Exposure attribution method and device for advertisement return

Abstract

The invention discloses an exposure attribution method and device for advertisement return, and the method comprises the steps: carrying out the attribution analysis of a conversion behavior in response to the conversion behavior implemented by a user in a business process, and obtaining the attribution data corresponding to the conversion behavior; the attribution data at least comprises an attribution channel; determining a return strategy corresponding to the attribution data based on the attribution data; and determining the return data based on the return strategy, and returning the return data to the attribution channel, thereby realizing reliable analysis of the conversion data, and providing rich and accurate sample data for formulating a later advertisement plan.

Description

Technical Field

[0001] The present disclosure generally relates to the field of backend technologies, and particularly relates to an exposure attribution method and apparatus for advertisement feedback. Background Art

[0002] In related technologies, advertisements are usually used to promote user conversion. However, in actual conversion attribution analysis, attribution can usually only be performed on users who directly convert through advertisements, while more customers will choose to implement conversion from the application market under the subtle influence of advertisements. In this conversion process, the current attribution logic cannot perceive that the conversion of users is due to their interest in a certain advertisement, and will attribute it to natural traffic, so such users cannot be attributed to the corresponding advertisement plan, thus affecting the formulation of advertisement plans. Summary of the Invention

[0003] In view of the above defects or deficiencies in the prior art, it is desirable to provide an exposure attribution method and apparatus for advertisement feedback.

[0004] In a first aspect, an embodiment of the present application provides an exposure attribution method for advertisement feedback, including:

[0005] In response to a conversion behavior implemented by a user in a business process, performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior; the attribution data at least includes an attribution channel;

[0006] Determining a feedback strategy corresponding to the attribution data based on the attribution data;

[0007] Determining feedback data based on the feedback strategy, and feeding back the feedback data to the attribution channel.

[0008] In some embodiments, the performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior includes:

[0009] Identifying whether an attribution clue is carried in the conversion behavior;

[0010] When the attribution clue is carried in the conversion behavior, obtaining the attribution data according to the attribution clue;

[0011] When the attribution clue is not carried in the conversion behavior, obtaining a user identifier corresponding to the user, and determining the attribution data based on the user identifier.

[0012] In some embodiments, the determining the attribution data based on the user identifier includes:

[0013] Based on the user identifier, obtaining prior browsing data of the user;

[0014] Determine the attribution data based on the prior browsing data.

[0015] In some embodiments, before obtaining the prior browsing data of the user based on the user identifier, the method further includes:

[0016] When the user browses the target advertisement previously, obtain the browsing data and generate a mapping relationship between the browsing data and the user identifier.

[0017] In some embodiments, the feedback strategy includes an advertising plan corresponding to the attribution channel, and determining the feedback data based on the feedback strategy includes:

[0018] Determine the user attributes corresponding to the user based on the advertising plan;

[0019] When the user attributes corresponding to the user are target attributes, determine the advertising plan as the feedback data.

[0020] In some embodiments, performing attribution analysis on the conversion behavior to obtain the attribution data corresponding to the conversion behavior includes:

[0021] Judging whether the conversion behavior is successfully attributed in sequence according to a preset attribution type order, and obtaining the attribution data corresponding to the conversion behavior when the attribution is successful.

[0022] In a second aspect, an exposure attribution device for advertisement feedback provided by an embodiment of the present application includes:

[0023] An analysis module, configured to perform attribution analysis on the conversion behavior in response to the conversion behavior implemented by the user in the business process, and obtain the attribution data corresponding to the conversion behavior; the attribution data at least includes an attribution channel;

[0024] A determination module, configured to determine a feedback strategy corresponding to the attribution data based on the attribution data;

[0025] A feedback module, configured to determine feedback data based on the feedback strategy and feedback the feedback data to the attribution channel.

[0026] In a third aspect, an electronic device provided by an embodiment of the present application includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the embodiment of the present application is implemented.

[0027] In a fourth aspect, a computer-readable storage medium provided by an embodiment of the present application has a computer program stored thereon. When the program is executed by a processor, the method described in the embodiment of the present application is implemented.

[0028] Fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the method described in the embodiments of the present application.

