An advertisement delivery position management system based on delivery effect analysis

Abstract

This invention relates to the field of advertising placement management technology, specifically disclosing an advertising placement management system based on placement effect analysis. The system includes: an advertising placement pre-selection module, an advertising placement determination module, an advertising placement optimization demand attribute judgment module, an advertising placement replacement module, and an advertising placement stop module. Before placement, this invention accurately collects and analyzes basic data for each advertising placement location to filter out effective alternative locations, avoiding resource waste caused by coarse data selection. During placement, it continuously monitors the advertising interaction data of alternative locations to determine the optimal placement location with the highest interaction intensity, improving the accuracy and timeliness of advertising placement, enhancing advertising effectiveness, reducing resource losses caused by ineffective placements, and improving the overall efficiency of advertising placement.

Description

Technical Field

[0001] This invention relates to the field of advertising placement management technology, specifically to an advertising placement management system based on placement performance analysis. Background Technology

[0002] With the development of information technology, advertising is becoming increasingly important in business promotion. Relying on experience, viewership ratings, or rough traffic data to simply determine the placement of advertisements can easily lead to a lot of waste of resources. At the same time, the effectiveness of each placement is subject to many dynamic factors, and traditional post-event phased evaluations are lagging behind, making it difficult to see problems in real time and achieve precise adjustments to the placement of advertisements.

[0003] For example, Chinese invention patent CN118246987B discloses an advertising placement management system based on campaign performance analysis, including an audience screening module, an audience behavior collection module, an interest ad matching module, and an advertising placement planning module. The audience screening module is used to filter out target audiences who have not viewed the campaign ads through monitoring information; the audience behavior collection module is used to obtain the trajectory information of target audiences around office buildings through monitoring information.

[0004] For example, Chinese invention patent CN115409553B discloses an advertising delivery system and method based on big data and location information. The system acquires the browsing data and location information of the target audience, preprocesses all acquired data to obtain a sample database, trains and learns on the sample database based on big data and deep learning to construct a delivery model, extracts features from the advertisers' ads, inputs the obtained keyword database into the delivery model to obtain matching probabilities, filters the ads based on the matching probabilities, and delivers the filtered ads in conjunction with location information.

[0005] The above-mentioned technology has at least the following technical problems: Current advertising placement management systems lack sufficient control over dynamic feedback during the placement process. When advertising results do not meet expectations during actual placement, they are unable to make accurate and reasonable responses, increasing the lag and blindness in advertising placement decisions and making it difficult to adapt to the dynamic changes in advertising results. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an advertising placement management system based on placement effect analysis, which can effectively solve the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention provides an advertising placement management system based on placement effect analysis, comprising: The ad placement pre-selection module is used to collect basic data on ad placements, analyze the basic placement potential value of each ad placement, and thereby filter out candidate ad placements and extract the basic placement potential value of each candidate ad placement.

[0008] The ad placement determination module is used to collect ad interaction data from each candidate ad placement location, analyze the ad interaction popularity value of each candidate ad placement location, and thus determine the optimal ad placement location.

[0009] The ad placement location optimization requirement attribute determination module is used to obtain the basic placement potential value and ad interaction heat value of the optimal ad placement location, and determine the optimization requirement attributes of the optimal ad placement location. The optimization requirement attributes of the optimal ad placement location include no optimization required, requirement location change, and requirement to stop placement.

[0010] The ad placement location change module is used to perform the first ad placement location change verification when the optimization demand attribute judgment result of the optimal ad placement location is a demand location change. It analyzes the evaluation value of the first ad placement location change verification, determines the demand for continued change verification, analyzes the optimal ad placement location based on the determination result of the continued change verification demand, and thus completes the ad placement location change.

[0011] The ad placement stop module is used to stop ad delivery and issue a reminder when the optimization demand attribute determination result of the optimal ad placement position is that the demand should be stopped.

