be an image or graphic that does not move, such as a GIF or JPEG. An animated creative can be an image or graphic that changes automatically. A rich media creative can be an image or graphic that a user can interact with. For example, a user can interact with a rich media creative by hovering over or clicking on the mouse.

[0022] 1.2 Digital Advertising Campaign Targeting

[0023] Advertisers may want to create advertising campaigns that target specific users. This is because advertisers may want to ensure that their marketing efforts reach relevant consumers, thereby achieving effective advertising spend. Advertisers may use marketing communication channels to target specific users. Marketing communication channels can be any communication channel used to establish a connection between advertisers and target users.

[0024] In traditional marketing, marketing communication channels can include newspapers, radio, television, billboards, telephone or door-to-door sales. In digital marketing, marketing communication channels can include websites, blogs, social media networks or search engines. Advertisers often choose marketing communication channels for their campaigns based on where their target audience spends their time. This ensures that the campaign reaches relevant users.

[0025] In traditional marketing, advertisers can create and execute advertising campaigns using marketing channels that target specific users. For example, advertisers can target viewers who watch a specific TV show on a specific TV station. Similarly, in digital marketing, advertisers can create and execute advertising campaigns using marketing channels that target specific users. However, digital marketing improves on traditional marketing by enabling advertisers to target users more effectively.

[0026] In digital marketing, advertisers can define the users to target based on one or more parameters. Advertisers can use these parameters to segment users based on demographic factors, behavioral patterns, or geographic locations. For example, in demographic segmentation, advertisers can segment the audience based on parameters such as gender, age, income, place of residence, education, marital status, ethnicity, or family size. In behavioral segmentation, advertisers can segment the audience based on specific consumption patterns. For example, advertisers can segment the audience based on parameters such as past purchase history or browsing history. In geographic segmentation, advertisers can segment the audience based on geographic regions. For example, advertisers can segment the audience based on parameters such as zip code. Advertisers can segment the audience based on various other types of parameters, such as when the user is active, how often the user uses the product, and whether the user is a previous visitor.

[0027] After defining what users the advertising campaign targets, the advertiser can publish the advertising campaign to a digital marketing channel, such as a website, blog, social media network, or search engine. For example, the advertiser can upload the creative of the advertising campaign to a website, blog, social media network, or search engine. In some cases, the advertiser can control the digital marketing channel. In other words, the advertiser can be the publisher of the digital marketing channel. In some other cases, the digital marketing channel may be operated by an external publisher. In this latter case, the advertiser can provide parameters to the publisher, which define what users the advertising campaign targets.

[0028] To achieve targeting, a publisher may provide various ad locations on its website, blog, social media network, or search engine where advertisers can post creatives from their ad campaigns. An ad location may be a specific group of areas on a website, blog, social media network, or search engine where advertisers can choose to place ads from an ad campaign for targeting purposes. In some cases, advertisers may target an ad campaign by selecting only ad locations with specific locations on a website, blog, social media network, or search engine. In some other cases, advertisers may target an ad campaign by selecting ad locations that are configured by the publisher to display the ad campaign based on whether the parameters of the ad campaign match the characteristics of the user, the user device, or the content of the website, blog, social media network, or search engine.

[0029] 1.3 Measuring the effectiveness of digital advertising campaigns

[0030] In addition to targeting users, advertisers also want to measure the effectiveness of digital advertising campaigns. There are a variety of ways to measure how effective digital advertising campaigns are. Many of these methods are not available in traditional marketing. This is because traditional marketing and digital marketing use different communication models. For example, in traditional marketing, advertising campaigns can provide push marketing. In push marketing, advertisers can share advertising campaigns with consumers, but consumers may not be able to communicate back to the advertiser. For example, if a potential consumer views an advertising message on television and subsequently purchases a product in-store, the advertiser may not be able to connect the purchase action to the advertising message.

[0031] In contrast, in digital marketing, an advertising campaign can provide inbound marketing. In inbound marketing, advertisers can share advertising campaigns with consumers, and consumers can post back to the advertiser. For example, in digital marketing, if a potential consumer views an advertising message on a website, or a consumer clicks on an advertising message to purchase a product, the advertiser may be able to associate the action (e.g., view or click) with the advertising message. Therefore, inbound marketing allows advertisers to elicit an immediate response from consumers to an advertising campaign. Because inbound marketing enables consumers to post back to advertisers, advertisers are able to collect various types of data from consumers in response to advertising campaigns. Advertisers can often use this data to evaluate the effectiveness of advertising campaigns and adjust marketing strategies when necessary.

[0032] Advertisers may collect various types of data from consumers in response to an advertising campaign. For example, advertisers may collect exposures, which represent the number of times a creative is displayed to a single consumer. Advertisers may collect clicks on creatives, which represent the number of times a consumer clicks on a creative. Advertisers may collect click-through rates for creatives. Click-through rates may represent the percentage of creative exposures that are clicked. This may be calculated by dividing the number of clicks on a creative by the number of exposures of the creative. Advertisers may collect conversions for creatives. Conversions may represent one of a set of desired actions taken by a consumer after clicking on a creative. For example, conversion actions may include making a purchase, filling out a form, subscribing to a service, or various other actions that a person of ordinary skill in the art would understand. Finally, advertisers may collect conversion rates for creatives. Conversion rates may represent the percentage of creative clicks that produce desired actions. This may be calculated by dividing the number of conversions for a creative by the number of clicks on the creative.

