Advertisement data management system based on market trend analysis

Abstract

The invention relates to the technical field of network advertisements, in particular to an advertisement data management system based on market trend analysis, which comprises a data acquisition module, an analysis module, a prediction module, a conversion rate calculation module and an adjustment module. The advertisement effect is reflected by acquiring click and purchase data after advertisement putting, the change trend of user behaviors is accurately recognized through the analysis module, and the user change rate is calculated, so that data support is provided for an advertisement putting strategy, and the advertisement putting strategy is more accurate by analyzing historical data, predicting the future purchase trend and comparing with predicted data in real time. The conversion rate change of the advertisement is accurately evaluated, a basis is provided for optimizing the advertisement effect, whether the advertisement putting duration needs to be adjusted or not is judged according to the comprehensive analysis of the user change rate and the conversion rate change rate, efficient utilization of advertisement resources is ensured, the accuracy and flexibility of advertisement putting are remarkably improved, the conversion rate and the return on investment of the advertisement are facilitated, and the user experience is improved. And continuous optimization and improvement of the advertisement effect are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of network advertising, and in particular to an advertising data management system based on market trend analysis. Background Art

[0002] With the rapid development of digital marketing, accurate evaluation and optimization of advertising effectiveness have become the key to improving the market competitiveness of enterprises. Traditional advertising data management methods are often limited to static data analysis and cannot capture user behavior trends and market dynamics in real time, resulting in a lack of flexibility and targeting in advertising strategies. Advertisers usually rely on experience and limited historical data to formulate delivery strategies, which makes it difficult to accurately predict advertising conversion effects and user response trends, and the return on investment is uncertain.

[0003] The patent document with publication number CN112801712A discloses a method and device for optimizing an advertising delivery strategy, the method comprising: obtaining attribute information of each advertising position, and multiple historical traffic data collected from each advertising position in multiple consecutive predetermined sampling time periods, quantifying the attribute information of each advertising position and the multiple historical traffic data respectively, generating an advertising position information sequence, inputting the advertising position information sequence into a trained traffic prediction model, obtaining high-dimensional features of the advertising position information sequence through a multi-level residual convolutional network, and determining the predicted traffic features of each advertising position based on the high-dimensional features and the traffic prediction function, obtaining the predicted traffic data of each advertising position within the time range to be predicted by performing dimensionality reduction processing on the predicted traffic features, and optimizing the advertising delivery strategy of each advertising position based on the predicted traffic data of each advertising position.

[0004] This shows the following problem: In the prior art, it is impossible to predict the market trend of advertising to adjust the advertising strategy, resulting in a poor conversion rate between advertising investment and return. Summary of the invention

[0005] To this end, the present invention provides an advertising data management system based on market trend analysis, which is used to overcome the problem in the prior art that the market trend of advertising cannot be predicted by comprehensively analyzing changes in the user market, so as to adjust the advertising delivery strategy and lead to poor conversion rate of advertising investment and return.

[0006] To achieve the above object, the present invention provides an advertising data management system based on market trend analysis, comprising:

[0007] A data acquisition module is used to obtain the real-time number of clicks and the real-time number of purchases of the advertisement after the advertisement delivery time is reached, so as to obtain real-time advertisement data, and to obtain historical advertisement data corresponding to the real-time advertisement data, including the historical number of clicks and the historical number of purchases;

[0008] An analysis module connected to the data acquisition module for analyzing user change trends and calculating user change rates based on the historical click times and the real-time click times;

[0009] A prediction module, connected to the data acquisition module, for predicting the number of purchases based on the historical number of clicks and the historical number of purchases to obtain a prediction result;

[0010] a conversion rate calculation module, which is connected to the data acquisition module and the prediction module respectively, and is used to compare the prediction result with the real-time purchase number to obtain a comparison result, and determine the conversion rate change rate based on the comparison result;

[0011] An adjustment module is respectively connected to the data acquisition module, the analysis module and the conversion rate evaluation module, and is used to determine whether to adjust the advertisement delivery duration according to the user change rate, and to calculate the adjustment rate according to the conversion rate change rate and the user change rate, and to adjust the advertisement delivery duration according to the adjustment rate.

