Data processing method and electronic device

Abstract

The present application relates to the technical field of terminals. Provided are a data processing method and an electronic device, in order to solve the problem of low accuracy in user lifetime values. The method, applied to a terminal, comprises: acquiring service data during the display of an advertisement in a first application of a terminal, wherein the service data is used for indicating exposure of the advertisement and / or an operation of a user on the advertisement, and the advertisement is used for promoting a second application; acquiring conversion behavior data in the second application, wherein the conversion behavior data is used for indicating a conversion value of a conversion behavior executed by the user in the second application; on the basis of service dimensions, aggregating the service data and the conversion behavior data, in order to obtain first data, wherein the first data is used for indicating a conversion value corresponding to the second application under the service dimensions; and sending the first data to a server.

Description

Data processing methods and electronic equipment

[0001] This application claims priority to Chinese patent application filed on July 31, 2024, with application number 202411046793.0 and entitled "Data Processing Method and Electronic Equipment", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of terminal technology, and in particular to data processing methods and electronic devices. Background Technology

[0003] In the advertising industry, a key metric for evaluating ad performance is Lifetime Value (LTV). Lifetime Value refers to the total revenue a user generates for an app developer throughout their entire lifecycle, from registration to uninstallation. For advertisers, after launching an ad campaign, obtaining the LTV of users across different dimensions is crucial for assessing the quality of users acquired in those dimensions. These dimensions can include channel, media, device, geographic location, and other factors. Furthermore, data such as customer acquisition cost can be combined to comprehensively evaluate the effectiveness of each dimension and optimize subsequent ad campaign strategies. Therefore, there is a pressing need to develop an effective data processing method for accurately determining the LTV of users across different dimensions. Summary of the Invention

[0004] This application provides a data processing method and electronic device. A terminal acquires business data and conversion behavior data, and aggregates the business data and conversion behavior data to obtain first data. The terminal sends the first data to a server, so that the server can aggregate the first data sent by different terminals. This improves data integrity, privacy, and security, and enhances the accuracy of LTV reports. Furthermore, it reduces the amount of data transmitted from the terminal to the server, alleviating the server's burden and improving system efficiency.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a data processing method applied to a terminal. The method includes: acquiring business data when an advertisement is displayed in a first application on the terminal, the business data being used to indicate the exposure of the advertisement and / or user actions on the advertisement, the advertisement being used to promote a second application; acquiring conversion behavior data within the second application, the conversion behavior data being used to indicate the conversion value of conversion behaviors performed by the user in the second application; aggregating the business data and conversion behavior data according to a business dimension to obtain first data, the first data being used to indicate the conversion value of the second application under the business dimension; and sending the first data to a server.

[0007] In this application, the terminal supports data aggregation. The terminal aggregates business data and conversion behavior data to obtain first data, which is then sent to the server for processing. No sensitive information needs to be sent during the interaction between the terminal and the server, thus improving data privacy and security. The terminal fully utilizes its local computing power to aggregate business data and conversion behavior data, significantly reducing the amount of data sent to the server, alleviating the cloud-side burden, and improving data processing efficiency. The data sent by the terminal to the server does not contain sensitive information, and the server can use all the data sent by the terminal to improve the accuracy of LTV reports.

[0008] The first application is the application that displays the advertisement on the terminal. Business data includes exposure data and / or operational data. Exposure data includes one or more of the following: the identifier of the advertising platform, the identifier of the advertising task corresponding to the advertisement, the identifier of the second application, the display duration of the advertisement, the identifier of the first application, the time the advertisement was displayed, and the region where the advertisement was displayed. Operational data includes one or more of the following: operation data on the advertisement, operation data on the skip control in the advertisement, and operation data on the close control in the advertisement. Operational data on the advertisement includes one or more of the following: clicking the advertisement to jump to the second application associated with the advertisement, clicking the advertisement to jump to the advertisement details page, or clicking the advertisement to download the second application associated with the advertisement.

[0009] In some examples, the first application in the terminal obtains the business data of the advertisement when it is displayed.

[0010] The second application is the application developed by the advertiser on the terminal. Conversion behavior data includes one or more of the following: second application identifier, conversion behavior, conversion value, and conversion time. Conversion behavior includes one or more of the following: installation, registration, consumption, liking, sharing, subscription, and content publishing.

[0011] In some examples, the second application pre-configures value conversion rules, which include the conversion value corresponding to different conversion behaviors. During a user's use of the second application, the application acquires the user's conversion behavior and determines the conversion value corresponding to that behavior based on the pre-configured value conversion rules. The second application then determines the conversion behavior data based on its own identifier and the conversion value.

[0012] Based on the first aspect, or any implementation of the first aspect above, multiple business data and multiple conversion behavior data are obtained; based on the business dimension, the business data and conversion behavior data are aggregated to obtain the first data, including: performing attribution calculations on the multiple business data and multiple conversion behavior data to obtain attribution results, the attribution results being used to indicate the relationship between conversion behavior data and business data; based on the business dimension, the conversion behavior data and business data with a relationship are aggregated to obtain the first data.

[0013] In some examples, the terminal analyzes and processes business data and conversion behavior data based on attribution models to obtain attribution results.

[0014] In this application, the terminal can filter out conversion behavior data associated with the user's business data on the advertisement through attribution calculation, and can also determine the impact and value of the user's business data in the first application on the conversion behavior data in the second application, so as to accurately generate LTV reports and accurately analyze the advertising performance.

[0015] In some examples, the terminal accumulates the conversion value of the same dimension based on the business dimension to obtain the first data.

[0016] In this application, data with the same business dimension are merged into one, which can greatly simplify the processing efficiency of subsequent analysis and save analysis resources and time.

[0017] In this application, the business data and conversion behavior data originate from the same device. The terminal aggregates the business data and conversion behavior data to obtain first data, linking the two. When the terminal interacts with the server, this first data is device-level data, identifying the terminal. The terminal no longer needs to send sensitive information (such as user identifiers) to identify the device, improving data privacy and security. Furthermore, the terminal performs data aggregation first, fully utilizing local computing power to process local data. The terminal sends processed, correlated data to the server, allowing the server to quickly process the data according to usage needs, reducing the amount of data transmitted to the server, alleviating the burden on the advertising server, and improving data processing efficiency.

[0018] In this application, the raw data is first aggregated at the terminal before being transmitted to the server. Compared to directly transmitting the raw data to the server for aggregation, this reduces network bandwidth usage and data transmission volume, significantly saving transmission costs and server resources. Furthermore, the terminal's initial processing of the raw data, transmitting only the necessary aggregation results, protects user data privacy and security, and reduces security risks such as data leakage. The terminal can provide real-time aggregation results, making it suitable for scenarios requiring rapid decision-making and response, offering greater flexibility and practicality.

[0019] According to the first aspect, or any of the above implementations of the first aspect, the business dimension includes one or more of the following dimensions: the application of the display advertisement, the advertising platform, or the advertising task; the business data includes one or more of the following data: the identifier of the first application, the identifier of the advertising platform of the advertisement, or the identifier of the advertising task corresponding to the advertisement.

[0020] In some examples, business dimensions can be represented by business attribute information related to ad display. Business dimensions may also include one or more of the following: ad display duration, the application promoted by the ad (i.e., the advertiser's app), operational data, ad display time, and ad display region. Business data may also include one or more of the following: ad display duration, the identifier of the advertiser's app promoted by the ad, the time the ad was displayed, the region where the ad was displayed, operational data related to the ad, operational data related to the skip control in the ad, and operational data related to the close control in the ad.

[0021] In this application, the terminal can analyze and aggregate data from different business dimensions, accurately identify the advertising performance across these dimensions, and meet diverse user needs. This facilitates the accurate generation of LTV reports and the optimization of advertising strategies.

[0022] According to the first aspect, or any implementation of the first aspect above, the method further includes: sending the terminal's device attribute information to the server.

[0023] In this embodiment, the terminal sends device attribute information to the server, which helps the server to analyze the advertising performance from the device perspective, so as to accurately locate the advertising performance and optimize advertising.

[0024] According to the first aspect, or any implementation of the first aspect above, the business data and conversion behavior data are aggregated according to the business dimension to obtain the first data, including: aggregating the business data and conversion behavior data according to the business dimension based on the terminal aggregation strategy; wherein, the terminal aggregation strategy includes one or more of the following: business dimension or aggregation period.

[0025] According to the first aspect, or any implementation of the first aspect above, the conversion behavior data includes the identifier of the second application and the conversion value; obtaining conversion behavior data within the second application includes: receiving user conversion behavior within the second application; and determining the conversion value of the conversion behavior according to the value conversion rules included in the terminal aggregation strategy.

[0026] In this application, for second applications without configured value conversion rules, the conversion value of each conversion behavior can be determined based on the value conversion rules in the edge aggregation strategy. The terminal performs precise aggregation based on the edge aggregation strategy, which can accurately extract effective data and improve data aggregation efficiency.

[0027] According to the first aspect, or any implementation of the first aspect above, the method further includes: receiving the end-side aggregation strategy sent by the server.

[0028] In some examples, the terminal can also receive updated end-to-end aggregation strategies sent by the server.

[0029] In this application, the terminal performs aggregation according to the terminal-side aggregation strategy configured by the server, so that the dimensions contained in the first data are more in line with user needs and more comprehensive.

[0030] In some examples, the endpoint aggregation strategy can be a pre-configured strategy in the endpoint.

[0031] Secondly, this application provides a data processing method applied to a server. The method includes: receiving first data from multiple terminals, wherein the first data is used to indicate the conversion value of a second application under a business dimension, the first data is obtained by the terminals after aggregating business data and conversion behavior data according to the business dimension, the business data is used to indicate the exposure of the advertisement and / or the user's operation on the advertisement when the advertisement is displayed in the first application of the terminal, the advertisement is used to promote the second application, and the conversion behavior data is used to indicate the conversion value of the conversion behavior performed by the user in the second application of the terminal.

[0032] According to the second aspect, or any implementation of the second aspect above, the method further includes: aggregating the first data of multiple terminals according to the target dimension to obtain second data, the second data being used to indicate the conversion value of the second application under the target dimension.

[0033] In this application, the server aggregates first data from multiple terminals based on multiple dimensions to obtain second data, which enables a more comprehensive and accurate analysis and evaluation of advertising effectiveness, providing a more comprehensive LTV report. This helps advertisers gain a deeper understanding of advertising performance, identify potential areas for improvement and optimization, and thus optimize their advertising strategies.

[0034] According to the second aspect, or any implementation of the second aspect above, the method further includes: receiving device attribute information of multiple terminals.

[0035] According to the second aspect, or any implementation of the second aspect above, the target dimension includes one or more of the following dimensions: business dimension or device dimension.

[0036] According to the second aspect, or any implementation of the second aspect above, the method further includes: generating an end-side aggregation strategy in response to a user configuration operation, wherein the end-side aggregation strategy includes one or more of the following: business dimension or aggregation period; and sending the end-side aggregation strategy to multiple terminals.

[0037] According to the second aspect, or any of the implementations of the second aspect above, the edge aggregation strategy also includes value conversion rules, which are used to determine the conversion value of conversion behavior.

[0038] Thirdly, this application provides a terminal comprising: a processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, and when the processor reads the computer-readable instructions from the memory, causing the terminal to perform the method as described in the first aspect and any embodiment of the first aspect.

[0039] Fourthly, this application provides a server comprising: a processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, wherein when the processor reads the computer-readable instructions from the memory, the terminal performs the method as described in the second aspect and any embodiment of the second aspect.

[0040] Fifthly, this application provides a data processing apparatus comprising: a processor and a memory, the memory being coupled to the processor, the memory being used to store computer-readable instructions, wherein when the processor reads the computer-readable instructions from the memory, the terminal performs a method as described in the second aspect and any one of the embodiments of the second aspect, or a method as described in the second aspect and any one of the embodiments of the second aspect.

[0041] In a sixth aspect, this application provides a chip system including at least one processor and at least one interface circuit. The at least one interface circuit is used to perform transceiver functions and send instructions to the at least one processor. The at least one processor executes the instructions and performs a method as described in the first aspect and any one of the embodiments of the first aspect, or a method as described in the second aspect and any one of the embodiments of the second aspect.

[0042] In a seventh aspect, this application provides a computer-readable storage medium comprising a computer program that, when executed on an electronic device, causes the electronic device to perform a method as described in the first aspect and any one of the embodiments of the first aspect, or a method as described in the second aspect and any one of the embodiments of the second aspect.

