Digital marketing effect analysis method and system based on big data

Abstract

The application provides a big data-based digital marketing effect analysis method and system, comprising: acquiring user browsing behavior data for different marketing contents under different marketing channels, and integrating and storing the browsing behavior data; analyzing the browsing behavior data based on the integration and storage results, determining the progressive conversion rates of different marketing contents at each marketing stage under different marketing channels, and performing correlation analysis on the progressive conversion rates of each marketing stage of each marketing content to determine the user conversion contribution under different marketing channels; determining the return on investment of different marketing channels based on the user conversion contribution, and tabulating and visually displaying the return on investment of different marketing channels. The user can intuitively and effectively understand the marketing effects of different marketing channels, and the reliability and accuracy of digital marketing effect analysis are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for analyzing the effectiveness of digital marketing based on big data. Background Technology

[0002] Currently, with the rapid development of internet technology, digital marketing has become an important means for enterprises to promote their products and services;

[0003] However, there are many problems with the current evaluation of digital marketing effectiveness. Traditional marketing effectiveness analysis often relies on simple indicators such as click-through rate and conversion rate, lacking comprehensive mining and in-depth analysis of large amounts of data. At the same time, due to the diversification of marketing channels, the data sources of different marketing channels are isolated. For example, social media data, website traffic data, and offline sales data have not been effectively integrated, resulting in one-sided and low accuracy in the analysis of marketing effectiveness, thus causing waste of resources and unsatisfactory marketing results.

[0004] Therefore, in order to overcome the above-mentioned shortcomings, the present invention provides a method and system for analyzing digital marketing effectiveness based on big data. Summary of the Invention

[0005] This invention provides a method and system for analyzing digital marketing effectiveness based on big data. It acquires user browsing behavior data for different marketing content across various marketing channels, integrates and analyzes this data, and accurately and effectively determines the progressive conversion rate of different marketing content at each marketing stage across different marketing channels. This provides data support for evaluating data-driven marketing effectiveness. Secondly, it performs correlation analysis on the progressive conversion rate of each marketing content at each marketing stage to determine the user conversion contribution of different marketing channels. This allows for the determination of the return on investment (ROI) for different marketing channels based on conversion contribution, enabling accurate and reliable determination of the digital marketing effectiveness of different marketing channels. Finally, the obtained ROI is presented in a tabular and visual format, facilitating a direct and effective understanding of the marketing effectiveness of different marketing channels for users. This also improves the reliability and accuracy of digital marketing effectiveness analysis.

[0006] This invention provides a method for analyzing digital marketing effectiveness based on big data, including:

[0007] Step 1: Obtain user browsing behavior data for different marketing content across different marketing channels, and integrate and store the browsing behavior data;

[0008] Step 2: Analyze browsing behavior data based on the integration and storage results, determine the progressive conversion rate of different marketing content under different marketing channels at each marketing stage, and conduct correlation analysis on the progressive conversion rate of each marketing content at each marketing stage to determine the user conversion contribution under different marketing channels.

[0009] Step 3: Determine the ROI of different marketing channels based on user conversion contribution, and then visualize the ROI of different marketing channels in a table.

[0010] Preferably, a digital marketing effectiveness analysis method based on big data includes, in step 1, acquiring user browsing behavior data for different marketing content across different marketing channels, including:

[0011] Extract the interface configuration parameters of the databases in different marketing channels, and connect the data collection terminal with the databases of different marketing channels according to the interface configuration parameters;

[0012] Based on user identity and the integration results, the system locks the browsing behavior data of different marketing content in the databases of different marketing channels, and then retrieves the locked browsing behavior data.

[0013] Preferably, a digital marketing effectiveness analysis method based on big data includes browsing behavior data such as likes, comments, and shares on social media platforms, visit time, page views, and registration information on the company's official website, and sales time, sales volume, and customer information at offline sales terminals.

[0014] Preferably, in a digital marketing effectiveness analysis method based on big data, step 1 involves integrating and storing browsing behavior data, including:

[0015] Establish integration standards for browsing behavior data, and classify browsing behavior data of different marketing content under different marketing channels in multiple dimensions based on the integration standards;

[0016] A data set sequence is obtained based on the multi-dimensional classification results, and cache space is allocated in the management terminal according to the composition of the data set sequence;

[0017] The data set sequence is stored in the allocated cache space.