[0012] Furthermore, the analysis module includes:

[0013] A trend analysis unit, used to divide the historical click counts into periods, and determine the user change trend based on the comparison results of the total number of historical clicks in adjacent periods;

[0014] A change rate calculation unit is connected to the trend analysis unit and is used to calculate the user change rate based on the historical number of clicks and the real-time number of clicks when determining that the user is in an increasing or decreasing trend.

[0015] Furthermore, the trend analysis unit comprises:

[0016] A period division subunit is used to set the calculation period;

[0017] A cycle calculation subunit, connected to the cycle division subunit, for calculating the total number of each historical click corresponding to each calculation cycle;

[0018] The trend analysis subunit is connected to the cycle calculation subunit to calculate the difference between the total number of historical clicks in the next cycle and the previous cycle of each adjacent cycle, and calculate the sum of all the differences, and determine the user change trend based on the sum of the differences.

[0019] Furthermore, the change rate calculation unit includes:

[0020] A graph drawing subunit, for drawing a scatter plot of the number of historical clicks in a coordinate axis according to the number of historical clicks when the number of users is in an increasing or decreasing trend, and fitting a straight line representing the changing trend;

[0021] The change rate calculation subunit is connected to the image drawing subunit and is used to calculate the slope of the straight line and the horizontal axis divided by the average of the total number of historical clicks to obtain the user change rate.

[0022] Furthermore, the prediction module includes:

[0023] A model training unit, used for training the prediction model according to a portion of the data of the historical number of clicks and the historical number of purchases to obtain a training model;

[0024] A model verification unit, connected to the model training subunit, for verifying the training model according to another part of the historical data to obtain a verification model;

[0025] A model optimization unit is connected to the model verification subunit and is used to optimize the verification model to predict the prediction result.

[0026] Furthermore, the conversion rate calculation module includes:

[0027] A comparison unit, used to compare the prediction result with the real-time purchase number to obtain a comparison result;

[0028] A calculation unit is connected to the comparison unit and is used to determine the change of the conversion rate according to the comparison result, and calculate the conversion rate change rate based on the determination result.

[0029] Furthermore, the calculation unit comprises:

[0030] A receiving subunit, used for receiving the comparison result;

[0031] A determination subunit, connected to the receiving subunit, is used to determine the change in conversion rate according to the comparison result, wherein:

[0032] If the predicted result is less than the real-time purchase number, it is determined that the conversion rate increases;

[0033] If the predicted result is equal to the real-time purchase number, it is determined that the conversion rate remains unchanged;

[0034] If the predicted result is greater than the real-time purchase number, it is determined that the conversion rate is reduced;

[0035] The calculation subunit is connected to the determination subunit and is used to calculate the conversion rate change rate when determining that the conversion rate increases or decreases.

[0036] Furthermore, the adjustment module includes:

[0037] an adjustment determination unit, configured to compare the user change rate with a preset user change rate threshold, and determine whether to adjust the advertisement delivery duration based on the comparison result;

[0038] An adjustment unit is connected to the adjustment determination unit, and is used to calculate the adjustment rate according to the conversion rate change rate and the user change rate when determining to adjust the advertisement delivery duration, and adjust the advertisement delivery duration according to a preset adjustment coefficient.

[0039] Furthermore, the adjustment unit includes:

[0040] An adjustment rate calculation subunit, used to assign weights to the conversion rate change rate and the user change rate to calculate an adjustment rate;

[0041] The adjustment value calculation subunit is connected to the adjustment rate calculation subunit and is used to determine the adjustment value according to the adjustment rate and the adjustment coefficient so as to adjust the advertisement delivery duration.

[0042] Furthermore, the data acquisition module includes:

[0043] A first acquisition unit is used to acquire the real-time number of clicks and the real-time number of purchases of the advertisement after the advertisement delivery time is reached;

[0044] The second acquisition unit is used to acquire the historical click times and the historical purchase times.

[0045] Compared with the prior art, the beneficial effect of the present invention lies in that, through the deep combination of real-time data and historical data, intelligent management and optimization of advertising delivery effects are realized, and the advertising effects are reflected in a timely manner by obtaining real-time click and purchase data after advertising delivery. The analysis module accurately identifies the changing trend of user behavior and calculates the user change rate, thereby providing data support for the advertising delivery strategy. The prediction module can efficiently predict future purchase trends by analyzing historical data. The conversion rate calculation module accurately evaluates the conversion rate changes of advertisements by comparing real-time and predicted data, providing an important basis for optimizing advertising effects. The adjustment module intelligently determines whether it is necessary to adjust the advertising delivery duration based on the comprehensive analysis of the user change rate and the conversion rate change rate, thereby ensuring the efficient use of advertising resources, significantly improving the accuracy and flexibility of advertising delivery, improving the conversion rate and return on investment of advertisements, and realizing continuous optimization and improvement of advertising effects.

