An advertising data analysis method, device, electronic device and storage medium

Abstract

The present application discloses an advertising data analysis method, device, electronic device and storage medium. The method includes: obtaining advertising conversion information and user node identifiers in a second block, where the advertising conversion information includes an advertising identifier of a target advertisement, as well as corresponding conversion time and conversion form; obtaining advertising exposure information of the target advertisement corresponding to the user node identifier from a first block, where the advertising exposure information includes the advertising identifier of the target advertisement, exposure time and delivery source node; in the case of obtaining at least two pieces of the advertising exposure information, determining an effective exposure time among the exposure times; determining the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement, and accurately storing advertising exposure and conversion data on the user side through blockchain technology, so that the attribution analysis of advertising conversion is more real and accurate.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular, to an advertising data analysis method, device, electronic device, and storage medium. Background Art

[0002] Advertisements are everywhere in life. Various advertisements can be recommended and displayed for users on many media platforms. The media advertisement delivery system can deliver advertisements to users according to the requirements of advertisers.

[0003] Advertisement conversion can largely reflect the value created by the advertisement. Generally, behaviors such as orders, downloads, or recharges brought about after advertisement delivery are called conversions. After an advertiser delivers an advertisement, it is necessary to analyze the specific reasons and sources of conversion, which is called advertisement attribution analysis and is an important step in evaluating the effect of advertisement delivery.

[0004] Different media can all deliver the same advertisement under the entrustment of an advertiser. The entire network has a large amount of data and complex data sources. In general advertisement attribution analysis, due to possible errors or fraud in its advertisement exposure data, and its data and analysis are limited to the proprietary data of the media, it is difficult or even impossible to determine the most direct reason for advertisement conversion due to network transmission reasons, media own functions, advertisement cheating traffic, and interests involved, etc. The advertisement attribution analysis is not accurate enough. Summary of the Invention

[0005] This application provides an advertising data analysis method, device, electronic device, and storage medium, which can accurately store advertisement exposure and conversion data on the user side through blockchain technology to obtain a true and accurate advertisement conversion attribution analysis.

[0006] On the one hand, an embodiment of this application provides an advertising data analysis method, including:

[0007] Obtain advertisement conversion information and user node identifiers in a second block, where the advertisement conversion information includes an advertisement identifier of a target advertisement, and a corresponding conversion time and conversion form;

[0008] Obtain advertisement exposure information of the target advertisement corresponding to the user node identifier from a first block, where the advertisement exposure information includes the advertisement identifier of the target advertisement, an exposure time, and a delivery source node;

[0009] In the case of obtaining at least two pieces of the advertisement exposure information, determine an effective exposure time among the exposure times;

[0010] Determine the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement.

[0011] On the other hand, an embodiment of the present application provides an advertising data analysis method, including:

[0012] When it is detected that the target advertisement is exposed, advertising exposure information is generated, and the advertising exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement;

[0013] Generate a first block to record the advertising exposure information and the user node identifier, and store the first block in the data sharing system;

[0014] When it is detected that a conversion operation of the target advertisement occurs, advertising conversion information is generated, and the advertising conversion information includes the advertisement identifier of the target advertisement and the corresponding conversion time and conversion form;

[0015] Generate a second block to record the advertising conversion information and the user node identifier, and store the second block in the data sharing system.

[0016] On the other hand, an embodiment of the present application provides an advertising data analysis device, including:

[0017] An acquisition module, configured to acquire the advertising conversion information and the user node identifier in the second block, where the advertising conversion information includes the advertisement identifier of the target advertisement and the corresponding conversion time and conversion form;

[0018] The acquisition module is further configured to acquire the advertising exposure information of the target advertisement corresponding to the user node identifier from the first block, where the advertising exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement;

[0019] A processing module, configured to determine an effective exposure time among the exposure times when at least two pieces of the advertising exposure information are acquired;

[0020] The processing module is further configured to determine the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement.

[0021] Optionally, the processing module is specifically configured to:

[0022] Among the at least two exposure times corresponding to the at least two pieces of advertising exposure information, determine the last exposure time before the conversion time as the effective exposure time.

[0023] Optionally, after determining the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement, the processing module is further configured to generate an attribution analysis result, where the attribution analysis result includes the advertisement identifier of the target advertisement, the effective exposure time, the conversion time, the conversion form, and the attribution node;

[0024] Determine the advertiser node corresponding to the target advertisement, and send the attribution analysis result to the advertiser node.

[0025] Optionally, the delivery source nodes of the at least two advertisement exposure information are different.

[0026] The conversion form includes any one or more of the following: a purchase operation, a download operation, a page jump operation, and a recharge operation triggered by the exposure of the target advertisement.

