Multi-channel advertisement putting effect monitoring and analyzing system

Abstract

The invention belongs to the technical field of advertisement putting effect analysis, and particularly relates to a multi-channel advertisement putting effect monitoring and analyzing system. On one hand, key performance data generated by each online channel for advertisement putting of a specified product of a target enterprise in a preset historical period is summarized; on the one hand, the overall advertisement putting effect index of the online channel of the specified product of the target enterprise in the preset historical period is evaluated and output, and on the other hand, the behavior path of each user participating in the advertisement putting interaction of the specified product of the target enterprise in each online channel is traced and cut; a single online channel interaction situation and a cross-online channel interaction situation are carefully considered, a direct contribution index and an indirect contribution index are correspondingly evaluated, a comprehensive contribution index of each online channel for an advertisement putting effect of a product specified by a target enterprise is fed back, the enterprise is helped to comprehensively grasp the advertisement putting effect of the online channels, and the advertisement putting efficiency is improved. And important support is provided for enterprises to realize efficient advertisement strategy optimization.

Description

Technical Field

[0001] The present invention belongs to the technical field of advertising effect analysis, and specifically relates to a multi-channel advertising effect monitoring and analysis system. Background Art

[0002] With the rapid development of digital marketing and the diversification of advertising channels, companies' advertising channels are becoming more diversified and no longer limited to traditional television, newspapers, magazines, etc. Online channels such as social media, search engines, and mobile applications have become a new battlefield for marketing. How to effectively monitor the effectiveness of advertising in different online advertising channels and accurately evaluate the return on investment of each channel has become an important issue in marketing decision-making.

[0003] In the prior art, there are also some solutions related to monitoring and analyzing the effect of advertising. For example, the multi-channel advertising delivery method, device, equipment and storage medium with Chinese patent publication number CN113743995B obtains the delivery effect data of multiple channel advertising delivery platforms to establish an initial delivery effect funnel model for each channel advertising delivery platform. Each initial delivery effect funnel model includes hierarchical information and conversion rates corresponding to multiple levels of advertising operation behavior events. By comparing the initial delivery effect funnel models of each channel advertising delivery platform, the initial delivery effect related indicators of each channel delivery platform can be mapped.

[0004] Another Chinese patent publication number CN118115210B is an advertising effect evaluation and analysis system based on data analysis, which includes a management platform, an information collection unit, a product popularity unit, an information supervision unit, a delivery evaluation unit, an effect fusion evaluation unit, a management evaluation unit and a delivery management unit. By analyzing from point to surface, it helps to improve the accuracy of the analysis results on the one hand, and on the other hand, it helps to improve the rationality of advertising management to improve the attractiveness and conversion effect of advertising. Analysis from three dimensions, namely, front, side and product, helps to provide data support for subsequent advertising effect analysis. On the premise that the advertising effect is qualified, the qualified advertising is further analyzed for revenue, so as to optimize and adjust the advertising strategy according to the information feedback, so as to improve the rate of return and the attractiveness of advertising, and further improve the conversion effect of advertising.

[0005] Although the above scheme proposes some solutions related to the monitoring and analysis of advertising effect, the existing technology still has limitations, which are specifically manifested as follows: the existing technology focuses on conventional key indicators for the analysis of advertising effect on various online channels, such as exposure, clicks and conversions. On the one hand, it is unable to provide the overall advertising effect indicators of the online channels of the target enterprise's designated products from a macro perspective, making it difficult for the enterprise to quickly grasp the comprehensive effectiveness of multi-channel delivery, which is not conducive to decision-making. On the other hand, it fails to effectively trace the user's interactive behavior with respect to the advertisements delivered on various online channels based on the user as the main body, and is unable to carefully consider the multi-stage changes of the user in the entire process of the interactive behavior path of the advertisement delivery, and fails to effectively consider the user's interactive advertising context across online channels, resulting in the unreliability and accuracy of the evaluation results of the advertising effect of each online channel. Summary of the invention

[0006] In order to overcome the shortcomings of the background technology, an embodiment of the present invention provides a multi-channel advertising effect monitoring and analysis system, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] The purpose of the present invention can be achieved through the following technical solutions: a multi-channel advertising effect monitoring and analysis system, including: an overall advertising effect evaluation module, a user behavior path tracing module, a user behavior path cutting module, an online channel contribution evaluation module, an online channel contribution feedback module and a cloud database.