[0029] The exposure attribution method and device for advertisement feedback provided by the embodiments of the present application can not only attribute the advertisement clues carried when actual conversion occurs, but also determine the attribution data corresponding to the conversion behavior without advertisement clues carried by the user when actual conversion occurs through attribution analysis, and then determine the corresponding feedback data to be fed back to the attribution channel, realizing reliable analysis of conversion data and providing rich and accurate sample data for the formulation of subsequent advertisement plans.

[0030] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Other features, objectives, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0032] Figure 1 The flowchart showing the exposure attribution method for advertisement feedback provided by an embodiment of the present application;

[0033] Figure 2 The flowchart showing the exposure attribution method for advertisement feedback provided by another embodiment of the present application;

[0034] Figure 3 The structural schematic diagram showing the system for implementing the exposure attribution method for advertisement feedback provided by an embodiment of the present application;

[0035] Figure 4 The flowchart showing the exposure attribution method for advertisement feedback provided by still another embodiment of the present application;

[0036] Figure 5 The flowchart showing the exposure attribution method for advertisement feedback provided by yet another embodiment of the present application;

[0037] Figure 6 The flowchart showing the exposure attribution method for advertisement feedback provided by yet another embodiment of the present application;

[0038] Figure 7 The flowchart showing the exposure attribution method for advertisement feedback provided by yet another embodiment of the present application;

[0039] Figure 8 The exemplary structural block diagram showing the exposure attribution device for advertisement feedback provided by the embodiments of the present application;

[0040] Figure 9 The figure shows a schematic structural diagram of a computer system suitable for an electronic device or a server for implementing the embodiments of the present application. Detailed implementation manners

[0041] The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. In addition, it should be noted that only the parts related to the invention are shown in the drawings for the convenience of description.

[0042] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0043] It should also be noted that the acquisition or use of the data in the embodiments of the present application requires the consent of the user. The relevant data can be obtained only after the user authorizes and permits, and the acquisition or use of the data complies with the provisions of relevant laws and regulations.

[0044] The purpose of advertising placement is to provide better user conversion for the owner, and a better understanding of user consumption habits and hobbies is the key to effectively formulating an advertising placement plan. However, in the actual application environment, a large number of users, after browsing the advertisement, do not enter the landing page by clicking on the advertisement to generate a conversion behavior, but after the advertisement arouses their interest, they search for the app in the app market for conversion. Due to the existing attribution process, which only focuses on the advertisement information carried by the user when the conversion actually occurs and does not consider the long-term impact of the advertising plan on the user, the advertising plan fails, thus affecting the customer acquisition efficiency.

[0045] Based on this, the present application proposes an exposure attribution method and device for advertising feedback, which can perform reliable attribution analysis on the conversion behavior of the user and obtain corresponding attribution data, provide reliable data support for formulating an advertising plan later, and thus improve the conversion efficiency of advertising placement.

[0046] In order to further illustrate the technical solutions provided by the embodiments of the present application, the following will be described in detail with reference to the accompanying drawings and specific implementation manners. Although the embodiments of the present application provide the method operation instruction steps as shown in the following embodiments or drawings, more or fewer operation instruction steps may be included in the method based on routine or non-creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. When the method is actually processed or the device executes, it can be executed in the order shown in the embodiments or drawings or executed in parallel.

[0047] Please refer toFigure 1 , Figure 1 shows a schematic flow chart of an exposure attribution method for advertisement feedback provided by an embodiment of the present application. As Figure 1 shown, the method includes:

[0048] Step 101, in response to a conversion behavior implemented by a user in a business process, perform attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior.

[0049] Among them, the attribution data at least includes an attribution channel.

[0050] It should be noted that the conversion behavior implemented by the user in the business process may be a behavior concerned by the advertisement publisher, including but not limited to registration, login, access, and credit granting, etc. The attribution data is attribution result or process data, including but not limited to one or more of user device ID, advertisement ID, trace ID, plan ID, click ID, and channel ID. Among them, the trace ID is an access ID assigned to the user after entering the landing page through the advertisement.

[0051] In a feasible embodiment, performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior includes:

[0052] Step 201, identify whether the conversion behavior carries an attribution clue.

[0053] It should be noted that the attribution clue is source information carried by the conversion behavior, such as an attribution channel or identification information corresponding to an advertisement plan. Optionally, it can be determined whether the conversion behavior carries an attribution clue through the path information of the conversion behavior. Preferably, obtain the track_id corresponding to the conversion behavior, and determine whether the conversion behavior carries an attribution clue through the track_id corresponding to the conversion behavior.