[0012] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: (1) This invention provides an advertising placement management system based on placement effect analysis. Before placement, it accurately collects and analyzes basic data of each advertising placement location to screen out effective alternative locations, avoiding resource waste caused by rough data selection. During placement, it continuously monitors the advertising interaction data of alternative locations to determine the optimal placement location with the highest interaction popularity, thereby improving the accuracy and timeliness of advertising placement and enhancing advertising effectiveness. By analyzing and optimizing demand indicators to determine the optimization demand attributes of the optimal location and performing targeted analysis and processing, it improves advertising effectiveness, reduces resource losses caused by ineffective placement, and enhances the overall efficiency of advertising placement.

[0013] (2) By judging the optimization demand attributes of the optimal advertising placement location, this invention can comprehensively analyze the actual placement effect of the optimal advertising placement location in terms of placement effect evaluation, providing data basis for optimization decision-making. In terms of placement strategy adjustment, based on the attribute judgment results, targeted analysis and processing can be carried out. This enhances the adaptability of advertising placement, avoids wasting resources in inefficient locations, and makes the evaluation of advertising placement location more scientific and accurate.

[0014] (3) By verifying the replacement of advertising placement locations, this invention can selectively identify replacement placement locations, improve the exposure efficiency of advertising, enhance the adaptability to fluctuations in placement effect, maintain the stability and effectiveness of advertising placement, and at the same time, by accurately determining the replacement of advertising placement locations, it can avoid resources being scattered in inefficient locations, thereby improving resource utilization. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Reference Figure 1 As shown, this invention provides an advertising placement management system based on placement effect analysis, comprising: The ad placement pre-selection module is used to collect basic data on ad placements, analyze the basic placement potential value of each ad placement, and thereby filter out candidate ad placements and extract the basic placement potential value of each candidate ad placement.

[0019] In this embodiment, the selection of candidate advertising placement locations is carried out through the following analysis process: Based on the basic data of the ad placement locations, the basic placement potential value of each ad placement location is obtained through analysis and processing.

[0020] In this embodiment, the basic placement potential value of each advertising location is analyzed in the following process: Collect basic data on ad placement locations, including total number of visitors to ad placement locations, standard deviation of traffic fluctuation, average click-through rate per user, and average number of visitors per day.

[0021] It should be noted that the basic data for ad placement can be obtained through system program logs.

[0022] Extract the reference baseline data stored in the database, including the reference total number of visitors, the reference traffic fluctuation standard deviation, the reference average click-through rate, and the reference daily average number of visitors.

[0023] For an ad placement, the total number of visitors is divided by a reference total number of visitors to obtain the total visitor reference percentage. The reference traffic fluctuation standard deviation is divided by the traffic fluctuation standard deviation to obtain the traffic fluctuation standard deviation reference percentage. The average click-through rate is divided by a reference average click-through rate to obtain the average click-through rate reference percentage. The average daily number of visitors is divided by a reference average daily number of visitors to obtain the average daily number of visitors reference percentage. The average of the total visitor reference percentage, the traffic fluctuation standard deviation reference percentage, the average click-through rate reference percentage, and the average daily number of visitors reference percentage is averaged to obtain the basic ad placement potential value, which characterizes the basic ad placement potential of the ad placement.

[0024] It should be noted that when the standard deviation of flow fluctuation is zero, the reference percentage of standard deviation of flow fluctuation is set to zero directly, and the division operation of this parameter is not performed.

[0025] Iterate through each ad placement location to obtain the basic ad placement potential value for each ad placement location.

[0026] It's important to note that the basic advertising potential value for each ad placement is derived through analysis of fundamental data. This analysis takes into account the interrelationships between these parameters. For example, when the total number of visitors increases, if the website or platform operates stably, the standard deviation of traffic fluctuation may decrease relatively, as more stable traffic will stabilize the overall traffic. Conversely, a larger standard deviation indicates decreased traffic stability, which may affect the growth trend of the total number of visitors. A surge in visitors will increase traffic fluctuations, thus affecting the standard deviation of traffic fluctuation. When the average click-through rate (CTR) increases, it means the ads are more attractive to visitors, potentially attracting more clicks and driving up the total number of visitors. Therefore, when the average daily number of visitors increases, the total number of visitors will inevitably increase.

[0027] Extract the preset basic deployment potential threshold from the database.