[0033] Additionally, advertisers can collect this data at various levels of interest to better assess the effectiveness of their ad campaigns. For example, advertisers can collect data at the campaign level. Data collected at the campaign level can measure how well the ad campaign performed overall across all publishers, placements, and creatives. Advertisers can also collect data at the publisher level. Data collected at the publisher level can measure how well the ad campaign performed across all ad placements for a publisher. Finally, advertisers can collect data at the creative level. Data collected at the creative level can measure how well a single creative performed across all publishers and placements.

[0034] After collecting this data, advertisers can use the data to determine more information about their customers, how effective their ad campaigns are, and how to change their ad strategies in the future. Advertisers can determine this information by combining the collected data with targeting information associated with the ad campaign. For example, an advertiser can create and execute an ad campaign that advertises a product to be sold to users in Washington, D.C. This ad campaign can collect the percentage of people who clicked on the creative in the ad campaign and purchased the product. Therefore, this conversion rate, together with the targeting parameters of the ad campaign, can provide an indication of how popular the product is in Washington, D.C.

[0035] Advertisers can also compare the effectiveness of different advertising campaigns by conducting various types of experimental tests. For example, advertisers can conduct A / B testing or bandit testing. A / B testing involves comparing two versions of something (A and B) with equal percentages (50 / 50), where the two versions are identical except for one variation that may affect user behavior. Version A may be the version currently used (control), while version B may be modified in some aspects. In the case of digital marketing, A / B testing may involve identifying changes to an advertising campaign that increase the results of interest (such as click-through rate of an advertising campaign). For example, an advertiser can create two advertising campaigns that are identical except for one dimension. The advertiser can then set up the two advertising campaigns so that 50% of users receive the control advertising campaign and 50% of users receive the variant advertising campaign. The advertiser can then run the two advertising campaigns for a period of time. After the period of time is over, the advertiser can determine whether the modification to the variant advertising campaign has increased the results of interest (e.g., click-through rate).

[0036] Advertisers can also conduct bandit testing. In a bandit test, three or more options are tested simultaneously, and more traffic (e.g., users) is shifted to the most successful option (e.g., the ad campaign with the highest conversion rate) at a given point in time, while other options continue to run and evaluate smaller percentages of traffic. Bandit testing involves constantly changing traffic in response to the real-time performance of the test. In other words, unlike A / B testing, bandit testing can be adaptive.

[0037] 1.4 Measuring Digital Marketing Effectiveness

[0038] As mentioned above, advertisers can publish advertising campaigns to digital marketing channels, such as websites, blogs, social media networks, or search engines. The type of data collected as part of this advertising campaign can be first-party data. First-party data can be data about the behavior, actions, or interests of consumers collected by advertisers or publishers through a direct relationship with their consumers. For example, first-party data can be collected directly on the advertiser's website. Since the data is collected directly on the advertiser's website, the advertiser can have a complete understanding of an individual customer (e.g., what he does, what he buys, etc.). In addition, since this data is collected by advertisers or publishers, there are two types of first-party data: advertiser first-party data and publisher first-party data. Digital marketers often use first-party data to learn valuable information about their customers and to evaluate and change their advertising campaigns.

[0039] First-party data may have several advantages over other types of data, such as second-party data and third-party data. First, because first-party data can be collected directly by advertisers or publishers, it is often the highest quality and most valuable type of data. Second, first-party data can often be collected for free to advertisers or publishers because it is captured on their own websites. In contrast, digital marketers may have to purchase second-party data and third-party data. Finally, because first-party data is collected directly by advertisers or publishers through a direct relationship with consumers, there are fewer privacy concerns than with the collection of second-party data and third-party data.

[0040] Despite these advantages of first-party data, digital marketers are often interested in using second-party data and third parties to learn additional information about their customers and evaluate their advertising campaigns. For example, in some cases, digital marketers may learn additional information about their customers and evaluate their advertising campaigns by combining first-party data, second-party data, and third-party data.

[0041] Digital marketers often use third-party data to measure the success of their digital advertising campaigns. Third-party data can be data about a customer's behavior, actions, or interests that is generated on other platforms and provided to digital marketers in an aggregated form. In other words, third-party data is information collected by entities that do not have a direct relationship with the consumer. Various companies and organizations may sell third-party data to advertisers. Advertisers can use third-party data to compile detailed profiles about their customers' tastes and behaviors as they browse the web. But third-party data is usually not very helpful to advertisers. This is because third-party data is collected by entities that do not have a direct relationship with the advertiser's consumers. As a result, third-party data is usually of lower quality.

[0042] Recently, digital marketers have begun to rely on second-party data to measure the success of digital marketing campaigns. Second-party data can be first-party data that a digital marketer obtains directly from another source. For example, a digital marketer may enter into a transaction with a retailer to receive customer information collected directly at the retailer. In other words, second-party data involves situations where a “first party” provides data to another entity.

[0043] Second-party data generally offers several advantages to digital marketers compared to first-party data and third-party data. First, second-party data is generally not accessible to the digital marketer's competitors. Second, digital marketers can use second-party data to form a larger and more complete picture of their customers. This is generally because second-party data gives digital marketers access to audiences that digital marketers otherwise would not be able to reach.

[0044] However, digital marketers often encounter challenges when processing second-party data. First, digital marketers may receive incomplete second-party data. Second, digital marketers may receive second-party data in an aggregated form without knowing how the second-party data was aggregated. As a result, digital marketers may not be able to learn information about their customers or determine the effectiveness of their advertising campaigns based on second-party data.