[0054] Exemplarily, when the conversion behavior implemented by the user is access, it can be determined whether the access behavior carries an attribution clue through the access path of the conversion behavior. Specifically, identify whether the access path includes a channel parameter corresponding to any attribution channel. If the access path contains a channel parameter, it is determined that the conversion behavior carries an attribution clue and is a direct conversion based on the advertisement plan. If the access path does not contain any known channel parameter, it is determined that the conversion behavior does not carry an attribution clue, and the conversion method needs to be further analyzed, including but not limited to natural conversion and indirect conversion subtly influenced by the advertisement plan.

[0055] Step 202, when the conversion behavior carries an attribution clue, obtain attribution data according to the attribution clue.

[0056] Specifically, parse the attribution clue to obtain at least part of the attribution data, and then query the database to supplement the missing attribution fields to obtain the attribution data.

[0057] Step 203: When no attribution clue is carried in the conversion behavior, obtain the user identifier corresponding to the user, and determine the attribution data based on the user identifier information.

[0058] Specifically, based on the user identifier, obtain the user's prior browsing data, and based on the prior browsing data, determine the attribution data. It should be understood that before the user performs the conversion behavior, by identifying the user's browsing behavior, when the user browses the target advertisement priorly, obtain the browsing data and generate the mapping relationship between the browsing data and the user identifier.

[0059] Among them, the target advertisement is the advertisement placed by the advertisement publisher, and the target advertisement can be one or more.

[0060] That is to say, when playing any target advertisement on any channel, the browsing data of the target advertisement and the user device identifier can be associated and stored to generate the mapping relationship between the browsing data and the user identifier. Then, when no attribution clue is carried in the user's conversion behavior, query the browsing data with the mapping relationship through the user identifier, and then determine the attribution data.

[0061] It can be understood that for the same user identifier, there can be at least one set of attribution data corresponding at the same time, that is, the same user identifier can correspond to at least one channel ID, at least one advertisement ID corresponding to each channel, and at least one plan ID corresponding to each advertisement ID.

[0062] Therefore, the present application provides two ways to obtain attribution data, which can ensure that attribution data for analysis can be obtained whether an attribution clue is carried in the conversion behavior implemented by the user, enabling the attribution analysis to be effectively implemented and improving the accuracy and reliability of the backhaul attribution.

[0063] Step 102: Determine the backhaul strategy corresponding to the attribution data based on the attribution data.

[0064] Among them, the backhaul strategy is used to determine the backhaul data. Preferably, the backhaul strategy includes at least one filtering strategy corresponding to the user attributes, so as to determine whether to backhaul from the attribution data through the at least one filtering strategy and the backhaul data when backhaul is required.

[0065] Step 103: Determine the backhaul data based on the backhaul strategy, and backhaul the backhaul data to the attribution channel.

[0066] In a feasible embodiment, the backhaul strategy includes the advertising plan corresponding to the attribution channel. Determining the backhaul data based on the backhaul strategy includes: determining the user attributes corresponding to the user based on the advertising plan, and when the user attributes corresponding to the user are the target attributes, determining the advertising plan as the backhaul data.

[0067] Optionally, the target attribute may be a preferred user attribute determined based on the type or audience group of the attribution channel. The target attributes corresponding to different attribution channels may be the same or different, and the present application does not make specific limitations in this regard.

[0068] That is to say, the feedback strategy filters the feedback data required by the attribution channel through user attributes. Specifically, the feedback strategy corresponding to the attribution channel can be determined according to the attribution channel in the attribution data, including but not limited to the target attribute corresponding to the attribution channel, and then the user attribute analysis is performed on the user. When the user attribute corresponding to the user is the same as the target attribute, it is determined that the user is an effective and high-quality conversion user of the attribution channel, and the attribution data corresponding to the user is used as the feedback data and fed back to the attribution channel. When the user attribute corresponding to the user is inconsistent with the target attribute, it is determined that the user is not an effective conversion user of the channel, and there is no need to feed back the attribution data of the user. At this time, it is determined that there is no feedback data to be fed back.