[0008] The overall advertising delivery effect evaluation module is connected to the user behavior path tracing module, the user behavior path tracing module is connected to the user behavior path cutting module, the user behavior path cutting module is connected to the online channel contribution evaluation module, the online channel contribution evaluation module is connected to the online channel contribution feedback module, and the cloud database is respectively connected to the overall advertising delivery effect evaluation module and the online channel contribution evaluation module.

[0009] The overall advertising effect evaluation module is used to summarize the key performance data generated by the advertising of the target enterprise's designated products through various online channels within a preset historical period, so as to evaluate and output the overall advertising effect indicators of the target enterprise's designated products through online channels within the preset historical period.

[0010] The user behavior path tracing module is used to locate the online channel where the user last interacted with the target enterprise's designated product advertising within a preset historical period, and use this as the tracing starting point to reversely trace the behavior paths of each user who participated in the target enterprise's designated product advertising interaction in each online channel.

[0011] The user behavior path cutting module is used to re-sort the behavior path segments of each user who participates in the target enterprise's designated product advertising interaction on each online channel with the purchase operation stage as the cutting node, and record them as the behavior path segments of each interactive user on each online channel.

[0012] The online channel contribution evaluation module is used to analyze each behavioral path segment of each interactive user in each online channel, and evaluate the direct contribution index and indirect contribution index of each online channel to the advertising effect of the designated product of the target enterprise.

[0013] The online channel contribution feedback module is used to comprehensively calculate direct contribution indicators and indirect contribution indicators, and to provide feedback on the comprehensive contribution indicators of each online channel for the advertising effect of the target enterprise's designated products.

[0014] The cloud database is used to store the user base of each online channel, the preset positioning weights of the target enterprise's designated products for each user group type label, and the preset attenuation rate of repeated advertising interaction behavior for user attractiveness at each operation stage.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) The present invention summarizes the key performance data generated by each online channel for the advertising of the target enterprise's designated products within a preset historical period, and comprehensively, accurately and efficiently evaluates and outputs the overall advertising effect indicators of the online channels of the target enterprise's designated products within a preset historical period, thereby helping the enterprise to have a macro understanding of the actual effect of the online channels in promoting the designated products.

[0016] (2) The present invention traces the behavioral paths of each user who participates in the advertising interaction of the target enterprise's designated products through each online channel, and effectively cuts them with the purchase operation stage as the cutting node, thereby achieving in-depth insights into the interactive behavior of online channel users participating in advertising, and providing a data basis and logical framework for the subsequent contribution evaluation of each online channel to the advertising effect of the target enterprise's designated products.

[0017] (3) The present invention carefully considers the interaction scenarios of a single online channel and the interaction scenarios across online channels, and correspondingly evaluates the direct contribution index and the indirect contribution index, thereby providing feedback on the comprehensive contribution index of each online channel to the advertising effect of the target enterprise's designated product, which helps the enterprise to have a clearer understanding of the actual contribution of each online channel, thereby promoting the formulation of a more accurate, scientific and reliable channel strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0019] Figure 1 It is a schematic diagram of module connection of the present invention.

[0020] Figure 2 A logical schematic diagram of the overall advertising effect indicators of the online channels of the target enterprise's designated products evaluated within a preset historical period in the present invention.

[0021] Figure 3 This is a logical diagram of direct contribution indicators for evaluating the advertising effect of each online channel on a designated product of a target enterprise according to the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Reference Figure 1 As shown, the present invention provides a multi-channel advertising effect monitoring and analysis system, including: an overall advertising effect evaluation module, a user behavior path tracing module, a user behavior path cutting module, an online channel contribution evaluation module, an online channel contribution feedback module and a cloud database.