[0045] For example, a digital marketer may create and execute an advertising campaign to collect data from a source. For example, the digital marketer may express an interest in knowing how many products are sold at a source each week. However, the digital marketer may not know how the source aggregates the data, i.e., the number of products sold to new customers each week, the number of products sold to existing customers each week, or the number of products sold to both new and existing customers each week. In other words, the digital marketer may not be able to control how the source aggregates the data. Therefore, the digital marketer may not be able to measure the effectiveness of an advertising campaign based on second-party data because second-party data is often incomplete or unclear how it is aggregated. As a result, the digital marketer may not be able to effectively change its advertising strategy.

[0046] 2. Exemplary Operating Environment

[0047] Figure 1 A functional block diagram 100 of an exemplary operating environment in which embodiments of the present disclosure may be implemented is shown. The block diagram 100 includes a first device 102, a second device 104, and a third device 106. The first device 102, the second device 104, and the third device 106 may be configured to communicate with a network 108. In various embodiments, the network 108 may include, but is not limited to, a wired and / or wireless intranet, an extranet, the Internet, cellular, Bluetooth, and / or any other local, short-range, ad hoc, regional, global communication network, and any combination thereof.

[0048] The first device 102 is a computer system (e.g., a stand-alone computer system, a server computer system, or a clustered computer system). The first device 102 may be controlled and configured by an advertiser. The first device 102 includes measurement data 114 (e.g., exposures, clicks, click-through rates, conversions, etc.). The first device 102 collects the measurement data 114 directly from its users. Therefore, the measurement data 114 may be first-party data. The measurement data 114 may include data about the behavior, actions, or interests of individual users of the first device 102. For example, the measurement data 114 may include data about the age, gender, race, or location of a single user of the first device 102. Therefore, the measurement data 114 may be captured at a granular level. This is possible because the measurement data 114 is captured directly at the first device 102.

[0049] The second device 104 is a computer system (e.g., a stand-alone computer system, a server computer system, or a clustered computer system). The second device 104 may be controlled and configured by an entity other than the advertiser that controls and configures the first device 102. For example, the second device 104 may be controlled and configured by a retailer. The second device 104 includes measurement data 116 (e.g., exposure, clicks, click-through rates, conversions, etc.). The second device 104 collects measurement data 116 directly from its users. Therefore, from the perspective of the second device 104, the measurement data 116 is first-party data. The measurement data 116 may include data about the behavior, actions, or interests of individual users of the second device 104. For example, the measurement data 116 may include data about the age, gender, race, or location of a single user of the second device 104. Therefore, the measurement data 116 can be captured at a granular level. This is possible because the measurement data 116 is captured directly at the second device 104.

[0050] In some embodiments, the first device 102 may have a data exchange agreement with the second device 104. The first device 102 may be configured to receive the measurement data 116 from the second device 104. Therefore, from the perspective of the first device 102, the measurement data 116 is second-party data. However, the first device 102 may not receive the measurement data 116 in its original form. For example, the first device 102 may receive the measurement data 116 without information about the age, gender, race, or location of an individual user of the second device 104. Instead, the first device 102 may receive the measurement data 116 from the second device 104 in an aggregated form.

[0051] The third device 106 is a computer system (e.g., a stand-alone computer system, a server computer system, or a clustered computer system). The third device 106 may be controlled and configured by an entity different from the entity that controls and configures the first device 102 and the second device 104. For example, the third device 106 may be controlled and configured by a data aggregator such as Acxiom.

[0052] The third device 106 includes measurement data 118 (e.g., impressions, clicks, click-through rates, conversions, etc.). The measurement data 118 may include measurement data that the third device 106 aggregates from the third-party data source 110 and the third-party data source 112. The third-party data source 110 and the third-party data source 112 may be websites, search engines, social media networks, or other data sources that will be understood by one of ordinary skill in the art. Therefore, the measurement data 118 may include general data about the behavior, actions, or interests of users on the third-party data source 110 and the third-party data source 112. In other words, the measurement data 118 may be captured data at a broad level rather than at a single user level.

[0053] In some embodiments, the first device 102 can have a data exchange protocol with the third device 106. The first device 102 can be configured to receive the measurement data 118 from the third device 106. Thus, from the perspective of the first device 102, the measurement data 118 is third-party data.

[0054] Similarly, in some embodiments, the second device 104 can have a data exchange protocol with the third device 106. The second device 104 can be configured to receive the measurement data 118 from the third device 106. Therefore, from the perspective of the second device 104, the measurement data 118 is also third-party data.

[0055] As described above, the first device 102 may have a data exchange agreement with the second device 104. The first device 102 may be configured to receive measurement data 116 from the second device 104. An advertiser at the first device 102 may define the type of measurement data 116 to be received from the second device 104 by creating and executing an advertising campaign at the second device 104. For example, an advertiser may create an advertising campaign that collects measurement data (e.g., exposure, clicks, click-through rates, conversions, etc.) for a targeted user. An advertiser may define what user to target based on one or more parameters. An advertiser may use these parameters to segment users based on demographic considerations, behavioral patterns, or geographic locations. An advertiser may also define what measurement data to collect. For example, an advertiser may create an advertising campaign that collects exposure, clicks, click-through rates, conversions, or conversion rates for a targeted user. An advertiser may evaluate the effectiveness of an advertising campaign based on the measurement data 116 obtained.