[0069] Therefore, the exposure attribution method for advertising feedback provided by the embodiments of the present application can not only attribute the advertising leads carried when actual conversion occurs, but also determine the attribution data corresponding to the conversion behavior without advertising leads carried when the user actually converts through attribution analysis, and then determine the corresponding feedback data and feed it back to the attribution channel, realizing reliable analysis of conversion data and providing rich and accurate sample data for the formulation of subsequent advertising plans.

[0070] In a specific embodiment of the present application, as Figure 3 shown, the system for implementing the exposure attribution method for advertising feedback includes a media channel layer, a service layer, and a database layer. Among them, the channel layer is various media channels, which serve as the corresponding attribution channels when attribution is successful, and the present application does not make specific limitations. The service layer includes an ocpc (Optimized Cost Per Click, advertising placement strategy) sub-layer, a basic service sub-layer, and a downstream dependency sub-layer. Among them, the ocpc layer includes service functions such as attribution service, display and click data parsing, receiving node messages, consuming login / credit / credit usage data, and feedback service. The exposure attribution method for advertising feedback proposed by the embodiments of the present application is executed by the attribution service function to realize attribution analysis of user conversion behavior and feed back the feedback data to the corresponding attribution channel. The basic service sub-layer may include functional modules that provide basic service functions such as a decision-making platform, CVR (Conversion Rate) analysis, and an operation platform. The downstream dependency sub-layer includes functional modules related to conversion analysis such as apps, landing pages, credit granting, and transactions. The database layer can set at least one database according to actual needs, including but not limited to mysql, oceanbase, etc., and the present application does not make specific limitations.

[0071] This application uses a Kafka cluster to receive real-time events from the buried point system in the target application. Flink is used as a real-time stream processing framework to implement real-time data processing. OceanBase, CKV, and MySQL are used as databases for data storage. Combined with Figure 4 to illustrate the solution of this application. Kafka collects user behaviors such as login / registration / access / credit granting in the target application. The Flink task consumes Kafka messages, encapsulates the behavior parameters, and the Kafka production task. The attribution service consumes the task messages. The attribution service sequentially queries whether the landing page has relevant data, whether the operation platform has mapping data, and queries the mapping data between the user ID and the channel. Then it queries the previously saved idmapping clue data from OceanBase and performs attribution analysis based on the multiple data queried, and stores the attribution result and attribution record in the MySQL database. Combined with Figure 5 to illustrate the process of previously saving the idmapping clue data. The device information triggers the Flink task to generate a mapping from uuid to device information. When behaviors such as login and access occur, the Flink task is triggered to generate a mapping from did to uuid. The Flink task also writes the above mappings to OceanBase as idmapping.

[0072] It should also be noted that for the previously viewed data, it is combined with Figure 6 to illustrate. Specifically, the exposure data of the target advertisement triggers the Flink task to process the viewed data and stores the viewed data in OceanBase.

[0073] Thus, the embodiment of this application uses the real-time capture function of Flink to capture the real-time events received by the Kafka cluster and timely trigger the attribution service and other functions, effectively improving the overall analysis efficiency.

[0074] In another embodiment, the exposure attribution method for advertisement feedback includes sequentially determining whether the conversion behavior is successfully attributed according to the preset attribution type order, and obtaining the attribution data corresponding to the conversion behavior when the attribution is successful.

[0075] Among them, the attribution types include registration attribution, click attribution, exposure attribution, and application attribution. Registration attribution is to find the media channels and advertising plans corresponding to the attribution clues carried when a user undergoes a registration and login conversion in the business process, mainly used to attribute users who click on an advertisement and then convert in the business process; click attribution targets users who click on an advertisement and enter the business process but do not register or log in. Such users do not continue to register after entering the advertisement landing page but instead go to download the app. Therefore, by looking up the device information carried when the user opens the app, exposure (viewing) and click clues are found, and the trace ID carried by the user when entering the advertisement landing page is found through the trace ID, which means successful attribution, and the corresponding media channels and advertising plans can also be found; exposure attribution targets users who see an advertisement but do not click on it, but are influenced by the advertisement and go to the app market to download the app. Therefore, by looking up the device information carried when the user opens the app, the advertisement viewed by the user is found, and then the corresponding media channels and advertising plans are found; application attribution is the situation where attribution cannot be successfully attributed to exposure attribution.