[0018] Preferably, in a big data-based digital marketing effectiveness analysis method, step 2 involves analyzing browsing behavior data based on the integrated and stored results to determine the progressive conversion rate of different marketing content across different marketing channels at each marketing stage. Furthermore, a correlation analysis is performed on the progressive conversion rate of each marketing content across each marketing stage to determine the user conversion contribution of different marketing channels, including:

[0019] Acquire the entire business process of digital marketing and divide the entire business process into stages based on the characteristics of each node;

[0020] Based on the phase division results, the business behavior characteristics of each phase are determined according to industry execution standards, and the business behavior characteristics are correlated with the phase division results;

[0021] A user behavior analysis model is constructed based on the correlation results, and the browsing behavior data of different marketing content under different marketing channels are analyzed based on the user behavior analysis model.

[0022] Based on the analysis results, the access characteristics of different users at each stage are determined, and the same object is tracked according to the execution logic between each stage based on the access characteristics.

[0023] Based on the tracking results of the same object, determine the changes in the group proportion of users between different stages, and based on the changes in the group proportion, determine the progressive conversion rate of different marketing content for different marketing channels at different marketing stages;

[0024] The digital marketing delivery ratio between adjacent stages is determined based on the progressive conversion rate. At the same time, the digital marketing weight of each stage is obtained, and the digital marketing delivery ratio between adjacent stages is weighted and averaged based on the digital marketing weight.

[0025] The user conversion contribution of different marketing channels is determined based on the weighted average results.

[0026] Preferably, in a big data-based digital marketing effectiveness analysis method, step 3 involves determining the return on investment (ROI) of different marketing channels based on user conversion contributions, and then visually displaying the ROI of different marketing channels in a table, including:

[0027] Tracing the user conversion contribution rate to determine the sub-user conversion rate of different marketing content under different marketing channels;

[0028] Obtain the marketing amount and marketing cost of different marketing content under different marketing channels, and convert the marketing amount based on the sub-user conversion rate to obtain the marketing conversion revenue of different marketing content under different marketing channels;

[0029] Based on sub-user conversion rates, marketing costs are converted to obtain target costs for different marketing content across different marketing channels;

[0030] The difference between the marketing conversion revenue and target cost for all the same marketing content under the same marketing channel is calculated to obtain the marketing revenue for each marketing channel, and the corresponding return on investment is obtained based on the marketing revenue.

[0031] Preferably, in a digital marketing effectiveness analysis method based on big data, step 3 involves tabulating and visually displaying the return on investment (ROI) of different marketing channels, including:

[0032] The ROI of different marketing channels is converted into charts to obtain corresponding visual display charts;

[0033] Visualize the charts and graphs on the visualization terminal.

[0034] This invention provides a digital marketing effectiveness analysis system based on big data, comprising:

[0035] The data integration and storage module is used to acquire user browsing behavior data on different marketing content under different marketing channels, and to integrate and store the browsing behavior data.

[0036] The data analysis module is used to analyze browsing behavior data based on the integrated and stored results, determine the progressive conversion rate of different marketing content under different marketing channels at each marketing stage, and perform correlation analysis on the progressive conversion rate of each marketing content at each marketing stage to determine the user conversion contribution under different marketing channels.

[0037] The marketing effectiveness determination module is used to determine the return on investment (ROI) of different marketing channels based on user conversion contributions, and to present the ROI of different marketing channels in a tabular and visual format.

[0038] Preferably, a big data-based digital marketing effectiveness analysis system includes a data integration and storage module, comprising:

[0039] The interface connection unit is used to extract the interface configuration parameters of the databases in different marketing channels, and connect the data collection terminal with the databases of different marketing channels according to the interface configuration parameters.

[0040] The data retrieval unit is used to lock browsing behavior data of different marketing content in the databases of different marketing channels based on user identity and the docking results, and to retrieve the locked browsing behavior data.

[0041] Preferably, a digital marketing effectiveness analysis system based on big data includes browsing behavior data such as likes, comments, and shares on social media platforms; visit time, page views, and registration information on the company's official website; and sales time, sales volume, and customer information at offline sales terminals.