[0056] Typically, however, advertisers at the first device 102 receive measurement data 116 that does not provide the advertiser with a way to effectively target future advertising campaigns. This is because the measurement data 116 is often incomplete or because the advertiser at the first device 102 does not know how the second device 104 aggregates the measurement data 116.

[0057] For example, an advertiser at the first device 102 may create and execute an advertising campaign at the second device 104 that collects the number of people who purchased a product at the second device 104 after seeing the advertising campaign. In response, the first device 102 receives measurement data 116 corresponding to the advertising campaign. However, the resulting measurement data may not include information about whether the purchaser is male or female, young or old, or a new customer or an existing customer. This missing information may enable advertisers to learn new things about their users. For example, an advertiser may want to know how many people in the Washington area purchased a product at the second device 104 after viewing the advertising campaign. In addition, this missing information may enable advertisers to target their ads more effectively in the future. For example, an advertiser may want to spend more money on an advertising campaign that focuses on purchasers who are new customers rather than on purchasers who are existing customers.

[0058] In addition, the advertiser may want to configure the first device 102 to automatically modify the advertising campaign based on its performance, taking into account the lost information. For example, the advertiser may want to configure the first device 102 to automatically compare the conversion rate performance of the advertising campaign based on whether the buyer is from the United States or Europe. The advertiser may also want to configure the first device 102 to automatically retarget the advertising campaign to users in the United States or Europe based on whether buyers from the United States or Europe have a higher conversion rate.

[0059] In addition to losing information, advertisers at the first device 102 may not know how the second device 104 aggregates the measurement data 116. This can reduce the advertiser's ability to compare the measurement data 116 with other first-party data, second-party data, or third-party data. For example, an advertiser at the first device 102 may create and execute an advertising campaign at the second device 104 that collects the number of people who purchased a product at the second device 104 after viewing the advertising campaign. The advertiser at the first device 102 may also execute the same advertising campaign at another device that provides second-party measurement data. The advertiser may want to reliably compare the effectiveness of the advertising campaign at the second device 104 and the other device. However, this may not be possible because the measurement data 116 from the second device 104 may be aggregated in a different manner than the measurement data from the other device. Similarly, the advertiser at the first device 102 may want to combine the measurement data 116 with other first-party data, second-party data, or third-party data. However, this may not be possible because the measurement data 116 may be aggregated in a different manner than another first-party data, second-party data, or third-party data.

[0060] 3. Systems and methods for analyzing second-party measurement data

[0061] Figure 2is a block diagram of a system 200 for analyzing second-party measurement data according to an exemplary embodiment. System 200 solves the following technical problem: how to generate and execute advertising campaigns that approximate the first-party data of a digital platform when the actual first-party data is not available or accessible from the digital platform. System 200 solves the technical problem by dynamically creating and executing sub-unit advertising campaigns based on the advertising campaign and the dimensions of interest on the digital platform. The creation of sub-unit advertising campaigns enables second-party data to be combined with other data (e.g., other second-party data or third-party data) to approximate the first party on a digital platform that the digital marketer does not have access to. In addition, even if the second-party data is aggregated differently from the other data, the creation of sub-unit advertising campaigns enables digital marketers to combine second-party data with other data (e.g., other second-party data or third-party data). Finally, the creation of sub-unit advertising campaigns can reduce the computing and storage costs associated with directly capturing first-party data. Reference Figure 1 discuss Figure 2 , but the present disclosure is not limited to this exemplary embodiment.

[0062] exist Figure 2 In the example of FIG. 2 , the data processing module 204 receives the ad campaign 202 and the interest dimension 208 . In response, the data processing module 204 generates the sub-unit ad campaign 206 based on the ad campaign 202 and the interest dimension 208 .

[0063] In some embodiments, the advertising campaign 202 may be an advertising campaign defined by an advertiser at the first device 102. The advertiser may create an advertising campaign 202 that targets users according to one or more parameters. The advertiser may use these parameters to segment users based on demographic considerations, behavioral patterns, or geographic locations. The advertising campaign 202 may be executed at a source such as the second device 104. In response, the second device 104 may generate measurement data 116 for the advertising campaign 202. The first device 102 may receive the measurement data 116.

[0064] For example, an advertiser at the first device 102 can create an advertising campaign 202 to collect the number of people who purchased a product at the second device 104 last week after seeing the advertising campaign 202. In response to executing the advertising campaign 202 at the second device 104, the first device 102 can receive measurement data 116 corresponding to the advertising campaign 202.

[0065] However, the measurement data 116 may be incomplete. For example, an advertiser at the first device 102 may want to collect the number of people in the geographic area who purchased a product at the second device 104 after seeing the advertising campaign 202. However, the measurement data 116 corresponding to the advertising campaign 202 may not have this information.

[0066] In some embodiments, the data processing module 204 can solve the missing data problem. First, the data processing module 204 can determine a set of dimensions that the source (such as the second device 104) uses to aggregate the measurement data 116 for the advertising campaign 202. For example, the data processing module 204 can determine that the set of dimensions that the second device 104 uses to aggregate the measurement data 116 includes product and week. As will be understood by one of ordinary skill in the art, the data processing module 204 can use a network service, a configuration file, or other mechanisms that will be understood by one of ordinary skill in the art to determine the set of dimensions.