[0046] Furthermore, by dividing the historical number of clicks into periods, we can deeply analyze the user's interaction patterns in different time periods and identify potential trend changes. This periodic comparative analysis not only reveals the dynamic changes in user interests and behaviors, but also helps advertisers understand the fluctuations in user demand in different market environments and time backgrounds, thereby providing a more accurate basis for advertising delivery strategies. The change rate calculation unit accurately calculates the user change rate by comparing the real-time number of clicks with the historical number of clicks, quickly identifies changes in user engagement, and promptly adjusts the advertising content and delivery strategy to adapt to the rapid changes in user demand, effectively improving the accuracy and effectiveness of advertising delivery, and thereby promoting an increase in the return on advertising investment.

[0047] Furthermore, by summarizing the total number of historical clicks in each calculation cycle, a reliable data basis is provided for subsequent trend analysis. By calculating the difference in the total number of historical clicks between adjacent cycles, it is possible to deeply identify the fluctuations in user behavior and accurately judge the user change trend through a comprehensive evaluation of the difference. This method not only improves the accuracy of determining user change trends, but also can capture potential market changes in a timely manner and quickly respond to changes in market demand, providing a scientific basis for the adjustment of advertising delivery strategies, thereby improving the effectiveness of advertising delivery and market competitiveness.

[0048] Furthermore, by converting the historical number of clicks into a scatter plot on the coordinate axis, it not only intuitively displays the distribution characteristics of the data, but also converts the complex data changes into a simple and clear linear expression by fitting a straight line representing the change trend. The slope of the trend line and the horizontal axis is used as a quantitative indicator of the user change rate, which realizes the accurate quantitative analysis of user behavior changes, intuitively presents the overall change trend of user behavior, enhances the readability of data analysis, significantly improves the scientificity and accuracy of user behavior trend analysis, and provides more accurate decision-making support for the optimization of advertising delivery strategies.

[0049] Furthermore, by utilizing part of the historical click and purchase data to construct an initial prediction model, the foundation for subsequent analysis is laid. The model verification unit uses another part of the data set to strictly verify the training model, effectively test the generalization ability and prediction performance of the model, and avoid overfitting problems. The model optimization unit continuously optimizes the model based on the verification results, and continuously adjusts the model parameters and learning strategies so that the prediction model can more accurately capture the complex changes in user behavior. This multi-level, iterative model construction method can significantly improve the accuracy of advertising purchase frequency prediction, provide more reliable and forward-looking decision support, accurately control the effectiveness of advertising delivery, and improve the efficiency of advertising resource utilization and return on investment.

[0050] Furthermore, by directly comparing the predicted results with the real-time purchase times, the deviation between the actual advertising effect and the expectation can be quickly obtained, providing a key data basis for subsequent analysis. Based on the comparison results, the calculation unit can intelligently judge the changing trend of the conversion rate and accurately calculate the conversion rate change rate, providing data support for the real-time adjustment of the advertising delivery strategy, helping to quickly identify problems in advertising delivery and adjust the strategy in time, which not only improves the accuracy of advertising delivery, but also significantly improves the utilization efficiency of advertising resources.

[0051] Furthermore, by quickly obtaining real-time comparison results, ensuring the timeliness and accuracy of data processing, and flexibly judging changes in conversion rates based on comparison results, we can grasp changes in advertising effects in real time and adjust advertising strategies in a timely manner. This not only improves the flexibility and accuracy of advertising delivery, but also optimizes resource allocation, ultimately improving the overall benefits of advertising and return on investment.

[0052] Furthermore, by comparing the real-time calculated user change rate with the preset threshold, it is possible to sensitively capture subtle changes in user behavior, providing a scientific basis for the dynamic adjustment of advertising delivery strategies. Through the conversion rate change rate and the user change rate, combined with the preset adjustment coefficient, the adjustment range of the advertising delivery duration can be calculated comprehensively and accurately, which can not only quickly respond to market changes, but also effectively balance the efficiency and cost of advertising delivery, significantly improving the targetedness and conversion rate of advertising delivery.