[0024] The overall advertising delivery effect evaluation module is connected to the user behavior path tracing module, the user behavior path tracing module is connected to the user behavior path cutting module, the user behavior path cutting module is connected to the online channel contribution evaluation module, the online channel contribution evaluation module is connected to the online channel contribution feedback module, and the cloud database is respectively connected to the overall advertising delivery effect evaluation module and the online channel contribution evaluation module.

[0025] The overall advertising delivery effect evaluation module is used to summarize the key performance data generated by each online channel for the advertising delivery of the target enterprise's designated products within a preset historical period, so as to evaluate and output the overall advertising delivery effect indicators of the online channels of the target enterprise's designated products within the preset historical period.

[0026] Specifically, the key performance data include exposure, clicks, conversions, new sales, and group type labels and identity logic indicators of each purchasing user, where the identity logic indicator value is 1 or 0, where 1 indicates that the purchasing user is an existing user and 0 indicates that the purchasing user is a new user.

[0027] Reference Figure 2Specifically, the evaluation and output of the overall advertising effect of the target enterprise's designated product online channel within the preset historical period refers to: the exposure generated by the advertising of the target enterprise's designated product on each online channel within the preset historical period , Clicks and conversion volume ,in is the number of each online channel, , is the number of online channels, according to the formula Analyze the basic effect index of the online channel of the target enterprise's designated product within the preset historical period, including The first The number of online channel users.

[0028] The new sales generated by the advertising of the target enterprise's designated products in each online channel during the preset historical period are accumulated to summarize the cumulative new sales generated by the advertising of the target enterprise's designated products in the online channels during the preset historical period, and the ratio of the new sales to the preset reference sales is analyzed to obtain the sales effect index of the online channels of the target enterprise's designated products during the preset historical period.

[0029] Extract the group type labels and identity logic indicators of each purchasing user generated by each online channel for the target enterprise's designated product advertising within the preset historical period, and analyze the user effect index of the target enterprise's designated product online channel within the preset historical period .

[0030] The basic effect index, sales effect index and user effect index are respectively added up with the products of their corresponding preset weights to obtain the overall advertising effect index of the online channels of the designated products of the target enterprise within the preset historical period.

[0031] The present invention summarizes the key performance data generated by each online channel for the advertising of the target enterprise's designated products within a preset historical period, comprehensively, accurately and efficiently evaluates and outputs the overall advertising effect indicators of the online channels of the target enterprise's designated products within the preset historical period, and helps enterprises to have a macro understanding of the actual effects of online channels in promoting designated products.

[0032] Specifically, the The specific analysis process includes: obtaining the number of purchasing users with each group type label generated by each online channel for the target enterprise's designated product advertising within the preset historical period, integrating each online channel, obtaining the cumulative number of purchasing users with each group type label generated by the online channel for the target enterprise's designated product advertising within the preset historical period, and performing ratio analysis with the total number of purchasing users generated by the online channel for the target enterprise's designated product advertising within the preset historical period to count the cumulative proportion of purchasing users corresponding to each group type label generated by the online channel for the target enterprise's designated product advertising within the preset historical period, and comparing it with the preset positioning weights of the target enterprise's designated product for each user group type label stored in the cloud database, calculating the absolute deviation ratio of each group type label generated by the online channel for the target enterprise's designated product advertising within the preset historical period relative to its preset positioning weight, and taking the negative value after mean calculation and substituting it into the natural exponential function to obtain the user positioning fit generated by the online channel for the target enterprise's designated product advertising within the preset historical period.

[0033] According to the identity logic indicators, the cumulative number of new users and the cumulative number of existing users generated by the online channels for the target enterprise's designated product advertising within the preset historical period are screened and counted, and the user expansion degree and user retention degree generated by the online channels for the target enterprise's designated product advertising within the preset historical period are analyzed respectively.

[0034] It should be noted that the above-mentioned user expansion degree is the ratio of the cumulative number of new users to the preset number of reference users, and the user retention degree is the ratio of the cumulative number of existing users to the preset number of reference users.