[0043] Eighthly, this application provides a computer program product comprising: a computer program or instructions that, when executed on a computer, cause the computer to perform a method as described in the first aspect and any one of the embodiments of the first aspect, or a method as described in the second aspect and any one of the embodiments of the second aspect.

[0044] The technical effects corresponding to any implementation method of aspects two through eight, and each aspect, can be found in the first aspect and the technical effects corresponding to any implementation method of the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0045] Figure 1 is a schematic diagram of the architecture of the advertising delivery system provided in an embodiment of this application;

[0046] Figure 2 is a diagram of the LTV computing architecture provided in an embodiment of this application;

[0047] Figure 3 is a schematic diagram of the hardware structure of the terminal provided in the embodiment of this application;

[0048] Figure 4 is a schematic diagram of the architecture of the data processing system provided in an embodiment of this application;

[0049] Figure 5 is a schematic flowchart of the data processing method provided in an embodiment of this application;

[0050] Figure 6 is a schematic diagram of the data processing framework of the terminal provided in the embodiment of this application;

[0051] Figure 7 is a schematic diagram of the data processing method provided in an embodiment of this application (II).

[0052] Figure 8 is a schematic diagram of the data processing framework of the server provided in an embodiment of this application;

[0053] Figure 9 is a timing diagram of the server generating an LTV report according to an embodiment of this application;

[0054] Figure 10 is a schematic diagram of the LTV report provided in an embodiment of this application;

[0055] Figure 11 is a flowchart of the method for configuring the end-side aggregation strategy provided in an embodiment of this application;

[0056] Figure 12 is a schematic flowchart of the data processing method provided in an embodiment of this application;

[0057] Figure 13 is a schematic diagram of the data processing device provided in an embodiment of this application;

[0058] Figure 14 is a schematic diagram of the chip system provided in an embodiment of this application. Detailed Implementation

[0059] The technical solutions of the embodiments of this application are described below with reference to the accompanying drawings. In the description of the embodiments of this application, the terminology used in the following embodiments is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions “a,” “an,” “the,” “the,” “the,” and “this” are intended to include expressions such as “one or more,” unless the context clearly indicates otherwise. It should also be understood that in the following embodiments of this application, “at least one” and “one or more” refer to one or more (including two).

[0060] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. The term "connection" includes direct connections and indirect connections, unless otherwise stated. "First" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated.

[0061] In the embodiments of this application, the words "exemplarily" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of the words "exemplarily" or "for example" is intended to present the relevant concepts in a specific manner.

[0062] To better understand the technical solution of this application, the specific implementation of the advertising delivery system will be described in detail first. Figure 1 shows an advertising delivery system provided in an embodiment of this application. This advertising delivery system may include a terminal 11 and an advertising server 12. The advertising delivery system may also be referred to as an internet advertising delivery system.

[0063] Terminal 11 may have one or more applications (APPs) installed (Figure 1 uses one APP installed on terminal 11 as an example). While running an APP, terminal 11 may request corresponding advertisements from advertising server 12 and display the advertisements returned by advertising server 12.

[0064] Specifically, an app can integrate a software development kit (SDK) for ad management. The app can call the SDK to send ad requests to the ad server to request corresponding ads. For example, during app operation, when a user action triggers the app to display an ad, the app calls the SDK to generate an ad request. Subsequently, the app receives the ad response returned by the SDK and displays the ad based on the information in the ad response.

[0065] The aforementioned SDK for ad management is an embedded ad toolkit provided to app developers, and can be built into the ad development code within the app. For example, an app developer integrates the SDK file into their development code documentation, thereby enabling the app to connect to the ad server.

[0066] By calling the SDK described above, terminal 11, such as the APP of terminal 11, can send an advertising request to the advertising server 12, request the corresponding advertisement, and receive the advertising response returned by the advertising server 12.

[0067] By calling the aforementioned SDK, terminal 11, such as its APP, can also collect business data when displaying advertisements and report it to the advertising server. For example, business data may include exposure data and / or operation data. Exposure data may include one or more of the following: advertisement information, advertisement display duration, time, and region. Operation data may include one or more of the following: operation data on the advertisement, operation data on the skip control in the advertisement, and operation data on the close control in the advertisement. For example, operation data on the advertisement may include clicking the advertisement to jump to the advertisement-related APP, clicking the advertisement to jump to the advertisement details page, or clicking the advertisement to download the advertisement-related APP.

[0068] The number of ad servers 12 can be one or more (Figure 1 uses an ad delivery system including one ad server as an example). An ad server can also be described as an ad platform.

[0069] Advertisers can issue advertising tasks on different ad servers 12, and the ad servers 12 execute these tasks. Specifically, ad servers 12 can display ads in apps that have their corresponding SDKs installed. For example, ad servers 12 can receive advertising requests from terminals 11, such as the apps on terminal 11. Ad servers 12 determine the corresponding advertising response based on the advertising request and can return the advertising response to terminals 11, such as the apps on terminal 11, so that the ads can be displayed in the apps. The SDKs corresponding to ad servers 12 can be installed in different apps, and the same app can also install SDKs corresponding to different ad servers to display ads provided by different ad servers.

[0070] The advertising server 12 can also receive business data reported by the terminal 11, such as the APP of the terminal 11, so as to evaluate the advertising effect in the future.

[0071] The advertising server 12 is also used to maintain the advertiser's advertising resource pool. This advertising resource pool stores advertising content uploaded by the advertiser (e.g., one or more of the text, images, videos, and audio appearing in the advertisement). The advertising resource pool may also store advertising information corresponding to the advertising content (e.g., one or more of the advertising format, advertising type, and download address).

[0072] Understandably, after developing their app (hereinafter referred to as the Advertiser App or Secondary App), advertisers want more users to use it. Therefore, advertisers can distribute ad tasks on an ad server to promote their app. Based on these ad tasks, the ad server can display ads for the Advertiser App in apps (hereinafter referred to as Media Apps or Primary Apps) that have the corresponding SDK installed, thus promoting the Advertiser App.

[0073] In some related technologies, cloud-based advertising servers determine LTV (Lifetime Value) based on user-identified data uploaded by the client-side app. Figure 2 shows the LTV calculation architecture based on user-identified data.

[0074] The client-side app collects and uploads data. Specifically, the media app displays ads promoting the advertiser's app by calling the ad server's SDK and collects the advertiser's app's business data. The media app then reports this business data to the ad server. This business data may include impression data and / or operation data, as well as user personal identifiers. The advertiser's app collects conversion behavior data of users during their use of the advertiser's app and reports this conversion behavior data to the ad server by calling the ad server's SDK. This conversion behavior data includes the conversion value corresponding to user registration, purchase, subscription, and other conversion behaviors, and also includes user personal identifiers.

[0075] The user's personal identifier can be a device-level identity document (ID). For example, the open anonymous device identifier (OAID).

[0076] Understandably, a user's personal identifier (PUI) can uniquely identify each user's device. By representing user identity through device-level identification, ad servers can accurately track and identify user behavior on the same device. Based on PUI, data from media apps and advertiser apps can also be linked to differentiate between different users. Furthermore, PUI helps ad servers obtain the complete conversion path, enabling them to understand the entire process from user exposure to ad engagement to the generation of conversion behavior data, which is beneficial for accurately calculating LTV (Lifetime Value) subsequently.

[0077] Specifically, the ad server comprises a data acquisition layer, a data processing layer, a data service layer, and a data application layer. The data acquisition layer persistently stores the data transmitted from the client-side app. The data processing layer processes the data; for example, it associates business data reported by media apps containing the same user identifier with conversion behavior data reported by advertiser apps, and then aggregates the data according to different dimensions to obtain the aggregation results. Data is processed through real-time / offline computing methods, and the aggregation results are written to the database. After processing the data, the ad server can provide aggregation results from different dimensions for user analysis. The database belongs to the data service layer, which also provides query functionality. The data application layer provides LTV reports. These LTV reports can display LTV metrics for specific time periods according to different dimensions, based on the advertiser's needs. Advertisers can query LTV reports from different dimensions and time periods to understand the effectiveness of their advertising campaigns and optimize subsequent advertising strategies.

[0078] Understandably, competition exists between ad servers on different channels and platforms, leading each ad server to prioritize protecting its own clients and data, avoiding sharing private data with competitors. Therefore, each ad server typically only obtains the ad performance data for the ads it runs. In some examples, a third-party server exists to monitor and manage ad servers across different channels or platforms. The ad server transmits the acquired data to the third-party server, which then determines the ad performance of each ad server. In other examples, advertisers possess servers with high computing power (described as advertiser servers). To prevent privacy breaches and ensure data security, the advertiser server can obtain business data related to the advertiser's app from multiple ad servers. It then aggregates this data based on conversion behavior data obtained from the advertiser's app and the business data obtained from the ad servers to generate an LTV report for the advertiser's app.

[0079] However, in the above example, the entire data processing process of the ad server is highly dependent on user personal identifiers. The ad server correlates data uploaded by the media app and the advertiser app based on the user's personal identifier to determine each user's data. This data is then analyzed and aggregated from different dimensions. While the ad server can trace user behavior and preferences based on their personal identifiers, improper data processing or inadequate security measures could lead to the leakage of users' personal information, posing privacy risks. Furthermore, users can configure whether to enable or disable user personal identifiers on their devices. If a user disables user personal identifiers, data from the media app or advertiser app on that device can be collected, but user personal identifiers cannot. Although the device can upload the data collected from the media app or advertiser app to the cloud-based ad server, the ad server cannot use the uploaded data because it lacks user personal identifiers. While the cloud-based ad server can acquire a large amount of data, the amount actually usable is relatively small. Typically, the collection of user personal identifiers incurs a loss of 10% or even higher. These losses can lead to a certain degree of deviation in the LTV (Lifetime Value) determined by the cloud-based ad server, reducing data accuracy and thus affecting advertisers' campaign decisions.

[0080] To address the problems existing in the aforementioned related technologies, this application provides a data processing method applicable to a terminal. A media app (i.e., a first application) on the terminal can collect business data, and an advertiser app (i.e., a second application) can collect conversion behavior data. The terminal can perform attribution calculations and data aggregation on the business data and conversion behavior data to obtain a first aggregation result (which can also be described as first data), and upload it to a server (such as the aforementioned third-party server). The server performs data aggregation based on the first aggregation results from different terminals to obtain a second aggregation result. The method provided in this application can improve data privacy and security, and enhance the integrity of the collected data, enabling comprehensive and accurate analysis and evaluation of advertising effectiveness to obtain precise LTV reports. The terminal can also fully utilize its local computing power to perform attribution calculations and aggregation on the data collected by the terminal, significantly reducing the amount of data transmitted to the server, alleviating the server burden, and improving data processing efficiency and system performance.

[0081] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0082] In this application embodiment, the terminal may also be referred to as terminal equipment, electronic equipment, user equipment (UE), mobile station (MS), mobile terminal (MT), etc.

[0083] In this embodiment, the terminal 11 can be a mobile phone, tablet computer, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), artificial intelligence (AI) terminal, etc. In this embodiment, no special restrictions are placed on the specific form of the terminal.

[0084] Figure 3 is a structural block diagram of the aforementioned terminal 11.

[0085] Terminal 11 may include a processor 110, an external memory interface 120, an internal memory 121, a USB interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a radio frequency module 150, a communication module 160, an audio module 170, a sensor module 180, a camera 193, a display screen 194, and a SIM card interface 195, etc.

[0086] The structure illustrated in this application embodiment does not constitute a limitation on terminal 11. It may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of both.

[0087] Processor 110 may include one or more processing units, such as an application processor (AP) and / or memory. These different processing units may be independent devices or integrated into one or more processors.

[0088] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor is a cache memory, which can store instructions or data that the processor has just used or that are used repeatedly. If the processor needs to use the instruction or data again, it can directly retrieve it from the memory. This avoids repeated accesses, reduces processor waiting time, and thus improves system efficiency.

[0089] In this embodiment, the processor 110 can acquire business data and conversion behavior data. The processor 110 can also perform attribution calculations on the business data and conversion behavior data to obtain attribution results. The processor 110 can also aggregate the attribution results according to business dimensions to obtain a first aggregation result. The processor 110 can also send the first aggregation result to the radio frequency module 150 or the communication module 160, so that the radio frequency module 150 or the communication module 160 can send the first aggregation result to the server.