[0028] If the basic placement potential value of an ad placement is less than or equal to the basic placement potential threshold, it means that the placement performs poorly in terms of basic indicators such as traffic data, page dwell time distribution, ad size and visibility, and is unlikely to provide sufficient potential exposure opportunities, attract user attention and display ad effects. Therefore, the ad placement should be removed to avoid unnecessary waste of resources and analysis work.

[0029] If the basic placement potential value of a certain ad placement is greater than the basic placement potential threshold, it indicates that the placement has certain advantages in the above basic indicators and has great development potential. It needs to be studied in depth. Therefore, the ad placement is recorded as a candidate ad placement so that it can be more comprehensively evaluated and optimized in the subsequent process, and provide data basis for accurately determining the final ad placement.

[0030] By iterating through each ad placement location in turn, the candidate ad placement locations are obtained.

[0031] The ad placement determination module is used to collect ad interaction data from each candidate ad placement location, analyze the ad interaction popularity value of each candidate ad placement location, and thus determine the optimal ad placement location.

[0032] In this embodiment, the optimal ad placement location is determined through the following analysis process: Based on the ad interaction data of each candidate ad placement, the ad interaction popularity value of each candidate ad placement is obtained through analysis and processing.

[0033] In this embodiment, the specific analysis process for the ad interaction popularity value of each candidate ad placement location is as follows: Collect ad interaction data for each candidate ad placement, including ad click count, interactive button click frequency, average dwell time on secondary pages, and peak duration of interaction for each candidate ad placement.

[0034] It should be noted that the peak duration of interaction refers to the duration during which the number of ad interactions reaches its peak within the preset monitoring period.

[0035] A secondary page is a new page that a user is redirected to after clicking a link on the page where the advertisement is placed (i.e., the primary page).

[0036] It should also be noted that the ad interaction data for each alternative ad placement can be collected from the system program logs.

[0037] Extract reference ad interaction data stored in the database, including reference ad click count, reference interaction button click frequency, average dwell time on reference secondary pages, and peak duration of reference interactions.

[0038] For a candidate ad placement, the ad click count of the candidate ad placement is divided by the reference ad click count to obtain an ad click count ratio factor. The interaction button click frequency of the candidate ad placement is divided by the reference interaction button click frequency to obtain an interaction button click frequency ratio factor. The average dwell time of the candidate ad placement's secondary page is divided by the average dwell time of the reference secondary page to obtain a secondary page average dwell time ratio factor. The peak duration of interaction of the candidate ad placement is divided by the peak duration of interaction to obtain an interaction peak duration ratio factor. The ad click count ratio factor, interaction button click frequency ratio factor, average dwell time of secondary page ratio factor, and peak duration of interaction ratio factor are averaged to obtain the ad interaction heat value of the candidate ad placement, which is used to characterize the ad interaction heat of the candidate ad placement.

[0039] It's important to note that the ad interaction popularity value for candidate ad placements, obtained through analysis of ad interaction data, takes into account the interrelationships between these parameters. For example, an increase in ad clicks attracts more users to the ad page, providing a larger base for increasing the click frequency of interactive buttons. This means there are more opportunities to trigger user clicks on interactive buttons, potentially leading to a higher click frequency. Simultaneously, the number of users entering secondary pages may also increase, and the average time users spend on secondary pages may lengthen due to further exploration of the ad content. A longer average time spent on secondary pages indicates that the ad content is more attractive to users, leading to deeper user engagement. This may result in them returning to the original ad page to interact again, increasing the click frequency of interactive buttons and potentially extending the duration of peak interaction times. Furthermore, a positive user experience on secondary pages may attract other users to click on the ad, increasing the overall ad click count. An increase in the duration of peak interaction indicates that the ad has generated significant user attention and active interaction within a specific time period. This may attract more users to click on the ad, increasing the number of ad clicks. Users are also more likely to click on the interaction button during the interaction process, increasing the frequency of interaction button clicks.

[0040] Iterate through each candidate ad placement location to obtain the ad interaction popularity value of each candidate ad placement location, and record the candidate ad placement location corresponding to the maximum ad interaction popularity value as the optimal ad placement location.

