E-commerce traffic conversion intelligent guidance analysis method, device, storage medium and electronic device

Abstract

The present invention discloses an intelligent guidance analysis method, device, storage medium and electronic device for e-commerce traffic diversion conversion. The method includes obtaining all advertising plans created on the media platform through an interface opened by the media platform, and automatically adding a third-party monitoring link to the selected advertising plan; collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling and generating a crowd package; obtaining e-commerce pre-link data and e-commerce post-link data, and associating the e-commerce pre-link data and e-commerce post-link data to calculate the return on investment of each advertising plan; analyzing the e-commerce conversion effect; providing auxiliary decision-making information through the return on investment of the advertising plan and the e-commerce conversion effect. The present invention connects the e-commerce pre-link data, realizes effective ROI calculation, and evaluates the quality of e-commerce conversion of non-domain people in multiple dimensions. Through multi-dimensional analysis and strategy recommendation, the conversion rate of e-commerce traffic diversion can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to an e-commerce traffic diversion conversion intelligent guidance analysis method, device, storage medium and electronic equipment. Background Art

[0002] In order to achieve e-commerce traffic diversion, the existing practice is to fill in the product page or activity page of the e-commerce platform in the landing page link when placing advertisements outside the e-commerce domain. After the user clicks the advertisement, he will automatically jump to the corresponding product page or activity page of the e-commerce platform to achieve traffic diversion. If you want to conduct secondary marketing for users reached by these external domains, you need to add third-party exposure monitoring and click monitoring links when placing advertisements to obtain the user device IDs of exposure and clicks. Then upload these IDs to the data system of the e-commerce platform and conduct secondary marketing within the e-commerce platform domain.

[0003] The problems and shortcomings of the prior art mainly include:

[0004] 1. By default, without adding third-party monitoring, the diverted users were only exposed to the advertisement once.

[0005] 2. Third-party monitoring to obtain user device ID requires analysis and processing of original logs, which has technical barriers.

[0006] 3. The ROI of overseas advertising cannot be effectively calculated.

[0007] 4. Overseas media populations lack the support of e-commerce conversion data, and cannot effectively evaluate the quality of overseas media populations.

[0008] 5. There is a lack of effective means to improve the conversion rate of e-commerce traffic.

[0009] 6. Each media and e-commerce platform has an independent system that needs to be manually operated separately, which is cumbersome.

[0010] In view of this, the present invention is proposed. Summary of the invention

[0011] The purpose of the present invention is to provide an e-commerce traffic conversion intelligent guidance analysis method, device, storage medium and electronic device to solve the problems existing in the prior art.

[0012] To solve the above problems, in a first aspect, an embodiment of the present invention provides an e-commerce traffic conversion intelligent guidance analysis method, comprising:

[0013] Through the interface opened by the media platform, all advertising plans created on the media platform are obtained, and third-party monitoring links are automatically added to the selected advertising plans;

[0014] Collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package based on the labeled monitoring data;

[0015] Acquire e-commerce pre-link data and e-commerce post-link data, and associate the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each of the advertising plans;

[0016] Analyze e-commerce conversion effects based on the population group;

[0017] Provide auxiliary decision-making information through the return on investment and e-commerce conversion effect of the advertising plan.

[0018] Optionally, the method of obtaining all advertising plans created on the media platform through an interface opened by the media platform and automatically adding a third-party monitoring link to the selected advertising plan includes:

[0019] Authorized media platform account;

[0020] Obtain detailed information on all advertising plans through the interfaces opened by the media platform;

[0021] Filter out the advertising plans that do not have third-party monitoring links added, and generate a list with selection controls;

[0022] Determining an advertising plan that requires third-party monitoring according to a selection operation performed by the customer on the list using the selection control;

[0023] Through the interface opened by the media platform, a third-party monitoring link is automatically added to the advertising plan that requires third-party monitoring.

[0024] Optionally, collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package according to the labeled monitoring data includes:

[0025] Obtain raw monitoring data and generate raw unstructured logs;

[0026] Split the original unstructured log fields and unify their formats to form structured logs;

[0027] Push structured logs to the big data computing platform to aggregate and sum indicators, process fields, and generate label tables;

[0028] Synchronize the tag table to the data analysis platform for real-time query;

[0029] Configure the visualization interface, perform intersection and difference selection on the labels in the label table, and generate corresponding population packages based on the selected labels.

