Marketing data processing method and system

Abstract

The present invention relates to the technical field of marketing strategy analysis, and in particular to a method and system for processing marketing data. The method comprises the following steps: obtaining a target platform marketing data set and performing data format division and caching processing and format unified conversion processing to obtain a target platform marketing standardized format data set; performing user browsing behavior analysis on the target platform marketing standardized format data set to obtain target platform marketing user browsing behavior data; obtaining marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to perform browsing interest screening processing to obtain a target platform user browsing interest product set; performing advertising browsing division processing and advertising marketing analysis on the target platform marketing user browsing behavior data, and performing marketing strategy analysis to generate a target platform product marketing strategy to execute corresponding target platform product personalized marketing activities. The present invention can provide more accurate and effective marketing strategy support.

Description

Technical Field

[0001] The present invention relates to the technical field of marketing strategy analysis, and in particular to a method and system for processing marketing data. Background Art

[0002] In today's digital marketing environment, companies are faced with the challenge of processing large amounts of marketing data. In addition, with the popularity of the Internet and mobile devices, user behavior data has exploded, and traditional data processing methods can no longer meet the needs of fast, accurate, and real-time data processing. In recent years, with the development of artificial intelligence and big data technologies, some new marketing data processing methods have begun to attract attention. These methods are mainly based on machine learning algorithms and data mining technologies, which can automatically analyze massive amounts of marketing data and discover potential connections and patterns between data, thereby providing companies with decision support and marketing optimization suggestions. However, existing marketing data processing methods still have problems with low data quality and lack of personalized analysis of marketing strategies, which limits their widespread application in actual marketing applications. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide a method and system for processing marketing data to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a method for processing marketing data includes the following steps:

[0005] Step S1: Obtain a target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain a target platform marketing standardized format data set;

[0006] Step S2: Analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data to obtain the target platform user browsing interest product set;

[0007] Step S3: Perform advertising browsing segmentation processing on the browsing behavior data of the target platform marketing users to obtain the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data; perform advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data to obtain the advertising marketing delivery timing of each user's browsing interest product;

[0008] Step S4: Based on the advertising marketing delivery opportunities of the products of interest browsed by each user, a marketing strategy analysis is performed on the target platform user browsing interest product set, and a target platform product marketing strategy is generated to execute corresponding personalized marketing activities for the target platform products.

[0009] The present invention first acquires the marketing data set of the target platform in order to establish the basis for analysis and decision-making. By collecting this data, information such as market trends, customer behavior, and product sales can be understood, thereby providing basic data guarantee for subsequent processing. The target platform marketing data set is divided into data formats and cached. The key to this step is to design various data format cache areas and temporarily store the target platform marketing data subsets in different data formats into various data format cache areas. This step designs and establishes cache areas in various data formats in order to effectively manage marketing data subsets in different formats. By temporarily storing data in these cache areas, data can be better organized and managed, which is convenient for subsequent processing and analysis. The design of this cache area can be flexibly adjusted according to the characteristics of the data and processing requirements to improve the efficiency and flexibility of data processing. At the same time, by performing format uniform conversion processing on the marketing data subsets in different format buffers of the target platform, the purpose is to unify the data in these buffers into the same data format for subsequent analysis and application. By unifying the data format, the complexity of data processing can be reduced, the availability and operability of data can be improved, and the marketing decision-making and strategy formulation of the subsequent processing process can be better supported. This format uniform conversion processing can make more efficient use of marketing data and provide strong support for the data process of subsequent marketing data, thereby better improving the quality of data processing. Secondly, by analyzing the user browsing behavior of the target platform marketing standardized format data set, the browsing behavior process of users for different contents can be understood, providing a basis for personalized recommendation and precision marketing. Such user browsing behavior analysis helps to better understand user needs, thereby improving the accuracy and user satisfaction of subsequent marketing services. Through the target platform marketing user browsing behavior data, various marketing products in the user browsing behavior process are obtained to build the user's browsing marketing product set. By collecting the marketing product information browsed by users, the user's interest and preference for different products can be understood, providing a basis for personalized recommendation and precision marketing. Such data acquisition and organization helps to better understand market demand and improve the matching degree and sales efficiency of products and services. The target platform users’ browsing behavior data is also used to perform browsing interest screening on the target platform users’ browsing marketing product set to determine the user’s browsing interest product set. This step can identify the user’s interest and preferred product information by analyzing the user’s browsing behavior for different products, thereby providing a basis for personalized recommendations and targeted marketing. Such interest screening helps to better understand user needs, thereby improving the pertinence and attractiveness of the subsequent marketing recommendation process. Then, the target platform marketing user browsing behavior data is subjected to advertising browsing segmentation processing to obtain the target platform users’ non-advertising browsing behavior data and advertising browsing behavior data.Through this step, it is possible to clearly distinguish whether users are exposed to advertisements when browsing products, providing a clear data basis for subsequent analysis. This processing helps to understand users' behavior patterns more finely, so as to formulate personalized advertising push strategies more effectively. By conducting advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' ad-free browsing behavior data, and the target platform users' ad browsing behavior data, the advertising marketing delivery timing of each user's browsing interest product is determined. This analysis helps to more accurately grasp the user's purchasing interests and behavior patterns, and provides guidance for the timing and method of advertising delivery, thereby improving the effectiveness of advertising and the user's purchase conversion rate. Finally, by conducting a marketing strategy analysis on the target platform users' browsing interest product set based on the advertising marketing delivery timing of each user's browsing interest product, we can determine the best product recommendation strategy, product promotion strategy and advertising delivery strategy, thereby generating an overall target platform product marketing strategy. This comprehensive strategy aims to make full use of users' interests and behavior data to achieve a more personalized and accurate marketing strategy analysis process. This step, by integrating different marketing strategies, can more effectively attract users' attention, improve purchase conversion rates, and maintain competitive advantages in a highly competitive market, thereby bringing more marketing opportunities and commercial value to the target platform.

[0010] Preferably, the present invention further provides a marketing data processing system for executing the marketing data processing method as described above, the marketing data processing system comprising:

[0011] The target platform marketing data processing module is used to obtain the target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain the target platform marketing standardized format data set;

[0012] The user browsing interest product screening module is used to analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data, thereby obtaining the target platform user browsing interest product set;

[0013] The module for mining the advertising marketing of interest products is used to process the browsing behavior data of target platform marketing users by advertising browsing, so as to obtain the advertising browsing behavior data of target platform users without advertising and the advertising browsing behavior data of target platform users; based on the target platform marketing standardized format data set, the advertising browsing behavior data of target platform users without advertising and the advertising browsing behavior data of target platform users, the advertising marketing analysis is performed on the target platform users' browsing interest product set, so as to obtain the advertising marketing delivery timing of each user browsing the interest product;

[0014] The target platform product marketing strategy analysis module is used to perform marketing strategy analysis on the target platform user browsing interest product set based on the advertising marketing delivery timing of each user browsing interest product, generate the target platform product marketing strategy, and execute the corresponding target platform product personalized marketing activities.

[0015] In summary, the present invention provides a marketing data processing system, which consists of a target platform marketing data processing module, a user browsing interest product screening module, an interest product advertising marketing delivery mining module and a target platform product marketing strategy analysis module. It can implement any marketing data processing method described in the present invention, and is used to combine the operations between computer programs running on each module to implement any marketing data processing method. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient marketing data processing process, thereby simplifying the operating procedures of the marketing data processing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 A schematic flow chart of the steps of the method for processing marketing data of the present invention;

[0018] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0019] Figure 3 for Figure 2 Detailed step flow chart of step S14 in FIG. DETAILED DESCRIPTION

[0020] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0022] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for processing marketing data, the method comprising the following steps:

[0024] Step S1: Obtain a target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain a target platform marketing standardized format data set;

[0025] Step S2: Analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data to obtain the target platform user browsing interest product set;

[0026] Step S3: Perform advertising browsing segmentation processing on the browsing behavior data of the target platform marketing users to obtain the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data; perform advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data to obtain the advertising marketing delivery timing of each user's browsing interest product;

[0027] Step S4: Based on the advertising marketing delivery opportunities of the products of interest browsed by each user, a marketing strategy analysis is performed on the target platform user browsing interest product set, and a target platform product marketing strategy is generated to execute corresponding personalized marketing activities for the target platform products.

[0028] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a flow chart of the steps of the method for processing marketing data of the present invention. In this example, the method for processing marketing data includes the following steps:

[0029] Step S1: Obtain a target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain a target platform marketing standardized format data set;

[0030] The embodiment of the present invention obtains a target platform marketing data set by collecting data related to marketing on the target platform from the log system in the target platform, including user behavior data, commodity sales data, browsing record data, market data, etc. At the same time, by classifying and dividing various data in the target platform marketing data set according to their characteristics and attributes, similar data formats are classified into the same category, thereby obtaining marketing data subsets in different format cache areas of the target platform. Secondly, by collecting and obtaining corresponding marketing decision data from the internal system of the target platform, including market research, competition analysis, sales forecast and other information, and using the Pearson correlation coefficient statistical method to perform correlation calculations on the extracted marketing decision data and marketing data subsets in different data formats, the correlation coefficients between each marketing data and the marketing decision data in the marketing data subset are quantitatively analyzed, and the correlation coefficients obtained by quantitative calculation are compared and judged according to the preset correlation threshold, so as to screen out the data with low correlation with the marketing decision, and screen out the data with high correlation with the marketing decision. Then, the screened relevant data subsets are converted and merged by using data format conversion technology to achieve mapping of data fields of marketing data subsets in different data formats, unify data units and data precision, unify data in different formats into the same format standard, eliminate the differences in data formats to reduce the complexity of data processing, and finally obtain the target platform marketing standardized format data set.