[0035] The user positioning fit, user expansion and user retention are accumulated to obtain the user effect index of the online channel of the target enterprise's designated product within the preset historical period.

[0036] The user behavior path tracing module is used to locate the online channel where the user last interacted with the target enterprise's designated product advertising within a preset historical period, and use this as the tracing starting point to reversely trace the behavior paths of each user who participated in the target enterprise's designated product advertising interaction in each online channel.

[0037] It should be noted that the above-mentioned reverse tracing of the user behavior path with the online channel where the last interaction was located as the tracing starting point is significant in that it can accurately understand the user's complete decision-making process after being exposed to the target company's designated product advertisement. Each user will go through multiple stages in the purchase decision-making process, from the initial exposure and cognition, click interest stimulation, to browsing consideration, jump link destination, and then to the formation of purchase intention, until the final purchase or abandonment. Through reverse tracing, the company can restore how the user obtains information, makes comparisons, and generates preferences from various online channels step by step. Therefore, although the system of the present invention uses the online channel where the last interaction was located as the tracing starting point and naming basis for the convenience of analysis and management, there are actually two situations for the user behavior path participating in the target company's designated product advertising interaction in a certain online channel. One is that it covers other online channels, and the other is that it only exists in this online channel.

[0038] The user behavior path cutting module is used to re-sort the behavior path segments of each user in each online channel who participates in the interaction of the target enterprise's designated product advertising, taking the purchase operation stage as the cutting node, and record them as the behavior path segments of each interactive user in each online channel.

[0039] It should be noted that each of the above-mentioned user's behavior path segments corresponds to each purchase operation history generated by the user within a preset historical period.

[0040] The embodiment of the present invention traces the behavior path of each user who participates in the interactive advertising of the target enterprise's designated products through each online channel, and effectively cuts it with the purchase operation stage as the cutting node, thereby achieving in-depth insight into the interactive behavior of online channel users participating in advertising, and providing a data basis and logical framework for the subsequent contribution evaluation of each online channel to the advertising effect of the target enterprise's designated products.

[0041] The online channel contribution evaluation module is used to analyze each behavioral path segment of each interactive user in each online channel, and evaluate the direct contribution index and indirect contribution index of each online channel to the advertising effect of the designated product of the target enterprise.

[0042] Reference Figure 3 As shown, specifically, the direct contribution index of evaluating each online channel for the advertising effect of the target enterprise's designated product includes: identifying whether there is a cross-online channel phenomenon in the behavior path segment, if not, recording the behavior path segment as a direct interaction path segment, if so, recording the behavior path segment as an indirect interaction path segment, so as to screen the direct interaction path segments and indirect interaction path segments of each interactive user in each online channel.

[0043] Set the order and basic attraction weight of each operation stage of advertising interaction behavior, extract each operation stage involved in each direct interaction path fragment of each interactive user in each online channel, combine the preset attenuation rate of user attraction for each operation stage of advertising interaction behavior stored in the cloud database, and analyze the effectiveness of each online channel in attracting each interactive user for the designated product advertising of the target enterprise. , is the ID of each interactive user, , The number of interactive users is used to calculate the advertising user attraction effectiveness index of each online channel for the target enterprise's designated products. , .

[0044] It should be noted that the basic attraction weights of the above-mentioned advertising interaction behavior at each operation stage are set based on the following: Exposure is the most basic stage of advertising interaction behavior, which mainly allows users to see the advertising content. At this stage, users are only passively exposed to advertising information and have not actively interacted with it, so their basic attraction weight value is the lowest.

[0045] When users actively click to get more information about the advertised product and enter the click stage, it shows that the user's curiosity or potential purchase intention is aroused. However, there may still be a certain degree of randomness in click behavior, such as the user may just click by mistake or simply browse the detail page without further understanding of the product. Therefore, its basic attraction weight is higher than exposure but lower than subsequent deeper interactive behavior.