[0090] USB port 130 can be a Mini USB port, Micro USB port, USB Type-C port, etc. The USB port can be used to connect a charger to charge terminal 11, or to transfer data between terminal 11 and peripheral devices. It can also be used to connect headphones for audio playback.

[0091] The charging management module 140 receives charging input from a charger, which can be either a wireless charger or a wired charger. While charging the battery, the charging management module can also supply power to the terminal device via the power management module 141.

[0092] The power management module 141 is used to connect the battery 142, the charging management module 140, and the processor 110. The power management module receives input from the battery and / or the charging management module to power the processor, internal memory, external memory, and display screen, etc. The power management module can also be used to monitor parameters such as battery capacity and battery health status (leakage current, impedance).

[0093] Antennas 1 and 2 are used to transmit and receive electromagnetic wave signals. Each antenna in terminal 11 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, a cellular antenna can be multiplexed as a wireless local area network diversity antenna. In some embodiments, the antennas can be used in conjunction with a tuning switch.

[0094] The radio frequency (RF) module 150 provides a communication processing module for wireless communication solutions, including 2G-5G, applied to the terminal 11. The RF module receives electromagnetic waves from the antenna 1, filters and amplifies the received electromagnetic waves, and transmits them to the modem for demodulation.

[0095] In the embodiment of the application, the radio frequency module 150 can communicate with the server. The radio frequency module 150 can also receive the first aggregation result sent by the processor 110, and the radio frequency module 150 can also send the first aggregation result to the server.

[0096] The communication module 160 provides a communication processing module for wireless communication solutions applied to the terminal 11, including wireless local area networks (WLAN), Bluetooth (BT), global navigation satellite system (GNSS), near field communication (NFC), and infrared (IR) technologies. The communication module 160 can be one or more devices integrating at least one communication processing module. The communication module receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to the processor.

[0097] In some embodiments, antenna 1 of terminal 11 is coupled to the radio frequency module, and antenna 2 is coupled to the communication module, enabling terminal 11 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, and / or IR technology. The GNSS may include Global Positioning System (GPS), Global Navigation Satellite System (GLONASS) and / or BeiDou Navigation Satellite System (BDS), Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0098] In the embodiment of the application, the communication module 160 can communicate with the server. The communication module 160 can receive the first aggregation result sent by the processor 110, and the communication module 160 can also send the first aggregation result to the server.

[0099] The display screen 194 is used to display images, videos, etc. The display screen includes a display panel. The display panel can be an LCD (liquid crystal display), OLED (organic light-emitting diode), active-matrix organic light-emitting diode (AMOLED), flexible light-emitting diode (FLED), MiniLED, MicroLED, Micro-OLED, quantum dot light-emitting diode (QLED), etc. In some embodiments, the terminal 11 may include one or N displays, where N is a positive integer greater than 1.

[0100] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element may be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. In some embodiments, terminal 11 may include one or N cameras, where N is a positive integer greater than 1.

[0101] The external storage interface 120 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the terminal 11. The external storage card communicates with the processor through the external storage interface to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0102] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of terminal 11 by running the instructions stored in internal memory 121. Memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc. The data storage area may store data created during the use of terminal 11, etc. In addition, memory 121 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, other volatile solid-state storage devices, universal flash storage (UFS), etc. In the embodiments of this application, the memory may store the end-side aggregation strategy configured by the server.

[0103] The audio module 170 is used to convert digital audio information into analog audio signals for output, and also to convert analog audio input into digital audio signals. The audio module can also be used for encoding and decoding audio signals.

[0104] The sensor module 180 of terminal 11 may specifically include: a pressure sensor, a distance sensor, an ambient light sensor, a fingerprint sensor, a temperature sensor, and a touch sensor, etc. The touch sensor, also known as a "touch panel," can be located on the display screen. It is used to detect touch operations applied to or near the display. Terminal 11 may also include components such as a battery 142 and a SIM card interface 195, etc., but this embodiment does not impose any limitations on these components.

[0105] Referring to Figure 4, this is a schematic diagram of the architecture of a data processing system applicable to the technical solution of this application embodiment. The data processing system includes a terminal device 41 and a cloud device 42. The number of terminal devices 41 can be one or more, and the number of cloud devices 42 can also be one or more (Figure 4 illustrates this using one terminal device and one cloud device as an example). For instance, an application on the terminal device 41 can integrate the SDKs corresponding to one or more cloud devices 42. Different applications on the terminal device 41 can integrate the SDKs corresponding to the same or different cloud devices 42.

[0106] For example, the data processing system can be the advertising delivery system mentioned above, the terminal device 41 can be the terminal 11 mentioned above, and the cloud device 42 can be the advertising server 12 mentioned above.

[0107] The edge device 41 may include a data acquisition module, a first processing module, and a second processing module. The cloud-side device 42 may include a third processing module and a report analysis module.

[0108] The data collection module can include media apps and advertiser apps.

[0109] Specifically, the media app integrates the SDK corresponding to the cloud-side device 42. By calling this SDK, the end-side device 41, such as the media app on the end-side device 41, can interact with the cloud-side device 42. For example, the media app can call the SDK to request advertisements from the cloud-side device 42 and display advertisements based on the advertisement response returned by the cloud-side device 42; when displaying advertisements, the media app can also call the SDK to collect business data of the advertisement. This business data is used to indicate the exposure of the advertisement and / or user actions on the advertisement, which is used to promote the advertiser's app. The media app can also send the business data to the first processing module.

[0110] The business data includes exposure data and / or operational data. Exposure data may include one or more of the following: the identifier of the ad server (i.e., the ad platform), the identifier of the ad task corresponding to the ad, the identifier of the media app, the display duration of the ad, the identifier of the advertiser app promoted by the ad, the time the ad was displayed, and the location information of the ad (i.e., the location information when the ad was displayed). Operational data may include one or more of the following: operation data on the ad, operation data on skipping controls in the ad, and operation data on closing controls in the ad. Operational data on the ad may include clicking the ad and being redirected to the ad-related app, clicking the ad and being redirected to the ad details page, or clicking the ad and downloading the ad-related app.

[0111] The advertiser's app is an app developed by the advertiser. This app can collect conversion behavior data from users within the advertiser's app. This conversion behavior data indicates the conversion value of user actions performed within the advertiser's app. The conversion behavior data can include the advertiser's app identifier and conversion value. It can also include conversion actions, with each action corresponding to a conversion value. Conversion actions can include one or more of the following: user installation, registration, purchase, liking, sharing, subscribing, and publishing content. The advertiser's app can also send business data to the first processing module.

[0112] In some examples, the data collection module sends data to the first processing module in real time. For instance, each time a media app displays an advertisement, it collects business data and sends it to the first processing module. During the operation of the advertiser's app, it sends conversion behavior data corresponding to user conversion actions to the first processing module in real time.

[0113] In other examples, the data collection module sends data to the first processing module according to a collection cycle. This collection cycle can be one day, three days, one week, one month, etc. For example, a media app sends the business data collected in each collection cycle to the first processing module. Similarly, an advertiser app sends the conversion behavior data collected in each collection cycle to the first processing module.

[0114] In some other examples, the data collection module sends data to the first processing module according to a preset quantity. For instance, a media app collects and stores business data; when the business data reaches a preset quantity (e.g., 10 items), it sends the stored preset quantity (10 items) of business data to the first processing module. Similarly, an advertising app collects and stores conversion behavior data; when the conversion behavior data reaches a preset quantity (e.g., 10 items), it sends the stored preset quantity (10 items) of conversion behavior data to the first processing module.

[0115] It is understood that in this embodiment, the edge device 41 is responsible for collecting data, performing attribution processing, and aggregation processing on the data. The business data in the edge device 41 is collected by the media app installed on the device, and the conversion behavior data is also collected by the advertiser app installed on the device. Both data have the same source, originating from the same device. Therefore, the business data and conversion behavior data in this embodiment do not need to carry user personal identifiers.

[0116] The first processing module can perform attribution processing on the business data transmitted by the media app in the acquisition module and the conversion behavior data transmitted by the advertiser app in the acquisition module to obtain attribution results. The first processing module can also transmit the attribution results to the second processing module.

[0117] Understandably, the edge device 41 performs attribution processing based on its own collected business data and conversion behavior data, which can accurately determine the correlation between conversion behavior data and business data. For example, it can determine which users' conversion behavior data for the advertiser's app is related to the business data when the media app displays ads, and which media app's business data it is associated with. In other words, conversion behavior data can be understood as the result of ad placement, while business data is the cause of ad placement. When a user displays an ad in the media app, they perform a certain action on that ad, which causes the advertiser's app to generate conversion behavior data. Through attribution, conversion behavior data can be linked to the business data that caused it, allowing for accurate evaluation of ad placement effectiveness. For example, when the media app on edge device 41 displays an ad, a user clicks on the ad and downloads the advertiser's app. Edge device 41 installs the advertiser's app, and the user registers an account and purchases items within the advertiser's app. The advertiser's app generates corresponding conversion behavior data based on these actions: installation, account registration, and item purchase.

[0118] The second processing module can aggregate data according to business dimensions based on the attribution results transmitted by the first processing module to obtain a first aggregation result. The second processing module can also transmit the first aggregation result to the third processing module of the cloud-side device 42. The business dimensions include, but are not limited to, the application of display advertising, advertising platforms, and advertising tasks.

[0119] Understandably, the edge device 41 can perform data aggregation, summarizing and statistically analyzing its own attribution results according to different business dimensions. Thus, the data in the first aggregation result is all data from the edge device 41 itself, which can be understood as device-level data. Subsequently, when the edge device 41 interacts with the cloud device 42, it no longer needs to send user personal identifiers to represent the device's identity. On the one hand, eliminating the need to send sensitive user personal identifiers during interaction between the edge device 41 and the cloud device 42 improves data privacy and security. On the other hand, the edge device 41 can fully utilize its local computing power to aggregate attribution data, significantly reducing the amount of data transmitted to the cloud device 42, alleviating the cloud's burden, and improving data processing efficiency.

[0120] The third processing module can aggregate the first aggregation result sent by the second processing module in the edge device 41 according to the target dimension using big data processing methods to obtain the second aggregation result. The target dimension includes, but is not limited to, business dimensions and device dimensions. The third processing module can also store the second aggregation result in the database of the cloud device 42 for later retrieval.

[0121] The database of cloud-side device 42 can also be described as a multidimensional data processing engine. The multidimensional data processing engine is a system in cloud-side device 42 used to store and query multidimensional data, and it typically supports complex query and analysis operations.

[0122] Understandably, the cloud-side device 42 can receive aggregated results sent by multiple end-side devices 41. The third processing module in the cloud-side device 42 aggregates the aggregated results of multiple end-side devices 41 from multiple dimensions, which can more comprehensively analyze and evaluate the advertising effect.

[0123] The report analysis module provides a report query function, allowing users to search for LTV reports. The module can also provide LTV analysis reports based on user needs. These LTV reports can include LTV data from different dimensions and time periods.

[0124] For example, cloud-side device 42 provides an LTV report. This LTV report can display LTV metrics for a specific time period according to the advertiser's needs and target dimensions. Advertisers can query LTV reports for different dimensions and time periods to understand the effectiveness of their advertising campaigns and optimize subsequent advertising strategies.

[0125] It is understood that the system architecture diagram shown in Figure 4 is only an example. In practical applications, the end-side device 41 may include more or fewer modules, and the cloud-side device 42 may also include more or fewer modules. This application embodiment does not limit the division of modules in the end-side device 41 and the cloud-side device 42.

[0126] Optionally, the cloud-side device 42 may further include a policy configuration module (not shown in Figure 4). The policy configuration module is used to configure the endpoint aggregation policy in response to user configuration operations. The policy configuration module is also used to update the endpoint aggregation policy in response to user update operations. The endpoint device 41 may further include an aggregation policy execution module (not shown in Figure 4). The policy configuration module is also used to send the endpoint aggregation policy to the policy execution module of the endpoint device 41. The policy execution module is used to send the endpoint aggregation policy to the second processing module, so that the second processing module can aggregate the attribution results according to the endpoint aggregation policy to obtain a first aggregation result.