[0053] Furthermore, by conducting a comprehensive evaluation based on the weights of the conversion rate change rate and the user change rate, it is ensured that the actual changes in the effectiveness of advertising can be accurately reflected in different situations, and the advertising delivery strategy can be automatically adjusted to improve the adaptability and response speed of the system. Based on the calculated adjustment rate and the preset adjustment coefficient, the adjustment range of the advertising delivery duration can be accurately determined, thereby optimizing the timing of advertising delivery, effectively avoiding waste of resources, maintaining competitive advantages in real-time market changes, and significantly improving the effectiveness and efficiency of advertising.

[0054] Furthermore, through a refined data collection mechanism, the real-time and accuracy of advertising data management have been significantly improved, laying a solid data foundation for subsequent user behavior prediction and advertising strategy optimization, capturing market changes in real time, and providing more accurate and efficient data analysis and decision-making support, which helps to improve the targeted nature of advertising. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 A system framework diagram of an advertisement data management system based on market trend analysis provided by an embodiment of the present invention;

[0056] Figure 2 It is a structural schematic diagram of an analysis module according to an embodiment of the present invention;

[0057] Figure 3 It is a schematic diagram of the structure of the prediction module according to an embodiment of the present invention;

[0058] Figure 4 It is a structural schematic diagram of an adjustment module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0060] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0061] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0062] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0063] See also Figure 1 ,like Figure 1 As shown, it is a system framework diagram of an advertisement data management system based on market trend analysis provided by an embodiment of the present invention;

[0064] Specifically, an embodiment of the present invention provides an advertising data management system based on market trend analysis, including:

[0065] A data acquisition module is used to obtain the real-time number of clicks and the real-time number of purchases of the advertisement after the advertisement delivery time is reached, so as to obtain real-time advertisement data, and to obtain historical advertisement data corresponding to the real-time advertisement data, including the historical number of clicks and the historical number of purchases;

[0066] An analysis module connected to the data acquisition module for analyzing user change trends and calculating user change rates based on the historical click times and the real-time click times;

[0067] A prediction module, connected to the data acquisition module, for predicting the number of purchases based on the historical number of clicks and the historical number of purchases to obtain a prediction result;

[0068] a conversion rate calculation module, which is connected to the data acquisition module and the prediction module respectively, and is used to compare the prediction result with the real-time purchase number to obtain a comparison result, and determine the conversion rate change rate based on the comparison result;

[0069] An adjustment module is respectively connected to the data acquisition module, the analysis module and the conversion rate evaluation module, and is used to determine whether to adjust the advertisement delivery duration according to the user change rate, and to calculate the adjustment rate according to the conversion rate change rate and the user change rate, and to adjust the advertisement delivery duration according to the adjustment rate.

[0070] Specifically, the duration of an advertisement is the specific duration of an advertisement on a shopping platform in one day, the number of clicks is the number of users who click on the advertisement to enter the product interface after seeing the advertisement, the number of purchases is the number of users who purchase the product after clicking on the advertisement to enter the product interface, and the conversion rate is the ratio of the number of people who click on the advertisement to enter the product interface and purchase the product to the total number of people who click on the advertisement to enter the product interface. When market trends change during advertisement delivery, users' intentions for the advertised products will change over time. By analyzing historical data on user change trends and adjusting the duration of advertisement delivery based on changes in user change rates and conversion rates, costs can be reduced.

[0071] Specifically, through the in-depth integration of real-time data and historical data, intelligent management and optimization of advertising effects are achieved. By obtaining real-time click and purchase data after advertising, the advertising effect is reflected in a timely manner, and the analysis module is used to accurately identify the changing trends of user behavior and calculate the user change rate, thereby providing data support for advertising strategies. The prediction module can efficiently predict future purchase trends through analysis of historical data. The conversion rate calculation module can accurately evaluate the changes in advertising conversion rates by comparing real-time and predicted data, providing an important basis for optimizing advertising effects. The adjustment module can intelligently determine whether it is necessary to adjust the advertising duration based on a comprehensive analysis of the user change rate and the conversion rate change rate, thereby ensuring efficient use of advertising resources, significantly improving the accuracy and flexibility of advertising, and improving the conversion rate and return on investment of advertising, thereby achieving continuous optimization and improvement of advertising effects.