[0031] Step S2: Analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data to obtain the target platform user browsing interest product set;

[0032] The embodiment of the present invention extracts the browsing record data of the user from the target platform marketing standardized format data set, including the user's basic information, browsing record, purchase record, etc., and calculates the user's browsing frequency and browsing depth therefrom. At the same time, the user's activity level and browsing conversion change pattern are analyzed by combining the user's browsing frequency and browsing depth, and the user's activity level and browsing conversion change pattern obtained by the analysis are comprehensively considered. The behavior simulation analysis method is used to simulate and analyze the browsing behavior process of each user on the target platform user browsing record data, so as to simulate and analyze the user's various browsing behaviors on the target platform, including browsing activity, preferred pages, browsing paths, etc., so as to obtain the browsing behavior data of the target platform marketing user. Secondly, the browsing behavior data of the target platform marketing user is used to obtain various marketing products browsed by the user during the browsing process, including the product categories and brands browsed by the user, and the various marketing products obtained are collected in a data set, so as to obtain the target platform user browsing marketing product set. Then, by combining the analyzed target platform marketing user browsing behavior data to obtain the number and duration of user browsing on each browsing marketing product, a browsing interest evaluation algorithm is used to evaluate and analyze each user browsing product in the target platform user browsing marketing product set to evaluate the user's browsing interest in different products, and compare and judge the user's browsing interest obtained by quantitative calculation according to a preset browsing interest threshold to divide it into two categories: interested and uninterested. At the same time, the products on the target platform are screened according to this classification result, and only those products that users consider to be of interest are retained, and finally the target platform user browsing interest product set is obtained.

[0033] Step S3: Perform advertising browsing segmentation processing on the browsing behavior data of the target platform marketing users to obtain the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data; perform advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data to obtain the advertising marketing delivery timing of each user's browsing interest product;

[0034] The embodiment of the present invention collects user browsing behavior data on a target platform, including users' browsing, clicking, purchasing and other behaviors on the platform, and identifies the appearance of advertisements during the user browsing behavior by using data analysis technology, and accurately understands whether each user's browsing behavior process is exposed to advertisements when browsing goods, thereby dividing the user browsing behavior data into browsing behavior without advertisements and browsing behavior with advertisements, thereby obtaining the browsing behavior data of users without advertisements on the target platform and the browsing behavior data of users with advertisements on the target platform. At the same time, by extracting sales information data related to each sales commodity on the target platform from the target platform marketing standardized format data set, including sales volume, sales amount, price and other information of various commodities, and further screening the commodity sales information data in the entire browsing behavior process according to the different browsing behavior states of users, so as to screen and obtain the commodity sales information for different user behavior states, including the commodity sales volume, sales amount, price and other information in the advertising-free and advertising-with states, and then, by combining and analyzing the marketing commodity sales information in the advertising-free browsing behavior state and the advertising-with browsing behavior state, the corresponding user browsing interest commodities in the target platform user browsing interest commodity set are matched and screened, so as to screen out the sales status of commodities related to user interests in the advertising-free state and the advertising-with state, and by using statistical chart tools or data visualization software to draw the sales change curves of each user browsing interest commodity in the advertising-free and advertising-with browsing behavior states, so as to intuitively compare the changing trends of user purchasing behaviors under the influence of different situations, and also by analyzing the time period with a huge sales deviation between the two change curves to determine the best advertising marketing delivery time, and finally obtain the advertising marketing delivery time of each user browsing interest commodity.

[0035] Step S4: Based on the advertising marketing delivery opportunities of the products of interest browsed by each user, a marketing strategy analysis is performed on the target platform user browsing interest product set, and a target platform product marketing strategy is generated to execute corresponding personalized marketing activities for the target platform products.

[0036] The embodiment of the present invention recommends the browsing interest products of each user in the target platform user browsing interest product set obtained by analysis, so as to recommend the corresponding browsing interest products or similar interest products to the target user, and selects a suitable recommendation strategy, such as content-based recommendation, collaborative filtering recommendation, etc. At the same time, by comprehensively considering the user's browsing history, purchase record and potential needs, it analyzes and determines the promotion strategy of the current browsing interest product, including discounts, full reduction, gifts, etc., so as to obtain the interest product recommendation strategy and promotion strategy. Secondly, by combining the advertising marketing delivery timings of each user's browsing interest products obtained through analysis (including user active time periods and preferred time periods for browsing interest products, etc.), a strategic analysis is conducted on the corresponding user browsing interest products in the target platform user browsing interest product set to determine the best advertising delivery strategy, such as increasing advertising exposure during time periods with high user activity and increasing the delivery of related advertisements when users browse specific interest products. Then, by comprehensively considering the interest product recommendation strategy, interest product promotion strategy and advertising delivery strategy and analyzing the correlation and complementary effects between them, a merger is performed to formulate a comprehensive product marketing strategy based on the results of the combined strategy, including adjusting the recommendation algorithm, optimizing promotional activities and adjusting the advertising delivery strategy, and corresponding personalized marketing activities for the target platform products are executed according to the formulated comprehensive marketing strategy, such as pushing personalized recommendations, conducting promotional activities and adjusting the advertising delivery strategy.

[0037] The present invention first acquires the marketing data set of the target platform in order to establish the basis for analysis and decision-making. By collecting this data, information such as market trends, customer behavior, and product sales can be understood, thereby providing basic data guarantee for subsequent processing. The target platform marketing data set is divided into data formats and cached. The key to this step is to design various data format cache areas and temporarily store the target platform marketing data subsets in different data formats into various data format cache areas. This step designs and establishes cache areas in various data formats in order to effectively manage marketing data subsets in different formats. By temporarily storing data in these cache areas, data can be better organized and managed, which is convenient for subsequent processing and analysis. The design of this cache area can be flexibly adjusted according to the characteristics of the data and processing requirements to improve the efficiency and flexibility of data processing. At the same time, by performing format uniform conversion processing on the marketing data subsets in different format buffers of the target platform, the purpose is to unify the data in these buffers into the same data format for subsequent analysis and application. By unifying the data format, the complexity of data processing can be reduced, the availability and operability of data can be improved, and the marketing decision-making and strategy formulation of the subsequent processing process can be better supported. This format uniform conversion processing can make more efficient use of marketing data and provide strong support for the data process of subsequent marketing data, thereby better improving the quality of data processing. Secondly, by analyzing the user browsing behavior of the target platform marketing standardized format data set, the browsing behavior process of users for different contents can be understood, providing a basis for personalized recommendation and precision marketing. Such user browsing behavior analysis helps to better understand user needs, thereby improving the accuracy and user satisfaction of subsequent marketing services. Through the target platform marketing user browsing behavior data, various marketing products in the user browsing behavior process are obtained to build the user's browsing marketing product set. By collecting the marketing product information browsed by users, the user's interest and preference for different products can be understood, providing a basis for personalized recommendation and precision marketing. Such data acquisition and organization helps to better understand market demand and improve the matching degree and sales efficiency of products and services. The target platform users’ browsing behavior data is also used to perform browsing interest screening on the target platform users’ browsing marketing product set to determine the user’s browsing interest product set. This step can identify the user’s interest and preferred product information by analyzing the user’s browsing behavior for different products, thereby providing a basis for personalized recommendations and targeted marketing. Such interest screening helps to better understand user needs, thereby improving the pertinence and attractiveness of the subsequent marketing recommendation process. Then, the target platform marketing user browsing behavior data is subjected to advertising browsing segmentation processing to obtain the target platform users’ non-advertising browsing behavior data and advertising browsing behavior data.Through this step, it is possible to clearly distinguish whether users are exposed to advertisements when browsing products, providing a clear data basis for subsequent analysis. This processing helps to understand users' behavior patterns more finely, so as to formulate personalized advertising push strategies more effectively. By conducting advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' ad-free browsing behavior data, and the target platform users' ad browsing behavior data, the advertising marketing delivery timing of each user's browsing interest product is determined. This analysis helps to more accurately grasp the user's purchasing interests and behavior patterns, and provides guidance for the timing and method of advertising delivery, thereby improving the effectiveness of advertising and the user's purchase conversion rate. Finally, by conducting a marketing strategy analysis on the target platform users' browsing interest product set based on the advertising marketing delivery timing of each user's browsing interest product, we can determine the best product recommendation strategy, product promotion strategy and advertising delivery strategy, thereby generating an overall target platform product marketing strategy. This comprehensive strategy aims to make full use of users' interests and behavior data to achieve a more personalized and accurate marketing strategy analysis process. This step, by integrating different marketing strategies, can more effectively attract users' attention, improve purchase conversion rates, and maintain competitive advantages in a highly competitive market, thereby bringing more marketing opportunities and commercial value to the target platform.