[0127] The data processing method in this application can be applied to other business scenarios such as advertising, e-commerce, media, education, health, and gaming. For example, in an advertising scenario, the terminal device and cloud device analyze and aggregate user conversion behavior data and business data according to the data processing method of this application to improve the accuracy of advertising effectiveness. In an e-commerce scenario, the terminal device and cloud device analyze user shopping behavior according to the data processing method of this application to optimize product recommendations and personalized marketing strategies. In a media scenario, the terminal device and cloud device analyze user interaction behavior on media platforms according to the data processing method of this application to optimize content recommendations and interaction strategies. In an education scenario, the terminal device and cloud device analyze user learning behavior according to the data processing method of this application to optimize tutoring programs and personalized learning material recommendations. In a health scenario, the terminal device and cloud device analyze user health data according to the data processing method of this application to optimize health management programs. In a gaming scenario, the terminal device and cloud device analyze user gaming behavior according to the data processing method of this application to optimize game design.

[0128] It is understood that the technical solution of this application can be extended to any business scenario in the prior art that generates analysis reports based on user personal identifiers. These business scenarios include, but are not limited to, in-app event analysis, funnel analysis, retention analysis, and user path analysis. In-app event analysis refers to understanding how users use and interact with the application by tracing and analyzing various events or actions that occur within the application. For example, in media applications, analyzing user likes, comments, and shares can help analyze user preferences and accurately push videos to users. Funnel analysis refers to identifying and optimizing bottlenecks and areas for improvement by analyzing user churn during the process of achieving a specific goal or conversion. For example, in e-commerce platforms, the various stages from browsing products to completing a purchase can be analyzed, and the process can be optimized based on the analysis results. Retention analysis refers to assessing user loyalty and improving retention strategies by analyzing user retention rates over a period of time. For example, analyzing user retention rates in the first week, first month, and first quarter after registration can help improve retention strategies. User path analysis refers to understanding user habits and interaction patterns by tracing and analyzing the sequence of user behaviors in a product or service.

[0129] The system architecture and business scenarios described in this application are intended to more clearly illustrate the technical solutions of this application, and do not constitute the only limitation on the technical solutions provided in this application. As those skilled in the art will know, with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in this application are also applicable to similar technical problems.

[0130] The following section uses the terminal device as the end device, the cloud device as a third-party server, and the business scenario as an advertising business scenario as an example to explain the data processing method of this application.

[0131] As shown in FIG5, in some embodiments of this application, a data processing method flowchart is provided by an embodiment of this application, applied to a data processing system including a terminal and a server. The method includes:

[0132] S501, the terminal acquires business data and conversion behavior data.

[0133] The business data can be data collected by the application (also described as the first application) on the terminal that displays the advertisement, specifically during the advertisement's display. This business data is used to indicate the advertisement's exposure and / or user actions on the advertisement, which promotes the advertiser's application (also described as the second application). The business data includes exposure data and / or operation data. Exposure data may include one or more of the following: the identifier of the advertisement's ad server (i.e., the ad platform), the identifier of the corresponding ad task, the identifier of the media app, the advertisement's display duration, the identifier of the advertiser's app promoted by the advertisement, the time the advertisement was displayed, and the region where the advertisement was displayed (i.e., the location information when the advertisement was displayed). Operation data may include one or more of the following: operation data on the advertisement, operation data on skipping controls within the advertisement, and operation data on closing controls within the advertisement. Operation data on the advertisement may include clicking the advertisement to jump to the advertisement's associated app (i.e., the second application), clicking the advertisement to jump to the advertisement's details page, or clicking the advertisement to download the advertisement's associated app (i.e., the second application).

[0134] The application used to display advertisements on the terminal (i.e., the first application) can be the media app described above. There can be one or more media apps on the terminal. Media apps can be of different types. One or more ad server SDKs can be integrated into the media app to perform advertising tasks for different ad servers and display different advertisements. The advertisements can be advertisements from push applications.

[0135] For example, a media app may include, but is not limited to, one or more of the following applications: game apps, video apps, music apps, entertainment apps, shopping apps, browser apps, social apps, lifestyle service apps, transportation apps, financial apps, reading apps, and education apps.

[0136] Understandably, when a media app displays an advertisement, users can perform actions such as clicking on the ad. Therefore, for each displayed ad, the media app can collect the corresponding exposure and click data, thereby obtaining the ad's business data.

[0137] Each media app on the terminal can collect corresponding business data when displaying advertisements. For example, if a media app displays an advertisement on the terminal but the user doesn't interact with it, the media app can still collect exposure data, which the terminal then uses as business data. Alternatively, if a media app detects a user's interaction with the currently displayed advertisement, the media app determines interaction data based on this interaction, and since the advertisement in the media app is also exposed, exposure data can also be collected. The terminal then determines business data based on both the exposure data and the interaction data.

[0138] In some examples, the terminal obtains multiple business data.

[0139] For example, taking business data including interaction time, ad server ID, ad task ID, advertiser APP ID, media APP ID and operation data as an example, the business data collected by each media APP in the terminal on May 1, 2024 can be summarized to generate a business data table as shown in Table 1.

[0140] Table 1

[0141] As shown in Table 1, on May 1, 2024, the terminal displayed two advertisements. The first was at 9:50 AM, when the media app with ID 201111111 displayed an advertisement from ad server A112233. This advertisement's task ID was Task111, and it promoted the advertiser app with ID 101111111. The user's action on this advertisement was a CLICK (click the ad), which could mean clicking the ad and downloading the ad-related app. That is, the user clicked the ad and downloaded the advertiser app with ID 101111111.

[0142] The second instance occurred at 10:04 AM, when a media app with ID 202222222 displayed an ad from ad server A112244. This ad's task ID was Task222, and it was used to promote an advertiser app with ID 102222222. The user's interaction with this ad was "IMPRESSION," meaning the user did not interact with the ad itself.

[0143] Conversion behavior data can be user conversion behavior data collected by an advertiser-developed application (or a second application) within the terminal. This conversion behavior data indicates the conversion value of user actions performed within the advertiser's app. The conversion behavior data can include one or more of the advertiser's app identifier, conversion value, conversion behavior, and conversion time. Conversion behavior data can also include conversion actions, with a corresponding conversion value. Conversion actions can include one or more of the following: user installation, registration, purchase, liking, sharing, subscription, and content publishing.

[0144] The application developed by the advertiser (i.e., the second application) can be the advertiser's app mentioned above. There can be one or more advertiser apps on the device. Advertiser apps can be of different types.

[0145] During a user's use of an advertiser's app, the terminal, such as the advertiser's app itself, can acquire the user's conversion behavior and generate conversion behavior data based on value conversion rules. These value conversion rules can be pre-configured within the advertiser's app. Value conversion rules can include the conversion value corresponding to different conversion behaviors. The conversion values ​​corresponding to different conversion behaviors within the value conversion rules can be different or the same. For example, advertiser app developers can set a corresponding conversion value for each conversion behavior and configure it in the value conversion rules.

[0146] For consumer-related conversions, the conversion value can be defined as the actual amount spent during that conversion. For non-consumer-related conversions, a corresponding conversion value can be assigned to each non-consumer-related conversion based on experience. For example, the conversion value for a user registering an account is 5; the conversion value for a user publishing content is 15; the conversion value for a user making a purchase in the advertiser's app is 50 (e.g., the actual amount spent is 50); the conversion value for a user liking something is 5; and the conversion value for a user sharing something is 10.

[0147] For example, taking the advertiser's app as a video app and the media app as a lifestyle service app, when a user clicks the icon of the lifestyle service app, the terminal responds by launching the lifestyle service app. During the launch of the lifestyle service app, the terminal displays a splash screen ad, which is an advertisement for the video app. When the user clicks the splash screen ad, the terminal responds by redirecting to the app store app and displaying the download page for the video app. When the user clicks the download control, the terminal responds by downloading and installing the video app. The terminal responds by displaying the main interface of the video app when the user clicks the video app icon. During the user's use of the video app, if the user registers an account within the video app, the video app responds by collecting conversion behavior data corresponding to this conversion behavior. The video app determines the conversion value (e.g., 5) corresponding to the registered account based on the pre-configured value conversion rules within the video app. The video app generates conversion behavior data based on the video app ID, the registered account conversion behavior, the conversion value corresponding to this conversion behavior, and the time when the user triggered the conversion behavior. The conversion behavior data can be "Time—Video App ID—Registered Account—5". In this way, the video app records the conversion behavior data corresponding to each conversion behavior of the user when using the video app.

[0148] Understandably, the value conversion rules for different advertiser apps can be the same or different. Conversion behavior data can be understood as the revenue generated for the advertiser app by users during their use of the app.

[0149] Each advertiser app on the device can collect corresponding conversion behavior data when a user uses the advertiser app. For example, during a user's use of the advertiser app, the advertiser app detects the user's conversion behavior and collects the corresponding conversion behavior data.

[0150] In some examples, the terminal obtains multiple conversion behavior data.

[0151] For example, taking conversion behavior data including conversion time, advertiser APP ID, and conversion value as an example, the conversion behavior data collected by each advertiser APP in the terminal on May 1, 2024 can be summarized to generate a conversion behavior data table as shown in Table 2.

[0152] Table 2

[0153] As shown in Table 2, on May 1, 2024, users generated multiple conversion behaviors across various advertiser apps. The first conversion occurred at 10:00 AM, with a user generating a conversion value of 5 in the advertiser app with ID 101111111. For example, the user registered an account at that time. The second conversion occurred at 10:05 AM, with a user generating a conversion value of 50 in the advertiser app with ID 102222222. For example, the user made a purchase at that time. The third conversion occurred at 10:10 AM, with a user generating a conversion value of 15 in the advertiser app with ID 101111111. For example, the user published content at that time. The fourth conversion occurred at 10:15 AM, with a user generating a conversion value of 50 in the advertiser app with ID 102222222. The fifth conversion occurred at 10:20 AM, with a user generating a conversion value of 30 in the advertiser app with ID 103333333.

[0154] Optionally, business data may also include device data. Device data refers to the terminal's device attribute information. For example, device data includes, but is not limited to, one or more of the following: device type, device manufacturer, and device model.

[0155] Optionally, conversion behavior data may also include device data.

[0156] Optionally, the advertiser's app can also integrate SDKs from other different ad servers to display ads. That is, an app can be both an advertiser's app and a media app.

[0157] Understandably, after developing their advertiser app, advertisers need to run ads to increase exposure and attract users to download and use the app. This embodiment of the application can be implemented after the advertiser has completed ad running. Specifically, the advertiser chooses to run ads on one or more ad servers to promote their developed advertiser app. Advertisers can publish ads on different ad servers.

[0158] For example, advertisers can first create an account on the ad server, and then create an ad task. The ad server executes the ad task to display the ad in an app that integrates the ad server's SDK. For instance, advertisers can select one or more of the following options on the ad server based on their needs: ad format, ad budget, time slot, geographic targeting, and target audience, and upload ad content to generate an ad task. The ad server can provide various ad formats, such as splash screen ads, banner ads, interstitial ads, video ads, rewarded ads, and feed ads. Advertisers can choose the ad format based on their budget and target audience. The ad budget is the amount of money the advertiser is prepared to invest. Advertisers can select the time slot and geographic targeting based on the target audience of their app. For example, if the advertiser's app is a lifestyle service app (e.g., the app includes a food delivery function), the advertiser might choose to push ads between 10 AM and 12 PM to increase exposure and accurately target the audience (e.g., working professionals, students). Alternatively, if the advertiser's app is a transportation app for a specific city, the advertiser could choose to advertise within that city to increase exposure. The target audience can be the target group selected by the advertiser's app. The advertising content consists of push notifications related to the advertiser's app, which may include, but are not limited to, one or more of the following: text, images, videos, download links, etc.

[0159] S502. The terminal performs attribution calculations on business data and conversion behavior data to obtain attribution results.

[0160] In some examples, the terminal acquires multiple business data and multiple conversion behavior data. The terminal performs attribution calculations on these data to obtain attribution results. These attribution results indicate the correlation between the conversion behavior data and the business data. The attribution results include related conversion behavior data and business data.

[0161] The terminal analyzes and calculates business data and conversion behavior data, and obtains attribution results based on the business data and related conversion behavior data. These attribution results may include one or more of the following: ad server ID, ad task ID, advertiser app ID, ad display duration, media app ID, region, conversion value, and conversion time.