[0067] In some embodiments, after determining the set of dimensions, the data processing module 204 may receive the dimensions of interest 208. An advertiser at the first device 102 may input the dimensions of interest 208 into the data processing module 204. The dimensions of interest 208 may be any data dimension missing from the measurement data 116. The dimensions of interest 208 may be a call to action, demographic, geographic area, or other types of dimensions as will be understood in the art. The dimensions of interest 208 may be selected from second-party data or third-party data. For example, the dimension of interest 208 may be a zip code, and the set of zip code values ​​may be stored in the second-party data or the third-party data.

[0068] In some embodiments, after receiving the dimension of interest 208, the data processing module 204 can generate a sub-unit ad campaign 206 based on the ad campaign 202, the determined set of dimensions, and the dimension of interest 208. For example, the data processing module 204 can generate a sub-unit ad campaign 206 that is identical to the ad campaign 202 but modified by the dimension of interest 208. The data processing module 204 can generate a separate sub-unit ad campaign 206 for each value within a range of values ​​for the dimension of interest 208. In some other embodiments, the data processing module 204 can generate a separate sub-unit ad campaign 206 for each value in a defined set of values ​​for the dimension of interest 208. In some embodiments, an advertiser at the first device 102 can define a set of values ​​that can be used for the dimension of interest 208.

[0069] For example, where the ad campaign 202 collects the number of people who purchased a product at the second device 104 last week and the dimension of interest 208 is zip code, the data processing module 204 can generate a sub-unit ad campaign 206 for each zip code value. For example, for a zip code value of 20001, the data processing module 204 can generate a sub-unit ad campaign 206 that collects the number of people in the 20001 zip code who purchased a product at the second device 104 last week.

[0070] In some embodiments, after the data processing module 204 generates the sub-unit advertising campaigns 206, the sub-unit advertising campaigns 206 may be simultaneously executed at a source (e.g., the second device 104). In response, the source may generate measurement data 210 for each sub-unit advertising campaign 206. The source may send the measurement data 210 for each sub-unit advertising campaign 206 to a device, such as the first device 102, for further analysis. Thus, the measurement data 210 for each sub-unit advertising campaign 206 represents second-party data from the perspective of the first device 102. The measurement data 210 for each sub-unit advertising campaign 206 represents a portion of the entire measurement data 116 for the advertising campaign 202. For example, the measurement data 210 for each sub-unit advertising campaign 206 may include the number of people who purchased a product at the second device 104 in a given zip code last week, rather than the total number of people who purchased a product at the second device 104 last week.

[0071] In some embodiments, the data processing module 204 can be configured to generate a sub-unit advertising campaign based on the sub-unit advertising campaign 206, a set of dimensions of the determined sub-unit advertising campaign 206, and another dimension of interest. The sub-sub-unit advertising campaigns can be executed simultaneously at a source, such as the second device 104. In response, the source can generate measurement data for each sub-sub-unit advertising campaign. As will be appreciated by one of ordinary skill in the art, the data processing module 204 can be configured to generate the sub-sub-unit advertising campaigns using a recursive algorithm.

[0072] Figure 3 is a block diagram of a system for processing measurement data from a subunit advertising campaign according to an exemplary embodiment. Figure 1 and Figure 2 Let's discuss Figure 3 , but the present disclosure is not limited to those exemplary embodiments.

[0073] exist Figure 3 In the example of FIG. 3 , the analysis module 302 receives the measurement data 210 for each subunit advertising campaign 206. In some embodiments, the analysis module may receive other measurement data 308 (e.g., other first-party measurement data, second-party measurement data, or third-party measurement data). In response, the analysis module 302 may output a new advertising strategy 304 and / or new user information 306.

[0074] In some embodiments, the analysis module 302 can analyze the measurement data 210 of the sub-unit advertising campaign 206 to learn new user information. For example, an advertiser at the first device 102 may want to determine how many people in the Washington area purchased a product last week at the second device 104, rather than how many people purchased the product last week overall at the second device 104. The analysis module 302 can be configured to output this new user information 306 based on the analysis of the measurement data 210 of the sub-unit advertising campaign 206.

[0075] In some embodiments, the analysis module 302 may compare the effectiveness of different sub-unit advertising campaigns based on the associated measurement data 210 of the different sub-unit advertising campaigns 206. For example, the analysis module 302 may be configured to determine whether a sub-unit advertising campaign 206 that collects how many new customers purchased a product at the second device 104 last week has a higher purchase conversion rate than a sub-unit advertising campaign 206 that collects how many existing customers purchased a product at the second device 104 last week. In some embodiments, the analysis module 302 may also compare the effectiveness of different sub-unit advertising campaigns 206 using various types of experimental tests. For example, the analysis module 302 may perform A / B testing or bandit testing on different sub-unit advertising campaigns 206. The analysis module 302 may be configured to output a new advertising strategy (e.g., a new advertising strategy 304) based on the comparison results. For example, the analysis module 302 may output a new advertising strategy 304 that indicates that a specific sub-unit advertising campaign 206 should be selected relative to another sub-unit advertising campaign 206. In some embodiments, the analysis module 302 may automatically select and execute a new or modified advertising campaign according to the new advertising strategy 304.