[0038] Preferably, step S1 comprises the following steps:

[0039] Step S11: Obtain target platform marketing data set;

[0040] Step S12: abstractly divide the target platform marketing data set into different data formats to obtain target platform marketing data subsets in different data formats;

[0041] Step S13: various data format buffers are designed, and the target platform marketing data subsets in different data formats are temporarily stored in the various data format buffers to obtain marketing data subsets in different format buffers of the target platform;

[0042] Step S14: acquiring the target platform marketing decision data, and performing marketing-related screening processing on the marketing data subsets in the target platform's different-format cache areas based on the target platform marketing decision data, to obtain the marketing decision-related data subsets in the target platform's different-format cache areas;

[0043] Step S15: Performing unified format conversion processing on the marketing decision-related data subsets in the target platform's different format buffer areas to obtain a target platform marketing standardized format data set.

[0044] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0045] Step S11: Obtain the target platform marketing data set;

[0046] The embodiment of the present invention collects data related to marketing on the target platform from the log system in the target platform, including user behavior data, product sales data, browsing record data, market data, etc., and finally obtains a target platform marketing data set.

[0047] Step S12: abstractly divide the target platform marketing data set into different data formats to obtain target platform marketing data subsets in different data formats;

[0048] The embodiment of the present invention classifies and divides the various data in the target platform marketing data set according to their characteristics and attributes to classify similar data formats into the same category. This process involves the identification and classification of data fields, so that it can better understand the structure and content of the data format, and ultimately obtain target platform marketing data subsets in different data formats.

[0049] Step S13: various data format buffers are designed, and the target platform marketing data subsets in different data formats are temporarily stored in the various data format buffers to obtain marketing data subsets in different format buffers of the target platform;

[0050] The embodiment of the present invention designs a cache area for storing various data formats, and temporarily stores the target platform marketing data subsets in different data formats obtained by analysis into the cache areas of various data formats. The cache areas of the data formats can be in the form of database tables, data files, memory data structures, etc., to improve the efficiency of data access and processing and reduce the overhead of data reading and writing, and finally obtain the marketing data subsets in the cache areas of different formats of the target platform.

[0051] Step S14: acquiring the target platform marketing decision data, and performing marketing-related screening processing on the marketing data subsets in the target platform's different-format cache areas based on the target platform marketing decision data, to obtain the marketing decision-related data subsets in the target platform's different-format cache areas;

[0052] The embodiment of the present invention obtains the marketing decision data of the target platform by collecting and acquiring corresponding marketing decision data from the internal system of the target platform, including market research, competition analysis, sales forecast and other information. At the same time, by using the Pearson correlation coefficient statistical method, correlation calculations are performed on the marketing decision data of the target platform and the marketing data subsets in different formats of the target platform cache area, so as to quantitatively analyze the correlation coefficients between each marketing data and the marketing decision data in the marketing data subset, and compare and judge the correlation coefficients obtained by quantitative calculation according to a preset correlation threshold, so as to screen out the data with low correlation with the marketing decision, and screen out the data with high correlation with the marketing decision, and finally obtain the marketing decision-related data subsets in different formats of the target platform cache area.

[0053] Step S15: Performing unified format conversion processing on the marketing decision-related data subsets in the target platform's different format buffer areas to obtain a target platform marketing standardized format data set.

[0054] The embodiment of the present invention uses data format conversion technology to convert and merge marketing decision-related data subsets in different format cache areas of the target platform, so as to achieve mapping of data fields of marketing data subsets in different cache areas, unify data units and data precision, etc., unify data in different formats into the same format standard, eliminate the differences in data formats to reduce the complexity of data processing, and finally obtain a marketing standardized format data set of the target platform.

[0055] The present invention first acquires the marketing data set of the target platform in order to establish the basis for analysis and decision-making. By collecting these data, market trends, customer behaviors, product sales and other information can be understood, thereby providing basic data guarantee for the subsequent processing process. Secondly, by abstractly dividing the data format of the target platform marketing data set, the key to this step is to perform appropriate format division and abstraction on the acquired marketing data for subsequent processing and analysis. By dividing the data according to different formats, the data can be better managed and utilized, and the efficiency and accuracy of data processing can be improved. This data format abstract division can be flexibly adjusted according to the characteristics and uses of the data to meet different needs. Then, by designing various data format buffers, and temporarily storing the target platform marketing data subsets of different data formats into various data format buffers, this step designs and establishes various data format buffers in order to effectively manage the marketing data subsets of different formats. By temporarily storing the data in these buffers, the data can be better organized and managed, which is convenient for subsequent processing and analysis. The design of this buffer can be flexibly adjusted according to the characteristics of the data and processing requirements to improve the efficiency and flexibility of data processing. Next, by obtaining the marketing decision data of the target platform and performing marketing-related screening processing on the marketing data subsets in different formats of the target platform cache area based on the marketing decision data of the target platform, the marketing decision data can be associated and analyzed with the marketing data subsets, so as to better understand the effect and impact of marketing activities, thereby guiding future marketing decisions and strategy adjustments. This screening processing based on marketing decision data can help enterprises grasp market demand and customer needs more accurately and improve the effectiveness and efficiency of marketing activities. Finally, by performing format uniform conversion processing on the marketing decision-related data subsets in different formats of the target platform cache area, the purpose is to unify the data in these cache areas into the same data format for subsequent analysis and application. By unifying the data format, the complexity of data processing can be reduced, the availability and operability of data can be improved, and the marketing decision and strategy formulation of the subsequent processing process can be better supported. This format uniform conversion processing can make more efficient use of marketing data and provide strong support for the data process of subsequent marketing data.

[0056] Preferably, step S14 comprises the following steps:

[0057] Step S141: performing histogram anomaly screening processing on the marketing data subsets in the target platform's different formats of the buffer area to obtain the marketing anomaly filtered data subsets in the target platform's different formats of the buffer area;

[0058] Step S142: performing missing interpolation compensation processing on the marketing filter data subsets in the target platform's different format buffer areas to obtain the marketing interpolation compensation data subsets in the target platform's different format buffer areas;

[0059] Step S143: Obtaining target platform marketing decision data;

[0060] Step S144: Calculating the marketing decision correlation coefficient for the marketing interpolation compensation data subsets in the target platform's different format buffers based on the target platform's marketing decision data, so as to obtain the correlation coefficient between each marketing data and the marketing decision in the target platform's different format buffers;

[0061] Step S145: Perform marketing-related screening on the marketing interpolation compensation data subsets in the target platform's different format cache areas according to the correlation coefficients between each marketing data and marketing decisions in the target platform's different format cache areas to obtain marketing decision-related data subsets in the target platform's different format cache areas.

[0062] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 Detailed step flow diagram of step S14 in the embodiment, step S14 includes the following steps:

[0063] Step S141: performing histogram anomaly screening processing on the marketing data subsets in the target platform's different formats of the buffer area to obtain the marketing anomaly filtered data subsets in the target platform's different formats of the buffer area;

[0064] The embodiment of the present invention uses a histogram drawing method to draw the data in the marketing data subsets in the different formats of the target platform cache area respectively, so as to draw a graph representing the data distribution, and analyze the uneven distribution of the data or the obvious deviation in the distribution, and at the same time mark and exclude the analyzed deviations, and finally obtain the marketing filtered data subsets in the different formats of the target platform cache area.

[0065] Step S142: performing missing interpolation compensation processing on the marketing filter data subsets in the target platform's different format buffer areas to obtain the marketing interpolation compensation data subsets in the target platform's different format buffer areas;

[0066] The embodiment of the present invention uses interpolation methods (including linear interpolation, polynomial interpolation, KNN interpolation, etc.) to supplement the missing or empty data in the marketing filtered data subsets in the different formats of the target platform cache area one by one, so as to infer the missing values ​​and fill these missing values ​​according to the characteristics and rules of the existing data, and finally obtain the marketing interpolation compensation data subsets in the different formats of the target platform cache area.

[0067] Step S143: Obtaining target platform marketing decision data;

[0068] The embodiment of the present invention collects and obtains corresponding marketing decision data from the internal system of the target platform, including market research, competition analysis, sales forecast and other information, and finally obtains the marketing decision data of the target platform.

[0069] Step S144: Calculating the marketing decision correlation coefficient for the marketing interpolation compensation data subsets in the target platform's different format buffers based on the target platform's marketing decision data, so as to obtain the correlation coefficient between each marketing data and the marketing decision in the target platform's different format buffers;

[0070] The embodiment of the present invention uses the Pearson correlation coefficient statistical method to perform correlation calculations on the target platform marketing decision data and the marketing interpolation compensation data subsets in different format cache areas of the target platform, so as to quantitatively analyze the correlation coefficients between each marketing data and the marketing decision data in the marketing interpolation compensation data subsets, and understand the degree of correlation between them, and finally obtain the correlation coefficients between each marketing data and the marketing decision in different format cache areas of the target platform.

[0071] Step S145: Perform marketing-related screening on the marketing interpolation compensation data subsets in the target platform's different format cache areas according to the correlation coefficients between each marketing data and marketing decisions in the target platform's different format cache areas to obtain marketing decision-related data subsets in the target platform's different format cache areas.

[0072] The embodiment of the present invention compares and judges the correlation coefficients between each marketing data and marketing decisions in different format cache areas of the target platform obtained by quantitative calculation according to a preset correlation threshold, so as to screen out the data with low correlation with the marketing decision, and screen out the data with high correlation with the marketing decision, and finally obtain a subset of marketing decision-related data in different format cache areas of the target platform.