[0162] Specifically, the terminal can analyze and process business data and conversion behavior data based on attribution models to obtain attribution results. Attribution models can include last-click models, linear attribution models, time decay models, location models, etc. Analysis and processing of business data and conversion behavior data can include techniques such as statistical analysis and data mining.

[0163] It is understandable that the terminal can determine the impact and value of user business data in the media app on conversion behavior data in the advertiser's app through attribution calculation. In the embodiments of this application, the attribution results may include conversion behavior data generated by advertising and related business data, so as to accurately analyze the effectiveness of advertising in the future.

[0164] In this embodiment, the terminal can periodically perform attribution calculations on business data and conversion behavior data to obtain attribution results. For example, the terminal processes business data and conversion behavior data according to an attribution period. For example, the attribution period can be one day, three days, one week, one month, etc.

[0165] Understandably, a terminal can include multiple different attribution periods. For example, a terminal might include three attribution periods: one day, one week, and one month. That is, the terminal performs attribution calculations daily based on the currently collected business data and conversion behavior data; every three days, it performs attribution calculations based on the business data and conversion behavior data collected within those three days; and each month, it performs attribution calculations by combining the business data and conversion behavior data from the entire month. Different attribution periods provide different perspectives for analyzing advertising effectiveness. Short-term periods (such as one day) provide rapid feedback and timely understanding of advertising performance. Long-term periods (such as one month) provide more stable and longer-term trend analysis. Advertisers can gain a more comprehensive understanding of advertising performance to effectively adjust their marketing strategies.

[0166] In this embodiment of the application, after obtaining the attribution result, the terminal storage stores the attribution result locally.

[0167] For example, taking an attribution period of one day and attribution results including conversion time, advertiser APP ID, conversion value, ad server ID, ad task ID, and media APP ID as an example, based on the example in S501, the terminal performs attribution calculations on the business data in Table 1 and the conversion behavior data in Table 2 to obtain the attribution result table shown in Table 3.

[0168] Based on Tables 1 and 2, it can be determined that at 09:50, the media app with ID 201111111 displayed an advertisement for the advertiser app with ID 101111111, and the user clicked on the advertisement and downloaded the advertiser app with ID 101111111. At 10:00, the user's action within the advertiser app with ID 101111111 generated a conversion value of 5. At 10:04, the media app with ID 202222222 displayed an advertisement for the advertiser app with ID 102222222, but the user did not interact with the advertisement. At 10:05, the user's action within the advertiser app with ID 102222222 generated a conversion value of 50. At 10:10, the user's action within the advertiser app with ID 101111111 generated a conversion value of 15. At 10:15, a user's actions in the advertiser's app with ID 102222222 generated a conversion value of 50. At 10:20, a user's actions in the advertiser's app with ID 103333333 generated a conversion value of 30.

[0169] By analyzing Tables 1 and 2, it can be determined that the conversion behavior data at 10:00 and 10:10 in Table 2 were generated after the user installed the advertiser's app with ID 101111111 at 09:50. That is, the two conversion behavior data at 10:00 and 10:10 in the terminal are associated with the business data at 09:50. The conversion behavior data at 10:05 and 10:15 in Table 2 were generated after the user saw an advertisement for the advertiser's app with ID 102222222 at 10:04. That is, the two conversion behavior data at 10:05 and 10:15 in the terminal are associated with the business data at 10:04. The source of the conversion behavior data at 10:20 is unrelated to the advertisement, and the installation source of the advertiser's app with ID 103333333 is unrelated to the advertisement; this advertiser's app may have been downloaded from a website. Therefore, the attribution results table only records conversion behavior data and business data related to the times of 10:00, 10:05, 10:10 and 10:15, as shown in Table 3.

[0170] Table 3

[0171] Understandably, the purpose of attribution calculation is to filter out conversion behavior data that is correlated with user business data related to advertising. Conversion behavior data can be understood as the result of ad placement, while business data is the cause of ad placement. Only after a user sees or interacts with an ad promoting an advertiser's app in the media app does a series of conversion behaviors be triggered within the advertiser's app, generating conversion behavior data. Different types of business data can also have different priorities in attribution calculation. For example, based on experience, if it is determined that a user clicking an ad is more likely to generate conversion behavior data than exposure data, then click data is given higher priority than exposure data. In attribution calculation, click data is calculated first.

[0172] S503: The terminal aggregates the attribution results based on the business dimensions to obtain the first aggregation result.

[0173] In this embodiment of the application, the terminal summarizes and analyzes the attribution results according to the business dimension to obtain the first aggregation result (which can also be described as the first data).

[0174] The first aggregation result is used to indicate the conversion value of the advertiser's app under this business dimension.

[0175] Specifically, the terminal can accumulate the conversion value of the same dimension based on business dimensions. These business dimensions can be represented by business attribute information related to ad display. Business dimensions may include one or more of the following: the application displaying the ad, the ad platform, the ad task, the application promoting the ad, operational data, ad display time, and ad display region. The information contained in the business dimension may be the same as or different from the information contained in the business data. For example, a business dimension may include one or more of the following business attribute information: ad server ID, ad task ID, advertiser app ID, ad display duration, media app ID, operational data, time, region, and conversion value.

[0176] In some examples, the terminal aggregates the attribution results according to the business dimensions specified in the terminal-side aggregation strategy, obtaining a first aggregation result. The terminal-side aggregation strategy can include business dimensions and an aggregation period. The terminal-side aggregation strategy can be a pre-configured strategy in the terminal or a strategy obtained by the terminal from a cloud server.

[0177] The pre-configured endpoint aggregation strategy in the terminal can be determined by the terminal based on historical data uploaded to the server. For example, taking the business dimension as an example, the terminal sorts each attribute information according to the historical data uploaded to the server, and determines the business dimension based on the attribute information that appears first in the sorted list.

[0178] The server-side aggregation strategy can be determined by server developers based on experience or historical data. For example, taking a business dimension as an example, server developers analyze a large number of advertising LTV reports to determine the business attribute information that advertisers are more concerned about, such as ad server ID, media ID, conversion value, time, etc., and determine the business dimension based on the above attribute information.

[0179] In some examples, the terminal can aggregate the attribution results based on the business dimensions in the pre-configured terminal-side aggregation strategy to obtain the first aggregation result.

[0180] In other examples, the terminal can aggregate the attribution results based on the business dimensions in the terminal-side aggregation strategy obtained from the server to obtain the first aggregation result.

[0181] The terminal can also obtain the updated end-side aggregation strategy from the server, and aggregate data according to the business dimensions in the updated end-side aggregation strategy to obtain the first aggregation result.

[0182] It's understandable that advertisers' ad placement and performance evaluation are handled by servers. Therefore, servers can analyze massive amounts of data to understand advertisers' needs. The business dimensions of server-side aggregation strategies are more comprehensive and better suited to advertisers' requirements compared to pre-configured business dimensions on the client-side. Furthermore, while pre-configured client-side aggregation strategies can be considered fixed, server-side aggregation strategies can be updated during use to meet different user needs.

[0183] In this embodiment, the terminal can periodically aggregate attribution results to obtain a first aggregated result. For example, the terminal can process the attribution results according to the aggregation period in the terminal-side aggregation strategy. For example, the aggregation period can be one day, three days, one week, one month, etc.

[0184] It is understandable that a terminal may include multiple different aggregation periods, and data aggregation is performed on the attribution results within each aggregation period according to the different aggregation periods.

[0185] In some examples, the terminal can aggregate the attribution results based on the business dimensions and aggregation period in the pre-configured terminal-side aggregation strategy to obtain the first aggregation result.

[0186] In other examples, the terminal can aggregate the attribution results based on the business dimensions and aggregation period in the terminal-side aggregation strategy obtained from the server, and obtain the first aggregation result.

[0187] Understandably, the endpoint aggregation strategy obtained from the server has higher priority than the endpoint aggregation strategy pre-configured in the terminal. If the terminal includes the endpoint aggregation strategy sent by the server, the terminal will perform data aggregation according to the endpoint aggregation strategy sent by the server. If the terminal does not include the endpoint aggregation strategy sent by the server, the terminal will perform data aggregation according to its own pre-configured endpoint aggregation strategy.

[0188] In other examples, the terminal can aggregate the attribution results based on the pre-configured terminal-side aggregation strategy and the terminal-side aggregation strategy obtained from the server to obtain the first aggregation result.

[0189] For example, if the endpoint aggregation strategy sent by the server only includes the aggregation period, then when the terminal performs aggregation, it aggregates data based on the business dimension in its own pre-configured endpoint aggregation strategy and the aggregation period in the endpoint aggregation strategy sent by the server.

[0190] On-device aggregation strategies can also include value conversion rules. This means that advertiser apps can configure value conversion rules when developing ads. For advertiser apps with configured value conversion rules, their corresponding conversion behavior data includes conversion value (i.e., when acquiring conversion behavior data, the advertiser app receives user conversion behavior within the app and determines the conversion value based on its value conversion rules. The advertiser app generates conversion behavior data based on the conversion behavior, advertiser app ID, conversion value, and conversion time). For advertiser apps without configured value conversion rules, their corresponding conversion behavior data includes the conversion behavior but not the conversion value. When an ad receives conversion behavior data that does not include conversion value, it can determine the conversion value of that conversion behavior based on the value conversion rules in the on-device aggregation strategy for subsequent data aggregation.

[0191] In some examples, the first aggregation result may include device data. For instance, where data collected by media apps and / or advertiser apps includes device data, the first aggregation result after attribution processing and data aggregation on the terminal may include device data.

[0192] In other examples, the first aggregation result may not include the device data. For example, if the data collected by a media app and / or an advertiser app does not include device data, the first aggregation result after the terminal performs attribution processing and data aggregation will not include device data.

[0193] For example, taking the terminal performing data aggregation based on the business dimensions and aggregation period in the terminal-side aggregation strategy obtained from the server as an example, the business dimensions include time, advertiser APP ID, conversion value, ad server ID, ad task ID and media APP ID, and the aggregation period is one day. Based on the example in S502, the terminal performs data aggregation on the attribution results in Table 3 to obtain the first aggregation result table described in Table 4.

[0194] Based on Table 3, it can be determined that the attribution results 10:00 and 10:10 have the same business dimension, and their conversion values ​​can be combined into a single first aggregated result. Similarly, the attribution results 10:05 and 10:15 also have the same business dimension, and their conversion values ​​can be combined into a single first aggregated result.

[0195] Table 4

[0196] Understandably, since the transformation values ​​in the two attribution results are summed, the time is represented by a date.

[0197] For example, Figure 6(a) shows the data processing architecture diagram of the terminal in this embodiment. Figure 6(b) shows the processing timing diagram of each module in the terminal in this embodiment. As described in the previous embodiments, the terminal may include a media APP, an advertiser APP, a first processing module, and a second processing module. The media APP and the advertiser APP are used to collect data. Specifically, the media APP sends business data to the first processing module, and the advertiser APP sends conversion behavior data to the first processing module. The first processing module and the second processing module are used to process the data. Specifically, the first processing module performs attribution calculation based on the business data and conversion behavior data to obtain attribution results. The first processing module sends the attribution results to the second processing module, and the second processing module aggregates the attribution results according to the business dimension to obtain a first aggregation result. The first processing module may also send the first aggregation result to the server so that the server can process the first aggregation result.

[0198] In this application, when aggregating business-dimensional data from the attribution results, data with the same business dimension can be merged into one, greatly simplifying subsequent analysis, saving resources and time. Furthermore, the data in the first aggregation result is all from the same terminal, which can be understood as device-level data. When the terminal interacts with the ad server subsequently, it no longer needs to send user-specific identifiers to represent the device. This improves data privacy and security, fully utilizes local computing power, reduces the amount of data transmitted to the ad server, alleviates the burden on the ad server, and improves data processing efficiency.

[0199] S504, The terminal sends the first aggregation result to the server.

[0200] The server in this context can be a third-party platform's server (i.e., a third-party server), which monitors multiple ad servers. Understandably, ad servers typically don't send local data to other ad servers. Therefore, a third-party platform's server is needed to monitor different ad servers and track ad performance across different channels (i.e., ad servers) to provide advertisers with detailed data analysis reports. After acquiring data from different ad servers, the third-party server processes and analyzes the performance of each ad server, and can then send the performance data for each ad server to the corresponding ad server.