[0027] Optionally, the processing module is further configured to

[0028] Determine multiple attribution nodes corresponding to the advertisement conversion information of the target advertisement within a preset period, and obtain the delivery volume of the target advertisement by the multiple attribution nodes within the preset period.

[0029] Obtain the number of times different delivery nodes are determined as the attribution nodes; according to the number of times different delivery nodes are determined as the attribution nodes and the delivery volume of the target advertisement by the different delivery nodes within the preset period, determine the node with the highest conversion contribution degree among the different delivery nodes.

[0030] On the other hand, an embodiment of the present application provides an advertisement data analysis device, including:

[0031] A processing module, configured to generate advertisement exposure information when detecting an exposure of a target advertisement, where the advertisement exposure information includes an advertisement identifier, an exposure time, and a delivery source node of the target advertisement;

[0032] A generation module, configured to generate a first block to record the advertisement exposure information and a user node identifier, and store the first block in a data sharing system;

[0033] The processing module is further configured to generate advertisement conversion information when detecting a conversion operation of the target advertisement, where the advertisement conversion information includes an advertisement identifier of the target advertisement and a corresponding conversion time and conversion form;

[0034] The generation module is further configured to generate a second block to record the advertisement conversion information and the user node identifier, and store the second block in the data sharing system.

[0035] On the other hand, an embodiment of the present application provides an electronic device, including an input device and an output device, further including: a processor, adapted to implement one or more instructions; and a computer storage medium storing one or more instructions, the one or more instructions being adapted to be loaded and executed by the processor to perform the advertisement data analysis method described in the above first or second aspect.

[0036] On the other hand, an embodiment of the present application provides a computer storage medium storing one or more instructions, the one or more instructions being adapted to be loaded and executed by a processor to perform the steps described in the above first or second aspect and any possible implementation manner thereof.

[0037] The present application obtains advertisement conversion information and user node identifiers in a second block. The advertisement conversion information includes an advertisement identifier of a target advertisement, a corresponding conversion time, and a conversion form. The advertisement exposure information of the target advertisement corresponding to the user node identifier is obtained from a first block. The advertisement exposure information includes the advertisement identifier of the target advertisement, an exposure time, and a delivery source node. In the case of obtaining at least two pieces of the advertisement exposure information, an effective exposure time is determined among the exposure times, and the delivery source node corresponding to the effective exposure time is determined as the attribution node for the conversion of the target advertisement. By using blockchain technology, advertisement exposure and conversion data on the user side can be accurately stored, and it is not limited to advertisement exposure data of a single platform, enabling more real and accurate attribution analysis of advertisement conversion, obtaining the advertisement delivery node that most directly causes the conversion, and obtaining an accurate attribution analysis result. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background art, the drawings required to be used in the embodiments of the present application or the background art will be described below.

[0039] Figure 1A A schematic structural diagram of a data sharing system provided by an embodiment of the present application;

[0040] Figure 1B A schematic structural diagram of a blockchain provided by an embodiment of the present application;

[0041] Figure 1C A schematic diagram of node interaction of a blockchain provided by an embodiment of the present application;

[0042] Figure 2 A schematic flowchart of an advertisement data analysis method provided by an embodiment of the present application;

[0043] Figure 3A A schematic flowchart of another advertisement data analysis method provided by an embodiment of the present application;

[0044] Figure 3B Schematic diagram of node interaction process for an advertising data analysis method provided by an embodiment of the present application;

[0045] Figure 4 Schematic diagram of the structure of an advertising data analysis device provided by an embodiment of the present application;

[0046] Figure 5 Schematic diagram of the structure of another advertising data analysis device provided by an embodiment of the present application;

[0047] Figure 6 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0048] In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0049] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0050] Referring to

[0051] See Figure 1AThe data sharing system shown. The data sharing system 100 refers to a system for data sharing between nodes. This data sharing system may include multiple nodes 101, and the multiple nodes 101 may refer to each client in the data sharing system. Each node 101 can receive input information during normal operation and maintain the shared data within the data sharing system based on the received input information. To ensure information interconnection within the data sharing system, there may be information connections between each node in the data sharing system, and nodes can transmit information through the above-mentioned information connections. For example, when any node in the data sharing system receives input information, other nodes in the data sharing system can obtain the input information according to the consensus algorithm and store the input information as data in the shared data, so that the data stored on all nodes in the data sharing system is consistent.