[0076] Specifically, as Figure 7 shown, identify the user's login / registration / access / application / credit-granting behavior, parse the user behavior into a unified format and write it into Kafka. The attribution service consumes Kafka to determine whether the user is logging in to the app for the first time / accessing the app for the first time / applying for credit. If so, perform registration attribution analysis to determine whether the attribution is successful. If the registration attribution is successful, parse the landing page clues, obtain the track_id, and query the database according to the track_id to supplement the attribution fields. If the registration attribution is not successful, perform click attribution analysis to determine whether the attribution is successful. If the click attribution is successful, construct an attribution record. If the click attribution is not successful, perform exposure attribution analysis to determine whether the attribution is successful. If the exposure attribution is successful, query the advertisement plan mapping and construct an attribution record. If the exposure attribution is not successful, determine whether there is a credit application record. If there is a credit application record, query the login / access records in the database attribution record to determine whether the most recent login or access can be found. If so, set the backtracking field and construct an attribution record. If not, set the attribution result to nature, and insert / update the attribution record in the database according to the supplemented attribution fields or the constructed attribution record or the set attribution result. Determine whether the database operation fails. If so, insert a failure record table and set the result to database operation failure and end the current process. If the user is not logging in to the app for the first time / accessing the app for the first time / applying for credit, directly end the process.

[0077] Further, after the exposure attribution method for advertisement feedback proposed in the embodiments of the present application is used to feedback the feedback data to the attribution channel, the attribution channel optimizes and trains the advertisement plan generation model based on the feedback data to improve the conversion rate of the advertisement plan formulated later.

[0078] Preferably, the attribution channel uses the optimized advertisement plan generation model to predict the conversion rate corresponding to the advertisement placement plan, and formulates the placement cost corresponding to the advertisement placement plan based on the prediction result. Thus, the embodiments of the present application can improve the accuracy and reliability of advertisement attribution, and further improve the accuracy and reliability of the calculation of the conversion rate of the advertisement placement plan, and further improve the accuracy and reliability of the formulation of the advertisement placement cost.

[0079] It should be noted that although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result.

[0080] Figure 8 The exemplary structural block diagram of the exposure attribution device for advertisement feedback provided by the embodiments of the present application is shown.

[0081] As Figure 8 shown, the exposure attribution device 10 for advertisement feedback includes:

[0082] An analysis module 11, configured to perform attribution analysis on the conversion behavior in response to the conversion behavior implemented by the user in the business process, and obtain attribution data corresponding to the conversion behavior; the attribution data at least includes an attribution channel;

[0083] A determination module 12, configured to determine a feedback strategy corresponding to the attribution data based on the attribution data;

[0084] A feedback module 13, configured to determine feedback data based on the feedback strategy, and feedback the feedback data to the attribution channel.

[0085] In some embodiments, the analysis module 11 is further configured to:

[0086] Identify whether an attribution clue is carried in the conversion behavior;

[0087] When the attribution clue is carried in the conversion behavior, obtain the attribution data according to the attribution clue;

[0088] When the attribution clue is not carried in the conversion behavior, obtain the user identifier corresponding to the user, and determine the attribution data based on the user identifier.

[0089] In some embodiments, the analysis module 11 is further configured to:

[0090] Obtain the user's prior browsing data based on the user identifier;

[0091] Determine the attribution data based on the prior browsing data.

[0092] In some embodiments, the analysis module 11 is further configured to:

[0093] When the user browses the target advertisement previously, obtain the browsing data and generate a mapping relationship between the browsing data and the user identifier.

[0094] In some embodiments, the feedback module 13 is further configured to:

[0095] Determine the user attributes corresponding to the user based on the advertisement plan;

[0096] When the user attributes corresponding to the user are target attributes, determine the advertisement plan as the feedback data.

[0097] In some embodiments, the analysis module 11 is further configured to:

[0098] Sequentially determine whether the conversion behavior is successfully attributed according to a preset attribution type order, and obtain the attribution data corresponding to the conversion behavior when the attribution is successful.

[0099] It should be understood that the various units or modules described in the exposure attribution device 10 for advertisement feedback correspond to the respective steps in the method described in the reference Figure 1 Therefore, the operations and features described above for the method also apply to the exposure attribution device 10 for advertisement feedback and the units included therein, and will not be repeated here. The exposure attribution device 10 for advertisement feedback can be pre-implemented in the browser or other security applications of the electronic device, or can be loaded into the browser or its security application of the electronic device by means of downloading, etc. The corresponding units in the exposure attribution device 10 for advertisement feedback can cooperate with the units in the electronic device to implement the solutions of the embodiments of the present application.