[0201] In this embodiment of the application, the terminal may send the first aggregation result to the server based on network protocols such as Hypertext Transfer Protocol (HTTP) or HTTP channel (HTTPS) for security purposes.

[0202] For example, the terminal sends the first aggregation result to the server via a JSON message format in HTTP.

[0203] In some examples, where the first aggregation result includes device data, the terminal sends the first aggregation result to the server.

[0204] In other examples, where the first aggregation result does not include device data, the terminal obtains the device data and packages the first aggregation result and the device data together before sending them to the server.

[0205] For example, based on the example in Table 3 of S503 above, the terminal sends the first aggregation result and device data to the server as follows:

[0206] S505. The server aggregates the data from the first aggregation result according to the target dimension to obtain the second aggregation result.

[0207] In this embodiment, the server aggregates the first aggregation result according to the target dimension using a big data processing method to obtain a second aggregation result. The second aggregation result is used to indicate the conversion value of the advertiser's app under that target dimension.

[0208] It is understandable that steps S501-S504 are steps executed by the end-side device. In practical applications, the relationship between the server and the terminal is usually one-to-many, meaning that the server can interact with multiple terminals and receive data sent by multiple terminals. For example, the server can receive the first aggregation result corresponding to different terminals respectively.

[0209] In some examples, where the first aggregation result includes device data, the server receives the first aggregation result from multiple terminals. The server then aggregates the first aggregation results from multiple terminals according to the target dimension to obtain the second aggregation result.

[0210] In other examples, where the first aggregation result does not include device data, the server receives the first aggregation result and device data from multiple terminals. The server then aggregates the first aggregation result and device data from the multiple terminals according to the target dimension to obtain a second aggregation result.

[0211] Optionally, taking the first aggregation result including device data as an example, as shown in Figure 7, the server in S505 performs data aggregation on the first aggregation result according to the target dimension to obtain the second aggregation result, which can be specifically implemented in the following steps: S5051-S5052.

[0212] S5051, The server stores the first aggregation result.

[0213] In some examples, the server records the first aggregation result sent by the terminal using log files or call detail records, and persists the first aggregation result (that is, it saves the first aggregation result in a persistent storage medium, such as a hard disk drive, solid-state drive, optical disk, magnetic tape, etc.).

[0214] Understandably, storing the initial aggregation results long-term ensures data integrity and accessibility even when the server is shut down or loses power. It also prevents data loss or corruption due to subsequent processing errors and allows data to be recovered from stored data, guaranteeing data integrity.

[0215] S5052. The server aggregates data based on the target dimension for the first aggregation result to obtain the second aggregation result.

[0216] In some examples, the server transforms the initial aggregation result into structured data with business semantic fields and stores it. For instance, based on the example in S504 above, the initial aggregation result sent by the terminal to the server is text data similar to a log file. This text data is then transformed into data in tabular or other structured formats. The structured data can be relational tables, columnar storage databases, data warehouses, etc. In relational tables, data is stored in rows or columns, with each column corresponding to an attribute or field. Columnar storage databases are a database structure where data is stored in columns, suitable for efficient storage and retrieval of large-scale data. Data warehouses are specifically designed for storing and managing large-scale datasets.

[0217] Optionally, the server can periodically transform and store the representation of the first aggregation result. For example, the server can convert multiple first aggregation results collected in each transformation cycle into structured aggregation data and store it according to the transformation cycle. The transformation cycle can be one day, three days, one week, one month, etc.

[0218] For example, taking a one-day conversion cycle and assuming the first aggregation result includes device data, the server generates a structured aggregation data table as shown in Table 5 based on the first aggregation results sent by each terminal.

[0219] Table 5

[0220] As shown in Table 5, on May 1, 2024, the server received the first aggregation results from terminals with device models A-11 and A-21, device type mobile phones, and device manufacturer A.

[0221] In some examples, after storing structured aggregated data from different terminals, the server summarizes and analyzes the structured aggregated data of the same dimension according to the target dimension to obtain a second aggregation result.

[0222] Specifically, the terminal accumulates the conversion value of items within the same target dimension. The target dimension can be represented using attribute information related to the evaluation of the advertisement. Target dimensions can include the business dimensions mentioned above, or one or more of the following: time dimension, geographic dimension, device dimension, etc. The business dimension can include one or more of the following business attribute information: ad server ID, ad task ID, advertiser APP ID, ad display duration, media APP ID, operation data, time, geographic location, conversion value, etc. The device dimension can include one or more of the following: device type, device manufacturer, device model, etc.

[0223] It is understandable that a server can include one or more target dimensions. Each target dimension can contain multiple sets of attribute information. Different target dimensions contain different sets of attribute information.

[0224] It is understandable that the key metric for evaluating the effectiveness of advertising is LTV, and conversion value is the basic unit for calculating LTV. Therefore, the attribute information in the aggregated results must include conversion value.

[0225] Optionally, the server can periodically aggregate structured data. For example, the server can process structured data according to an aggregation cycle, such as one day, three days, one week, or one month.

[0226] It is understandable that a server can include multiple different aggregation periods, and perform data aggregation on the structured aggregated data within each aggregation period according to the different aggregation periods.

[0227] In some examples, the server aggregates structured aggregated data according to a cloud-side aggregation strategy to obtain a second aggregation result. This cloud-side aggregation strategy can be determined by server developers based on experience or historical data. The cloud-side aggregation strategy may include one or more of the following: target dimension and aggregation cycle.

[0228] For example, server developers can analyze a large number of ad LTV reports to identify the ad-related attributes that advertisers are most interested in evaluating, such as ad server ID, media ID, conversion value, time, device type, and device model. Based on these attributes, they can determine target dimensions so that when advertisers query LTV later, the processing speed can be accelerated.

[0229] For example, based on the example in Table 5 above, taking an aggregation period of one day, and with target dimension 1 including business dimensions, which include advertiser APP ID, ad server ID and conversion value, we get the second aggregation result table shown in Table 6.

[0230] Specifically, based on the target dimension, we determine the advertiser app ID, conversion value, and ad server ID columns in Table 5, ignoring the other columns. Based on these three columns, we find that the advertiser app ID and ad server ID are the same for the two groups with a conversion value of 20 and a conversion value of 70, respectively. These two groups can be merged into one. This results in the second aggregation result table shown in Table 6.

[0231] Table 6

[0232] For example, based on the example in Table 5 above, with an aggregation period of one day, and target dimension 2 including business dimension and device dimension, where the business dimension includes advertiser APP ID and conversion value, and the device dimension includes device model, the second aggregation result table shown in Table 7 is obtained.

[0233] Specifically, based on the target dimension, we determine the advertiser's app ID, conversion value, and device model in Table 5, ignoring the other columns. Based on these three columns, we find that the advertiser's app ID and device model are the same in the two groups with conversion values ​​of 50 and 70, respectively. These two groups can be merged into one. This results in the second aggregation result table shown in Table 7.

[0234] Table 7

[0235] Understandably, the server can summarize and aggregate structured aggregated data according to different target dimensions to obtain the corresponding second aggregation result.

[0236] Optionally, the server writes the second aggregation result to the database so that advertisers can quickly query the aggregation result through the database.

[0237] Understandably, the server performs a second data aggregation on the data from multiple terminals to summarize the data, so that the aggregation results can be quickly queried and LTV reports can be generated quickly.

[0238] Optionally, the server can choose not to perform data aggregation and instead write the structured aggregated data completely into the database. Subsequently, when the server receives an LTV report task created by an advertiser, it queries the database for the target data corresponding to that LTV report task, aggregates the target data according to the target dimensions in the LTV report task, and obtains a second aggregation result. An LTV report can then be generated based on this second aggregation result.

[0239] For example, as shown in Figure 8(a), this is a data processing architecture diagram of the server in this embodiment. As described in the foregoing embodiments, as shown in Figure 8(a), the server may include a data acquisition layer, a data processing layer, a data service layer, and a data application layer. The server may include a third processing module. The third processing module can perform corresponding operations in the data acquisition layer, the data processing layer, and the data service layer. For example, the server receives first aggregated data sent by a terminal (the terminal is not shown in Figure 8(a)). The third processing module persists the first aggregated data in the data acquisition layer, and then the data acquisition layer pushes the stored data in batches to the message queue of the data processing layer so that the data processing layer can process the data (the message queue is not shown in Figure 8(a)). The third processing module performs structured storage of the data in the data processing layer, and performs data aggregation on the structured data according to the target dimension to obtain a second aggregation result. The third module stores the second aggregation result in the database in the data service layer for subsequent query and other operations. The data application layer may include generating an LTV report in response to advertiser operations. As shown in Figure 8(b), this is a processing timing diagram of each module when the server performs data aggregation in this embodiment. The specific steps are described above and will not be repeated here.

[0240] It is understandable that S501-S505 above are illustrated using a third-party server as an example. In other examples, the advertiser has a server with strong computing power (i.e., the advertiser server), which can obtain relevant data from the third-party server and aggregate that data. The method also includes: taking advertiser 1 as an example, after step S504, the server can package the relevant data of advertiser 1 from the first aggregation results sent by multiple terminals and send it to advertiser 1's server. Advertiser 1's server then aggregates this data to obtain a third aggregation result. Advertiser 1's server generates an LTV report based on the third aggregation result so that advertiser 1 can evaluate advertising effectiveness and optimize its advertising strategy.

[0241] Understandably, in other examples, the terminal interacts directly with the advertiser server. The terminal only obtains data related to that advertiser and sends the aggregated data to the advertiser server for further processing. Specifically, the terminal obtains the advertiser identifier (such as the advertiser's app ID), business data, and conversion behavior data corresponding to that advertiser identifier. Based on the advertiser identifier, attribution calculations are performed on the business data and the corresponding conversion behavior data to obtain the attribution result for that advertiser identifier. Based on the business dimensions, the attribution result for that advertiser identifier is aggregated to obtain the first aggregation result. The terminal sends this first aggregation result to the advertiser server. The advertiser server receives the first aggregation results for the advertiser identifier from multiple terminals and aggregates them according to the target dimensions to obtain the second aggregation result.

[0242] Optionally, after S505, when the server receives an operation from an advertiser to query an LTV report, the server generates an LTV report in response to the operation. Figure 9 shows the processing timing diagram of each module when the server generates an LTV report in this embodiment. The server may include a data application layer and a data service layer. Advertisers can create LTV report tasks on the display interface provided by the server according to their usage needs. Then, the server, such as the server's data application layer, can receive the LTV report task. The LTV report task may include different target dimensions, different lifecycles (lifecycle can be understood as the time period during which the user uses the advertiser's APP after activation), advertiser APP ID, etc. The data application layer initiates a query request to the data service layer in response to the LTV report task. The data service layer receives the query request sent by the data application layer and queries the database to determine the analysis results. Specifically, the data service layer converts the query request into a structured query language (SQL) query request and sends the SQL query request to the database in the data service layer. The database responds to the SQL query request, executes the query, determines the corresponding analysis results, and returns the analysis results to the data service layer. The data service layer encapsulates and formats the analysis results, then sends the processed data to the data application layer. The data application layer receives the data and generates and displays an LTV report based on it. This LTV report can be a chart, table, or other form of visualization, used to help advertisers understand and analyze LTV data for the selected dimensions.

[0243] For example, Figure 10 shows an LTV report in chart form. As shown in Figure 10(a), the horizontal axis in the LTV report represents the measurement period, and the vertical axis represents LTV data (e.g., conversion value). The advertiser's app runs ads on ad servers 1, 2, and 3. Therefore, the advertiser sets the query dimension of this LTV report to the ad server ID to view the performance of ads on ad servers 1, 2, and 3. LTV for the day represents the LTV data generated on the day the advertiser's app is activated. LTV2 represents the LTV data generated on the day the advertiser's app is activated and the following two days (two days in total). LTV3 represents the LTV data generated from the day the advertiser's app is activated to the third day (three days in total). LTV4 represents the LTV data generated from the day the advertiser's app is activated to the fourth day (four days in total). LTV5 represents the LTV data generated from the day the advertiser's app is activated to the fifth day (five days in total). LTV6 represents the LTV data generated from the day the advertiser's app is activated to the sixth day (six days in total). LTV7 represents the LTV data generated from the day the advertiser's app is activated to the seventh day (one week). LTV14 represents the LTV data generated from the day the advertiser's app is activated to two weeks after activation. LTV30 represents the LTV data generated from the day the advertiser's app is activated to one month after activation. Users can click the option symbol to the right of "By Ad Server" in the LTV report to display multiple target dimensions as shown in Figure 10(b). Users can select different dimensions to view the corresponding LTV reports.