[0073] The present invention first performs histogram anomaly screening on a subset of marketing data in different formats of the target platform's cache area, aiming to identify and exclude outliers in the data to ensure the accuracy and reliability of subsequent analysis. This step can identify potential outliers through histogram anomaly screening and remove them from the data set, thereby reducing the interference of outliers on data analysis and decision-making. This anomaly screening process helps to improve data quality and make subsequent data analysis and decision-making more reliable and effective. Secondly, by performing missing interpolation compensation processing on a subset of marketing filtered data in different formats of the target platform's cache area, it aims to fill in the missing values ​​in the data to ensure the integrity and continuity of the data. Through interpolation compensation, missing values ​​can be inferred based on the characteristics and rules of the existing data, making the data set more complete and available. This missing interpolation compensation process helps to make full use of data resources and reduce data information loss, thereby improving the accuracy and reliability of data analysis. Then, by obtaining the marketing decision data of the target platform, the purpose is to combine the marketing data and decision data for further analysis and application. The marketing decision data includes information such as market research, competition analysis, and sales forecasts, which are important bases for formulating marketing strategies and decisions. By obtaining these data, we can better understand the market demand and competition situation, and provide guidance and support for the subsequent marketing processing process. Next, by calculating the marketing decision correlation coefficient of the marketing interpolation compensation data subset of the target platform's different format buffer areas based on the marketing decision data of the target platform, the degree of correlation between marketing data and marketing decisions can be explored. By calculating the correlation coefficient, the correlation between different marketing data and marketing decisions can be quantified, thereby helping the subsequent processing process to better understand the impact and role of marketing data on decisions. This correlation coefficient calculation helps to determine which marketing data has a greater impact on decisions, and provides a scientific basis and reference for subsequent marketing decisions. Finally, marketing-related screening is performed on the marketing interpolation compensation data subsets in different format cache areas of the target platform according to the correlation coefficients between each marketing data and marketing decisions in different format cache areas of the target platform to determine the marketing data indicators that have a significant impact on marketing decisions. This step can identify key data indicators that have a greater impact on decisions through this marketing-related screening process, providing more accurate and effective support for subsequent marketing decisions. This screening process helps to optimize the use of marketing data, thereby improving the accuracy and effectiveness of marketing decisions.

[0074] Preferably, step S2 comprises the following steps:

[0075] Step S21: extracting user browsing record data from the target platform marketing standardized format data set to obtain the target platform user browsing record data;

[0076] The embodiment of the present invention extracts the user's browsing history data from the target platform marketing standardized format data set, including the user's basic information, browsing history, purchase history, etc., to gain an in-depth understanding of the user's behavior and preferences on the platform, and ultimately obtain the target platform user's browsing history data.

[0077] Step S22: Performing statistical analysis on the browsing frequency and browsing depth of the target platform user browsing record data to obtain the target platform user browsing frequency and the target platform user browsing depth;

[0078] The embodiment of the present invention uses a frequency statistical analysis method to perform a statistical calculation of the browsing frequency of the user browsing process on the target platform user browsing record data to calculate the user's browsing frequency, that is, the number of times the user browses within a certain period of time, thereby obtaining the target platform user browsing frequency. At the same time, the user browsing process on the target platform user browsing record data is analyzed by using a browsing depth statistical analysis method to statistically analyze the user's browsing depth, that is, the number of pages browsed by the user or the depth of browsing in each browsing process, and finally obtain the target platform user browsing depth.

[0079] Step S23: performing user browsing behavior analysis on the target platform user browsing record data based on the target platform user browsing frequency and the target platform user browsing depth to obtain the target platform marketing user browsing behavior data;

[0080] The embodiment of the present invention analyzes the user's activity level and the changing pattern of browsing conversion by combining the browsing frequency and browsing depth of the target platform users, and comprehensively considers the changing pattern of the user's activity level and browsing conversion obtained by analysis, and uses a behavioral simulation analysis method to simulate and analyze the browsing behavior process of each user on the target platform user browsing record data, so as to simulate and analyze the various browsing behaviors of users on the target platform, including browsing activity, preferred pages, browsing paths, etc., and finally obtain the browsing behavior data of the target platform marketing users.

[0081] Step S24: obtaining various marketing commodities in the process of user browsing behavior through the target platform marketing user browsing behavior data, so as to obtain the target platform user browsing marketing commodity set;

[0082] The embodiment of the present invention uses the browsing behavior data of the target platform marketing users to obtain various marketing products that the users have browsed during the browsing process, including the product categories and brands browsed by the users, and collects the various marketing products obtained into a data set, and finally obtains the marketing product set browsed by the target platform users.

[0083] Step S25: Based on the browsing behavior data of the target platform marketing users, the target platform users' browsing interest set of marketing products is screened to obtain the target platform users' browsing interest product set.

[0084] The embodiment of the present invention obtains the number and duration of users' browsing stay on each browsed marketing product by combining the target platform marketing user browsing behavior data obtained by analysis, and uses a browsing interest evaluation algorithm to evaluate and analyze each user's browsed product in the target platform user browsing marketing product set to evaluate the user's browsing interest level for different products, and compares and judges the user's browsing interest level obtained by quantitative calculation according to a preset browsing interest threshold to divide it into two categories: interested and uninterested, and at the same time, screens the products on the target platform according to this classification result, and only retains those products that the user considers to be of interest, and finally obtains the target platform user browsing interest product set.

[0085] The present invention firstly extracts the user browsing record data from the marketing standardized format data set of the target platform, aiming to extract the user browsing record data from the marketing standardized format data set, so as to gain an in-depth understanding of the user's behavior and preferences on the platform. By extracting the user's browsing record, the user's interests and behavioral habits can be analyzed, providing a basis for subsequent personalized recommendations and marketing strategies. Such data extraction processing can help to better understand the user, thereby improving the pertinence and effectiveness of subsequent marketing activities. Secondly, by statistically analyzing the browsing frequency and browsing depth of the target platform user browsing record data, it aims to reveal the user's attention to the platform content and the depth of browsing behavior. This step can discover the user's activity and preference for different content by statistically analyzing the user's browsing frequency and browsing depth, providing a basis for subsequent user behavior analysis and personalized recommendations. This statistical analysis helps to better understand the user's behavioral characteristics, thereby optimizing marketing strategies and user experience. Then, by analyzing the browsing behavior of the target platform users based on the browsing frequency and browsing depth of the target platform users, the user browsing behavior is analyzed to reveal the user's interests and preferences. By analyzing the user's browsing behavior, the user's interest and focus on different content can be understood, providing a basis for personalized recommendation and precision marketing. Such user browsing behavior analysis helps to better understand user needs, thereby improving the accuracy of subsequent marketing services and user satisfaction. Next, various marketing products in the user's browsing behavior process are obtained through the target platform marketing user browsing behavior data to build the user's browsing marketing product set. By collecting the marketing product information browsed by the user, the user's interest and preference for different products can be understood, providing a basis for personalized recommendation and precision marketing. Such data acquisition and organization helps to better understand market demand and improve the matching degree and sales efficiency of products and services. Finally, by performing browsing interest screening on the target platform user's browsing marketing product set based on the target platform marketing user browsing behavior data, the user's browsing interest product set can be determined. This step can identify the user's interest and preference product information by analyzing the user's browsing behavior for different products, thereby providing a basis for personalized recommendation and targeted marketing. Such interest screening helps to better understand user needs, thereby improving the pertinence and attractiveness of the subsequent marketing recommendation process.

[0086] Preferably, step S23 includes the following steps:

[0087] Step S231: Analyze the time series changes of the browsing frequency of the target platform users to obtain the time series change trend data of the browsing frequency of the target platform users;

[0088] The embodiment of the present invention analyzes the browsing frequency of target platform users by using corresponding time series analysis methods (such as moving average method, exponential smoothing method or seasonal decomposition, etc.) to reveal the trend of its time series changes, and find out whether there are obvious periodic or trend changes in the user's browsing behavior, and finally obtain the time series change trend data of the browsing frequency of target platform users.

[0089] Step S232: analyzing the browsing activity change pattern of the target platform user browsing record data according to the target platform user browsing frequency time series change trend data to obtain the target platform user browsing activity change pattern data;

[0090] The embodiment of the present invention uses a change pattern analysis method to analyze the corresponding user browsing activity in the target platform user browsing record data by combining the analyzed target platform user browsing frequency time series change trend data, so as to identify the pattern or regularity of the user's browsing activity changing over time, and reveal the user's activity level in different time periods, thereby gaining insight into the user's browsing habits and behavioral characteristics, and finally obtaining the target platform user browsing activity change pattern data.

[0091] Step S233: performing page conversion pattern mining and analysis on the target platform user browsing record data based on the target platform user browsing depth to obtain the target platform user browsing page conversion pattern data;

[0092] The embodiment of the present invention uses a sequence pattern mining analysis technology to analyze the browsing record data of the target platform users by combining the browsing depth of the target platform users obtained by analysis, so as to discover the patterns or regularities of page conversions of users during the browsing process, such as which pages the users often transfer from and the frequency and path of conversion, etc., and finally obtain the browsing page conversion pattern data of the target platform users.

[0093] Step S234: Analyze the browsing behavior of target platform user browsing record data based on the target platform user browsing activity change pattern data and the target platform user browsing page conversion pattern data to obtain the target platform marketing user browsing behavior data.

[0094] The embodiment of the present invention uses a behavioral simulation analysis method to simulate and analyze the browsing behavior processes of each user on the target platform user browsing record data by combining the target platform user browsing activity change pattern data and the target platform user browsing page conversion pattern data obtained by analysis, so as to comprehensively consider the user's browsing activity change pattern and page conversion pattern to simulate and analyze the user's various browsing behaviors on the target platform, including browsing activity, preferred pages, browsing paths, etc., and finally obtain the target platform marketing user browsing behavior data.