[0100] Among the several modules or units mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0101] Next, with reference to Figure 9 , Figure 9 shows a schematic structural diagram of a computer system of an electronic device or a server suitable for implementing the embodiments of the present application,

[0102] As Figure 9 shown, the computer system includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage section 908 into a random access memory (RAM) 903. In the RAM 903, various programs and data required for the operation instructions of the system are also stored. The CPU 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0103] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as required. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as required so that a computer program read from it can be installed into the storage section 908 as required.

[0104] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart Figure 2 can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 909, and / or installed from the removable medium 911. When the computer program is executed by the central processing unit (CPU) 901, the above functions defined in the system of the present application are executed.

[0105] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0106] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operation instructions of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the foregoing module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two connected blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that executes the specified functions or operation instructions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0107] The units or modules involved in the embodiments described in this application can be implemented in software or in hardware. The described units or modules can also be provided in a processor. For example, it can be described as: a processor includes an analysis module, a determination module, and a feedback module. Among them, the names of these units or modules do not constitute a limitation on the units or modules themselves in some cases. For example, the analysis module can also be described as "for performing attribution analysis on the conversion behavior implemented by the user in the business process in response to the conversion behavior, obtaining attribution data corresponding to the conversion behavior; the attribution data includes at least an attribution channel".

[0108] On the other hand, this application also provides a computer-readable storage medium. The computer-readable storage medium can be included in the electronic device described in the above embodiments, or can exist alone without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are executed by one or more processors, they are used to perform the exposure attribution method for advertising feedback described in this application.

[0109] The above description is only a preferred embodiment of this application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in this application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in this application.

Claims

1. An exposure attribution method for advertising feedback, characterized in that, including: responding to a conversion behavior implemented by a user in a business process, performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior; the attribution data at least includes an attribution channel; determining a feedback strategy corresponding to the attribution data based on the attribution data; determining feedback data based on the feedback strategy and transmitting the feedback data to the attribution channel.

2. The exposure attribution method for advertisement feedback according to claim 1, wherein The performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior includes: identifying whether an attribution clue is carried in the conversion behavior; when the attribution clue is carried in the conversion behavior, obtaining the attribution data according to the attribution clue; when the attribution clue is not carried in the conversion behavior, obtaining a user identifier corresponding to the user and determining the attribution data based on the user identifier.

3. The exposure attribution method for advertisement feedback according to claim 2, wherein The determining the attribution data based on the user identifier includes: obtaining prior browsing data of the user based on the user identifier; determining the attribution data based on the prior browsing data.

4. The exposure attribution method for advertisement feedback according to claim 3, wherein Before obtaining the prior browsing data of the user based on the user identifier, it further includes: when the user browses a target advertisement priorly, obtaining the browsing data and generating a mapping relationship between the browsing data and the user identifier.

5. The exposure attribution method for advertisement feedback according to claim 1, wherein The feedback strategy includes an advertising plan corresponding to the attribution channel, and the determining feedback data based on the feedback strategy includes: determining user attributes corresponding to the user based on the advertising plan; when the user attributes corresponding to the user are target attributes, determining the advertising plan as the feedback data.

6. The exposure attribution method for advertisement feedback according to claim 1, wherein The performing attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior includes: sequentially determining whether the conversion behavior is successfully attributed according to a preset attribution type sequence, and obtaining attribution data corresponding to the conversion behavior when the attribution is successful.

7. An exposure attribution device for advertisement feedback, characterized in that including: an analysis module, configured to respond to a conversion behavior implemented by a user in a business process, perform attribution analysis on the conversion behavior to obtain attribution data corresponding to the conversion behavior; the attribution data at least includes an attribution channel; a determination module, configured to determine a feedback strategy corresponding to the attribution data based on the attribution data; a feedback module, configured to determine feedback data based on the feedback strategy and transmit the feedback data to the attribution channel.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the exposure attribution method for advertising feedback as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the exposure attribution method for advertising feedback as described in any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the exposure attribution method for advertising feedback as described in any one of claims 1-6.

Images