[0052] For each node in the data sharing system, there is a corresponding node identifier, and each node in the data sharing system can store the node identifiers of other nodes in the data sharing system, so as to broadcast the generated block to other nodes in the data sharing system according to the node identifiers of other nodes in the future. Each node can maintain a node identifier list as shown in the following table, and store the node name and node identifier corresponding to each other in the node identifier list. Among them, the node identifier can be an IP (Internet Protocol) address and any other information that can be used to identify the node. Only the IP address is used as an example in Table 1 for illustration.

[0053] Node Name Node Identifier Node 1 117.114.151.174 Node 2 117.116.189.145 … … Node N 119.123.789.258

[0054] Each node in the data sharing system stores an identical blockchain. The blockchain consists of multiple blocks. Refer to Figure 1B , the blockchain consists of multiple blocks. The genesis block includes a block header and a block body. The block header stores the input information feature value, version number, timestamp, and difficulty value. The block body stores the input information; the next block of the genesis block uses the genesis block as the parent block, and the next block also includes a block header and a block body. The block header stores the input information feature value of the current block, the block header feature value of the parent block, version number, timestamp, and difficulty value, and so on, so that the block data stored in each block in the blockchain is associated with the block data stored in the parent block, ensuring the security of the input information in the block.

[0055] To better illustrate the method in the embodiments of the present application, refer to Figure 1C , Figure 1C is a schematic diagram of node interaction of a blockchain provided by an embodiment of the present application. As shown in Figure 1CAs shown in the figure, multiple delivery nodes, advertiser nodes, and user nodes can be set up in the data sharing system (where there can be multiple advertiser nodes and user nodes, and only for illustration in the figure), to execute an advertising data analysis method of this application. Among them, the delivery node can be regarded as the media side, such as a media platform or a media server, which performs advertising delivery operations on user nodes. The advertiser node is the funder with advertising delivery needs, and the delivery node can deliver advertisements according to the advertiser's requirements; the user node can be understood as the user-side terminal. Through the above media platform or the client of the application used by the user, the user can receive and view the delivered advertisements, perform data interaction in the blockchain, so that the delivery node can timely obtain and integrate the real advertisement exposure information and advertisement conversion information of the user node, and perform analysis not limited to the data within a single delivery node, so that the attribution analysis of advertisement conversion is more real and accurate.

[0056] The embodiments of this application will be described below with reference to the accompanying drawings in the embodiments of this application.

[0057] Please refer to Figure 2 , Figure 2 which is a schematic flowchart of an advertising data analysis method provided by the embodiments of this application. The method may include:

[0058] 201. When it is detected that a target advertisement is exposed, generate advertisement exposure information, where the advertisement exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement.

[0059] The data sharing system involved in the embodiments of this application can be the above blockchain system, and data sharing and processing in the data sharing system can be implemented based on the blockchain. The delivery system involved in the embodiments of this application refers to a system that delivers advertisements to users in a network media platform, which belongs to a node in the blockchain network and can be understood as a server on the media side, delivering advertisements to user nodes in the blockchain network.

[0060] The execution subject in the embodiments of this application can be the above user node, such as the terminal used on the user side, which can receive and output advertisements from the delivery node through the client. In specific implementation, the above terminal can also be called a terminal device, including but not limited to other portable devices such as mobile phones, laptop computers, or tablet computers with a touch-sensitive surface (for example, a touch screen display and / or a touchpad), and can implement location services and navigation functions through an application. It should also be understood that in some embodiments, the above device is not a portable communication device, but a desktop computer with a touch-sensitive surface (for example, a touch screen display and / or a touchpad).

[0061] A blockchain may include multiple delivery nodes, user nodes, and advertiser nodes. In the embodiments of this application, any one user node is taken as an example for illustration first. Among them, the above advertisement exposure information may be generated by an application or plug-in of the user node acting as a client. Generally, corresponding advertisement exposure information is generated when an advertisement is exposed. For the convenience of the delivery node to perform statistics, control, and optimization, it is sent to the server that delivers the advertisement and the third-party detection system in the form of an exposure request.

[0062] In the embodiments of this application, when it is detected that a target advertisement is exposed, the above advertisement exposure information may be generated, which may specifically include the advertisement identifier, exposure time, and delivery source node of the target advertisement, and may also include the advertisement position, etc. The target advertisement is the advertisement exposed this time. The above advertisement identifier may be a name, code, or string. The above delivery source node is the delivery node that delivers the target advertisement this time, which may specifically be the delivery node address. The above advertisement position refers to the area position where the advertisement is exposed, which may specifically be the advertisement position number or string. Only one advertisement can be displayed in each advertisement position at the same time.

[0063] 202. Generate a first block to record the above advertisement exposure information and the user node identifier, and store the first block in the data sharing system.