[0030] Optionally, the acquiring e-commerce pre-link data and e-commerce post-link data, and associating the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each of the advertising plans includes:

[0031] Obtain e-commerce pre-link data through the interfaces opened by media platforms;

[0032] Obtain e-commerce post-link data through promotional links on e-commerce platforms;

[0033] Obtain the relationship between advertising plans and e-commerce platform promotion links through the interfaces opened by the media platform;

[0034] Calculate the ROI for each advertising campaign.

[0035] Optionally, analyzing the e-commerce conversion effect according to the population package includes:

[0036] The population package is pushed to the e-commerce data platform, and the intersection calculation is performed with the e-commerce behavior data and model population provided by the e-commerce data platform to obtain the number of intersections, and the e-commerce conversion rate of each population package is calculated.

[0037] Optionally, providing auxiliary decision information through the return on investment and e-commerce conversion effect of the advertising plan includes:

[0038] Based on the calculated return on investment and e-commerce conversion rate of the population package, generate multi-dimensional reports to show to customers, and configure indicator sorting controls.

[0039] Optionally, the functions implemented by the method are integrated into a system.

[0040] In a second aspect, an embodiment of the present invention provides an e-commerce traffic conversion intelligent guidance analysis device, comprising:

[0041] The third-party monitoring link creation module is used to obtain all advertising plans created on the media platform through the interface opened by the media platform, and automatically add a third-party monitoring link to the selected advertising plan;

[0042] A labeling processing module, used for collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package according to the label-processed monitoring data;

[0043] A front-end and back-end link data association module, used to obtain e-commerce front-end link data and e-commerce back-end link data, and associate the e-commerce front-end link data with the e-commerce back-end link data to calculate the return on investment of each of the advertising plans;

[0044] A conversion effect association module, used to analyze e-commerce conversion effects based on the population package;

[0045] The auxiliary decision-making module is used to provide auxiliary decision-making information through the return on investment and e-commerce conversion effect of the advertising plan.

[0046] In a third aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, wherein the program implements the method of the first aspect when executed by a processor.

[0047] In a fourth aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0048] one or more processors; and

[0049] A storage device is used to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of the first aspect.

[0050] The embodiments of the present invention provide an e-commerce traffic conversion intelligent guidance analysis method, device, storage medium and electronic device, which have the following beneficial effects: convenient data reflux, visualized crowd selection, and lower technical barriers to data reflux and application. It intuitively reflects the advertising ROI and e-commerce conversion data, so that the e-commerce traffic effect has data support. Through multi-dimensional analysis and strategy recommendation, the e-commerce traffic conversion rate is effectively improved and the trial and error cost is reduced. One-stop integration of multiple channels improves the multi-platform operation efficiency of e-commerce marketing. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of an e-commerce traffic conversion intelligent guidance analysis method according to an embodiment of the present invention is shown;

[0052] Figure 2 A block diagram of an e-commerce traffic conversion intelligent guidance analysis device according to an embodiment of the present invention is shown;

[0053] Figure 3 A block diagram of a computing device capable of implementing various embodiments of the present invention is shown. DETAILED DESCRIPTION

[0054] The principle and spirit of the present invention will be described below with reference to several exemplary embodiments shown in the accompanying drawings. It should be understood that these embodiments are described only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way.

[0055] In the description of the embodiments of the present invention, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0056] Please refer to Figure 1 In order to solve the above problems, an embodiment of the present invention provides an e-commerce traffic conversion intelligent guidance analysis method, the method 100 includes:

[0057] Step 101, through the interface opened by the media platform, all advertising plans created on the media platform are obtained, and a third-party monitoring link is automatically added to the selected advertising plan.

[0058] A third-party monitoring link refers to a link provided by a third party other than the media platform and the advertising client. It is used to monitor the advertising data delivered by the media platform and can be configured in the form of a URL. The third-party monitoring link usually includes fields such as the advertising plan and the user information field that touches the advertisement. The user information field includes information such as the user's IP address, operating system type, and device model. In addition, other fields related to the advertising effect may also be included, such as exposure, clicks, advertising expenses, tasks, products, materials, behaviors, and other fields. When an advertisement is displayed or clicked, the user end accessing the media platform server or the media platform server will request the third-party monitoring link address, thereby providing data to the server of the third-party monitoring platform.