[0042] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0043] By acquiring user browsing behavior data on different marketing content across various marketing channels, and integrating and analyzing this data, we can accurately and effectively determine the progressive conversion rates of different marketing content at each marketing stage across different marketing channels. This provides data support for evaluating the effectiveness of data-driven marketing. Secondly, we conduct correlation analysis on the progressive conversion rates of each marketing content at each marketing stage to determine the user conversion contribution of different marketing channels. This allows us to determine the return on investment (ROI) of different marketing channels based on conversion contribution, enabling accurate and reliable determination of the digital marketing effectiveness of different channels. Finally, we present the obtained ROI in a tabular and visual format, facilitating users' intuitive and effective understanding of the marketing effectiveness of different channels. This also improves the reliability and accuracy of digital marketing effectiveness analysis.

[0044] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained through the structures particularly pointed out in this application.

[0045] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0046] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0047] Figure 1 This is a flowchart of a digital marketing effectiveness analysis method based on big data, as described in an embodiment of the present invention.

[0048] Figure 2 This is a flowchart of step 1 in a big data-based digital marketing effectiveness analysis method according to an embodiment of the present invention;

[0049] Figure 3 This is a structural diagram of a big data-based digital marketing effectiveness analysis system according to an embodiment of the present invention. Detailed Implementation

[0050] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0051] In one embodiment, a big data-based digital marketing effectiveness analysis method includes:

[0052] Step 1: Obtain user browsing behavior data for different marketing content across different marketing channels, and integrate and store the browsing behavior data;

[0053] Step 2: Analyze browsing behavior data based on the integration and storage results, determine the progressive conversion rate of different marketing content under different marketing channels at each marketing stage, and conduct correlation analysis on the progressive conversion rate of each marketing content at each marketing stage to determine the user conversion contribution under different marketing channels.

[0054] Step 3: Determine the ROI of different marketing channels based on user conversion contribution, and then visualize the ROI of different marketing channels in a table.

[0055] In this embodiment, different marketing channels refer to platforms or websites that need to publish or promote digital content, such as social media platforms, corporate websites, and e-commerce service platforms.

[0056] In this embodiment, different marketing content refers to digital information that needs to be promoted, such as product details or technical services.

[0057] In this embodiment, browsing behavior data refers to data such as user clicks on different marketing content through different marketing channels and browsing time, specifically including likes, comments, and shares on social media platforms, access time, page views, and registration information on the company's official website, and sales time, sales quantity, and customer information at offline sales terminals.

[0058] In this embodiment, the marketing stage refers to all stages from when a user learns about marketing content through marketing channels to when they purchase it. For example, it could be the awareness stage, interest stage, decision-making stage, and purchase stage.

[0059] In this embodiment, the progressive conversion rate refers to the transmission effect between each marketing stage, such as the probability or proportion of a user entering the interest stage through the cognition stage and the probability or proportion of entering the decision-making stage through the interest stage.

[0060] In this embodiment, user conversion contribution refers to the promotion of different marketing content through different marketing channels, that is, the purchase or ordering of different marketing content by users through different marketing channels.

[0061] In this embodiment, the return on investment refers to the user conversion contribution under different marketing channels, that is, determined by the number of users acquired, the marketing costs and marketing revenues of different marketing channels.

[0062] In this embodiment, tabular visualization refers to creating corresponding visual tables of the return on investment (ROI) of different marketing channels, thereby displaying the ROI of different marketing channels. For example, it can be visualized by generating line charts or bar charts.

[0063] The working principle and beneficial effects of the above technical solution are as follows: By acquiring user browsing behavior data of different marketing content under different marketing channels, and integrating and analyzing the browsing behavior data, the progressive conversion rate of different marketing content under different marketing channels at each marketing stage can be accurately and effectively determined, providing data support for data marketing effect evaluation. Secondly, the progressive conversion rate of each marketing content at each marketing stage is correlated and analyzed to determine the user conversion contribution of different marketing channels. Then, the return on investment of different marketing channels can be determined through conversion contribution, and the digital marketing effect of different marketing channels can be accurately and reliably determined through return on investment. Finally, the obtained return on investment is tabulated and visualized, making it easy for users to intuitively and effectively understand the marketing effect of different marketing channels. At the same time, the reliability and accuracy of digital marketing effect analysis are improved.