[0046] The browsing stage means that after users click to enter the ad details page, they spend a certain amount of time viewing the detailed information of the product, such as the product's features, functions, price, etc., which shows that users have a more practical interest in the product and actively collect information to evaluate whether the product meets their needs. Compared with clicking, browsing behavior can better reflect the user's active participation and attention to the product, because users need to invest more time and energy. However, at this time, users may still be in a state of comparison and waiting, and have not yet clearly expressed their intention to buy. Therefore, its basic attraction weight is higher than clicking but lower than subsequent deeper interactive behaviors.

[0047] Jump usually refers to users jumping from an ad page to a product's official website, purchase page, or other important purchase-related pages. This behavior indicates that users are not only interested in the product, but have a strong desire to learn more about the purchase process, product details, or view more user reviews. The jump stage is an important step for users to move toward purchasing behavior, indicating that users have crossed the simple information collection stage and begun to transition to the actual purchase decision stage. It has a greater driving effect on the final purchase behavior, so its basic attraction weight is higher than browsing but lower than subsequent deeper interactive behaviors.

[0048] Purchase behavior is the ultimate goal of advertising interaction and the stage that best reflects the effectiveness of advertising. When users complete a purchase, it means that the advertisement successfully guides users through the entire decision-making process, from product awareness, interest generation, information collection to the final purchase decision. Therefore, it has the highest weight among all interactive behaviors.

[0049] Exemplarily, the basic attraction weights of each operation stage of the advertising interaction behavior are set to exposure: 0.05, click: 0.15, browse: 0.2 jump: 0.25, purchase: 0.35.

[0050] Collect the sales generated by advertising of designated products of target enterprises through various online channels within the preset historical period and delivery costs Calculate the advertising revenue growth effectiveness index of each online channel for the target enterprise’s designated products .

[0051] The advertising user attraction effectiveness index and the advertising revenue growth effectiveness index are accumulated to obtain the direct contribution index of each online channel to the advertising effect of the target enterprise's designated products.

[0052] Specifically, the operation stages of the advertising interactive behavior are exposure, click, browse, jump and purchase in order from low to high.

[0053] Specifically, the The specific analysis process includes: taking the basic attraction weight corresponding to the highest-order operation stage involved in the direct interaction path segment of the online channel interactive user as the main attraction factor .

[0054] Count the number of repetitions of each operation stage except the highest level in the direct interaction path segment of online channel interactive users , is the number of each operation stage involved in the direct interaction path fragment except the highest order level, , is the number of operation stages involved in the direct interaction path segment except the highest order level, which is given by the formula Analyze the secondary attraction factors of each operation stage except the highest level in the direct interaction path fragment of online channel interactive users, including They are the other levels involved in the direct interaction path fragment except the highest level. The basic attraction weight of each operation stage, the preset decay rate of repeated appearance for user attraction, is an index variable representing the number of repetitions.

[0055] By formula Calculate the effective attraction level presented by the direct interaction path fragments of users interacting with online channels. is a natural constant, and then the effective attraction degree presented by each direct interaction path segment of each interactive user in each online channel is obtained, and the maximum effective attraction degree presented by the direct interaction path segments is screened out as the effective attraction degree of each online channel for each interactive user within it for the designated product advertising of the target enterprise.

[0056] Specifically, the .

[0057] Specifically, the indirect contribution index of each online channel for evaluating the advertising effect of the target enterprise's designated product includes: extracting each indirect interaction path segment of each interactive user in each online channel, taking each interactive user as the main body, and arranging each online channel and its terminal operation stage contained in each indirect interaction path segment of each interactive user in chronological order, analyzing the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user, arranging the effective contribution ratio sequence of each online channel corresponding to the indirect interaction path segment of the interactive user, and obtaining the indirect contribution index of each online channel for the advertising effect of the target enterprise's designated product through mean calculation.

[0058] It should be noted that the specific process of collating the effective contribution ratio sequence of each online channel corresponding to the indirect interaction path fragment of each interactive user is as follows: select a certain online channel as an example, traverse the effective contribution ratio of each online channel contained in each indirect interaction path fragment of each interactive user, screen and extract the effective contribution ratio related to the selected online channel, and incorporate the extracted effective contribution ratio into the effective contribution ratio sequence of the indirect interaction path fragment of the interactive user corresponding to the online channel. Similarly, by repeating the above operation for each online channel, the sorting of the effective contribution ratio sequence of the indirect interaction path fragment of each online channel corresponding to the interactive user can be completed.