[0095] The present invention first analyzes the time series change of the browsing frequency of the target platform users to explore the browsing situation of the platform content in different time periods by analyzing the time series change of the browsing frequency of the users, and finds whether the browsing behavior of the users has obvious periodic or trend changes, thereby providing more accurate data support for the subsequent processing process. Secondly, by analyzing the change pattern of the browsing activity of the target platform users according to the time series change trend data of the browsing frequency of the target platform users, the change pattern of the browsing activity of the users can be explored, and the user's activity in different time periods can be revealed, thereby gaining insight into the browsing habits and behavioral characteristics of the users. For example, it can be found that the browsing activity of users is higher on weekends or holidays, while it is relatively lower on weekdays, which provides guidance for formulating targeted marketing strategies. Such analysis helps to better understand the user's behavior pattern, thereby providing basic data guarantee for the subsequent processing process. Then, by mining and analyzing the page conversion pattern of the browsing record data of the target platform users based on the browsing depth of the target platform users, the page switching path and behavior trajectory of the users in the browsing process can be understood, thereby finding the user's points of interest and focus. For example, we can find the conversion pattern of users switching to the shopping cart page after browsing the product page, and then to the checkout page, thereby inferring the user's purchase intention and behavior habits. Such analysis can provide basic data guarantee for the subsequent browsing behavior analysis process. Finally, by analyzing the browsing behavior of target platform users based on the browsing activity change pattern data of the target platform users and the browsing page conversion pattern data of the target platform users, and comprehensively analyzing the browsing activity change pattern and page conversion pattern of the users, we can fully understand the browsing behavior characteristics and preferences of users, and provide a more accurate basis for the subsequent marketing data processing process.

[0096] Preferably, step S25 comprises the following steps:

[0097] Step S251: Based on the target platform marketing user browsing behavior data, the browsing stay monitoring and analysis of each user browsing product in the target platform user browsing marketing product set is performed to obtain the browsing stay number and browsing stay duration of each user browsing product on the target platform;

[0098] The embodiment of the present invention monitors and analyzes each user's browsed product in the target platform user's browsed marketing product set by combining the target platform marketing user browsing behavior data obtained by analysis, so as to monitor the number and duration of the user's clicks, scrolling, staying and other behaviors on the target platform from the corresponding user browsing behaviors, track and record them, and statistically calculate the number of times and the length of time the user stays on each browsed product, and finally obtain the number of browsing and staying times and the browsing and staying duration of each user browsing product on the target platform.

[0099] Step S252: Analyze the browsing behavior data of the target platform marketing user to obtain the browsing behavior trajectory of the target platform marketing user;

[0100] The embodiment of the present invention uses methods such as time series analysis and path simulation analysis to simulate the behavior trajectory of users' clicks, browsing, purchases, etc. on the browsing behavior data of target platform marketing users, so as to simulate and analyze the browsing behavior trajectory of users on the target platform, and reveal the key paths and behavior patterns of users in the browsing process, and finally obtain the browsing behavior trajectory of target platform marketing users.

[0101] Step S253: Marking each user-browsed product in the target platform user-browsed marketing product set as a browsing path conversion node on the target platform marketing user browsing behavior trajectory to obtain each user-browsing path conversion product node;

[0102] The embodiment of the present invention marks each user-browsed product in the target platform user's marketing product set as a browsing path conversion node on the target platform marketing user's browsing behavior trajectory, so as to understand the user's conversion path and flow between different products, and more clearly understand the user's browsing conversion path on the target platform, and finally obtain each user's browsing path conversion product node.

[0103] Step S254: Based on the browsing behavior trajectory of the target platform marketing user, a browsing conversion bottleneck analysis is performed on each user browsing path conversion commodity node to obtain user browsing conversion bottleneck status data between each user browsing commodity node;

[0104] The embodiment of the present invention uses data analysis technology to analyze the commodity conversion nodes of each user's browsing path in combination with the browsing characteristics of the browsing behavior trajectory of the target platform marketing user, so as to identify the bottleneck points in the user's browsing conversion process. This involves counting and comparing the conversion nodes in the user's behavior trajectory, and finding the conversion bottlenecks between the user's browsing commodity nodes, while discovering the key hindering factors in the user's conversion process, and finally obtaining the user's browsing conversion bottleneck status data between the user's browsing commodity nodes.

[0105] Step S255: performing conversion attenuation rate statistics calculation on the user browsing conversion bottleneck status data between each user browsing commodity node to obtain the user browsing conversion attenuation rate between each user browsing commodity node;

[0106] The embodiment of the present invention uses mathematical statistical methods to analyze and calculate the user browsing conversion bottleneck status data between each user browsing product node, so as to quantify the conversion efficiency and loss of users in the browsing process, and find out the problems and improvement space in the user conversion process, and finally obtain the user browsing conversion attenuation rate between each user browsing product node.

[0107] Step S256: performing a time correction analysis on the browsing dwell time of each user browsing the product on the target platform according to the user browsing conversion decay rate between each user browsing the product node, and obtaining the browsing dwell conversion correction time of each user browsing the product on the target platform;

[0108] The embodiment of the present invention firstly takes the user browsing conversion decay rate between each user browsing commodity nodes as a key indicator. This decay rate reflects the change in the efficiency of user conversion between different commodity nodes. Then, by combining the decay rate obtained by quantitative calculation, mathematical modeling and statistical analysis techniques are used to perform correction analysis on the browsing residence time of each user browsing commodities on the target platform, so as to adjust the length of time the user stays at each node according to the size of the decay rate, so as to reflect the actual browsing residence behavior of the user in the conversion process, and ensure that the corrected residence time is closer to the actual behavior of the user, and finally obtain the corrected browsing residence conversion time of each user browsing commodities on the target platform.

[0109] Step S257: Based on the number of browsing stays of each user browsing the product on the target platform and the modified browsing stay conversion time, a browsing interest evaluation analysis is performed on each user browsing the product in the marketing product set of the target platform users to obtain a user browsing interest evaluation value of each user browsing the product;

[0110] The embodiment of the present invention uses a browsing interest evaluation algorithm to evaluate and analyze the products browsed by each user in the target platform user marketing product set by combining the two browsing degree quantitative indicators, namely the number of browsing stays and the corrected browsing stay conversion time, obtained by analyzing the target platform users, so as to evaluate the user's browsing interest in different products, discover and understand the user's preferences for different products, and finally obtain the user browsing interest evaluation value of each user browsing the product.

[0111] Step S258: performing browsing interest screening processing on each user-browsed product in the target platform user-browsed marketing product set according to the user-browsing interest evaluation value of each user-browsed product, and obtaining the target platform user-browsing interest product set.

[0112] The embodiment of the present invention compares and judges the user browsing interest evaluation values ​​of each user browsing products obtained by quantitative calculation according to a preset browsing interest threshold. If the user browsing interest evaluation value is greater than or equal to the preset browsing interest threshold, it means that the corresponding user has a greater purchase interest in the browsing products. If the user browsing interest evaluation value is less than the preset browsing interest threshold, it means that the corresponding user has a smaller purchase interest or is not interested in the browsing products. The products are then divided into two categories: interested and not interested. According to the classification result, the products on the target platform are screened, and only those products that the users consider to be of interest are retained, thereby finally obtaining a target platform user browsing interest product set.

[0113] The present invention first monitors and analyzes the browsing stay of each user in the target platform user's browsing marketing commodity set based on the target platform marketing user's browsing behavior data, so as to monitor the number of users' browsing stays and the length of stay on each browsing commodity, and can deeply understand the user's interest and attention to different commodities. This analysis can provide valuable market insights for subsequent processing, help better understand user preferences, optimize commodity display and promotion strategies, and improve user purchase conversion rate and satisfaction. By analyzing the browsing behavior trajectory of the target platform marketing user's browsing behavior data to analyze the user's browsing behavior trajectory on the platform, the key path and behavior pattern of the user in the browsing process can be revealed. This analysis can help understand the user's browsing habits and behavioral preferences, so as to more accurately perform personalized recommendations and marketing activities. At the same time, by marking the target platform user as a browsing path conversion node on the user's behavior trajectory when browsing each commodity in the marketing commodity set, so as to obtain the user's conversion node on the behavior trajectory of different commodities, the user's conversion path and flow between different commodities can be understood, which helps to better understand the user's purchase decision-making process and behavior trajectory, and optimize commodity layout and promotion strategies. Based on the browsing behavior trajectory of the target platform marketing users, the browsing conversion bottleneck analysis is also performed on the conversion nodes of each user browsing path to identify the conversion bottleneck of the user in the browsing process, which can find the obstacles and problems on the user conversion path and provide optimization suggestions for the subsequent processing process, thereby improving the user's purchase conversion rate and user experience. Secondly, by performing statistical calculations on the conversion decay rate of the user browsing conversion bottleneck status data between each user browsing product node, the conversion efficiency and loss of users in the browsing process can be quantified, which helps the subsequent processing process to identify and solve the problems in the conversion process, thereby providing basic data guarantee for the subsequent processing process. Then, by performing a time correction analysis on the browsing stay time of each user browsing product on the target platform according to the user browsing conversion decay rate between each user browsing product node, this analysis can help identify the relationship between the user's stay time and the conversion rate when browsing the product, thereby adjusting and correcting the user's stay time when browsing the product to make it more in line with the user's conversion needs. By accurately calculating the conversion correction time, the browsing experience of users on the target platform can be optimized and the user's interest in the product and willingness to buy can be improved. Next, by conducting a browsing interest evaluation analysis on each user browsing products in the target platform user marketing product set based on the number of browsing stays and the corrected browsing stay conversion time of each user browsing products on the target platform, and comprehensively considering the user's browsing times and the corrected stay time, the user's interest in different products can be more accurately evaluated. This analysis helps to discover and understand user preferences for products, and provide accurate personalized recommendations and marketing strategies for subsequent processing, thereby improving user purchase conversion rates and user satisfaction.Finally, by filtering the browsing interests of each product in the marketing product set browsed by the target platform users according to the user browsing interest evaluation values ​​of each user browsing the product, the products in which the users are not very interested can be excluded, thereby obtaining the browsing interest product set of the target platform users. This screening process helps to optimize the product display and promotion strategy, increase users' attention to and willingness to buy products, and increase sales and market share.