[0064] The user nodes added to the blockchain can publish and share data in the blockchain. For advertisement exposure, a block can be generated to record the advertisement exposure information, and at the same time, the user node identifier can be recorded to determine at which node the exposure occurs. Specifically, the generated first block may include the exposure time, advertisement position, delivery source node address, user node address of the target advertisement, and may also include the advertisement order information and advertisement material information of the target advertisement. Among them, the above advertisement order information includes the order number and advertiser identifier, and the above advertisement material information includes the material number and material download link. Among them, the advertiser nodes in the blockchain network entrust the delivery nodes to deliver advertisements to users, and corresponding advertisement order information will be generated when entrusting. And the advertisement material can be understood as the video or picture finally presented to the user.

[0065] The advertisement exposure information stored in the blockchain can be obtained by the delivery node, so that the delivery node can know the advertisement exposure situation on the user side in a timely and accurate manner, which is convenient for data management, statistics, and strategy optimization of advertisement delivery.

[0066] 203. In the case of detecting a conversion operation of the above target advertisement, generate advertisement conversion information, where the advertisement conversion information includes the advertisement identifier of the above target advertisement and the corresponding conversion time and conversion form.

[0067] The behaviors such as orders, downloads, or recharges brought about after general advertisement placement are called conversions. The advertiser allows the media side to embed codes or report relevant data to the media through background requests, thereby completing corresponding analyses.

[0068] For user nodes, when a conversion operation of the target advertisement is detected, advertisement conversion information can be generated, including the advertisement identifier of the target advertisement, the corresponding conversion time, and the conversion form. The above conversion form is the conversion result caused by the conversion operation. In an optional implementation manner, it includes any one or more of the following: a purchase operation, a download operation, a page jump operation, and a recharge operation triggered by the exposure of the target advertisement.

[0069] 204. Generate a second block to record the above advertisement conversion information and the above user node identifier, and store the second block in the data sharing system.

[0070] Specifically, similar to the situation of advertisement exposure, the advertisement conversion information can be uploaded to the blockchain to generate the above second block to record the advertisement conversion information and the user node identifier, and store it in the data sharing system, so that the placement node can timely and accurately know the advertisement conversion situation on the user side, facilitating data statistics and analysis of advertisement conversion.

[0071] An important evaluation function after advertisement placement is attribution analysis, which is to analyze which click or exposure of which media actually brought about the advertisement conversion, so as to optimize the placement and preferentially select the media. However, the attribution analysis currently done by the media side can only be limited to its own data. In essence, it is to find the real reason for the conversion on incomplete data, and the effect is very limited.

[0072] Through the secure, accurate, and complete characteristics of the blockchain, the exposure data can be obtained completely, reducing false exposures. At the same time, the data no longer needs to go through a third-party monitoring system, reducing the interaction with the third-party monitoring and logarithmic errors. And based on the distributed accounting and secure and unified characteristics of the blockchain, it can provide real and effective advertisement conversion information across the network. The media that joins the blockchain can obtain complete data across media, thereby making correct attribution analysis, which can help advertisers optimize advertisement placement and also find the placement function points that need to be optimized for their own media.

[0073] In this application, when it is detected that a target advertisement is exposed, advertisement exposure information is generated. The advertisement exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement. A first block is generated to record the advertisement exposure information and the user node identifier, and the first block is stored in the data sharing system. When it is detected that a conversion operation of the target advertisement occurs, advertisement conversion information is generated. The advertisement conversion information includes the advertisement identifier of the target advertisement, the corresponding conversion time, and the conversion form. A second block is generated to record the advertisement conversion information and the user node identifier, and the second block is stored in the data sharing system. By using blockchain technology to store advertisement exposure information and advertisement conversion information, false exposures are reduced, and multiple delivery nodes across platforms can obtain the exposure and conversion situations of the target advertisement in a timely manner, making the attribution analysis of advertisement conversion more real and accurate.

[0074] Please refer to Figure 3A , Figure 3A which is a schematic flowchart of another advertisement data analysis method provided by an embodiment of this application. As Figure 3A shown, the method may include:

[0075] 301. Obtain the advertisement conversion information and the user node identifier in the second block. The advertisement conversion information includes the advertisement identifier of the target advertisement, the corresponding conversion time, and the conversion form.

[0076] The data sharing system involved in the embodiments of this application may be the above blockchain network. The delivery system refers to a system that delivers advertisements to users in a network media platform and belongs to a node in the blockchain network. It can be understood as the delivery system server on the media side, which delivers advertisements to user nodes in the blockchain network.