[0064] In one embodiment, a digital marketing effectiveness analysis method based on big data, such as Figure 2 As shown, in step 1, user browsing behavior data for different marketing content across different marketing channels is obtained, including:

[0065] Step 101: Extract the interface configuration parameters of the databases in different marketing channels, and connect the data collection terminal with the databases of different marketing channels according to the interface configuration parameters;

[0066] Access the backend of different marketing channels and determine the corresponding database interface configuration parameters based on the access results. The interface configuration parameters include the type of database interface and the communication requirements during data communication.

[0067] Based on the interface configuration parameters, the communication interface of the data collection terminal is adapted, and after successful adaptation, the data collection terminal is connected to the databases of different marketing channels.

[0068] Step 102: Based on the user's identity and the integration results, lock the browsing behavior data of different marketing content in the databases of different marketing channels, and retrieve the locked browsing behavior data;

[0069] The address information of the data stored in the databases of different marketing channels is extracted, and the access terminal information of different browsing behavior data is determined based on the address information extraction results;

[0070] The system identifies the user's target terminal based on the user's identity, and determines the correspondence between browsing behavior data and user identity based on the target terminal and access terminal information. Based on this correspondence, it locks the browsing behavior data of different marketing content in the databases of different marketing channels.

[0071] In one embodiment, a digital marketing effectiveness analysis method based on big data includes, in step 1, integrating and storing browsing behavior data, including:

[0072] Establish integration standards for browsing behavior data, and based on these standards, classify browsing behavior data of different marketing content across different marketing channels in a multi-dimensional manner, specifically including:

[0073] The integration standard for browsing behavior data is obtained based on the management terminal, and the integration standard is parsed to determine the limited subject items in the integration standard, where the subject item is the user identity identifier.

[0074] Extract the data attribution characteristics of browsing behavior data of different marketing content under different marketing channels, and determine the attribution object of different browsing behavior data based on the data attribution characteristics. Here, the attribution characteristics are the data sources of browsing behavior data, and the attribution object is the user ID determined according to the data source.

[0075] The system matches the attribution object with the user's identity identifier. Based on the matching results, the browsing behavior data of the same user under different marketing channels is aggregated to obtain a data set sequence, where the data set sequence is the sum of all browsing behavior data of each user under different marketing channels.

[0076] A data set sequence is obtained based on the multi-dimensional classification results, and cache space is allocated in the management terminal according to the composition of the data set sequence, specifically including:

[0077] Determine the number of bytes in the data set sequence and the data set, and allocate cache space for each data set in the management terminal based on the number of bytes;

[0078] The data set sequence is stored in the allocated cache space.

[0079] In one embodiment, a big data-based digital marketing effectiveness analysis method, in step 2, analyzes browsing behavior data based on the integrated and stored results to determine the progressive conversion rate of different marketing content across different marketing channels at each marketing stage, and performs correlation analysis on the progressive conversion rate of each marketing content across each marketing stage to determine the user conversion contribution of different marketing channels, including:

[0080] The entire business process of digital marketing is obtained, and the entire business process is divided into stages based on the characteristics of each node business. The entire business process refers to all aspects of digital marketing, and the characteristics of each node business are the types and features of each aspect business.

[0081] Based on the phase division results, the business behavior characteristics of each phase are determined according to the industry execution standards, and the business behavior characteristics are associated with the phase division results. The industry execution standards are known in advance, and the business behavior characteristics are the business situation and business content of each phase.

[0082] A user behavior analysis model is constructed based on the correlation results, and the browsing behavior data of different marketing content under different marketing channels are analyzed based on the user behavior analysis model.

[0083] Based on the analysis results, the access characteristics of different users at each stage are determined, and the same object is tracked according to the execution logic between each stage based on the access characteristics. The access characteristics are the viewing type, browsing time and click information of digital marketing content by users at each stage, and the same object tracking is to track the access characteristics of the same user at different stages.

[0084] Based on the tracking results of the same object, the changes in the proportion of users in each stage are determined, and based on the changes in the proportion of users, the progressive conversion rate of different marketing content under different marketing channels in each marketing stage is determined. The change in the proportion of users is the change in the number of users in each stage, that is, the change in the number of users.