[0244] Optionally, users can configure endpoint aggregation policies on the server and send these policies to the endpoints so that the endpoints can aggregate data according to the policies. Users can also update the endpoint aggregation policies on the server, and the server will send the updated policies to the endpoints so that the endpoints can aggregate data according to the updated policies. Alternatively, users can configure cloud-side aggregation policies on the server, and the server will aggregate data according to these cloud-side aggregation policies. Users can also update cloud-side aggregation policies on the server, and the server will aggregate data according to the updated cloud-side aggregation policies.

[0245] Figure 11 shows the flowchart of the method for configuring the end-side aggregation strategy.

[0246] S1101, Configure the server-side aggregation strategy.

[0247] In some examples, the server can respond to configuration actions by users (such as server developers) and configure client-side aggregation strategies. These strategies can include one or more of the following: aggregation period, aggregation dimension, reported device data, and value conversion rules. The number of aggregation periods can be one or more, for example, 1 day, 3 days, 7 days, 30 days, etc. The aggregation dimension can be the business dimension mentioned above, which can include one or more of the following: ad server ID, ad task ID, advertiser app ID, ad display duration, media app ID, operation data, time, region, conversion value, etc. The reported device data can include one or more of the following: device type, device manufacturer, device model, etc. Value conversion rules include different conversion behaviors and their corresponding conversion values. For example, for paid conversion behaviors, the conversion value can be defined as the amount corresponding to the conversion behavior. For non-paid conversion behaviors, a corresponding conversion value can be set for each conversion behavior.

[0248] Understandably, if the advertiser's app has configured value conversion rules, the conversion behavior data obtained by the terminal will include conversion value. In this case, the terminal aggregates data based on the conversion behavior data containing conversion value. If the advertiser's app has not configured value conversion rules, the conversion behavior data obtained by the terminal will include conversion behavior but not conversion value. In this case, when aggregating data, the terminal first determines the conversion value corresponding to the advertiser's app's conversion behavior according to the terminal-side aggregation strategy, and then performs data aggregation based on that conversion value.

[0249] In this embodiment of the application, the server can also store the configured end-side aggregation strategy in the database.

[0250] Optionally, the server can also update the client-side aggregation strategy in response to user update operations.

[0251] In other examples, the server can also respond to the advertiser's configuration actions by configuring an on-device aggregation strategy for the advertiser's app. This on-device aggregation strategy is only used by the terminal to process data related to the advertiser's app. The server can send this on-device aggregation strategy to the terminal so that the terminal can aggregate the data from the advertiser's app according to the strategy.

[0252] Optionally, the server can also respond to the advertiser's update operation and update the aggregation strategy on the advertiser's app side.

[0253] Understandably, the server can respond to configuration operations by users (such as server developers) and configure cloud-side aggregation policies on the server. These client-side aggregation policies can include one or more of the following: aggregation period and aggregation dimension. The number of aggregation periods can be one or more, for example, aggregation periods may include 1 day, 3 days, 7 days, 30 days, etc. The aggregation dimension can be the target dimension mentioned above, which can include one or more of the following: business dimension, device dimension, time dimension, geographic dimension, etc.

[0254] Understandably, the server can configure the advertiser's app cloud aggregation strategy on the server, based on the advertiser's settings. This advertiser app cloud aggregation strategy is only used by the server to process data related to that advertiser app.

[0255] Optionally, the server can also update the cloud-side aggregation strategy in response to user update operations. Alternatively, the server can also update the advertiser's app cloud-side aggregation strategy in response to advertiser update operations.

[0256] S1102, The server sends the end-side aggregation policy to the terminal.

[0257] In this embodiment of the application, the server may proactively push the terminal-side aggregation strategy to the terminal based on network protocols such as Hypertext Transfer Protocol (HTTP) or HTTP Channel (HTTPS) for security purposes.

[0258] Terminals can also send aggregation policy requests to the server to request the terminal-side aggregation policy.

[0259] For example, the server sends the end-to-end aggregation strategy to the terminal via a JSON message format in HTTP.

[0260] For example, the endpoint aggregation strategy includes aggregation periods of 1 day, 3 days, 7 days, and 30 days. Taking the business dimension as an example, the endpoint aggregation strategy sent by the server to the terminal is as follows:

[0261] Optionally, if the user updates the endpoint aggregation policy, the server can also send the updated endpoint aggregation policy to the endpoint.

[0262] S1103. The terminal aggregates the attribution results according to the terminal-side aggregation strategy to obtain the first aggregation result.

[0263] In this embodiment of the application, the terminal receives the end-side aggregation strategy and stores the end-side aggregation strategy.

[0264] In this embodiment, S503 can be replaced by the terminal aggregating the obtained attribution results according to the terminal-side aggregation strategy to obtain the first aggregation result.

[0265] In some examples, for attribution data that includes conversion behavior but not conversion value, the terminal first determines the conversion value corresponding to the conversion behavior in the attribution data according to the value conversion rules in the terminal aggregation strategy. Then, the processed data is aggregated to obtain the first aggregation result.

[0266] Optionally, if the user updates the endpoint aggregation strategy, the terminal receives and stores the updated endpoint aggregation strategy, and performs data aggregation according to the updated endpoint aggregation strategy.

[0267] For example, taking the advertiser app with ID 101111111 as an example, on device A, a user installed and used the advertiser app with ID 101111111 on May 1, 2024. During use, multiple conversion behaviors occurred. This advertiser app has been configured with value conversion rules, and the conversion behavior data obtained by the terminal includes conversion value. According to the terminal-side aggregation strategy in S1102, for example, the business dimensions in the terminal-side aggregation strategy include conversion time, advertiser app ID, conversion value, ad server ID, ad task ID, and media app ID. Starting from the activation date of May 1, 2024, the terminal collects data on the user's use of the advertiser app according to the aggregation cycle and performs attribution calculations, obtaining the attribution result table shown in Table 8.

[0268] Table 8

[0269] Based on the end-side aggregation strategy exemplified in S1102, the attribution result is aggregated to obtain the first aggregation result table shown in Table 9.

[0270] Table 9

[0271] Based on Table 8, aggregation will begin on the activation date of May 1, 2024. For aggregation period 1, the data from May 1, 2024 should be accumulated to obtain the first aggregation result for that period. For aggregation period 3, the data from May 1 to May 3, 2024 should be accumulated to obtain the first aggregation result for that period. For aggregation period 7, the data from May 1 to May 7, 2024 should be accumulated to obtain the first aggregation result for that period. For aggregation period 30, the data from May 1 to May 30, 2024 should be accumulated to obtain the first aggregation result for that period. This process will yield the first aggregation result for each aggregation period.

[0272] In this application, through the aforementioned method, after advertising is delivered, the media app on the terminal collects business data, and the advertiser app collects conversion behavior data. The terminal performs attribution calculations and data aggregation on this business data and conversion behavior data to obtain a first aggregation result. The server aggregates data based on the first aggregation results from different terminals to obtain a second aggregation result. The data in the first aggregation result all originates from the same terminal, eliminating the need to collect user personal identifiers, thus improving data privacy and security. The first aggregation result contains all conversion behavior data related to the advertisement, allowing for complete data collection, which is beneficial for the server to provide a more comprehensive and accurate LTV report. Moreover, the terminal can fully utilize its local computing power to perform attribution calculations and aggregation on terminal data, significantly reducing the amount of data transmitted to the server, alleviating server load, and improving data processing efficiency and system performance. The server can perform multi-dimensional data aggregation based on the first aggregation results from multiple terminals, enabling a more comprehensive analysis and evaluation of advertising effectiveness, thus providing a more accurate LTV report. This helps advertisers gain a deeper understanding of advertising effectiveness, identify potential areas for improvement and optimization, and ultimately optimize their advertising strategies.

[0273] It should be understood that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0274] Figure 12 shows a flowchart of another data processing method provided in an embodiment of this application, applied to a terminal. The method includes:

[0275] S1201. Obtain business data when an advertisement is displayed in the first application on the terminal. The business data is used to indicate the exposure of the advertisement and / or the user's operation on the advertisement. The advertisement is used to promote the second application.

[0276] The first application is the media app mentioned above, and the second application is the advertiser app mentioned above.

[0277] The aforementioned terminals include terminal 1 to terminal N, where N is a positive integer. That is, the number of the aforementioned terminals can be one or more.

[0278] The specific implementation method of the terminal obtaining business data when displaying advertisements in the first application of the terminal in this embodiment can be found in S501 above, and will not be repeated here.

[0279] S1202. Obtain conversion behavior data within the second application. The conversion behavior data is used to indicate the conversion value of conversion behaviors performed by the user in the second application.

[0280] The conversion behavior data includes the identifier of the second application and its conversion value.

[0281] In some examples, if value conversion rules are pre-configured in the second application, then the conversion behavior data within the second application can be obtained, including: receiving the user's conversion behavior within the second application and determining the conversion value corresponding to the conversion behavior based on the pre-configured value conversion rules in the second application.

[0282] In other examples, if no value conversion rules are configured in the second application, but the terminal-side aggregation strategy includes value conversion rules, then the conversion behavior data within the second application is obtained, including: receiving the user's conversion behavior within the second application, and determining the conversion value corresponding to the conversion behavior based on the value conversion rules included in the terminal-side aggregation strategy.

[0283] Among them, the terminal-side aggregation strategy can be a strategy pre-configured in the terminal or a strategy obtained by the terminal from the cloud-side server.

[0284] In this embodiment of the application, the terminal may also receive the end-side aggregation strategy sent by the server.

[0285] The specific implementation method of the terminal obtaining the conversion behavior data in the second application in this embodiment can be found in S501 above, and will not be repeated here.

[0286] S1203. Based on the business dimension, aggregate the business data and conversion behavior data to obtain the first data. The first data is used to indicate the conversion value of the second application under the business dimension.

[0287] The first data is the first aggregation result mentioned above.

[0288] In some examples, if multiple business data and multiple conversion behavior data are obtained, the business data and conversion behavior data are aggregated according to the business dimension to obtain the first data. This includes: performing attribution calculations on the multiple business data and multiple conversion behavior data to obtain attribution results, which are used to indicate the relationship between conversion behavior data and business data; and aggregating the conversion behavior data and business data with relationships according to the business dimension to obtain the first data.

[0289] The business dimension includes one or more of the following: the application displaying the advertisement (i.e., the media app), the advertising platform, or the advertising task. The business data includes one or more of the following: the identifier of the first application, the identifier of the advertising platform for the advertisement, or the identifier of the advertising task corresponding to the advertisement.

[0290] Optionally, business dimensions can be represented by business attribute information related to ad display. Business dimensions may also include ad display duration, the application promoted by the ad (i.e., the advertiser's app), operational data, ad display time, and ad display region. Business data may also include one or more of the following: ad display duration, the identifier of the advertiser's app promoted by the ad, the time the ad was displayed, the region where the ad was displayed, operational data related to the ad, operational data related to the skip control in the ad, and operational data related to the close control in the ad.

[0291] It's understandable that business dimensions correspond to business data, and a business dimension can include dimensions corresponding to one or more data points within the business data. For example, business data might include media app ID, advertising platform ID, advertiser app ID, and ad display duration. Business dimensions might include media app ID, advertising platform ID, and advertiser app ID. During data aggregation, data is aggregated based on these three dimensions: media app ID, advertising platform ID, and advertiser app ID.

[0292] In some examples, business data and conversion behavior data are aggregated according to business dimensions to obtain the first data, including: aggregating business data and conversion behavior data according to business dimensions based on the client-side aggregation strategy.

[0293] The endpoint aggregation strategy includes one or more of the following: business dimension or aggregation period. The endpoint aggregation strategy can be a strategy pre-configured in the endpoint or a strategy obtained by the endpoint from the cloud server.

[0294] In this embodiment of the application, the terminal aggregates business data and conversion behavior data according to the business dimension to obtain the first data. The specific implementation method can be found in S502-S503 above, and will not be repeated here.

[0295] S1204, Send the first data to the server.

[0296] In some embodiments, if the first data does not include the terminal's device attribute information, the terminal may also send the terminal's device attribute information to the server.

[0297] The device attribute information refers to the device data mentioned above.