[0059] It should be noted that the above-mentioned indirect contribution index of each online channel to the advertising effect of the target enterprise's designated product obtained by the mean calculation specifically refers to the mean calculation of each element value in the effective contribution proportion sequence of the indirect interaction path fragment of each online channel corresponding to the interactive users, and the mean calculation result is used as the indirect contribution index to obtain the indirect contribution index of each online channel to the advertising effect of the target enterprise's designated product.

[0060] Specifically, the specific analysis process of the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user includes: extracting each online channel and its terminal operation stage contained in the indirect interaction path segment of the interactive user, taking the basic attraction weight of the terminal operation stage as the reference attraction weight, and obtaining the reference attraction weight of each online channel contained in the indirect interaction path segment of the interactive user. , It is the ID of each online channel included in the indirect interaction path segment of the interactive user. , The number of online channels included in the indirect interaction path segment of interactive users.

[0061] The sequence level of each online channel terminal operation stage contained in the interactive user's indirect interaction path segment is compared with the sequence level of its previous online channel terminal operation stage. If the sequence level of a certain online channel terminal operation stage is higher than the sequence level of its previous online channel terminal operation stage, the basic attraction weight of the online channel terminal operation stage is subtracted from the basic attraction weight of its previous online channel terminal operation stage, and the calculated difference is further subjected to ratio analysis with the basic attraction weight of its previous online channel terminal operation stage. The ratio analysis result is used as the operation promotion factor of the online channel. If the sequence level of a certain online channel terminal operation stage is lower than the sequence level of its previous online channel terminal operation stage, the operation promotion factor of the online channel is set to 0, thereby obtaining the operation promotion factors of each online channel contained in the interactive user's indirect interaction path segment. .

[0062] Collect the timestamps corresponding to the terminal operation stages of each online channel contained in the indirect interaction path segment of the interactive user, calculate the interval duration of each terminal operation stage of the online channel contained in the indirect interaction path segment of the interactive user relative to the last terminal operation stage of the online channel, and substitute the interval duration into the preset time decay function to obtain the time decay factor of each online channel contained in the indirect interaction path segment of the interactive user .

[0063] It should be noted that the above preset time decay function is specifically: ,in is the preset attenuation factor, whose unit is the reciprocal of the time variable unit, is the interval duration.

[0064] By formula Analyze the effective contribution ratio of each online channel contained in the indirect interaction path segments of interactive users, and then obtain the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user.

[0065] The online channel contribution feedback module is used to comprehensively analyze direct contribution indicators and indirect contribution indicators to provide feedback on the comprehensive contribution indicators of each online channel for the advertising effect of the target enterprise's designated product.

[0066] It should be noted that the above-mentioned comprehensive contribution index is the cumulative value of the direct contribution index and the indirect contribution index.

[0067] The embodiments of the present invention carefully consider the interaction scenarios of single online channels and cross-online channels, and correspondingly evaluate direct contribution indicators and indirect contribution indicators, so as to provide feedback on the comprehensive contribution indicators of each online channel for the advertising effect of the target enterprise's designated products, which helps the enterprise to have a clearer understanding of the actual contribution of each online channel, thereby promoting the formulation of more accurate, scientific and reliable channel strategies.

[0068] The cloud database is used to store the user base of each online channel, store the preset positioning weights of the target enterprise's designated products for each user group type label, and store the preset attenuation rate of the user's attractiveness caused by the repeated appearance of each operation stage of the advertising interaction behavior.

[0069] The data sources in the cloud database of this embodiment are shown in Table 1 below.