[0085] The digital marketing delivery ratio between adjacent stages is determined based on the progressive conversion rate. At the same time, the digital marketing weight of each stage is obtained, and the digital marketing delivery ratio between adjacent stages is weighted and averaged based on the digital marketing weight. The digital marketing delivery ratio is the user group conversion between adjacent stages determined by the progressive conversion rate, such as the delivery effect from the interest stage to the understanding stage. Specifically, it can be the number of user conversions. The digital marketing weight is the importance of digital marketing at different stages.

[0086] The user conversion contribution of different marketing channels is determined based on the weighted average results.

[0087] To further improve the effectiveness of digital marketing and ensure the relevance of marketing content to the user group, an embodiment is proposed: a big data-based digital marketing effectiveness analysis method. This method, based on access characteristics and the execution logic between different stages, tracks the same object and includes:

[0088] Based on the tracking results of the same object, we determine the attention of the same object to different marketing content at different stages. At the same time, we extract the performance characteristics of the same marketing content at different stages under different marketing channels. Among them, the performance characteristics are the form of marketing content at different stages and the basic situation of digital marketing.

[0089] The attention level is associated with the performance characteristics of the marketing content, and the changes in the access behavior characteristics of the same target audience at different stages are determined based on the association results of the same marketing content. The changes in access behavior characteristics are the viewing time and the number of times users share the marketing content at different stages.

[0090] Based on changes in access behavior characteristics, the conversion rate of the same marketing content at different stages is obtained. At the same time, the user group composition of each marketing content at different stages is determined, and the determined user group composition is divided into groups. The group division is based on age groups to divide users of different age groups in the user group structure.

[0091] Key behavioral information is extracted from the performance characteristics of marketing content at different stages to determine the corresponding marketing style. The marketing style, marketing content conversion rate, and group level segmentation results are then associated with the same marketing content. The key behavioral information refers to the types of copy and video in the marketing content at different stages, and the association of the main body refers to the association of the marketing style, marketing content conversion rate, and group level segmentation results corresponding to the same marketing content.

[0092] Based on the subject association results, determine the marketing effect of each marketing content under each marketing channel, and sort the marketing effects of each marketing content under each marketing channel in descending order.

[0093] Based on the return on investment (ROI) of each marketing content under each marketing channel, the marketing content under each marketing channel is adaptively allocated according to the descending order of the results. The adaptive allocation means that the marketing content with an ROI lower than a preset threshold is displayed in the marketing channel with an ROI higher than the preset threshold.

[0094] The marketing content is dynamically optimized and adjusted based on the adaptive allocation results.

[0095] In one embodiment, a big data-based digital marketing effectiveness analysis method, in step 3, determines the return on investment (ROI) of different marketing channels based on user conversion contributions, and then tabulates and visualizes the ROI of different marketing channels, including:

[0096] Tracing the user conversion contribution rate to determine the sub-user conversion rate of different marketing content under different marketing channels;

[0097] Obtain the marketing amount and marketing cost of different marketing content under different marketing channels, and convert the marketing amount based on the sub-user conversion rate to obtain the marketing conversion revenue of different marketing content under different marketing channels;

[0098] Based on sub-user conversion rates, marketing costs are converted to obtain target costs for different marketing content across different marketing channels;

[0099] The difference between the marketing conversion revenue and target cost for all the same marketing content under the same marketing channel is calculated to obtain the marketing revenue for each marketing channel, and the corresponding return on investment is obtained based on the marketing revenue.

[0100] In one embodiment, a big data-based digital marketing effectiveness analysis method, step 3, involves tabulating and visually displaying the return on investment (ROI) of different marketing channels, including:

[0101] The ROI of different marketing channels is transformed into charts to obtain corresponding visual displays, including:

[0102] Obtain the return on investment for different marketing channels and determine the target value for the corresponding return on investment;

[0103] Retrieve various types of visualization chart templates from the visualization chart library and determine the basic data requirements for each type of visualization chart template. The basic data requirements are the data format requirements of the visualization chart template.

[0104] Based on the basic data requirements, the target value corresponding to the rate of return on investment is converted and imported, and corresponding visualization charts are generated based on the conversion and import results;

[0105] Visualizing charts and graphs on a visualization terminal includes:

[0106] Receive visualization requests from visualization terminals, parse the visualization requests, and determine the intended category of the visualization charts to be displayed, where the intended category is the type of charts the user expects to see;

[0107] When an intention category exists, the corresponding visualization chart will be displayed on the visualization terminal.