[0059] In one embodiment, the configuration of the third-party monitoring link adopts the tool identification method, that is, the tool identification generated by the third-party monitoring tool is added to the back of the advertising landing page link to form a third-party monitoring link. In this way, when the advertising plan landing page corresponding to the monitoring link is visited, the visiting user terminal will initiate a request via https and send the user information of the user terminal to the server of the third-party monitoring platform.

[0060] In one embodiment, the advertising platform account is first authorized through the OAuth2.0 interface, and then the open Marketing API provided by the media platform is used to obtain detailed information on all advertising plans published by the merchant on the media platform and generate a list.

[0061] Next, filter out the advertising plans that do not have third-party monitoring links added and present them in a list. For example, a merchant has released three advertising plans on a media platform, namely Advertising Plan A, Advertising Plan B, and Advertising Plan C. The three advertising plans are presented in a list, and a checkable control is added after each advertising plan, as well as a virtual button for confirming the addition of a third-party monitoring link, such as "One-click Add". If neither advertising plan A nor B is configured with a third-party monitoring link, the customer who places the advertisement can check the box on their device, and then click the "One-click Add" virtual button. In this way, the system will edit the advertising plan according to the predetermined third-party monitoring link generation rules, and automatically add third-party monitoring links to advertising plans A and B, such as splicing the third-party monitoring link to the end of the advertising plan landing page URL.

[0062] Step 102, collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a population package based on the labeled monitoring data.

[0063] In one embodiment, step 102 includes:

[0064] 102a, the original monitoring data is obtained by the monitoring end server in the form of https request, forming an original unstructured log. As described above, the monitoring end server obtains the monitoring data, including various fields, which are first directly added to the log and saved on the monitoring end server.

[0065] 102b, the system splits and unifies the format of the fields in the original unstructured log to form a standard structured log.

[0066] 102c, push the structured log to a big data computing platform, such as MaxCompute, and use SQL to aggregate and sum indicators and process fields to form a label table.

[0067] 102d, synchronize the tag table to a data analysis platform, such as AnalyticDB or Hologres, for real-time query by the front-end system.

[0068] 102e, configure the visualization interface, circle the labels of the label table by intersection, union and difference, and form a crowd package. A crowd package refers to a set of "people with a type of characteristics", and the characteristics here are the labels after intersection, union and difference. For example, for a game product, there may be dimensions such as gender, age, income, and game type, and the dimensions correspond to multiple labels that can be selected. In some embodiments, the labels in these dimensions can be selected by union, such as the age label is "20-30 years old" and the game type label is "role-playing type". In this way, after performing the "intersection" operation, a crowd package with both labels is obtained. The "and" operation covers more than two labels in the same dimension. For example, circling "male" and "female" in the age dimension is a union operation, and the crowd package finally generated includes males and females. Difference corresponds to the "not" operation in Boolean logic, or the "difference" operation in the set.

[0069] Through step 102, the system performs operations such as format conversion, unification, and summation on each field in the monitoring request to form label data of different dimensions. These label data are visualized in the system, allowing users to circle the intersection, union, and difference combinations between labels to form population packages that meet specific business scenarios.

[0070] Step 103: Obtain e-commerce pre-link data and e-commerce post-link data, and associate the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each of the advertising plans.

[0071] Step 103 specifically includes:

[0072] 103a. Obtain e-commerce pre-link data (such as exposure, clicks, advertising expenditure, etc.) through the Marketing API opened by the media platform.

[0073] 103b, obtain e-commerce post-link data (payment amount, number of payment orders, etc.) through e-commerce platforms, such as Taobao Alliance / JD Alliance promotion links.

[0074] 103c, obtain the association between the advertising plan and the Taobao Alliance / JD Alliance promotion link through the Marketing API opened by the media platform.

[0075] 103d. Calculate the ROI (return on investment) of each advertising plan using the formula ROI=payment amount / advertising cost.

[0076] For example, the server obtains from the media platform that the advertising cost of advertising plan A is 1,000 yuan, and also obtains the promotion link of the e-commerce platform associated with the advertising plan from the media platform, and then obtains the corresponding sales payment amount of 2,000 yuan based on the promotion link. According to the formula ROI = payment amount / advertising cost, the ROI of the advertising plan is 200%.