[0114] Preferably, step S3 comprises the following steps:

[0115] Step S31: performing advertisement appearance recognition analysis on the target platform marketing user browsing behavior data to obtain advertisement appearance status data of each user browsing behavior on the target platform;

[0116] The embodiments of the present invention collect user browsing behavior data on a target platform, including users' browsing, clicking, purchasing and other behaviors on the platform, and identify the appearance of advertisements during the user's browsing behavior by using data analysis technology, that is, determine the content and frequency of advertisements that users are exposed to during the browsing process, and accurately understand whether each user's browsing behavior process is exposed to advertisements when browsing goods, and finally obtain the advertisement appearance status data of each user's browsing behavior on the target platform.

[0117] Step S32: Based on the advertisement appearance status data of each user browsing behavior on the target platform, the target platform marketing user browsing behavior data is subjected to advertisement browsing classification processing to obtain the target platform user non-advertising browsing behavior data and the target platform user advertising browsing behavior data;

[0118] The embodiment of the present invention divides the corresponding user browsing behavior processes in the target platform marketing user browsing behavior data by combining the advertising appearance status data of each user browsing behavior on the target platform obtained by analysis, so as to divide the user browsing behavior data into browsing behavior without advertisements and browsing behavior with advertisements according to whether there are advertisements in the user's browsing behavior, and finally obtains the browsing behavior data without advertisements of the target platform users and the browsing behavior data of advertisements of the target platform users.

[0119] Step S33: extracting and processing the commodity sales data of the target platform marketing standardized format data set to obtain the target platform marketing commodity sales information data;

[0120] The embodiment of the present invention extracts sales information data related to each sales commodity on the target platform from the target platform marketing standardized format data set, including sales volume, sales revenue, price and other information of various commodities, and cleans and pre-processes the extracted data, including removing outliers, supplementing missing values, unifying data format, etc., to finally obtain the target platform marketing commodity sales information data.

[0121] Step S34: performing browsing behavior product sales screening processing on the target platform marketing product sales information data based on the target platform user's non-advertising browsing behavior data and the target platform user's advertising browsing behavior data, so as to obtain the marketing product sales information data in the non-advertising browsing behavior state and the marketing product sales information data in the advertising browsing behavior state;

[0122] The embodiment of the present invention screens and processes the target platform marketing commodity sales information data by combining the target platform user's ad-free browsing behavior data and the target platform user's ad browsing behavior data obtained by division, and uses a matching and filtering method to further screen the commodity sales information data in the entire browsing behavior process according to the user's different browsing behavior states, so as to screen and obtain commodity sales information for different user behavior states, including commodity sales volume, sales volume, price and other information in the ad-free and ad-with states, and finally obtain the marketing commodity sales information data in the ad-free browsing behavior state and the marketing commodity sales information data in the ad-with browsing behavior state.

[0123] Step S35: Based on the marketing product sales information data in the state of no advertising browsing behavior and the marketing product sales information data in the state of advertising browsing behavior, the target platform user browsing interest product set is analyzed for advertising marketing, and the advertising marketing delivery timing of each user browsing interest product is obtained.

[0124] The embodiment of the present invention combines the marketing commodity sales information data obtained by analysis in a browsing behavior state without advertisements (including commodity sales status caused by users independently searching or browsing commodity pages in the target platform without being exposed to advertisements, such as information on the types, quantities, and prices of purchased commodities) and the marketing commodity sales information data in a browsing behavior state with advertisements (including commodity sales status generated in commodity pages of the target platform when users browse advertisements, such as information on the types, quantities, and prices of purchased commodities), and matches and filters the corresponding user browsing interest commodities in the target platform user browsing interest commodity set, so as to filter out the marketing commodity sales information data in a browsing behavior state without advertisements and in a browsing behavior state with advertisements. The sales status of commodities related to user interests can be analyzed, and the sales change curves of each user's browsing interest commodities under the browsing behavior states of no advertisements and with advertisements can be drawn by using statistical chart tools or data visualization software, so as to intuitively compare the changing trends of user purchasing behaviors under the influence of different situations. At the same time, by comparing and analyzing the drawn sales change curves of each user's browsing interest commodities without advertisements and the sales change curves of the browsing interest commodities with advertisements, the fluctuations and trends of the sales status of each user's browsing interest commodities under different situations can be analyzed, and the time period with a huge sales deviation between the two change curves can be analyzed to determine the best advertising marketing delivery timing, and finally the advertising marketing delivery timing for each user's browsing interest commodities can be obtained.

[0125] The present invention firstly analyzes the advertising appearance status of the target platform marketing user browsing behavior data, and can accurately understand whether each user browsing behavior process is exposed to advertisements when browsing commodities, and then obtains the advertising appearance status data of each user browsing behavior on the target platform. The key of this step is that it can provide a key data basis for subsequent analysis, help better understand the relationship between user behavior and advertising exposure, and thus more effectively formulate advertising marketing strategies. Secondly, the target platform marketing user browsing behavior data is processed by advertising browsing division based on the advertising appearance status data of each user browsing behavior on the target platform, so as to obtain the advertising-free browsing behavior data and advertising browsing behavior data of the target platform users. Through this step, it is possible to clearly distinguish whether the user is exposed to advertisements when browsing commodities, and provide a clear data basis for subsequent analysis. This processing helps to understand the user's behavior pattern more finely, so as to more effectively formulate personalized advertising push strategies. Then, the commodity sales data extraction processing is performed on the target platform marketing standardized format data set to obtain the commodity sales information data of the target platform. The key of this step is that it can provide a sales data basis for subsequent analysis, help better understand the sales situation of commodities, and provide an important basis for the formulation of advertising marketing strategies. Next, the target platform marketing product sales information data is screened based on the target platform users' ad-free browsing behavior data and the target platform users' ad-free browsing behavior data to obtain the marketing product sales information data in the ad-free browsing behavior state and the ad-free browsing behavior state. Through this analysis, the product sales of different user groups under the condition of ad exposure can be compared, so as to better evaluate the impact of advertising on sales and provide data support for the optimization of advertising marketing strategies. Finally, the target platform users' browsing interest product set is analyzed for advertising marketing based on the marketing product sales information data in the ad-free browsing behavior state and the marketing product sales information data in the ad-free browsing behavior state to determine the advertising marketing delivery timing of each user's browsing interest product. This analysis helps to more accurately grasp the user's purchasing interests and behavior patterns, and provides guidance for the timing and method of advertising delivery, thereby improving the effectiveness of advertising and the user's purchase conversion rate.

[0126] Preferably, step S35 includes the following steps:

[0127] Step S351: Based on the marketing product sales information data in the ad-free browsing behavior state, each user browsing interest product in the target platform user browsing interest product set is subjected to ad-free matching screening processing to obtain the sales information data of each user browsing interest product in the ad-free browsing behavior state;

[0128] The embodiment of the present invention combines the marketing product sales information data obtained by analysis in a state of advertising-free browsing behavior (including the product sales status caused by users independently searching or browsing product pages in the target platform without being exposed to advertisements, such as information such as the type, quantity, and price of purchased products) with the corresponding user browsing interest products in the target platform user browsing interest product set, so as to screen out the sales status of products related to user interests in a state of advertising-free browsing behavior, and finally obtain the sales information data of each user browsing interest product in a state of advertising-free browsing behavior.

[0129] Step S352: Based on the marketing product sales information data in the state of advertising browsing behavior, each user browsing interest product in the target platform user browsing interest product set is subjected to advertising matching screening processing to obtain the sales information data of each user browsing interest product in the state of advertising browsing behavior;

[0130] The embodiment of the present invention combines the marketing product sales information data obtained by analysis in the state of advertising browsing behavior (including the product sales status generated in the product page of the target platform when the user browses the advertisement, such as the type, quantity, price and other information of the purchased product) and also matches and filters the corresponding user browsing interest products in the target platform user browsing interest product set, so as to filter out the sales status of products related to the user's interest in the state of advertising, and understand the sales status of users of different interest products after being exposed to the advertisement, and finally obtain the sales information data of each user browsing interest product in the state of advertising browsing behavior.