[0041] The ad placement location optimization requirement attribute determination module is used to obtain the basic placement potential value and ad interaction heat value of the optimal ad placement location, and determine the optimization requirement attributes of the optimal ad placement location. The optimization requirement attributes of the optimal ad placement location include no optimization required, requirement location change, and requirement to stop placement.

[0042] In this embodiment, the optimization demand attributes for determining the optimal ad placement location are analyzed in the following specific steps: The optimization requirements for the optimal ad placement include no optimization required, placement change, and placement stoppage.

[0043] Within the preset monitoring period, the basic placement potential value and advertising interaction popularity value of the optimal advertising placement position are obtained through analysis and processing.

[0044] The optimization requirements for the optimal ad placement are obtained by analyzing the basic placement potential value and ad interaction popularity value of the optimal ad placement location.

[0045] In this embodiment, the optimization requirements for the optimal ad placement location are analyzed in the following specific steps: The optimal advertising placement's base potential value is extracted based on the base potential value of each advertising placement location.

[0046] Extract the optimization demand correction coefficients corresponding to each basic placement potential value range stored in the database, and map them to the optimization demand correction coefficients corresponding to the basic placement potential value range of the optimal ad placement position, which are denoted as the optimization demand correction coefficients of the optimal ad placement position.

[0047] It should be noted that the higher the basic placement potential value of the optimal ad placement, the better the basic condition of the optimal ad placement, and the higher the optimization requirements for it. The corresponding optimization requirement correction coefficient for the optimal ad placement should be larger. By extracting the optimization requirement correction coefficient of the optimal ad placement based on its basic placement potential value, we can accurately evaluate and adjust the placement, ensuring the reasonable allocation of optimization resources and avoiding under-optimization or over-optimization of high-quality placements. This makes the overall ad placement strategy more scientific and precise. At the same time, a larger correction coefficient fully considers its high starting point and high potential when calculating optimization requirement indicators, thus setting higher optimization standards for the ad placement.

[0048] Within a preset monitoring period, advertising interaction data for the optimal advertising placement locations is collected, and the data is analyzed to obtain the advertising interaction popularity value for the optimal advertising placement locations.

[0049] In a specific embodiment, the analysis method for the ad interaction popularity value of the optimal ad placement position is as follows: Within the preset monitoring period, collect ad interaction data for the optimal ad placement location, including the number of ad clicks, interaction button click frequency, average dwell time on secondary pages, and peak duration of interaction.

[0050] Based on the ad interaction data of the optimal ad placement location and the reference ad interaction data, the ad interaction popularity value of the optimal ad placement location is obtained through analysis and processing. The ad interaction popularity value of the optimal ad placement location is used to characterize the interaction popularity of the optimal ad placement location.

[0051] It should be noted that the specific method for analyzing the ad interaction heat value of the optimal ad placement position is the same as that for analyzing the ad interaction heat value of the alternative ad placement positions, so it will not be repeated here.

[0052] The optimization demand index for the optimal ad placement is obtained by multiplying the optimization demand correction coefficient of the optimal ad placement by the reciprocal of the ad interaction heat value of the optimal ad placement.

[0053] In a specific embodiment, by analyzing and processing the optimization demand correction coefficient and the ad interaction heat value of the optimal ad placement location, an optimization demand index for the optimal ad placement location is obtained. This enables in-depth analysis and accurate evaluation of the optimal ad placement location. The optimization demand correction coefficient is determined based on the basic placement potential value of each ad placement location, which can fully consider the inherent advantages and potential of the location, making the evaluation more targeted and scientific. Combined with the ad interaction heat value, it can comprehensively and dynamically reflect the actual user response to the ad. The resulting optimization demand index provides key data support for optimizing ad placement strategies, avoiding unnecessary resource investment and strategy adjustments, ensuring the stability and consistency of ad placement, improving the timeliness and accuracy of ad placement decisions, and enhancing the overall intelligence and adaptability of the ad placement management system.

[0054] Extract the first and second verification indicators of the optimization requirements from the database.

[0055] It should be noted that the first verification metric for optimizing requirements is less than the second verification metric for optimizing requirements.