[0070] Table 1 Detailed description of data sources in cloud database

[0071]

[0072] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A multi-channel advertising effect monitoring and analysis system, characterized in that: The system includes: The overall advertising effect evaluation module is used to summarize the key performance data generated by the advertising of the target enterprise's designated products through various online channels within a preset historical period, so as to evaluate and output the overall advertising effect indicators of the target enterprise's designated products through online channels within the preset historical period; The user behavior path tracing module is used to locate the online channel where the user last interacted with the target enterprise's designated product advertising within a preset historical period, and use this as the tracing starting point to reversely trace the behavior paths of each user who participated in the target enterprise's designated product advertising interaction in each online channel; The user behavior path cutting module is used to re-sort the behavior path segments of each user who participates in the interaction of the target enterprise's designated product advertising in each online channel, taking the purchase operation stage as the cutting node, and record them as the behavior path segments of each interactive user in each online channel; The online channel contribution evaluation module is used to analyze the behavior path segments of each interactive user in each online channel, and evaluate the direct contribution index and indirect contribution index of each online channel to the advertising effect of the target enterprise's designated product; The online channel contribution feedback module is used to integrate direct contribution indicators and indirect contribution indicators to provide feedback on the comprehensive contribution indicators of each online channel for the advertising effect of the target enterprise's designated products; The cloud database is used to store the user base of each online channel, the preset positioning weights of the target enterprise's designated products for each user group type label, and the preset attenuation rate of repeated advertising interaction behavior for user attractiveness at each operation stage.

2. A multi-channel advertising effect monitoring and analysis system according to claim 1, characterized in that: The key performance data include exposure, clicks, conversions, new sales, and group type labels and identity logic indicators of each purchasing user, where the identity logic indicator value is 1 or 0, where 1 indicates that the purchasing user is an existing user and 0 indicates that the purchasing user is a new user.

3. A multi-channel advertising effect monitoring and analysis system according to claim 2, characterized in that: The evaluation and output of the overall advertising effect index of the online channels of the target enterprise's designated products within the preset historical period includes: the exposure generated by the advertising of the target enterprise's designated products on each online channel within the preset historical period , Clicks and conversion volume ,in is the number of each online channel, , is the number of online channels, according to the formula Analyze the basic effect index of the online channel of the target enterprise's designated product within the preset historical period, including The first The number of online channel users; Summarize the cumulative additional sales generated by online channels for the target enterprise's designated products during the preset historical period, and compare them with the preset reference sales to obtain the sales effect index of the online channels for the target enterprise's designated products during the preset historical period; Extract the group type labels and identity logic indicators of each purchasing user generated by each online channel for the target enterprise's designated product advertising within the preset historical period, and analyze the user effect index of the target enterprise's designated product online channel within the preset historical period ; The basic effect index, sales effect index and user effect index are respectively added up with the products of their corresponding preset weights to obtain the overall advertising effect index of the online channels of the designated products of the target enterprise within the preset historical period.

4. A multi-channel advertising effect monitoring and analysis system according to claim 3, characterized in that: Said The specific analysis process includes: counting the cumulative proportion of users who purchased products corresponding to each group type label generated by online channels for the target enterprise's designated product advertising within the preset historical period, comparing it with the preset positioning weights of the target enterprise's designated product for each user group type label stored in the cloud database, and analyzing the user positioning fit generated by online channels for the target enterprise's designated product advertising within the preset historical period; According to the identity logic indicators, the cumulative number of new users and the cumulative number of existing users generated by the online channels for the target enterprise's designated product advertising within the preset historical period are screened and counted, and the user expansion and user retention generated by the online channels for the target enterprise's designated product advertising within the preset historical period are analyzed respectively; The user positioning fit, user expansion and user retention are accumulated to obtain the user effect index of the online channel of the target enterprise's designated product within the preset historical period.