[0077] The execution subject in the embodiments of this application may be any delivery node in the blockchain, such as the server of the delivery system. As the media side, it can deliver advertisements to user nodes according to the requirements of advertisers. The user node may be a terminal (client) used on the user side and can receive and output advertisements from the delivery node. In specific implementation, the above terminal may also be referred to as a terminal device, including but not limited to other portable devices such as mobile phones, laptop computers, or tablet computers having a touch-sensitive surface (e.g., a touch screen display and / or a touchpad), and can implement location services and navigation functions through an application program. It should also be understood that in some embodiments, the device is not a portable communication device but a desktop computer having a touch-sensitive surface (e.g., a touch screen display and / or a touchpad).

[0078] The method in the embodiments of this application can be in such as Figure 2executed after the user-side method in the illustrated embodiment, where for clearer explanation, corresponding to the description in Figure 2 the illustrated embodiment, the user node in the blockchain can generate a block to record the advertisement exposure information and advertisement conversion information of the user node, that is, report the true advertisement exposure and conversion of the user node in a timely manner, which will not be elaborated here.

[0079] The placement node in the blockchain can obtain the advertisement exposure information generated by the user node for exposure statistics and obtain the advertisement conversion information for attribution analysis. Specifically, for the advertisement conversion information in the obtained second block, the placement node can perform attribution analysis on the current conversion of the target advertisement to find out the direct reason for this conversion.

[0080] 302. Obtain the advertisement exposure information of the target advertisement corresponding to the above user node identifier from the first block, where the advertisement exposure information includes the advertisement identifier, exposure time, and placement source node of the target advertisement.

[0081] For the advertisement exposure information provided by the user node in the block, the placement node can obtain it. It can be obtained immediately when detecting the generation of a block storing advertisement exposure information, or obtained periodically in batches. This application embodiment does not limit this; in the case of obtaining the above advertisement conversion information, the placement node can also obtain all the advertisement exposure information of the target advertisement at the user node for analysis.

[0082] 303. In the case of obtaining at least two pieces of the above advertisement exposure information, determine the effective exposure time among the above exposure times.

[0083] For the same user node, the same advertisement may be exposed once or multiple times, and accordingly, there may be one or more pieces of advertisement exposure information of the user node. For the case of advertisement conversion, in the case of obtaining at least two pieces of the above advertisement exposure information, advertisement attribution analysis can be performed. First, the effective exposure time, that is, the exposure time that leads to conversion, can be determined among the above exposure times.

[0084] Generally, the entire advertisement chain is to place an advertisement -> generate an exposure -> generate a click -> generate conversion behaviors such as download, registration, and recharge. When analyzing the conversion, the last click or exposure principle can be based on.

[0085] In an optional implementation manner, the determining the effective exposure time among the above exposure times includes:

[0086] Among the at least two exposure times corresponding to the at least two pieces of advertisement exposure information, determine the last exposure time before the above conversion time as the above effective exposure time.

[0087] Specifically, the above advertisement exposure information can be sorted in the order of the corresponding exposure time, and an exposure time chain of the target advertisement can be obtained. The last exposure time before the above conversion time can be determined from it as the effective exposure time.

[0088] Optionally, the placement source nodes of the above at least two advertisement exposure information are different;

[0089] The above conversion forms include any one or more of the following: purchase operation, download operation, page jump operation, and recharge operation triggered by the exposure of the above target advertisement.

[0090] 304. Determine the placement source node corresponding to the above effective exposure time as the attribution node for the conversion of the above target advertisement.

[0091] Specifically, the determined above effective exposure time can be considered as the exposure time that caused the conversion this time. It can be understood that the exposure caused by the placement source node corresponding to this effective exposure time led to the conversion of the advertisement and is valuable. Therefore, in the attribution analysis, the placement source node corresponding to the above effective exposure time can be determined as the attribution node for this conversion of the target advertisement.

[0092] Optionally, after the above step 304, the method further includes:

[0093] Generate an attribution analysis result, where the above attribution analysis result includes the advertisement identifier of the above target advertisement, the above effective exposure time, the above conversion time, the above conversion form, and the above attribution node;

[0094] Determine the advertiser node corresponding to the above target advertisement, and send the above attribution analysis result to the above advertiser node.

[0095] After determining the attribution node, an attribution analysis result can be generated. Optionally, attribution analysis can be performed on each advertisement conversion and statistically integrated periodically to obtain a more comprehensive analysis result.