[0298] The specific implementation of the terminal sending the first data to the server in this embodiment can be found in S504 above, and will not be repeated here.

[0299] Correspondingly, after the terminal executes S1204, the server executes S1205.

[0300] S1205. The server receives the first data from the terminal, wherein the first data is used to indicate the conversion value of the second application under the business dimension. The first data is obtained by the terminal after aggregating business data and conversion behavior data according to the business dimension. The business data is used to indicate the exposure of the advertisement and / or the user's operation on the advertisement when the advertisement is displayed in the first application of the terminal. The advertisement is used to promote the second application. The conversion behavior data is used to indicate the conversion value of the conversion behavior performed by the user in the second application of the terminal.

[0301] It is understandable that the server can receive the first data from each of terminals from terminal 1 to terminal N. That is, the server can receive the first data from multiple terminals. Alternatively, the server can also receive the first data from a single terminal.

[0302] In some examples, if the first data does not include the device attribute information of the terminal, the server may also receive device attribute information of multiple terminals.

[0303] After the server receives the first data from multiple terminals, it can also process that first data.

[0304] Specifically, the server can also aggregate the first data from multiple terminals based on the target dimension to obtain second data, which is used to indicate the conversion value of the second application under the target dimension.

[0305] The target dimension includes one or more of the following dimensions: business dimension or device dimension.

[0306] Optionally, the target dimension can be represented using attribute information related to the evaluation of the advertisement. The target dimension may also include time dimension, geographic dimension, etc.

[0307] In this embodiment of the application, the server receives first data from multiple terminals, and the specific implementation method for processing the first data can be found in S505 above, which will not be repeated here.

[0308] In this embodiment of the application, the server can also be configured with an end-side aggregation strategy.

[0309] Specifically, the server generates an end-side aggregation policy in response to the user configuration operation and sends the end-side aggregation policy to multiple terminals.

[0310] Among them, the endpoint aggregation strategy includes one or more of the following: business dimension or aggregation cycle.

[0311] In some examples, the endpoint aggregation strategy may also include a value conversion rule, which is used to determine the conversion value of a conversion action.

[0312] Optionally, the endpoint aggregation strategy may also include the device data that needs to be reported, so that the terminal can send its own device data to the server, which is beneficial for the server to aggregate data according to the device dimension.

[0313] Optionally, the server can also respond to user update operations, update the end-side aggregation policy, and send the updated end-side aggregation policy to multiple terminals.

[0314] The specific implementation of the server's response to the user configuration operation to generate the end-side aggregation strategy in this embodiment can be found in S1101-S1102 above, and will not be repeated here.

[0315] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0316] This application embodiment can divide the above-described electronic device into functional modules based on the method example described above. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing module. The integrated modules can be implemented in hardware or as software functional modules. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0317] Based on the same inventive concept, this application provides a data processing device. The data processing device provided in this application is applied to the terminal 11 shown in FIG3. FIG13 is a schematic diagram of the structure of a data processing device provided in this application. The data processing device can be used to implement the methods described in the above method embodiments. Exemplarily, the data processing device may specifically include: a processing module 1301, a transceiver module 1302, and a display module 1303.

[0318] The processing module 1301 is used to support the data processing device in performing any of the processing functions shown in Figures 5-12. In this embodiment, taking the data processing device applied to a terminal as an example, the processing module 1301 is used to acquire business data when an advertisement is displayed in a first application on the terminal. The business data is used to indicate the exposure of the advertisement and / or the user's actions on the advertisement, which is used to promote a second application. The processing module 1301 is also used to acquire conversion behavior data within the second application. The conversion behavior data is used to indicate the conversion value of the conversion behavior performed by the user in the second application. The processing module 1301 is also used to aggregate the business data and conversion behavior data according to a business dimension to obtain first data, which is used to indicate the conversion value of the second application under the business dimension.

[0319] The transceiver module 1302 is used to support the data processing device in performing the transceiver functions of any one of Figures 5-12. In this embodiment, the transceiver module 1302 is used to send first data to the server. The transceiver module 1302 is also used to send the terminal's device attribute information to the server. The transceiver module 1302 is also used to receive the end-side aggregation strategy sent by the server.

[0320] The display module 1303 is used to support the data processing device in performing the display function of any one of Figures 5-12. In this embodiment, the display module 1303 is used to display advertisements.

[0321] The technical effects of the data processing device shown in Figure 13 can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here. The processing module 1301 involved in the data processing device shown in Figure 13 can be implemented by a processor or processor-related circuit components, and can be a processor or a processing module. The display module 1303 can be implemented by display screen-related components.

[0322] This application also provides a chip system, as shown in FIG14. The chip system 1400 includes at least one processor 1401 and at least one interface circuit 1402. As an example, when the chip system 1400 includes one processor and one interface circuit, the processor can be the processor 1401 shown in the solid box in FIG14 (or the processor 1401 shown in the dashed box), and the interface circuit can be the interface circuit 1402 shown in the solid box in FIG14 (or the interface circuit 1402 shown in the dashed box). When the chip system 1400 includes two processors and two interface circuits, the two processors include the processor 1401 shown in the solid box and the processor 1401 shown in the dashed box in FIG14, and the two interface circuits include the interface circuit 1402 shown in the solid box and the interface circuit 1402 shown in the dashed box in FIG14. This is not a limitation.

[0323] Processor 1401 and interface circuit 1402 can be interconnected via lines. For example, interface circuit 1402 can be used to receive signals. As another example, interface circuit 1402 can be used to send signals to other devices (e.g., processor 1401). Exemplarily, interface circuit 1402 can read instructions stored in memory and send the instructions to processor 1401. When the instructions are executed by processor 1401, the steps in the above embodiments can be performed. Of course, the chip system may also include other discrete devices, and this application embodiment does not specifically limit this.

[0324] Optionally, there can be one or more processors in the chip system. The processor can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor that reads software code stored in memory.

[0325] Optionally, the chip system may also include a memory (not shown in Figure 14). There may be one or more memories, which may be integrated with the processor or disposed separately from it; this application does not limit this. For example, the memory may be a non-transient processor, such as read-only memory (ROM), which may be integrated with the processor on the same chip or disposed separately on different chips. This application does not specifically limit the type of memory or the arrangement of the memory and processor. For example, the chip system may be a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-a-chip (SoC), a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0326] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The method steps disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0327] This application also provides a computer storage medium storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device performs the method described in the above-described method embodiments.

[0328] Computer-readable storage media include, but are not limited to, any of the following: USB flash drive, portable hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media capable of storing program code.

[0329] This application provides a computer program product, which includes a computer program or instructions that, when run on a computer, cause the computer to perform the methods described in the above-described method embodiments.

[0330] In addition, this application also provides an apparatus, which may specifically be a chip, component or module. The apparatus may include a connected processor and a memory. The memory is used to store computer execution instructions. When the apparatus is running, the processor can execute the computer execution instructions stored in the memory to cause the apparatus to perform the methods in the above-described method embodiments.

[0331] In addition, this application also provides a system, which may specifically be a chip, component or module. The system may include a connected processor and a memory. The memory is used to store computer execution instructions. When the system is running, the processor can execute the computer execution instructions stored in the memory to enable the system to perform the methods in the above-described method embodiments.

[0332] In this embodiment, the electronic device, computer storage medium, computer program product or chip are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects of the corresponding method provided above, and will not be repeated here.

[0333] The steps of the methods or algorithms described in conjunction with the embodiments of this application can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, compact disc read-only memory (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an application-specific integrated circuit (ASIC).

[0334] Through the above description of the embodiments, those skilled in the art will clearly understand that, for the sake of convenience and brevity, the division of the above functional modules is only used as an example. In practical applications, the above functions can be assigned to different functional modules as needed; that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0335] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The embodiments can be combined with or referenced to each other without conflict. The apparatus embodiments described above are merely illustrative; for example, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0336] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0337] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0338] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially or in other words, the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. The software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0339] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A data processing method, characterized in that, Applied to a terminal, the method includes: The system acquires business data when an advertisement is displayed in a first application on the terminal. The business data is used to indicate the exposure of the advertisement and / or the user's operation on the advertisement. The advertisement is used to promote a second application. Acquire conversion behavior data within the second application, the conversion behavior data being used to indicate the conversion value of conversion behaviors performed by the user in the second application; Based on the business dimension, the business data and the conversion behavior data are aggregated to obtain first data, which is used to indicate the conversion value of the second application under the business dimension. Send the first data to the server.

2. The method according to claim 1, characterized in that, Multiple sets of business data and multiple sets of conversion behavior data were obtained; The aggregation of the business data and the conversion behavior data according to the business dimension to obtain the first data includes: Attribution calculations are performed on multiple sets of business data and multiple sets of conversion behavior data to obtain attribution results, which are used to indicate the correlation between conversion behavior data and business data; Based on the aforementioned business dimensions, the conversion behavior data and business data with related relationships are aggregated to obtain the first data.

3. The method according to claim 1 or 2, characterized in that, The business dimension includes one or more of the following dimensions: application of display advertising, advertising platform or advertising task; The business data includes one or more of the following: the identifier of the first application, the identifier of the advertising platform of the advertisement, or the identifier of the advertising task corresponding to the advertisement.

4. The method according to any one of claims 1-3, characterized in that, The method further includes: Send the terminal's device attribute information to the server.

5. The method according to any one of claims 1-4, characterized in that, The aggregation of the business data and the conversion behavior data according to the business dimension to obtain the first data includes: According to the terminal-side aggregation strategy, the business data and the conversion behavior data are aggregated according to the business dimensions; The endpoint aggregation strategy includes one or more of the following: the business dimension or the aggregation period.

6. The method according to any one of claims 1-5, characterized in that, The conversion behavior data includes the identifier of the second application and its conversion value; The acquisition of conversion behavior data within the second application includes: Receive user conversion behavior within the second application; The conversion value of the conversion behavior is determined based on the value conversion rules included in the end-side aggregation strategy.

7. The method according to claim 5 or 6, characterized in that, The method further includes: Receive the end-side aggregation strategy sent by the server.

8. A data processing method, characterized in that, Applied to a server, the method includes: The system receives first data from multiple terminals, wherein the first data is used to indicate the conversion value of the second application under the business dimension, the first data is obtained by the terminal after aggregating business data and conversion behavior data according to the business dimension, the business data is used to indicate the exposure of the advertisement and / or the user's operation on the advertisement when the advertisement is displayed in the first application of the terminal, the advertisement is used to promote the second application, and the conversion behavior data is used to indicate the conversion value of the conversion behavior performed by the user in the second application of the terminal.

9. The method according to claim 8, characterized in that, The method further includes: Based on the target dimension, the first data from the multiple terminals is aggregated to obtain second data, which is used to indicate the conversion value of the second application under the target dimension.

10. The method according to claim 8 or 9, characterized in that, The method further includes: Receive device attribute information from the multiple terminals.

11. The method according to any one of claims 8-10, characterized in that, The target dimension includes one or more of the following dimensions: business dimension or device dimension.

12. The method according to any one of claims 8-11, characterized in that, The method further includes: A client-side aggregation strategy is generated in response to a user configuration operation, wherein the client-side aggregation strategy includes one or more of the following: the business dimension or the aggregation period; The end-side aggregation strategy is sent to the multiple terminals.

13. The method according to claim 12, characterized in that, The endpoint aggregation strategy also includes value conversion rules, which are used to determine the conversion value of conversion behaviors.

14. A terminal, characterized in that, The terminal includes a processor and a memory coupled to the processor. The memory is used to store computer-readable instructions, and when the processor reads the computer-readable instructions from the memory, the terminal causes the terminal to perform the method as described in any one of claims 1-7.

15. A server, characterized in that, The server includes a processor and a memory coupled to the processor, the memory being used to store computer-readable instructions, which, when read from the memory by the processor, cause the terminal to perform the method as described in any one of claims 8-13.

16. A chip system, characterized in that, It includes at least one processor and at least one interface circuit, the at least one interface circuit being used to perform transceiver functions and send instructions to the at least one processor, the at least one processor executing the instructions, the at least one processor performing the method as described in any one of claims 1-7 or 8-13.

17. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program that, when run on an electronic device, causes the electronic device to perform the method as described in any one of claims 1-7 or 8-13.

18. A computer program product, characterized in that, The computer program product includes: a computer program or instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1-7 or 8-13.

Images