[0072] Please continue reading Figure 2 ,like Figure 2As shown, it is a schematic diagram of the structure of the analysis module of an embodiment of the present invention;

[0073] Specifically, the analysis module includes:

[0074] A trend analysis unit, used to divide the historical click counts into periods, and determine the user change trend based on the comparison results of the total number of historical clicks in adjacent periods;

[0075] A change rate calculation unit is connected to the trend analysis unit and is used to calculate the user change rate based on the historical number of clicks and the real-time number of clicks when determining that the user is in an increasing or decreasing trend.

[0076] Specifically, the historical number of clicks is the number of times users clicked on the advertisement every day in the past few days. The number of user clicks is used to reflect the number of users who are interested in the advertised products. The historical number of clicks in the past is divided into periods, and the total number of historical clicks in each period is analyzed to determine the changing trend of the historical number of clicks to reflect the changing trend of users.

[0077] Specifically, by dividing the historical number of clicks into periods, we can deeply analyze the user's interaction patterns in different time periods and identify potential trend changes. This periodic comparative analysis not only reveals the dynamic changes in user interests and behaviors, but also helps advertisers understand the fluctuations in user demand in different market environments and time backgrounds, thereby providing a more accurate basis for advertising delivery strategies. The change rate calculation unit accurately calculates the user change rate by comparing the real-time number of clicks with the historical number of clicks, quickly identifies changes in user engagement, and promptly adjusts the advertising content and delivery strategy to adapt to the rapid changes in user demand, effectively improving the accuracy and effectiveness of advertising delivery, thereby promoting an increase in the return on advertising investment.

[0078] Specifically, the trend analysis unit includes:

[0079] A period division subunit is used to set the calculation period;

[0080] A cycle calculation subunit, connected to the cycle division subunit, for calculating the total number of each historical click corresponding to each calculation cycle;

[0081] The trend analysis subunit is connected to the cycle calculation subunit to calculate the difference between the total number of historical clicks in the next cycle and the previous cycle of each adjacent cycle, and calculate the sum of all the differences, and determine the user change trend based on the sum of the differences.

[0082] Specifically, when the sum of the difference values ​​is greater than 0, it is determined as an increasing trend, or when it is less than 0, it is determined as a decreasing trend.

[0083] In this embodiment, the number of advertisement clicks in the past 40 days is obtained, and the calculation period is set to 10 days. The total number of historical clicks in each period is calculated to be 140 clicks, 130 clicks, 120 clicks and 110 clicks, and the difference between the total number of historical clicks in the subsequent period and the previous period in each adjacent period is calculated to be -10, -10, and -10. The total of the differences is -40, which is less than 0, and is determined to be a decreasing trend.

[0084] Specifically, by summarizing the total number of historical clicks in each calculation cycle, a reliable data basis is provided for subsequent trend analysis. By calculating the difference in the total number of historical clicks between adjacent cycles, it is possible to deeply identify fluctuations in user behavior and accurately judge user change trends through comprehensive evaluation of the differences. This method not only improves the accuracy of determining user change trends, but also can capture potential market changes in a timely manner, quickly respond to changes in market demand, and provide a scientific basis for the adjustment of advertising delivery strategies, thereby improving advertising delivery effectiveness and market competitiveness.

[0085] Specifically, the change rate calculation unit includes:

[0086] A graph drawing subunit, for drawing a scatter plot of the number of historical clicks in a coordinate axis according to the number of historical clicks when the number of users is in an increasing or decreasing trend, and fitting a straight line representing the changing trend;

[0087] The change rate calculation subunit is connected to the image drawing subunit and is used to calculate the slope of the straight line and the horizontal axis divided by the average of the total number of historical clicks to obtain the user change rate.

[0088] In the specific implementation process, the total number of historical clicks is 140 clicks, 130 clicks, 120 clicks and 110 clicks, and the slope of the fitted curve is -13. The change rate is -10% which is the slope 13 divided by the average of the total number of historical clicks 125.

[0089] Specifically, by converting the historical number of clicks into a scatter plot on the coordinate axis, it not only intuitively displays the distribution characteristics of the data, but also converts the complex data changes into a simple and clear linear expression by fitting a straight line representing the change trend. The slope of the trend line and the horizontal axis is used as a quantitative indicator of the user change rate, which realizes the accurate quantitative analysis of user behavior changes, intuitively presents the overall change trend of user behavior, enhances the readability of data analysis, significantly improves the scientificity and accuracy of user behavior trend analysis, and provides more accurate decision-making support for the optimization of advertising delivery strategies.