5. A multi-channel advertising effect monitoring and analysis system according to claim 3, characterized in that: The direct contribution index of evaluating each online channel for the advertising effect of the designated product of the target enterprise includes: identifying whether the behavior path segment has a cross-online channel phenomenon, if not, recording the behavior path segment as a direct interaction path segment, if so, recording the behavior path segment as an indirect interaction path segment, thereby screening each direct interaction path segment and each indirect interaction path segment of each interactive user of each online channel; Set the order and basic attraction weight of each operation stage of advertising interaction behavior, extract each operation stage involved in each direct interaction path fragment of each interactive user in each online channel, combine the preset attenuation rate of user attraction for each operation stage of advertising interaction behavior stored in the cloud database, and analyze the effectiveness of each online channel in attracting each interactive user for the designated product advertising of the target enterprise. , is the ID of each interactive user, , The number of interactive users is used to calculate the advertising user attraction effectiveness index of each online channel for the target enterprise's designated products. , ; Collect the sales generated by advertising of designated products of target enterprises through various online channels within the preset historical period and delivery costs Calculate the advertising revenue growth effectiveness index of each online channel for the target enterprise’s designated products ; The advertising user attraction effectiveness index and the advertising revenue growth effectiveness index are accumulated to obtain the direct contribution index of each online channel to the advertising effect of the target enterprise's designated products.

6. A multi-channel advertising effect monitoring and analysis system according to claim 5, characterized in that: The operation stages of the advertising interactive behavior are exposure, click, browse, jump and purchase in order from low to high.

7. A multi-channel advertising effect monitoring and analysis system according to claim 5, characterized in that: Said The specific analysis process includes: taking the basic attraction weight corresponding to the highest-order operation stage involved in the direct interaction path segment of the online channel interactive user as the main attraction factor ; Count the number of repetitions of each operation stage except the highest level in the direct interaction path segment of online channel interactive users , is the number of each operation stage involved in the direct interaction path fragment except the highest order level, , is the number of operation stages involved in the direct interaction path segment except the highest order level, which is given by the formula Analyze the secondary attraction factors of each operation stage except the highest level in the direct interaction path fragment of online channel interactive users, including They are the other levels involved in the direct interaction path fragment except the highest level. The basic attraction weight of each operation stage, the preset decay rate of repeated appearance for user attraction, is the index variable representing the number of repetitions; By formula Calculate the effective attraction level presented by the direct interaction path fragments of users interacting with online channels. is a natural constant, and then the effective attraction degree presented by each direct interaction path segment of each interactive user in each online channel is obtained, and the maximum effective attraction degree presented by the direct interaction path segments is screened out as the effective attraction degree of each online channel for each interactive user within it for the designated product advertising of the target enterprise.

8. A multi-channel advertising effect monitoring and analysis system according to claim 5, characterized in that: Said .

9. A multi-channel advertising effect monitoring and analysis system according to claim 5, characterized in that: The indirect contribution index of evaluating each online channel for the advertising effect of the target enterprise's designated product includes: extracting each indirect interaction path segment of each interactive user in each online channel, taking each interactive user as the main body, arranging each online channel and its terminal operation stage contained in each indirect interaction path segment of each interactive user in chronological order, analyzing the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user, arranging the effective contribution ratio sequence of each online channel corresponding to the indirect interaction path segment of the interactive user, and obtaining the indirect contribution index of each online channel for the advertising effect of the target enterprise's designated product through mean calculation.

10. A multi-channel advertising effect monitoring and analysis system according to claim 9, characterized in that: The specific analysis process of the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user includes: extracting each online channel contained in the indirect interaction path segment of the interactive user and its terminal operation stage, taking the basic attraction weight of the terminal operation stage as the reference attraction weight, and obtaining the reference attraction weight of each online channel contained in the indirect interaction path segment of the interactive user. , It is the ID of each online channel included in the indirect interaction path segment of the interactive user. , The number of online channels included in the indirect interaction path segment of interactive users; Compare the sequence of each online channel terminal operation stage contained in the interactive user's indirect interaction path segment with the sequence of the previous online channel terminal operation stage to obtain the operation driving factors of each online channel contained in the interactive user's indirect interaction path segment ; Collect the corresponding timestamps of the terminal operation stages of each online channel contained in the indirect interaction path segment of the interactive user, and obtain the time attenuation factor of each online channel contained in the indirect interaction path segment of the interactive user ; By formula Analyze the effective contribution ratio of each online channel contained in the indirect interaction path segments of interactive users, and then obtain the effective contribution ratio of each online channel contained in each indirect interaction path segment of each interactive user.

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