[0056] If the optimization requirement index for the optimal ad placement is less than the first verification index for optimization requirements, then the optimization requirement attribute for the optimal ad placement will be marked as no optimization required.

[0057] If the optimization demand index for the optimal ad placement is less than the first verification index for optimization demand, it indicates that the traffic base provided by the basic placement potential of this position can effectively support ad exposure. At the same time, the ad interaction heat also indicates that users respond positively to the ad content. All indicators combined show that this position does not need to be adjusted or optimized on a large scale. Continuing to maintain the current placement strategy can maintain good ad placement results without additional investment of resources to make changes. This can effectively save optimization resources and maintain the consistency and stability of the placement.

[0058] If the optimization requirement index of the optimal ad placement is greater than or equal to the first verification index of optimization requirement and less than or equal to the second verification index of optimization requirement, then the optimization requirement attribute of the optimal ad placement is recorded as a requirement position change.

[0059] If the optimization requirement index of the optimal ad placement is greater than or equal to the first verification index of optimization requirements and less than or equal to the second verification index of optimization requirements, it indicates that although the optimal ad placement has certain basic advantages and interactive effects, there is still room for improvement after comprehensive evaluation. It is necessary to change the placement to seek a more suitable display environment, hoping to improve the exposure efficiency of the ad and attract more user interaction in the new position, thereby improving the ad placement effect.

[0060] If the optimization demand index of the optimal ad placement is greater than the second verification index of optimization demand, then the optimization demand attribute of the optimal ad placement will be recorded as demand stoppage.

[0061] If the optimization requirement metric for the optimal ad placement exceeds the second validation metric, it indicates that the placement is performing poorly in terms of traffic acquisition, user interaction, and conversion rates. Continuing to run ads in this placement may lead to a significant waste of resources and reduce ad performance. Therefore, to avoid further losses, ad placement in this placement should be discontinued.

[0062] This invention, by determining the optimization demand attributes of the optimal advertising placement location, comprehensively analyzes the actual performance of the optimal advertising placement location in terms of campaign effectiveness evaluation, providing data-driven support for optimization decisions. Regarding adjustments to the placement strategy, based on the attribute determination results, targeted analysis and processing can be performed. This enhances the adaptability of advertising placement, avoids wasting resources on inefficient locations, and makes the evaluation of advertising placement location more scientific and accurate.

[0063] The ad placement location change module is used to perform the first ad placement location change verification when the optimization demand attribute judgment result of the optimal ad placement location is a demand location change. It analyzes the evaluation value of the first ad placement location change verification, determines the demand for continued change verification, analyzes the optimal ad placement location based on the determination result of the continued change verification demand, and thus completes the ad placement location change.

[0064] In this embodiment, the verification evaluation value of the initial ad placement location change is analyzed. The specific analysis process is as follows: Each alternative ad placement after removing the optimal ad placement is denoted as a replacement verification ad placement.

[0065] Based on the ad interaction popularity value of each candidate ad placement, extract the ad interaction popularity value of each replacement verification ad placement.

[0066] The ad interaction popularity values ​​of each alternative verification ad placement are arranged in descending order, and the order of arrangement is recorded as the alternative verification priority order of each alternative verification ad placement.

[0067] Extract the replacement verification ad placement corresponding to the maximum ad interaction popularity value of each replacement verification ad placement, and record it as the first replacement verification ad placement.

[0068] The initial ad placement location change verification was completed based on the first change verification.

[0069] Within a preset time period, the parameters for the first advertisement placement location change verification are collected, and the analysis and processing are used to obtain the first advertisement placement location change verification evaluation value. The first advertisement placement location change verification evaluation value is used to characterize the effect of the first advertisement placement location change verification.

[0070] The verification parameters for ad placement changes include ad link response time, content loading completion time, interaction feedback time, and interaction success rate.

[0071] It should be noted that the verification parameters for the change of ad placement can all be extracted from the system program logs.

[0072] Extract the reference parameters for verifying ad placement changes stored in the database, including reference link response time, reference content loading completion time, reference interaction feedback time, and reference interaction success rate.