[0096] Currently, advertising attribution analysis relies on data reported by media parties and advertisers for analysis, and it is impossible to analyze the whole-network data across media, resulting in many incorrect analysis conclusions, ignoring the role of other media and overestimating the value of its own media. This is neither conducive to optimizing the placement of its own media nor to the advertising effect of advertisers. With the secure, accurate, and complete characteristics of blockchain, this application can obtain the exposure, click, and conversion data completely, achieve more accurate attribution analysis, and improve the advertising effect of the media itself. At the same time, it can provide the attribution analysis results to advertisers, specifically including the advertisement identifier of the target advertisement, the above-mentioned effective exposure time, the above-mentioned conversion time, the above-mentioned conversion form, and the above-mentioned attribution node, etc., so that the advertiser node can intuitively and accurately know the placement node with greater value in the advertisement conversion, that is, the attribution node, reducing the blind placement of advertisers, making advertisers more targeted, and also promoting the healthy development of the industry.

[0097] In one implementation, multiple attribution nodes corresponding to the advertisement conversion information of the target advertisement within a preset period can be determined, and the placement volume of the multiple attribution nodes for the target advertisement within the preset period can be obtained;

[0098] The number of times different placement nodes are determined as the above-mentioned attribution nodes is obtained; according to the number of times different placement nodes are determined as the above-mentioned attribution nodes and the placement volume of different placement nodes for the target advertisement within the preset period, the node with the highest conversion contribution degree is determined among the different placement nodes.

[0099] The above-mentioned preset period can be set by the placement node or the advertiser node of the target advertisement. Specifically, through the above method, multiple attribution nodes corresponding to the advertisement conversion information of the target advertisement within the preset period can be determined, and the placement node that makes a contribution can be determined for the attribution analysis of each conversion. It should be noted that the attribution analysis can be carried out for the advertisement conversions of a large number of different user nodes. For these users, the attribution nodes determined for each conversion of the target advertisement can be the same, that is, one placement node may cause the conversion of the target advertisement at multiple user nodes after placement, and even more than one conversion.

[0100] Therefore, statistics can be further carried out to obtain the number of times different placement nodes are determined as the above-mentioned attribution nodes. At the same time, the placement volume of these placement nodes for the target advertisement within the preset period can be obtained, and then the node with the highest conversion contribution degree can be determined among the different placement nodes.

[0101] Specifically, based on the data obtained above, for the preset period, the conversion contribution degree of each placement node can be measured by the ratio of the number of times the placement node is determined as the attribution node to the placement volume of the target advertisement by the placement node. The higher the ratio, the higher the conversion contribution degree. Optionally, weights of other items can also be set for calculation, such as the exposure duration (viewing duration) of the target advertisement, the feedback (good or bad) scores of users on each placement node, etc. Finally, the comprehensive contribution degrees of the above-mentioned placement nodes can be output.

[0102] After determining the node with the highest conversion contribution degree among the above different placement nodes, it can be recommended to the advertiser node of the target advertisement, which is beneficial for the advertiser to select the advertisement placement node that can bring more conversions and improve the advertising placement effect. At the same time, correct attribution analysis can enable the placement nodes to form healthy competition and urge the media itself to improve the placement effect.

[0103] To understand the advertisement frequency control of the present application more clearly, please refer to Figure 3B , Figure 3B which is a schematic diagram of the node interaction process of an advertisement data analysis method. Among them, two placement nodes can be regarded as a media platform A (server) and a media platform B (server). Taking an advertiser node and a user node in the blockchain as an example, the advertisement attribution analysis process is introduced. Specifically, the method includes:

[0104] (a1) and (a2), the advertiser places advertisements on media platforms A and B and sets placement requirements. A and B will conduct advertisement placement according to their respective placement requirements;

[0105] (b1) Media platform A places an advertisement for the user node, and an exposure occurs. The user node generates a block to record the advertisement exposure information;

[0106] (b2) Media platform B places an advertisement for the user node, and an exposure occurs. The user node generates a block to record the advertisement exposure information (the exposure of (b2) is later);

[0107] (c) After the advertisement is exposed at the user node, a conversion occurs. The user node generates a block to record the advertisement conversion information;

[0108] (d1) Media platform B can receive the block, obtain the above advertisement exposure information and conversion exposure information for analysis, and determine the correct attribution node according to the exposure time and conversion time therein to obtain the attribution analysis result (here, it is an example that the placement exposure of media platform B generates a conversion, and the analysis result attributes it to B);

[0109] There may be (d2), that is, media platform A can also execute the steps described in (d1), and still attribute it to B instead of itself.

[0110] Optionally, advertisers can join the blockchain to view the above exposure and conversion data, confirm whether each media meets their delivery requirements and whether the attribution analysis is reasonable.

[0111] Generally speaking, media do not share data with each other. Even if they do, the data is not trustworthy. They usually take credit for the exposure and conversion of advertising, especially the conversion effect, in order to gain more budget from advertisers.