[0090] Please continue reading Figure 3 ,like Figure 3 As shown, it is a schematic diagram of the structure of the prediction module of an embodiment of the present invention;

[0091] Specifically, the prediction module includes:

[0092] A model training unit, used for training the prediction model according to a portion of the data of the historical number of clicks and the historical number of purchases to obtain a training model;

[0093] A model verification unit, connected to the model training subunit, for verifying the training model according to another part of the historical data to obtain a verification model;

[0094] A model optimization unit is connected to the model verification subunit and is used to optimize the verification model to predict the prediction result.

[0095] Specifically, choose a suitable machine learning algorithm to build a prediction model, such as support vector machine, random forest, neural network, etc., use part of the historical clicks and historical purchases to train the model, and use the other part to verify the model.

[0096] Specifically, by using part of the historical click and purchase data to build an initial prediction model, the foundation is laid for subsequent analysis. The model verification unit uses another part of the data set to strictly verify the training model, effectively test the generalization ability and prediction performance of the model, and avoid overfitting problems. The model optimization unit continuously optimizes the model based on the verification results, and constantly adjusts the model parameters and learning strategies so that the prediction model can more accurately capture the complex changes in user behavior. This multi-level, iterative model building method can significantly improve the accuracy of advertising purchase prediction, provide more reliable and forward-looking decision support, accurately control the effectiveness of advertising delivery, and improve the efficiency of advertising resource utilization and return on investment.

[0097] Specifically, the conversion rate calculation module includes:

[0098] A comparison unit, used to compare the prediction result with the real-time purchase number to obtain a comparison result;

[0099] A calculation unit is connected to the comparison unit and is used to determine the change of the conversion rate according to the comparison result, and calculate the conversion rate change rate based on the determination result.

[0100] Specifically, by directly comparing the predicted results with the real-time purchase times, the deviation between the actual advertising effect and the expected effect can be quickly obtained, providing key data basis for subsequent analysis. Based on the comparison results, the calculation unit can intelligently judge the changing trend of the conversion rate and accurately calculate the conversion rate change rate, providing data support for the real-time adjustment of the advertising delivery strategy, helping to quickly identify problems in advertising delivery and adjust the strategy in time, which not only improves the accuracy of advertising delivery, but also significantly improves the utilization efficiency of advertising resources.

[0101] Specifically, the computing unit includes:

[0102] A receiving subunit, used for receiving the comparison result;

[0103] A determination subunit, connected to the receiving subunit, is used to determine the change in conversion rate according to the comparison result, wherein:

[0104] If the predicted result is less than the real-time purchase number, it is determined that the conversion rate increases;

[0105] If the predicted result is equal to the real-time purchase number, it is determined that the conversion rate remains unchanged;

[0106] If the predicted result is greater than the real-time purchase number, it is determined that the conversion rate is reduced;

[0107] The calculation subunit is connected to the determination subunit and is used to calculate the conversion rate change rate when determining that the conversion rate increases or decreases.

[0108] During the specific implementation process, the predicted result of the purchase number for a total of 100 clicks is 40 purchases, while the actual real-time purchase number is 30. If the predicted result is greater than the real-time purchase number, it is determined that the conversion rate has decreased. The calculated conversion rate change rate is the ratio of the real-time purchase number to the total number of clicks minus the ratio of the predicted result to the total number of clicks, which is a decrease of 10%.

[0109] Specifically, by quickly obtaining real-time comparison results, ensuring the timeliness and accuracy of data processing, and flexibly judging changes in conversion rates based on comparison results, we can grasp changes in advertising effects in real time and adjust advertising strategies in a timely manner. This not only improves the flexibility and accuracy of advertising delivery, but also optimizes resource allocation, ultimately improving the overall benefits of advertising and return on investment.

[0110] Please continue reading Figure 4 ,like Figure 4 As shown, it is a schematic diagram of the structure of the adjustment module according to an embodiment of the present invention;

[0111] Specifically, the adjustment module includes:

[0112] an adjustment determination unit, configured to compare the user change rate with a preset user change rate threshold, and determine whether to adjust the advertisement delivery duration based on the comparison result;

[0113] An adjustment unit is connected to the adjustment determination unit, and is used to calculate the adjustment rate according to the conversion rate change rate and the user change rate when determining to adjust the advertisement delivery duration, and adjust the advertisement delivery duration according to a preset adjustment coefficient.