[0073] The link response time ratio is obtained by dividing the reference link response time by the link response time. The content loading completion time ratio is obtained by dividing the reference content loading completion time by the content loading completion time. The interaction feedback time ratio is obtained by dividing the reference interaction feedback time by the interaction feedback time. The interaction success rate ratio is obtained by dividing the interaction success rate by the reference interaction success rate. The link response time ratio, content loading completion time ratio, interaction feedback time ratio, and interaction success rate ratio are averaged to obtain the initial ad placement position change verification evaluation value, which is used to characterize the effectiveness of the initial ad placement position change verification.

[0074] It should be noted that if the link response time, content loading completion time, or interactive operation feedback time is zero, the corresponding parameter division will not be performed, and the proportional coefficient of the corresponding parameter will be set to zero directly.

[0075] It's important to note that the evaluation value for the initial ad placement change verification, derived from the analysis and processing of the initial ad placement change verification parameters, takes into account the interrelationships between these parameters. For example, a longer link response time means users have to wait longer after clicking the ad link to be redirected to the target page. This may reduce user patience and decrease their willingness to continue interacting, thus lowering the success rate of the interaction. Furthermore, longer link response times are often associated with server or network issues, which may further affect content loading speed, leading to increased content loading completion time. Longer content loading completion time may cause users to abandon browsing the ad content while waiting, also reducing the success rate of the interaction. Increased interaction feedback time will further decrease the success rate of the interaction.

[0076] In this embodiment, the optimal replacement ad placement is analyzed based on the results of the continued replacement verification demand determination, thereby completing the replacement of ad placement. The specific analysis process is as follows: The continued replacement verification requirement includes both no need for continued replacement verification and a requirement for continued replacement verification.

[0077] Extract the preset threshold for verifying and evaluating ad placement changes from the database.

[0078] If the initial ad placement location change verification evaluation value is less than the ad placement location change verification evaluation threshold, the need to continue the change verification will be recorded as no need to continue the change verification, and the ad placement location change verification will be stopped. The initial ad placement location will be recorded as the optimal change ad placement location.

[0079] If the initial ad placement change verification evaluation value is less than the ad placement change verification evaluation threshold, it means that the initially selected ad placement for replacement performed well in the actual verification. It met or exceeded the expected standards in key indicators such as link response time, content loading completion time, interactive operation feedback time, and interactive operation success rate, which can effectively guarantee the ad placement effect. There is no need to conduct further placement change verification. The initial replacement position can be directly determined as the optimal replacement ad placement position, and the verification process can be stopped to save time and resources.

[0080] If the initial ad placement change verification evaluation value is greater than or equal to the ad placement change verification evaluation threshold, the request to continue verification will be recorded as a request to continue verification. Ad placement change verification will be performed sequentially according to the priority of each ad placement change verification, until the current ad placement change verification evaluation value is less than the ad placement change verification evaluation threshold. At this point, the request to continue verification will be recorded as no need to continue verification, the ad placement change verification will be stopped, and the current ad placement will be recorded as the optimal ad placement change.

[0081] If the initial ad placement change verification evaluation value is greater than or equal to the ad placement change verification evaluation threshold, it indicates that the initial change of placement has certain deficiencies in the verification and has failed to fully meet the expected performance standards. It is necessary to continue to verify the ad placement change of other candidate placements in the order of the pre-determined replacement verification priority of each replacement verification ad placement to continuously find better placements.

[0082] When the evaluation value obtained from a certain replacement verification is less than the threshold, it means that a better performance and more compliant placement has been found in various verification indicators. At this time, the requirement to continue replacement verification is marked as no longer needed, the verification process is stopped, and the current ad placement is determined as the optimal replacement ad placement, thereby completing the optimization and replacement of ad placement and ensuring that the ad can achieve the best placement effect in the most suitable position.

[0083] This invention verifies the replacement of advertising placements, enabling targeted selection of replacement placements, improving advertising exposure efficiency, enhancing adaptability to fluctuations in placement performance, maintaining the stability and effectiveness of advertising placements, and, by accurately determining replacement advertising placements, avoiding the dispersion of resources in inefficient locations, thereby improving resource utilization.

[0084] The ad placement stop module is used to stop ad delivery and issue a reminder when the optimization demand attribute determination result of the optimal ad placement position is that the demand should be stopped.