[0131] Step S353: Plotting sales change curves for the sales information data of the interested products browsed by each user in the state of no advertisement browsing behavior and the sales information data of the interested products browsed by each user in the state of advertisement browsing behavior, so as to generate the sales change curve without advertisement and the sales change curve with advertisement of the interested products browsed by each user;

[0132] The embodiment of the present invention uses a mathematical statistical analysis method to analyze the sales information data of each user's browsing interest products in a non-advertising browsing behavior state and the sales information data of each user's browsing interest products in a browsing behavior state with advertisements, so as to analyze the sales volume, sales revenue and other indicators of each user's browsing interest products in a non-advertising or advertising state, and draws the sales change curves of each user's browsing interest products in the non-advertising and advertising browsing behavior states by using statistical chart tools or data visualization software, so as to intuitively compare the changing trends of user purchasing behaviors under the influence of different advertisements, and finally generate a non-advertising sales change curve and an advertising sales change curve for each user's browsing interest products.

[0133] Step S354: Analyze the advertising marketing delivery timing of the sales change curve without advertisements and the sales change curve with advertisements of the products of interest to each user, and obtain the advertising marketing delivery timing of the products of interest to each user.

[0134] The embodiment of the present invention compares and analyzes the sales change curves without advertisements and the sales change curves with advertisements of the products of interest to each user, so as to analyze the fluctuations and trends of the sales status of the products of interest to each user under different situations, and determines the best time for advertising and marketing delivery by analyzing the time period with huge sales deviation between the two change curves, and finally obtains the advertising and marketing delivery timing for the products of interest to each user.

[0135] The present invention firstly performs a non-advertising matching and screening process on each user-browsed interest product in the target platform user-browsed interest product set based on the marketing product sales information data in the non-advertising browsing behavior state, so as to clearly understand the sales status of users for different interest products without the influence of advertising. The key of this step is to help understand the true purchasing behavior of users, provide a key data basis for subsequent analysis, and help to more accurately evaluate the impact of advertising on sales. Secondly, by performing an advertising matching and screening process on each user-browsed interest product in the target platform user-browsed interest product set based on the marketing product sales information data in the advertising browsing behavior state, through this process, it is possible to understand the sales status of users for different interest products after being exposed to advertising, so as to more comprehensively evaluate the impact of advertising on sales. This step helps to understand the incentive effect of advertising on user purchasing behavior, and provides data support for the optimization of advertising marketing strategies. Then, by drawing a sales change curve for each user-browsed interest product in the non-advertising browsing behavior state and the advertising browsing behavior state, a corresponding sales change curve is drawn, so that the changing trend of user purchasing behavior under the influence of different advertisements can be intuitively compared, so as to better understand the impact mechanism of advertising on sales. Finally, based on the analysis of the sales change curve, the advertising marketing timing is analyzed for the sales change curve without advertisements and the sales change curve with advertisements of the products that each user is interested in browsing. Through this analysis, the time period with a huge deviation between the two curves can be analyzed to determine the best advertising marketing timing, so as to maximize the effect of the advertisement and improve the user's purchase conversion rate. This step helps to formulate advertising strategies more accurately, thereby providing data support for the subsequent marketing strategy analysis process.

[0136] Preferably, step S4 comprises the following steps:

[0137] Step S41: performing an analysis of the target platform user's browsing interest product set on the recommendation and promotion strategy of the interest product set, and obtaining the target platform user's browsing interest product recommendation strategy and the target platform user's browsing interest product promotion strategy;

[0138] The embodiment of the present invention recommends the browsing interest commodities of each user in the target platform user browsing interest commodity set obtained by analysis, so as to recommend the corresponding browsing interest commodities or similar interest commodities to the target user, and selects a suitable recommendation strategy, such as content-based recommendation, collaborative filtering recommendation, etc., so as to obtain the target platform user browsing interest commodity recommendation strategy. At the same time, by comprehensively considering the user's browsing history, purchase record and potential demand, the promotion strategy of the current browsing interest commodity is analyzed and determined, including discounts, full reduction, gifts, etc., and finally the target platform user browsing interest commodity promotion strategy is obtained.

[0139] Step S42: analyzing the advertising delivery strategy of the target platform user browsing interest products set based on the advertising marketing delivery timing of each user browsing interest products, and obtaining the advertising delivery strategy of the target platform user browsing interest products;

[0140] The embodiment of the present invention combines the analyzed advertising marketing delivery timings of each user's browsing interest products (including user active time periods and preferred time periods for browsing interest products, etc.) to perform strategic analysis on the corresponding user browsing interest products in the target platform user browsing interest product set to determine the best advertising delivery strategy, such as increasing advertising exposure during time periods with high user activity and increasing the delivery of related advertisements when users browse specific interest products, and finally obtaining the advertising delivery strategy for the target platform user browsing interest products.

[0141] Step S43: Generate a target platform product marketing strategy by merging the target platform user browsing interest product recommendation strategy, the target platform user browsing interest product promotion strategy and the target platform user browsing interest product advertising strategy to execute the corresponding target platform product personalized marketing activities.

[0142] The embodiment of the present invention comprehensively considers the recommendation strategy of target platform users' browsing interest commodities, the promotion strategy of target platform users' browsing interest commodities, and the advertising delivery strategy of target platform users' browsing interest commodities, and analyzes the correlation and complementary effects between them for merging, so as to formulate a comprehensive commodity marketing strategy according to the result of the merged strategy, including adjusting the recommendation algorithm, optimizing the promotional activities and adjusting the advertising delivery strategy, and executes the corresponding personalized marketing activities of the target platform commodities according to the formulated comprehensive marketing strategy, such as pushing personalized recommendations, carrying out promotional activities and adjusting the advertising delivery strategy.

[0143] The present invention first analyzes the interest commodity recommendation and promotion strategy of the interest commodity set browsed by the target platform users, aiming to explore the user's preferences and purchasing behavior patterns, and comprehensively considers the user's browsing history, purchase record and potential needs, and can formulate a highly targeted interest commodity recommendation strategy and promotion strategy. This process can not only improve the user's stickiness to the target platform, but also help increase sales and improve user satisfaction. The formulation of the recommendation strategy can effectively guide users to browse and purchase, and the promotion strategy can stimulate the user's desire to buy and promote sales growth. Then, by analyzing the advertising delivery strategy of the interest commodity set browsed by the target platform users based on the advertising marketing delivery timing of each user browsing the interest commodity, the optimal advertising delivery strategy can be determined, including delivery time, frequency, content and audience positioning. This process helps to maximize the advertising effect, improve the return on advertising investment, and better meet the personalized needs of users, and enhance brand exposure and awareness. Finally, by merging the target platform users' browsing interest product recommendation strategy, the target platform users' browsing interest product promotion strategy and the target platform users' browsing interest product advertising strategy, the overall target platform product marketing strategy can be generated. This comprehensive strategy aims to make full use of users' interest and behavior data to achieve more personalized and precise marketing activities. This step, by integrating different marketing strategies, can more effectively attract users' attention, improve purchase conversion rate, and maintain competitive advantage in the highly competitive market, thereby bringing more marketing opportunities and commercial value to the target platform.

[0144] Preferably, the present invention further provides a marketing data processing system for executing the marketing data processing method as described above, the marketing data processing system comprising:

[0145] The target platform marketing data processing module is used to obtain the target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain the target platform marketing standardized format data set;

[0146] The user browsing interest product screening module is used to analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data, thereby obtaining the target platform user browsing interest product set;

[0147] The module for mining the advertising marketing of interest products is used to process the browsing behavior data of target platform marketing users by advertising browsing, so as to obtain the advertising browsing behavior data of target platform users without advertising and the advertising browsing behavior data of target platform users; based on the target platform marketing standardized format data set, the advertising browsing behavior data of target platform users without advertising and the advertising browsing behavior data of target platform users, the advertising marketing analysis is performed on the target platform users' browsing interest product set, so as to obtain the advertising marketing delivery timing of each user browsing the interest product;

[0148] The target platform product marketing strategy analysis module is used to perform marketing strategy analysis on the target platform user browsing interest product set based on the advertising marketing delivery timing of each user browsing interest product, generate the target platform product marketing strategy, and execute the corresponding target platform product personalized marketing activities.

[0149] In summary, the present invention provides a marketing data processing system, which consists of a target platform marketing data processing module, a user browsing interest product screening module, an interest product advertising marketing delivery mining module and a target platform product marketing strategy analysis module. It can implement any marketing data processing method described in the present invention, and is used to combine the operations between computer programs running on each module to implement any marketing data processing method. The internal structures of the system cooperate with each other, which can greatly reduce repetitive work and manpower investment, and can quickly and effectively provide a more accurate and efficient marketing data processing process, thereby simplifying the operating procedures of the marketing data processing system.