[0108] When no intended category exists, the visualization charts for each category will be displayed sequentially on the visualization terminal for a preset duration.

[0109] In one embodiment, a big data-based digital marketing effectiveness analysis system, such as Figure 3 As shown, it includes:

[0110] The data integration and storage module is used to acquire user browsing behavior data on different marketing content under different marketing channels, and to integrate and store the browsing behavior data.

[0111] The data analysis module is used to analyze browsing behavior data based on the integrated and stored results, determine the progressive conversion rate of different marketing content under different marketing channels at each marketing stage, and perform correlation analysis on the progressive conversion rate of each marketing content at each marketing stage to determine the user conversion contribution under different marketing channels.

[0112] The marketing effectiveness determination module is used to determine the return on investment (ROI) of different marketing channels based on user conversion contributions, and to present the ROI of different marketing channels in a tabular and visual format.

[0113] In one embodiment, a big data-based digital marketing effectiveness analysis system includes a data integration and storage module, comprising:

[0114] The interface connection unit is used to extract the interface configuration parameters of the databases in different marketing channels, and connect the data collection terminal with the databases of different marketing channels according to the interface configuration parameters.

[0115] Access the backend of different marketing channels and determine the corresponding database interface configuration parameters based on the access results. The interface configuration parameters include the type of database interface and the communication requirements during data communication.

[0116] Based on the interface configuration parameters, the communication interface of the data collection terminal is adapted, and after successful adaptation, the data collection terminal is connected to the databases of different marketing channels.

[0117] The data retrieval unit is used to lock browsing behavior data of different marketing content in the databases of different marketing channels based on user identity and the docking results, and to retrieve the locked browsing behavior data.

[0118] The system identifies the user's target terminal based on the user's identity, and determines the correspondence between browsing behavior data and user identity based on the target terminal and access terminal information. Based on this correspondence, it locks the browsing behavior data of different marketing content in the databases of different marketing channels.

[0119] In one embodiment, a big data-based digital marketing effectiveness analysis system includes browsing behavior data such as likes, comments, and shares on social media platforms; visit time, page views, and registration information on a company's official website; and sales time, sales volume, and customer information at offline sales terminals.

[0120] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A big data-based digital marketing effect analysis method, characterized by, The method comprises the following steps: Step 1: Obtain user browsing behavior data of different marketing contents under different marketing channels, and integrate and store the browsing behavior data; Step 2: Based on the integration and storage results, analyze the browsing behavior data to determine the progressive conversion rates of different marketing contents at each marketing stage under different marketing channels, and perform correlation analysis on the progressive conversion rates of each marketing stage of each marketing content to determine the user conversion contribution under different marketing channels; Step 3: Based on the user conversion contribution, determine the return on investment of different marketing channels, and tabulate and visually display the return on investment of different marketing channels; In step 2, based on the integration and storage results, the browsing behavior data is analyzed to determine the progressive conversion rates of different marketing contents at each marketing stage under different marketing channels, and the correlation analysis is performed on the progressive conversion rates of each marketing stage of each marketing content to determine the user conversion contribution under different marketing channels, which comprises: Obtain the business whole process of digital marketing, and divide the business whole process into stages based on the node business characteristics; Based on the stage division results, determine the business behavior characteristics of each stage according to the industry execution standard, and associate the business behavior characteristics with the stage division results; Based on the association results, build a user behavior analysis model, and analyze the browsing behavior data of different marketing contents under different marketing channels based on the user behavior analysis model; Based on the analysis results, determine the access characteristics of different users at each stage, and track the same object according to the execution logic between stages based on the access characteristics; Based on the same object tracking results, determine the group proportion change of users between stages, and determine the progressive conversion rates of different marketing contents at each marketing stage under different marketing channels based on the group proportion change; Based on the progressive conversion rates, determine the digital marketing transmission ratio between adjacent stages, and obtain the digital marketing weight of each stage, and perform weighted average on the digital marketing transmission ratio between adjacent stages based on the digital marketing weight; Based on the weighted average results, determine the user conversion contribution under different marketing channels.