[0085] This invention provides an advertising placement management system based on campaign performance analysis. Before campaigning, it accurately collects and analyzes basic data for each advertising placement location to filter out effective alternative locations, avoiding resource waste caused by rough data-driven location selection. During campaigning, it continuously monitors advertising interaction data for alternative locations to determine the optimal placement location with the highest interaction activity, improving the accuracy and timeliness of advertising placement and enhancing its effectiveness. By analyzing and optimizing demand indicators to determine the optimization requirements of the optimal location and performing targeted analysis and processing, it improves advertising effectiveness, reduces resource losses caused by ineffective placements, and enhances the overall efficiency of advertising campaigns.

[0086] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, 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. An advertisement distribution position management system based on distribution effect analysis, characterized by, include: The ad placement pre-selection module is used to collect basic data on ad placements, analyze the basic placement potential value of each ad placement, and thereby filter out each candidate ad placement, and extract the basic placement potential value of each candidate ad placement. The ad placement determination module is used to collect ad interaction data from each candidate ad placement location, analyze the ad interaction popularity value of each candidate ad placement location, and thus determine the optimal ad placement location. The ad placement location optimization requirement attribute determination module is used to obtain the basic placement potential value and ad interaction heat value of the optimal ad placement location, and determine the optimization requirement attribute of the optimal ad placement location. The optimization requirement attribute of the optimal ad placement location includes no optimization required, requirement location change, and requirement to stop placement. The ad placement location change module is used to perform the first ad placement location change verification when the optimization demand attribute judgment result of the optimal ad placement location is a demand location change. It analyzes the evaluation value of the first ad placement location change verification, determines the demand for continued change verification, analyzes the optimal replacement ad placement location based on the determination result of the continued change verification demand, and thus completes the ad placement location change. The ad placement stop module is used to stop ad delivery and issue a reminder when the optimization demand attribute determination result of the optimal ad placement position is that the demand should be stopped.

2. The advertising placement management system based on placement effect analysis according to claim 1, characterized in that: The specific analysis process for screening each candidate ad placement location is as follows: Based on the basic data of the ad placement locations, the basic placement potential value of each ad placement location is obtained through analysis and processing. Extract the preset basic deployment potential threshold from the database; If the basic placement potential value of a certain ad placement is less than or equal to the basic placement potential threshold, then the ad placement will be removed. If the basic advertising potential value of a certain advertising placement is greater than the basic advertising potential threshold, then the advertising placement will be recorded as a candidate advertising placement. By iterating through each ad placement location in turn, the candidate ad placement locations are obtained.

3. The advertising placement management system based on placement effect analysis according to claim 2, characterized in that: The basic placement potential value of each advertising location is analyzed in the following process: Collect basic data on ad placement locations, including the historical total number of visitors to the ad placement location, standard deviation of traffic fluctuation, average click-through rate per user, and average number of visitors per day; The basic placement potential value of each advertising placement position is obtained by analyzing and processing the basic data of the advertising placement position. The basic placement potential value of each advertising placement position is used to characterize the basic placement potential of each advertising placement position.

4. The advertising placement management system based on placement effect analysis according to claim 1, characterized in that: The specific analysis process for determining the optimal ad placement location is as follows: Based on the ad interaction data of each candidate ad placement, the ad interaction popularity value of each candidate ad placement is obtained through analysis and processing. The candidate ad placement position corresponding to the maximum ad interaction popularity value is recorded as the optimal ad placement position.

5. The advertising placement management system based on placement effect analysis according to claim 4, characterized in that: The specific analysis process for the ad interaction popularity value of each candidate ad placement position is as follows: Collect ad interaction data for each candidate ad placement, including ad click count, interactive button click frequency, average dwell time on secondary pages, and peak duration of interaction for each candidate ad placement. The advertising interaction heat value of each candidate advertising placement is obtained by analyzing and processing the advertising interaction data of each candidate advertising placement. The advertising interaction heat value of each candidate advertising placement is used to characterize the advertising interaction heat of each candidate advertising placement.