[0077] Through step 103, the data of the front and back links of e-commerce are connected to achieve effective calculation of ROI.

[0078] Step 104: Analyze the e-commerce conversion effect according to the population group.

[0079] In step 104, the data returned through monitoring in step 102 can be labeled to form population packages of multiple dimensions such as media, tasks, commodities, materials, and behaviors. In step 104, these population packages are pushed to the data analysis platforms of e-commerce such as Brand Data Bank and JD Data Square, and can be intersected with the e-commerce behaviors and model populations of Taobao and JD.com to obtain the number of intersections, and then the e-commerce conversion rate of each population package can be calculated.

[0080] In one embodiment, step 104 may include the following steps:

[0081] 104a, automatically splitting the returned data into crowd packages of multiple dimensions such as media, tasks, products, materials, and behaviors according to the returned data labels;

[0082] 104b, call the crowd push interface of Brand Data Bank and JD Data Square to push the crowd package to the corresponding platform;

[0083] 104c, through RPA, simulate the intersection of the crowd package and the e-commerce behavior / model crowd to obtain the number of intersections;

[0084] 104d, calculate the e-commerce conversion rate by the intersection number and the total number of people in the crowd package, for example, the intersection number can be divided by the total number of people in the crowd package.

[0085] Step 105: providing auxiliary decision-making information through the return on investment and e-commerce conversion effect of the advertising plan.

[0086] For example, based on the calculated ROI and e-commerce conversion rate of the population package, a multi-dimensional report can be presented to the customer, allowing sorting by various indicators. The advertising plans, products, materials, and user orientations that rank high in ROI and e-commerce conversion rate of the population package represent the content with high e-commerce traffic conversion rate. Customers can increase the budget for this part with high conversion rate and reduce or terminate the part with low conversion rate, thereby achieving the effect of improving the conversion rate of e-commerce traffic.

[0087] All the above steps can be integrated into a system that connects to the data interfaces of media platforms and e-commerce platforms to form a one-stop service. For example, media platforms (Big Engine and Tencent Advertising) are connected through open Marketing APIs, and e-commerce platforms such as Brand Data Bank can push crowd packages through the data bank push function in the marketing accelerator. Another example is that JD Data Square can push crowd packages through the JD Data Square push function in JD CDP.

[0088] The account authorization and operation processes of all platforms are unified in one system to achieve one-stop service.

[0089] like Figure 2 As shown, the embodiment of the present invention further provides an e-commerce traffic conversion intelligent guidance analysis device 200, comprising:

[0090] The third-party monitoring link creation module 201 is used to obtain all advertising plans created on the media platform through the interface opened by the media platform, and automatically add a third-party monitoring link to the selected advertising plan;

[0091] The labeling processing module 202 is used to collect monitoring data corresponding to the advertising plan through the third-party monitoring link, label the monitoring data, and generate a crowd package based on the label-processed monitoring data;

[0092] The pre- and post-link data association module 203 is used to obtain the e-commerce pre-link data and the e-commerce post-link data, and associate the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each of the advertising plans;

[0093] A conversion effect association module 204, used to analyze the e-commerce conversion effect according to the population package;

[0094] The auxiliary decision module 205 is used to provide auxiliary decision information through the return on investment and e-commerce conversion effect of the advertising plan.

[0095] It should be understood that the above-mentioned program modules have a one-to-one correspondence with the steps described in the method embodiment, and the technical solutions described in the method embodiment can also be applied to the specific configuration of each program module. To avoid repetition, they will not be described here.

[0096] According to an embodiment of the present invention, the present invention also provides an electronic device, a readable storage medium and a computer program product.

[0097] Figure 3A block diagram of a computing device 600 capable of implementing multiple embodiments of the present invention is shown. Electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0098] like Figure 3 As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0099] A number of components in the device 600 are connected to the I / O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0101] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0103] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, 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 foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0105] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0106] A computer system may include clients and servers. Clients and servers are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship to each other.

[0107] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0108] The invention concept is described in detail using specific examples herein, and the description of the above embodiments is only used to help understand the core idea of ​​the invention. It should be pointed out that any obvious modification, equivalent substitution or other improvement made by a person of ordinary skill in the art without departing from the invention concept should be included in the protection scope of the present invention.