[0076] In some embodiments, the analysis module 302 may combine the measurement data 210 for one or more sub-unit advertising campaigns 206 with other measurement data 308. In some embodiments, the other measurement data 308 may include first-party data, second-party data, or third-party data. In some embodiments, the analysis module 302 may determine a dimensional difference between the measurement data 210 of one or more sub-unit advertising campaigns 206 and the other measurement data 308. If the analysis module 302 determines that there is a dimensional difference, the data processing module 204 may be used to repair the difference by creating a new advertising campaign based on the one or more sub-unit advertising campaigns 206 and the missing dimension of interest. In some other embodiments, the analysis module 302 may combine the two measurement data based on the shared dimensions of the measurement data 210 of the one or more sub-unit advertising campaigns 206 and the other measurement data 308. The analysis module 302 may be configured to output new user information 306 based on the analysis of the combined measurement data. In some other embodiments, the analysis module 302 may be configured to output a new advertising strategy 304 based on the analysis of the combined measurement data.

[0077] Figure 4 4 is a flow chart of a method 400 for analyzing an advertising campaign according to an embodiment. The method 400 may be performed by processing logic, which may include hardware (e.g., circuits, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions executed on a processing device), or a combination thereof. It should be understood that not all steps may be required to perform the disclosure provided herein. In addition, as will be appreciated by those of ordinary skill in the art, some of these steps may be performed simultaneously or in parallel. Figure 4 The different orders are shown.

[0078] Will refer to Figure 1 , Figure 2 and Figure 3 4 to describe method 400. However, method 400 is not limited to these exemplary embodiments.

[0079] At 402 , the data processing system 204 receives the advertising campaign 202 . The advertising campaign 202 can be executed on a source (eg, the second device 104 ) that generates second-party measurement data corresponding to the advertising campaign 202 .

[0080] At 404, the data processing system 204 determines a set of dimensions that a source (e.g., the second device 104) uses to aggregate data for the advertising campaign 202. In some embodiments, the data processing module 204 may determine the set of dimensions using a web service, a configuration file, or other mechanisms that will be understood by one of ordinary skill in the art.

[0081] In 406, the data processing system 204 receives the dimension of interest 208. The dimension of interest 208 may not be present in the determined set of dimensions. In some embodiments, the advertiser may define the dimension of interest 208. In some embodiments, the dimension of interest 208 may be selected from the second-party data or the third-party data. For example, the dimension of interest 208 may be a zip code, and the set of zip code values ​​may be stored in the second-party data or the third-party data.

[0082] At 408, the data processing system 204 creates a sub-unit ad campaign 206 based on the ad campaign 202, the determined set of dimensions, and the dimension of interest 208. In some embodiments, the data processing system 204 may create a sub-unit ad campaign 206 for each value within a range of values ​​for the dimension of interest 208. For example, the data processing system 204 may create a sub-unit ad campaign 206 for each zip code value within a range of available zip code values.

[0083] At 410, the analysis module 302 receives measurement data 210 associated with the execution of each sub-unit advertising campaign 206. The received measurement data 210 can approximate the first-party data. In other words, the combination of the dimensions of interest 208 from the second-party data or third-party data and the existing second-party data returned in response to running the advertising campaign 202 on the source (e.g., the second device 104) can approximate the first party collected directly by the source for the advertising campaign 202.

[0084] At 412, the analysis module 302 analyzes the measurement data 210 associated with each sub-unit advertising campaign 206 to output new user information 306 or new advertising strategies 304. In some embodiments, the analysis module 302 analyzes the measurement data 210 by combining it with other measurement data, such as first-party data, second-party data, or third-party data. In some embodiments, the analysis module 302 uses machine learning techniques to analyze the measurement data 210. In some embodiments, the analysis module 302 analyzes the measurement data 210 in conjunction with conducting experimental tests on different sub-unit advertising campaigns 206.

[0085] 4. Exemplary Computer System Implementation

[0086] For example, one or more computer systems (such as Figure 5 The computer system 500 shown in the figure can be used to implement various embodiments. The computer system 500 can be used, for example, to implement Figure 4The method 400 of . For example, the computer system 500 may determine a resource block allocation for a portion of the tone space using a lookup table. According to some embodiments, the computer system 500 may also map a plurality of tones to resource blocks based on the determined resource block allocation. The computer system 500 may be any computer capable of performing the functions described herein.

[0087] Computer system 500 may be any well-known computer capable of performing the functions described herein.

[0088] Computer system 500 includes one or more processors (also referred to as central processing units or CPUs), such as processor 504. Processor 504 is connected to a communication infrastructure or bus 506.

[0089] One or more processors 504 may each be a graphics processing unit (GPU). In an embodiment, a GPU is a processor of a dedicated electronic circuit designed to process mathematically intensive applications. A GPU may have a parallel structure that efficiently processes large blocks of data in parallel, such as mathematically intensive data commonly used in computer graphics applications, images, videos, etc.

[0090] The computer system 500 also includes user input / output devices 503 , such as a monitor, keyboard, pointing device, etc., that communicate with the communication infrastructure 506 through the user input / output interface 502 .

[0091] The computer system 500 also includes a main memory or primary storage 508, such as a random access memory (RAM). The main memory 508 may include one or more levels of cache. The main memory 508 has stored therein control logic (ie, computer software) and / or data.

[0092] The computer system 500 may also include one or more secondary storage devices or memories 510. The secondary storage 510 may include, for example, a hard disk drive 512 and / or a removable storage device or drive 514. The removable storage drive 514 may be a floppy disk drive, a magnetic tape drive, an optical disk drive, an optical storage device, a tape backup device, and / or any other storage device / drive.