[0112] For example, the data between media platforms are usually not connected. For media platforms A and B, A only has A's exposure and click data, and B only has B's exposure and click data. When analyzing the reasons for conversion, it is assumed that a1-a4 and b1-b4 are the exposure and click data generated by media A and B, respectively, which correspond to four delivery times.

[0113] Then A can only analyze its own data a1, a2, a3, and a4, and then draw a subjective conclusion: a4 is the final one that plays a role. Similarly, B can only analyze b1, b2, b3, and b4, and conclude that its own b4 is the final one that plays a role. However, on the real user side, what you may see is a1, a2, b1, b2, b3, a3, a4, and b4. From a global perspective, it is ultimately the subsequent conversion brought about by b4. Everyone should recognize the contribution of b4 to the conversion. This is the correct attribution analysis. That is, the original media A would mistakenly attribute it to a4, but now A needs to recognize the value of b4. For advertisers, such attribution analysis is of practical value, rather than the original multiple media each attributing the conversion to a certain exposure of their own node. The solution in the embodiment of this application can clarify the correct media side of the advertising conversion attribution.

[0114] In an embodiment of the present application, by obtaining the advertisement conversion information and the user node identifier in the second block, the advertisement conversion information includes the advertisement identifier of the target advertisement and the corresponding conversion time and conversion form, and obtaining the advertisement exposure information of the target advertisement corresponding to the user node identifier from the first block, the advertisement exposure information includes the advertisement identifier, exposure time and delivery source node of the target advertisement. When at least two of the advertisement exposure information are obtained, the effective exposure time is determined in the exposure time, and the delivery source node corresponding to the effective exposure time is determined as the attribution node of the conversion of the target advertisement. By accurately storing the advertisement exposure and conversion data on the user side through blockchain technology, it is not limited to the advertisement exposure data of a single platform, and a more real and accurate attribution analysis of the advertisement conversion is performed to obtain the attribution analysis result that directly causes the conversion.

[0115] Based on the description of the above advertising data analysis method embodiment, the present application embodiment also discloses an advertising data analysis device. Figure 4, the advertisement data analysis device 400 includes:

[0116] An acquisition module 410, configured to acquire advertisement conversion information and user node identifiers in a second block, where the advertisement conversion information includes an advertisement identifier of a target advertisement and corresponding conversion time and conversion form;

[0117] The acquisition module 410 is further configured to acquire advertisement exposure information of the target advertisement corresponding to the user node identifier from a first block, where the advertisement exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement;

[0118] A processing module 420, configured to determine an effective exposure time among the exposure times when at least two pieces of the advertisement exposure information are acquired;

[0119] The processing module 420 is further configured to determine the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement.

[0120] According to an embodiment of the present application, Figure 3A and / or Figure 3B Each step involved in the method executed by the delivery node shown in Figure 4 can be executed by the advertisement data analysis device 400 shown in

[0121] The advertisement data analysis device 400 in the embodiment of the present application acquires advertisement conversion information and user node identifiers in a second block, where the advertisement conversion information includes an advertisement identifier of a target advertisement and corresponding conversion time and conversion form, acquires advertisement exposure information of the target advertisement corresponding to the user node identifier from a first block, where the advertisement exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement, determines an effective exposure time among the exposure times when at least two pieces of the advertisement exposure information are acquired, and determines the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement. By accurately storing advertisement exposure and conversion data on the user side through blockchain technology, it is possible to include advertisement exposure data not limited to a single platform, making the attribution analysis of advertisement conversion more real and accurate.

[0122] Based on the description of the above method embodiment and device embodiment, the embodiment of the present application further provides another advertisement data analysis device. Please refer to Figure 5 , the advertisement data analysis device 500 includes:

[0123] A processing module 510, configured to generate advertisement exposure information when detecting that a target advertisement is exposed, where the advertisement exposure information includes the advertisement identifier, exposure time, and delivery source node of the target advertisement;

[0124] A generation module 520 is configured to generate a first block to record the above-mentioned advertisement exposure information and the user node identifier, and store the first block in the data sharing system;

[0125] The above-mentioned processing module 520 is further configured to generate advertisement conversion information in the case of detecting a conversion operation of the above-mentioned target advertisement, where the advertisement conversion information includes the advertisement identifier of the above-mentioned target advertisement and the corresponding conversion time and conversion form;

[0126] The above-mentioned generation module 520 is further configured to generate a second block to record the above-mentioned advertisement conversion information and the above-mentioned user node identifier, and store the second block in the data sharing system.