Claims

1. An intelligent guidance analysis method for e-commerce traffic conversion, characterized in that: include: Through the interface opened by the media platform, all advertising plans created on the media platform are obtained, and third-party monitoring links are automatically added to the selected advertising plans; Collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package based on the labeled monitoring data; Acquire e-commerce pre-link data and e-commerce post-link data, and associate the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each of the advertising plans; Analyze e-commerce conversion effects based on the population group; Provide decision-making assistance information through the return on investment and e-commerce conversion effect of the advertising plan; Analyzing the e-commerce conversion effect according to the population package includes: Push the crowd package to the e-commerce data platform, perform intersection calculation with the e-commerce behavior data and model crowd provided by the e-commerce data platform, obtain the number of intersections, and calculate the e-commerce conversion rate of each crowd package; The auxiliary decision-making information provided by the return on investment and e-commerce conversion effect of the advertising plan includes: Based on the calculated return on investment and e-commerce conversion rate of the population package, generate multi-dimensional reports to show to customers, and configure indicator sorting controls.

2. The e-commerce traffic conversion intelligent guidance analysis method according to claim 1 is characterized in that: The method of obtaining all advertising plans created on the media platform through the interface opened by the media platform and automatically adding a third-party monitoring link to the selected advertising plan includes: Authorized media platform account; Obtain detailed information on all advertising plans through the interfaces opened by the media platform; Filter out the advertising plans that do not have third-party monitoring links added, and generate a list with selection controls; Determining an advertising plan that requires third-party monitoring according to a selection operation performed by the customer on the list using the selection control; Through the interface opened by the media platform, a third-party monitoring link is automatically added to the advertising plan that requires third-party monitoring.

3. The e-commerce traffic conversion intelligent guidance analysis method according to claim 1 is characterized in that: The collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package according to the labeled monitoring data includes: Obtain raw monitoring data and generate raw unstructured logs; Split the original unstructured log fields and unify their formats to form structured logs; Push structured logs to the big data computing platform to aggregate and sum indicators, process fields, and generate label tables; Synchronize the tag table to the data analysis platform for real-time query; Configure the visualization interface, perform intersection and difference selection on the labels in the label table, and generate corresponding population packages based on the selected labels.

4. The e-commerce traffic conversion intelligent guidance analysis method according to claim 1 is characterized in that: The acquiring of e-commerce pre-link data and e-commerce post-link data, and associating the e-commerce pre-link data with the e-commerce post-link data to calculate the return on investment of each advertising plan includes: Obtain e-commerce pre-link data through the interfaces opened by media platforms; Obtain e-commerce post-link data through promotional links on e-commerce platforms; Obtain the relationship between advertising plans and e-commerce platform promotion links through the interfaces opened by the media platform; Calculate the ROI for each advertising campaign.

5. The e-commerce traffic conversion intelligent guidance analysis method according to claim 4 is characterized in that: Integrate the functions implemented by the method in a system.

6. An intelligent guidance and analysis device for e-commerce traffic conversion, characterized in that: include: The third-party monitoring link creation module is used to obtain all advertising plans created on the media platform through the interface opened by the media platform, and automatically add a third-party monitoring link to the selected advertising plan; A labeling processing module, used for collecting monitoring data corresponding to the advertising plan through the third-party monitoring link, labeling the monitoring data, and generating a crowd package according to the label-processed monitoring data; A front-end and back-end link data association module, used to obtain e-commerce front-end link data and e-commerce back-end link data, and associate the e-commerce front-end link data with the e-commerce back-end link data to calculate the return on investment of each of the advertising plans; A conversion effect association module, used to analyze e-commerce conversion effects based on the population package; A decision-making support module, used to provide decision-making support information based on the return on investment and e-commerce conversion effect of the advertising plan; Analyzing the e-commerce conversion effect according to the population package includes: Push the crowd package to the e-commerce data platform, perform intersection calculation with the e-commerce behavior data and model crowd provided by the e-commerce data platform, obtain the number of intersections, and calculate the e-commerce conversion rate of each crowd package; The auxiliary decision-making information provided by the return on investment and e-commerce conversion effect of the advertising plan includes: Based on the calculated return on investment and e-commerce conversion rate of the population package, generate multi-dimensional reports to show to customers, and configure indicator sorting controls.

7. A storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

8. An electronic device, comprising: one or more processors; as well as A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

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