[0114] Specifically, if the user change rate is greater than the preset user change rate threshold, it is determined to adjust the advertisement delivery duration. If the user change rate is less than or equal to the preset user change rate threshold, it is determined not to adjust the advertisement delivery duration. In the specific implementation process, the user change rate threshold is 5%, and the user change rate of the advertisement is reduced by 10%. The user change rate is greater than the preset user change rate threshold, and it is determined to adjust the advertisement delivery duration.

[0115] Specifically, by comparing the real-time calculated user change rate with the preset threshold, it is possible to sensitively capture subtle changes in user behavior, providing a scientific basis for the dynamic adjustment of advertising delivery strategies. Through the conversion rate change rate and the user change rate, combined with the preset adjustment coefficient, it is possible to comprehensively and accurately calculate the adjustment range of the advertising delivery duration, which can not only quickly respond to market changes, but also effectively balance the efficiency and cost of advertising, significantly improving the targeting and conversion rate of advertising.

[0116] Specifically, the adjustment unit includes:

[0117] An adjustment rate calculation subunit, used to assign weights to the conversion rate change rate and the user change rate to calculate an adjustment rate;

[0118] The adjustment value calculation subunit is connected to the adjustment rate calculation subunit and is used to determine the adjustment value according to the adjustment rate and the adjustment coefficient so as to adjust the advertisement delivery duration.

[0119] In the specific implementation process, the adjustment coefficient is the degree of adjustment of the advertising delivery duration. In the present embodiment, it is 100, the user change rate weight is 40%, the conversion rate weight is 60%, the advertising delivery duration is 200 minutes, the user change rate is -10%, and the conversion rate is -10%. Then, the product of the user change rate and the user change rate weight plus the product of the conversion rate and the conversion rate weight is calculated to obtain an adjustment rate of -10%. The calculated sum is multiplied by the adjustment coefficient to reduce the advertising duration by 10 minutes to 190 minutes.

[0120] Specifically, by conducting a comprehensive evaluation based on the weights of the conversion rate change rate and the user change rate, we can ensure that the actual changes in the effectiveness of advertising can be accurately reflected in different situations, automatically adjust the advertising strategy, improve the adaptability and response speed of the system, and accurately determine the adjustment range of the advertising duration based on the calculated adjustment rate and the preset adjustment coefficient, thereby optimizing the timing of advertising delivery, effectively avoiding waste of resources, and being able to maintain competitive advantages in real-time market changes, significantly improving the effectiveness and efficiency of advertising.

[0121] Specifically, the data acquisition module includes:

[0122] A first acquisition unit, configured to acquire the real-time number of clicks and the real-time number of purchases of the advertisement after the advertisement delivery time is reached;

[0123] The second acquisition unit is used to acquire the historical click times and the historical purchase times.

[0124] Specifically, the data acquisition module obtains the historical number of clicks on the advertisements on the advertising platform in the past days and the historical number of purchases of the products by entering the product interface, and obtains the real-time number of clicks and real-time purchases during the advertisement delivery time on the day.

[0125] Specifically, through a refined data collection mechanism, the real-time and accuracy of advertising data management have been significantly improved, laying a solid data foundation for subsequent user behavior prediction and advertising strategy optimization, capturing market changes in real time, and providing more accurate and efficient data analysis and decision-making support, which helps to improve the targeted nature of advertising.

[0126] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0127] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An advertising data management system based on market trend analysis, characterized in that: include: A data acquisition module, for acquiring the real-time number of clicks and the real-time number of purchases of the advertisement when the advertisement delivery time reaches a preset value, so as to obtain real-time advertisement data, and for acquiring historical advertisement data corresponding to the real-time advertisement data, including the historical number of clicks and the historical number of purchases; An analysis module connected to the data acquisition module for analyzing user change trends and calculating user change rates based on the historical click times and the real-time click times; A prediction module, connected to the data acquisition module, for predicting the number of purchases based on the historical number of clicks and the historical number of purchases to obtain a prediction result; a conversion rate calculation module, which is connected to the data acquisition module and the prediction module respectively, and is used to compare the prediction result with the real-time purchase number to obtain a comparison result, and determine the conversion rate change rate based on the comparison result; An adjustment module is respectively connected to the data acquisition module, the analysis module and the conversion rate evaluation module, and is used to determine whether to adjust the advertisement delivery duration according to the user change rate, and to calculate the adjustment rate according to the conversion rate change rate and the user change rate, and to adjust the advertisement delivery duration according to the adjustment rate.