[0150] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0151] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for processing marketing data, characterized in that: The following steps are involved: Step S1: Obtain a target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; Performing unified format conversion processing on the marketing data subsets in the target platform's different format buffer areas to obtain a target platform marketing standardized format data set; wherein step S1 includes the following steps: Step S11: Obtain the target platform marketing data set; Step S12: abstractly divide the target platform marketing data set into different data formats to obtain target platform marketing data subsets in different data formats; Step S13: various data format buffers are designed, and the target platform marketing data subsets in different data formats are temporarily stored in the various data format buffers to obtain marketing data subsets in different format buffers of the target platform; Step S14: acquiring the marketing decision data of the target platform, and performing marketing-related screening processing on the marketing data subsets of the target platform's different-format cache areas based on the marketing decision data of the target platform, to obtain the marketing decision-related data subsets of the target platform's different-format cache areas; wherein step S14 includes the following steps: Step S141: performing histogram anomaly screening processing on the marketing data subsets in the target platform's different formats of the buffer area to obtain the marketing anomaly filtered data subsets in the target platform's different formats of the buffer area; Step S142: performing missing interpolation compensation processing on the marketing filter data subsets in the target platform's different format buffer areas to obtain the marketing interpolation compensation data subsets in the target platform's different format buffer areas; Step S143: Obtaining target platform marketing decision data; Step S144: Calculating the marketing decision correlation coefficient for the marketing interpolation compensation data subsets in the target platform's different format buffers based on the target platform's marketing decision data, so as to obtain the correlation coefficient between each marketing data and the marketing decision in the target platform's different format buffers; Step S145: performing marketing-related screening processing on the marketing interpolation compensation data subsets in the target platform's different-format buffer areas according to the correlation coefficients between each marketing data and the marketing decision in the target platform's different-format buffer areas, to obtain the marketing decision-related data subsets in the target platform's different-format buffer areas; Step S15: performing a unified format conversion process on the marketing decision-related data subsets in the target platform's different format buffer areas to obtain a target platform marketing standardized format data set; Step S2: Analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data to obtain the target platform user browsing interest product set; wherein, step S2 includes the following steps: Step S21: extracting user browsing record data from the target platform marketing standardized format data set to obtain the target platform user browsing record data; Step S22: Performing statistical analysis on the browsing frequency and browsing depth of the target platform user browsing record data to obtain the target platform user browsing frequency and the target platform user browsing depth; Step S23: Analyze the browsing behavior of the target platform user browsing record data based on the target platform user browsing frequency and the target platform user browsing depth to obtain the target platform marketing user browsing behavior data; wherein, step S23 includes the following steps: Step S231: Analyze the time series changes of the browsing frequency of the target platform users to obtain the time series change trend data of the browsing frequency of the target platform users; Step S232: analyzing the browsing activity change pattern of the target platform user browsing record data according to the target platform user browsing frequency time series change trend data to obtain the target platform user browsing activity change pattern data; Step S233: performing page conversion pattern mining and analysis on the target platform user browsing record data based on the target platform user browsing depth to obtain the target platform user browsing page conversion pattern data; Step S234: Analyze the browsing behavior of the target platform user browsing record data based on the target platform user browsing activity change pattern data and the target platform user browsing page conversion pattern data to obtain the target platform marketing user browsing behavior data; Step S24: obtaining various marketing commodities in the process of user browsing behavior through the target platform marketing user browsing behavior data, so as to obtain the target platform user browsing marketing commodity set; Step S25: Based on the target platform marketing user's browsing behavior data, the target platform user's browsing interest screening processing is performed on the marketing product set to obtain the target platform user's browsing interest product set; wherein step S25 includes the following steps: Step S251: Based on the target platform marketing user browsing behavior data, the browsing stay monitoring and analysis of each user browsing product in the target platform user browsing marketing product set is performed to obtain the browsing stay number and browsing stay duration of each user browsing product on the target platform; Step S252: Analyze the browsing behavior data of the target platform marketing user to obtain the browsing behavior trajectory of the target platform marketing user; Step S253: Marking each user-browsed product in the target platform user-browsed marketing product set as a browsing path conversion node on the target platform marketing user browsing behavior trajectory to obtain each user-browsing path conversion product node; Step S254: Based on the browsing behavior trajectory of the target platform marketing user, a browsing conversion bottleneck analysis is performed on each user browsing path conversion commodity node to obtain user browsing conversion bottleneck status data between each user browsing commodity node; Step S255: performing conversion attenuation rate statistics calculation on the user browsing conversion bottleneck status data between each user browsing commodity node to obtain the user browsing conversion attenuation rate between each user browsing commodity node; Step S256: performing a time correction analysis on the browsing dwell time of each user browsing the product on the target platform according to the user browsing conversion decay rate between each user browsing the product node, and obtaining the browsing dwell conversion correction time of each user browsing the product on the target platform; Step S257: Based on the number of browsing stays of each user browsing the product on the target platform and the modified browsing stay conversion time, a browsing interest evaluation analysis is performed on each user browsing the product in the marketing product set of the target platform users to obtain a user browsing interest evaluation value of each user browsing the product; Step S258: performing browsing interest screening processing on each user-browsed product in the target platform user-browsed marketing product set according to the user-browsing interest evaluation value of each user-browsed product, and obtaining the target platform user-browsing interest product set; Step S3: Perform advertising browsing segmentation processing on the browsing behavior data of the target platform marketing users to obtain the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data; perform advertising marketing analysis on the target platform users' browsing interest product set based on the target platform marketing standardized format data set, the target platform users' advertising-free browsing behavior data and the target platform users' advertising browsing behavior data to obtain the advertising marketing delivery opportunities for each user's browsing interest products; wherein, step S3 includes the following steps: Step S31: performing advertisement appearance recognition analysis on the target platform marketing user browsing behavior data to obtain advertisement appearance status data of each user browsing behavior on the target platform; Step S32: Based on the advertisement appearance status data of each user browsing behavior on the target platform, the target platform marketing user browsing behavior data is subjected to advertisement browsing classification processing to obtain the target platform user non-advertising browsing behavior data and the target platform user advertising browsing behavior data; Step S33: extracting and processing the commodity sales data of the target platform marketing standardized format data set to obtain the target platform marketing commodity sales information data; Step S34: performing browsing behavior product sales screening processing on the target platform marketing product sales information data based on the target platform user's non-advertising browsing behavior data and the target platform user's advertising browsing behavior data, so as to obtain the marketing product sales information data in the non-advertising browsing behavior state and the marketing product sales information data in the advertising browsing behavior state; Step S35: Based on the marketing product sales information data in the state of no advertising browsing behavior and the marketing product sales information data in the state of advertising browsing behavior, the target platform user browsing interest product set is analyzed for advertising marketing, and the advertising marketing delivery opportunity of each user browsing interest product is obtained; wherein, step S35 includes the following steps: Step S351: Based on the marketing product sales information data in the ad-free browsing behavior state, each user browsing interest product in the target platform user browsing interest product set is subjected to ad-free matching screening processing to obtain the sales information data of each user browsing interest product in the ad-free browsing behavior state; Step S352: Based on the marketing product sales information data in the state of advertising browsing behavior, each user browsing interest product in the target platform user browsing interest product set is subjected to advertising matching screening processing to obtain the sales information data of each user browsing interest product in the state of advertising browsing behavior; Step S353: Plotting sales change curves for the sales information data of the interested products browsed by each user in the state of no advertisement browsing behavior and the sales information data of the interested products browsed by each user in the state of advertisement browsing behavior, so as to generate the sales change curve without advertisement and the sales change curve with advertisement of the interested products browsed by each user; Step S354: Analyze the advertising marketing placement timing of the sales change curve without advertisements and the sales change curve with advertisements of the products of interest to each user, and obtain the advertising marketing placement timing of the products of interest to each user; Step S4: Based on the advertising marketing delivery opportunities of the products of interest browsed by each user, a marketing strategy analysis is performed on the target platform user browsing interest product set, and a target platform product marketing strategy is generated to execute corresponding personalized marketing activities for the target platform products.

2. The method for processing marketing data according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing an analysis of the target platform user's browsing interest product set on the recommendation and promotion strategy of the interest product set, and obtaining the target platform user's browsing interest product recommendation strategy and the target platform user's browsing interest product promotion strategy; Step S42: analyzing the advertising delivery strategy of the target platform user browsing interest products set based on the advertising marketing delivery timing of each user browsing interest products, and obtaining the advertising delivery strategy of the target platform user browsing interest products; Step S43: Generate a target platform product marketing strategy by merging the target platform user browsing interest product recommendation strategy, the target platform user browsing interest product promotion strategy and the target platform user browsing interest product advertising strategy to execute the corresponding target platform product personalized marketing activities.

3. A marketing data processing system, characterized in that: Used to execute the marketing data processing method according to claim 1, the marketing data processing system comprises: The target platform marketing data processing module is used to obtain the target platform marketing data set, and perform data format division and cache processing on the target platform marketing data set to obtain marketing data subsets in different format cache areas of the target platform; perform format uniform conversion processing on the marketing data subsets in different format cache areas of the target platform to obtain the target platform marketing standardized format data set; The user browsing interest product screening module is used to analyze the user browsing behavior of the target platform marketing standardized format data set to obtain the target platform marketing user browsing behavior data; obtain various marketing products in the user browsing behavior process through the target platform marketing user browsing behavior data to obtain the target platform user browsing marketing product set; perform browsing interest screening on the target platform user browsing marketing product set based on the target platform marketing user browsing behavior data, thereby obtaining the target platform user browsing interest product set; The module for mining the advertising marketing of interest products is used to process the browsing behavior data of target platform marketing users by advertising browsing, so as to obtain the advertising-free browsing behavior data of target platform users and the advertising browsing behavior data of target platform users; based on the target platform marketing standardized format data set, the advertising-free browsing behavior data of target platform users and the advertising browsing behavior data of target platform users, the advertising marketing analysis is performed on the target platform users' browsing interest product set, so as to obtain the advertising marketing delivery timing of each user browsing the interest product; The target platform product marketing strategy analysis module is used to perform marketing strategy analysis on the target platform user browsing interest product set based on the advertising marketing delivery timing of each user browsing interest product, generate the target platform product marketing strategy, and execute the corresponding target platform product personalized marketing activities.

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