[0093] The removable storage drive 514 can interact with a removable storage unit 518. The removable storage unit 518 includes a computer usable or readable storage device having computer software (control logic) and / or data stored thereon. The removable storage unit 518 can be a floppy disk, a magnetic tape, an optical disk, a DVD, an optical storage disk, and / or any other computer data storage device. The removable storage drive 514 reads from and / or writes to the removable storage unit 518 in a well-known manner.

[0094] According to an exemplary embodiment, secondary memory 510 may include other devices, tools, or other methods for allowing computer programs and / or other instructions and / or data to be accessed by computer system 500. Such devices, tools, or other methods may include, for example, a removable storage unit 522 and an interface 520. Examples of removable storage unit 522 and interface 520 may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an EPROM or PROM) and an associated socket, a memory stick and a USB port, a memory card and an associated memory card slot, and / or any other removable storage unit and associated interface.

[0095] The computer system 500 may also include a communication or network interface 524. The communication interface 524 enables the computer system 500 to communicate and interact with any combination of remote devices, remote networks, remote entities (individually and collectively represented by reference numeral 528), etc. For example, the communication interface 524 may allow the computer system 500 to communicate with the remote device 528 via a communication path 526, which may be wired and / or wireless and may include any combination of a LAN, a WAN, the Internet, etc. Control logic and / or data may be transmitted to and from the computer system 500 via the communication path 526.

[0096] In an embodiment, a tangible non-transitory device or article of manufacture, also referred to herein as a computer program product or program storage device, includes a tangible non-transitory computer usable or readable medium having control logic (software) stored thereon. This includes, but is not limited to, computer system 500, main memory 508, auxiliary memory 510, and removable storage units 518 and 522, and tangible articles of manufacture embodying any combination of the foregoing. When executed by one or more data processing devices (such as computer system 500), such control logic causes such data processing devices to operate as described herein.

[0097] Based on the teachings contained in this disclosure, it will be apparent to those skilled in the relevant art how to use the Figure 5 The embodiments of the present disclosure may be made and used with data processing devices, computer systems, and / or computer architectures other than those shown. Specifically, the embodiments may operate with software, hardware, and / or operating system implementations other than those described herein.

[0098] It should be understood that it is the detailed description section, rather than any other section, that is intended to be used to interpret the claims. The other sections may present one or more but not all exemplary embodiments contemplated by the inventors, and are therefore not intended to limit the present invention and the appended claims in any way.

[0099] Although the present disclosure describes exemplary embodiments of exemplary fields and applications, it should be understood that the present disclosure is not limited thereto. Other embodiments and modifications thereof are possible and are within the scope and spirit of the present disclosure. For example, and without limiting the generality of this paragraph, the embodiments are not limited to the software, hardware, firmware, and / or entities shown in the drawings and / or described herein. In addition, the embodiments (whether or not explicitly described herein) have significant utility for fields and applications outside the examples described herein.

[0100] Embodiments are described herein by means of functional building blocks that illustrate implementations of specific functions and their relationships. For ease of description, the boundaries of these functional building blocks are arbitrarily defined herein. Alternative boundaries may be defined as long as the specific functions and relationships (or their equivalents) can be properly performed. In addition, alternative embodiments may perform functional blocks, steps, operations, methods, etc., in an order different from that described herein.

[0101] "One embodiment", "embodiment", "exemplary embodiment" or similar phrases mentioned herein indicate that the described embodiments may include specific features, structures or characteristics, but each embodiment may not necessarily include specific features, structures or characteristics. Moreover, such phrases do not necessarily refer to the same embodiment. In addition, when describing specific features, structures or characteristics in conjunction with the embodiments, whether or not explicitly mentioned or described herein, one or more technicians in the relevant fields are able to incorporate such features, structures or characteristics into other embodiments. In addition, some embodiments may be described using the expressions "connect" and "connect" and their derivatives. These terms are not necessarily intended to be synonyms for each other. For example, the terms "connect" and / or "connect" may be used to describe some embodiments to indicate that two or more elements are in direct physical or electrical contact with each other. However, the term "connect" may also mean that two or more elements are not in direct contact with each other, but still cooperate or interact with each other.

[0102] The breadth and scope of the present disclosure should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A computer-implemented method for obtaining measurement data from a device, the measurement data approximating first-party measurement data of an advertising campaign executed at the device, the method comprising: receiving, by at least one processor, the advertising campaign; determining, by the at least one processor, using a configuration file stored at the device or a network service of the device, a set of dimensions that the device uses to aggregate measurement data of the advertising campaign; defining, by the at least one processor, a dimension of interest; determining, by the at least one processor, that a defined dimension of interest is not present in the set of dimensions used by the apparatus to aggregate measurement data for the advertising campaign; storing, by the at least one processor, the advertising campaign and the defined dimensions of interest in a memory; dividing, by the at least one processor, the advertising campaign into a plurality of sub-unit advertising campaigns using the defined dimension of interest, wherein each of the plurality of sub-unit advertising campaigns comprises a respective value in a range of values ​​defined by the defined dimension of interest, and each of the plurality of sub-unit advertising campaigns comprises the set of dimensions used by the apparatus to aggregate measurement data for the advertising campaign; In response to the partitioning, sending, by the at least one processor, each sub-unit advertising campaign to the device for simultaneous execution by the device; as well as The measurement data is received by the at least one processor from the device in response to the sending, wherein the measurement data approximates the first-party measurement data of the advertising campaign executed on the device, and the first-party measurement data of the advertising campaign on the device is inaccessible to the at least one processor.