[0127] In one embodiment, the advertisement data analysis device 500 in the embodiments of the present application can be used to perform a series of processes, including, for example Figure 2 the methods in the embodiments shown, and Figure 3A or Figure 3B the methods executable by the user node in

[0128] and so on. It can interact with the foregoing advertisement data analysis device 400 to implement the advertisement data analysis method in the embodiments of the present application, which will not be elaborated here. Figure 6 Referring to

[0129] Figure 2 Figure 3A Figure 3B or

[0130] the methods in the embodiments shown and so on.

[0130] Embodiments of the present application also provide a computer storage medium (Memory). The computer storage medium is a memory device in a terminal and is used to store programs and data. It can be understood that the computer storage medium here can include both the built-in storage medium in the terminal and, of course, the extended storage medium supported by the terminal. The computer storage medium provides a storage space, and the operating system of the terminal is stored in this storage space. Moreover, one or more instructions suitable for being loaded and executed by the processor 601 are stored in this storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory; optionally, it can also be at least one computer storage medium located far from the aforementioned processor.

[0131] In one embodiment, one or more instructions stored in the computer storage medium can be loaded and executed by the processor 601 to implement the corresponding steps of the method in the above embodiment; in a specific implementation, one or more instructions in the computer storage medium can be loaded and executed by the processor 601 Figure 2 、 Figure 3A and / or Figure 3B any steps of the method in, which will not be elaborated here.

[0132] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices and modules can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0133] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. The couplings, direct couplings, or communication connections shown or discussed with each other can be through some interfaces, and the indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0134] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0135] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media. The available medium can be a read-only memory (ROM), a random access memory (RAM), a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape, a magnetic disk, or an optical medium, such as a digital versatile disc (DVD), or a semiconductor medium, such as a solid state disk (SSD), etc.

Claims

1. An advertising data analysis method, characterized in that, Including: Obtain the advertisement conversion information and the user node identifier in the second block, where the advertisement conversion information includes the advertisement identifier of the target advertisement and the corresponding conversion time and conversion form; Obtain the advertisement exposure information of the target advertisement corresponding to the user node identifier from the first block, where the advertisement exposure information includes the advertisement identifier of the target advertisement, the exposure time, and the delivery source node; In the case of obtaining at least two pieces of the advertisement exposure information, determine the effective exposure time among the exposure times; Determine the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement.

2. The method according to claim 1, wherein The determining the effective exposure time among the exposure times includes: Among the at least two exposure times corresponding to the at least two pieces of advertisement exposure information, determine the last exposure time before the conversion time as the effective exposure time.

3. The method according to claim 2, wherein After determining the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement, the method further includes: Generate an attribution analysis result, where the attribution analysis result includes the advertisement identifier of the target advertisement, the effective exposure time, the conversion time, the conversion form, and the attribution node; Determine the advertiser node corresponding to the target advertisement, and send the attribution analysis result to the advertiser node.

4. The method according to any one of claims 1-3, characterized in that, The delivery source nodes of the at least two pieces of the advertisement exposure information are different; The conversion form includes any one or more of the following: a purchase operation, a download operation, a page jump operation, and a recharge operation triggered by the exposure of the target advertisement.

5. The method according to claim 4, wherein The method further includes: Determine multiple attribution nodes corresponding to the advertisement conversion information of the target advertisement within a preset period, and obtain the delivery volume of the target advertisement by the multiple attribution nodes within the preset period; Obtain the number of times different delivery nodes are determined as the attribution nodes; according to the number of times different delivery nodes are determined as the attribution nodes and the delivery volume of the target advertisement by the different delivery nodes within the preset period, determine the node with the highest conversion contribution degree among the different delivery nodes.

6. An advertising data analysis device, characterized in that, Including: An acquisition module, configured to obtain the advertisement conversion information and the user node identifier in the second block, where the advertisement conversion information includes the advertisement identifier of the target advertisement and the corresponding conversion time and conversion form; The acquisition module is further configured to obtain the advertisement exposure information of the target advertisement corresponding to the user node identifier from the first block, where the advertisement exposure information includes the advertisement identifier of the target advertisement, the exposure time, and the delivery source node; A processing module, configured to determine the effective exposure time among the exposure times in the case of obtaining at least two pieces of the advertisement exposure information; The processing module is further configured to determine the delivery source node corresponding to the effective exposure time as the attribution node for the conversion of the target advertisement.

7. An electronic device, comprising an input device and an output device, characterized in that, Further including: A processor, adapted to implement one or more instructions; And, A computer storage medium storing one or more instructions, where the one or more instructions are adapted to be loaded and executed by the processor to perform the advertisement data analysis method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more instructions, and the one or more instructions are adapted to be loaded and executed by a processor to perform the advertising data analysis method according to any one of claims 1-5.

9. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, the advertising data analysis method according to any one of claims 1-5 is implemented.

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