6. The advertising placement management system based on placement effect analysis according to claim 1, characterized in that: The specific analysis process for determining the optimization requirement attributes for the optimal ad placement position is as follows: The optimization requirements for the optimal ad placement include no optimization required, placement change, and placement stoppage. Within the preset monitoring period, the basic placement potential value and advertising interaction popularity value of the optimal advertising placement position are obtained through analysis and processing. The optimization requirements for the optimal ad placement are obtained by analyzing the basic placement potential value and ad interaction heat value of the optimal ad placement position. Extract the first and second verification indicators of optimization requirements from the database; If the optimization requirement index for the optimal ad placement is less than the first verification index for optimization requirements, then the optimization requirement attribute for the optimal ad placement will be marked as no optimization required. If the optimization requirement index of the optimal ad placement is greater than or equal to the first verification index of optimization requirement and less than or equal to the second verification index of optimization requirement, then the optimization requirement attribute of the optimal ad placement is recorded as a requirement position change. If the optimization demand index of the optimal ad placement is greater than the second verification index of optimization demand, then the optimization demand attribute of the optimal ad placement will be recorded as demand stoppage.

7. The advertising placement management system based on placement effect analysis according to claim 6, characterized in that: The specific analysis process for the optimization requirements of the optimal ad placement location is as follows: Extract the base placement potential value of the optimal ad placement position based on the base placement potential value of each ad placement position; Extract the optimization demand correction coefficients corresponding to each basic placement potential value range stored in the database, and map the optimization demand correction coefficients corresponding to the basic placement potential value range of the optimal ad placement position, which are denoted as the optimization demand correction coefficients of the optimal ad placement position. Within a preset monitoring period, collect advertising interaction data from the optimal advertising placement locations, and analyze and process the data to obtain the advertising interaction popularity value for the optimal advertising placement locations. Based on the optimization requirement correction coefficient of the optimal ad placement position and the ad interaction heat value of the optimal ad placement position, the optimization requirement index of the optimal ad placement position is obtained through analysis and processing. The optimization demand index for the optimal ad placement position is used to characterize the degree of optimization demand for the optimal ad placement position.

8. The advertising placement management system based on placement effect analysis according to claim 1, characterized in that: The analysis of the initial ad placement change verification evaluation value is as follows: Each alternative ad placement after removing the optimal ad placement is recorded as a replacement verification ad placement. The ad interaction heat value of each alternative ad placement is extracted based on the ad interaction heat value of each candidate ad placement. Arrange the ad interaction popularity values ​​of each replacement verification ad placement in descending order, and record the order of arrangement as the replacement verification priority order of each replacement verification ad placement. Extract the replacement verification ad placement corresponding to the maximum ad interaction popularity value of each replacement verification ad placement, and record it as the first replacement verification ad placement. The initial ad placement location verification was completed based on the first change. Within a preset time period, the parameters for the first advertisement placement location change verification are collected, and the analysis and processing are used to obtain the first advertisement placement location change verification evaluation value. The first advertisement placement location change verification evaluation value is used to characterize the effect of the first advertisement placement location change verification.

9. The advertising placement management system based on placement effect analysis according to claim 8, characterized in that: The optimal replacement ad placement is analyzed based on the results of the continued replacement verification demand determination, thereby completing the replacement of ad placement. The specific analysis process is as follows: The requirement to continue verification includes both no need for continued verification and a requirement to continue verification. Extract the preset threshold for verifying and evaluating ad placement changes from the database; If the initial ad placement location change verification evaluation value is less than the ad placement location change verification evaluation threshold, the need to continue the change verification will be recorded as no need to continue the change verification, and the ad placement location change verification will be stopped. The initial ad placement location will be recorded as the optimal change ad placement location. If the initial ad placement change verification evaluation value is greater than or equal to the ad placement change verification evaluation threshold, the request to continue verification will be recorded as a request to continue verification. Ad placement change verification will be performed sequentially according to the priority of each ad placement change verification, until the current ad placement change verification evaluation value is less than the ad placement change verification evaluation threshold. At this point, the request to continue verification will be recorded as no need to continue verification, the ad placement change verification will be stopped, and the current ad placement will be recorded as the optimal ad placement change.

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