2. The advertising data management system based on market trend analysis according to claim 1, characterized in that: The analysis module comprises: A trend analysis unit, used to divide the historical click counts into periods, and determine the user change trend based on the comparison results of the total number of historical clicks in adjacent periods; A change rate calculation unit is connected to the trend analysis unit and is used to calculate the user change rate based on the historical number of clicks and the real-time number of clicks when determining that the user is in an increasing or decreasing trend.

3. The advertising data management system based on market trend analysis according to claim 2, characterized in that: The trend analysis unit comprises: A period division subunit is used to set the calculation period; A cycle calculation subunit, connected to the cycle division subunit, for calculating the total number of each historical click corresponding to each calculation cycle; The trend analysis subunit is connected to the cycle calculation subunit to calculate the difference between the total number of historical clicks in the next cycle and the previous cycle of each adjacent cycle, and calculate the sum of all the differences, and determine the user change trend based on the sum of the differences.

4. The advertising data management system based on market trend analysis according to claim 2, characterized in that: The change rate calculation unit comprises: A graph drawing subunit, for drawing a scatter plot of the number of historical clicks in a coordinate axis according to the number of historical clicks when the number of users is in an increasing or decreasing trend, and fitting a straight line representing the changing trend; The change rate calculation subunit is connected to the image drawing subunit and is used to calculate the slope of the straight line and the horizontal axis divided by the average of the total number of historical clicks to obtain the user change rate.

5. The advertising data management system based on market trend analysis according to claim 1, characterized in that: The prediction module comprises: A model training unit, used for training the prediction model according to a portion of the data of the historical number of clicks and the historical number of purchases to obtain a training model; A model verification unit, connected to the model training subunit, for verifying the training model according to another part of the historical data to obtain a verification model; A model optimization unit is connected to the model verification subunit and is used to optimize the verification model to predict the prediction result.

6. The advertising data management system based on market trend analysis according to claim 1, characterized in that: The conversion rate calculation module includes: A comparison unit, used to compare the prediction result with the real-time purchase number to obtain a comparison result; A calculation unit is connected to the comparison unit and is used to determine the change of the conversion rate according to the comparison result, and calculate the conversion rate change rate based on the determination result.

7. The advertising data management system based on market trend analysis according to claim 6, characterized in that: The computing unit comprises: A receiving subunit, used for receiving the comparison result; A determination subunit, connected to the receiving subunit, is used to determine the change in conversion rate according to the comparison result, wherein: If the predicted result is less than the real-time purchase number, it is determined that the conversion rate increases; If the predicted result is equal to the real-time purchase number, it is determined that the conversion rate remains unchanged; If the predicted result is greater than the real-time purchase number, it is determined that the conversion rate is reduced; The calculation subunit is connected to the determination subunit and is used to calculate the conversion rate change rate when determining that the conversion rate increases or decreases.

8. The advertising data management system based on market trend analysis according to claim 1, characterized in that: The adjustment module comprises: an adjustment determination unit, configured to compare the user change rate with a preset user change rate threshold, and determine whether to adjust the advertisement delivery duration based on the comparison result; An adjustment unit is connected to the adjustment determination unit, and is used to calculate the adjustment rate according to the conversion rate change rate and the user change rate when determining to adjust the advertisement delivery duration, and adjust the advertisement delivery duration according to a preset adjustment coefficient.

9. The advertising data management system based on market trend analysis according to claim 8, characterized in that: The adjustment unit comprises: An adjustment rate calculation subunit, used to assign weights to the conversion rate change rate and the user change rate to calculate an adjustment rate; The adjustment value calculation subunit is connected to the adjustment rate calculation subunit and is used to determine the adjustment value according to the adjustment rate and the adjustment coefficient so as to adjust the advertisement delivery duration.

10. The advertising data management system based on market trend analysis according to claim 1, characterized in that: The data acquisition module comprises: A first acquisition unit, configured to acquire the real-time number of clicks and the real-time number of purchases of the advertisement after the advertisement delivery time is reached; The second acquisition unit is used to acquire the historical click times and the historical purchase times.

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