2. The method according to claim 1, further comprising: The dimension of interest is defined, by the at least one processor, based at least in part on a second dimension of interest, wherein the second dimension of interest is representative of the dimension of interest. The method of claim 1 , wherein the dimension of interest represents a demographic characteristic, a behavioral pattern, or a geographic area.

4. The method according to claim 1, further comprising: The measurement data is processed by the at least one processor based on other first-party measurement data, second-party measurement data, or third-party measurement data.

5. The method according to claim 1, further comprising: The measurement data is analyzed by the at least one processor.

6. The method according to claim 5, further comprising: generating, by the at least one processor, another advertising campaign in response to analyzing the measurement data; as well as The other advertising campaign is sent, by the at least one processor, to the device for execution by the device.

7. The method according to claim 1, further comprising: A respective second sub-unit advertising campaign is created, by the at least one processor, for each value in a range of values ​​of a second dimension of interest based at least in part on the advertising campaign, wherein the second dimension of interest is present in the set of dimensions of the advertising campaign.

8. The method according to claim 7, further comprising: sending, by the at least one processor, each second sub-unit advertising campaign to a second device for execution by the second device; receiving, by the at least one processor, second measurement data from the second device based at least in part on the second transmission; as well as The second measurement data is processed by the at least one processor based at least in part on the measurement data.

9. A system for obtaining measurement data from a device, the measurement data approximating first-party measurement data of an advertising campaign executed at the device, the system comprising: Memory; and at least one processor coupled to the memory and configured to: Receive advertising campaigns; determining, using a configuration file stored at the device or a network service of the device, a set of dimensions that the device uses to aggregate measurement data for the advertising campaign; Define the dimensions of interest; determining that a defined dimension of interest is not present in the set of dimensions used by the apparatus to aggregate measurement data for the advertising campaign; storing the advertising campaign and the defined dimensions of interest in the memory; dividing the advertising campaign into a plurality of sub-unit advertising campaigns using the defined dimension of interest, wherein each of the plurality of sub-unit advertising campaigns includes a respective value in a range of values ​​defined by the defined dimension of interest, and each of the plurality of sub-unit advertising campaigns includes the set of dimensions used by the apparatus to aggregate measurement data for the advertising campaign; In response to the partitioning, sending each sub-unit advertising campaign to the device for simultaneous execution by the device; as well as The measurement data is received from the device in response to each sub-unit advertising campaign being sent to the device for execution by the device, wherein the measurement data approximates the first-party measurement data of the advertising campaign executed on the device, and the first-party measurement data of the advertising campaign on the device is inaccessible to the at least one processor.

10. The system of claim 9, wherein the at least one processor is further configured to: The dimension of interest is defined based at least in part on a second dimension of interest, wherein the second dimension of interest is representative of the dimension of interest.

11. The system of claim 9, wherein the at least one processor is further configured to: The measurement data is analyzed.

12. The system of claim 11, wherein the at least one processor is further configured to: generating another advertising campaign in response to analyzing the measurement data; and The other advertising campaign is sent to the device for execution by the device.

13. The system of claim 9, wherein the at least one processor is further configured to: creating a respective second sub-unit advertising campaign for each value in a range of values ​​of a second dimension of interest based at least in part on the advertising campaign, wherein the second dimension of interest is present in the set of dimensions of the advertising campaign; sending each second subunit advertising campaign to a second device for execution by the second device; receiving second measurement data from the second device based at least in part on each second subunit advertising campaign being second transmitted to the second device for execution by the second device; as well as The second measurement data is processed based at least in part on the measurement data.

14. A non-transitory computer readable medium having stored thereon instructions that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising: Receive advertising campaigns; determining, using a configuration file stored at a second computing device or a network service of the second computing device, a set of dimensions that the second computing device uses to aggregate measurement data for the advertising campaign; Define the dimensions of interest; determining that a defined dimension of interest is not present in the set of dimensions used by the apparatus to aggregate measurement data for the advertising campaign; storing the advertising campaign and the defined dimensions of interest in a memory; dividing the advertising campaign into a plurality of sub-unit advertising campaigns using the defined dimension of interest, wherein each of the plurality of sub-unit advertising campaigns includes a respective value in a range of values ​​defined by the defined dimension of interest, and each of the plurality of sub-unit advertising campaigns includes the set of dimensions used by the second computing device to aggregate measurement data for the advertising campaign; In response to the partitioning, sending, by the at least one processor, each sub-unit advertising campaign to the device for simultaneous execution by the second computing device; as well as The measurement data is received from the second computing device in response to the sending, wherein the measurement data approximates first-party measurement data of the advertising campaign executed on the second computing device, and the first-party measurement data of the advertising campaign on the device is inaccessible to the at least one computing device.

15. The non-transitory computer readable medium of claim 14, the operations further comprising: The dimension of interest is defined based at least in part on a second dimension of interest, wherein the second dimension of interest is representative of the dimension of interest.

16. The non-transitory computer readable medium of claim 14, the operations further comprising: The measurement data is analyzed.

17. The non-transitory computer readable medium of claim 16, the operations further comprising: generating another advertising campaign in response to analyzing the measurement data; as well as The other advertising campaign is sent to the device for execution by the second computing device.

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