2. The big data-based digital marketing effect analysis method of claim 1, wherein, In step 1, the browsing behavior data of different marketing contents under different marketing channels is obtained, which comprises: Extract the interface configuration parameters of the database in different marketing channels, and connect the data collection end with the database of different marketing channels according to the interface configuration parameters; Based on the user identity, lock the browsing behavior data of different marketing contents in the database of different marketing channels according to the connection results, and retrieve the locked browsing behavior data. 3.The big data-based digital marketing effect analysis method of claim 1, wherein, The browsing behavior data includes like, comment and share data on social media platforms, access time, browsing page and registration information data on enterprise official websites, and sales time, sales quantity and customer information on offline sales terminals.

4. The big data based digital marketing effect analysis method according to claim 1, characterized in that, In step 1, the browsing behavior data is integrated and stored, which comprises: Determine the integration standard of the browsing behavior data, and perform multi-dimensional classification on the browsing behavior data of different marketing contents under different marketing channels based on the integration standard; Based on the multi-dimensional classification results, obtain the data set sequence, and allocate cache space according to the composition of the data set sequence in the management terminal; The data set sequence is stored in the allocated cache space.

5. The big data based digital marketing effect analysis method according to claim 1, wherein, In step 3, the return on investment of different marketing channels is determined based on the user conversion contribution, and the return on investment of different marketing channels is tabulated and visually displayed, including: Trace the user conversion contribution rate to determine the sub-user conversion rate of different marketing contents under different marketing channels; Obtain the marketing amount and marketing cost of different marketing contents under different marketing channels, and convert the marketing amount based on the sub-user conversion rate to obtain the marketing conversion income of different marketing contents under different marketing channels; Convert the marketing cost based on the sub-user conversion rate to obtain the target cost of different marketing contents under different marketing channels; Subtract the marketing conversion income and target cost of all the same marketing contents under the same marketing channel to obtain the marketing income of each marketing channel, and obtain the corresponding return on investment based on the marketing income.

6. The big data based digital marketing effect analysis method according to claim 1, wherein, In step 3, the return on investment of different marketing channels is tabulated and visually displayed, including: Convert the return on investment of different marketing channels into a corresponding visualization chart; Visualize the visualization chart on a visualization terminal. 7.A big data-based digital marketing effect analysis system, characterized by, It includes: A data integration and storage module for obtaining user browsing behavior data on different marketing contents under different marketing channels and integrating and storing the browsing behavior data; A data analysis module for analyzing the browsing behavior data based on the integration and storage results, determining the progressive conversion rate of different marketing contents at each marketing stage under different marketing channels, and performing correlation analysis on the progressive conversion rate of each marketing stage of each marketing content to determine the user conversion contribution under different marketing channels; A marketing effect determination module for determining the return on investment of different marketing channels based on the user conversion contribution, and tabulating and visually displaying the return on investment of different marketing channels; The data analysis module includes: Obtain the business whole process of digital marketing, and divide the business whole process into stages based on node business characteristics; Determine the business behavior characteristics of each stage based on the stage division results according to industry execution standards, and associate the business behavior characteristics with the stage division results; Based on the association results, construct a user behavior analysis model, and analyze the browsing behavior data of different marketing contents under different marketing channels based on the user behavior analysis model; Determine the access characteristics of different users at each stage based on the analysis results, and track the same object according to the execution logic between stages based on the access characteristics; Determine the group proportion change of users between stages based on the same object tracking results, and determine the progressive conversion rate of different marketing contents at each marketing stage under different marketing channels based on the group proportion change; Determine the digital marketing transfer ratio between adjacent stages based on the progressive conversion rate, and obtain the digital marketing weight of each stage, and weight average the digital marketing transfer ratio between adjacent stages based on the digital marketing weight; Determine the user conversion contribution under different marketing channels based on the weighted average result.

8. The big data based digital marketing effect analysis system according to claim 7, wherein, The data integration and storage module includes: The interface docking unit is used to extract interface configuration parameters of databases in different marketing channels and dock the data collection end with the databases of the different marketing channels according to the interface configuration parameters. The data retrieval unit is used to lock the browsing behavior data of different marketing contents in the databases of the different marketing channels according to the docking results based on the user identity, and retrieve the locked browsing behavior data.

9. The big data based digital marketing effect analysis system according to claim 8, wherein, The browsing behavior data includes like, comment and share data of a social media platform, access time, browsed page and registration information data of an enterprise official website, and sale time, sale quantity and customer information of an offline sale terminal.

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