Generative AI marketing system

Abstract

The invention provides a generative AI marketing system, and belongs to the technical field of marketing. The system comprises an acquisition module, a determination module, an analysis module and a calculation module, the acquisition module is used for acquiring historical advertisement input cost data and historical platform marketing data; the determination module is used for determining the cis-state sample data and the mutation sample data in a preset time period according to the historical platform marketing data, and calculating trend influence data based on the cis-state sample data; the analysis module is used for analyzing a preset number of homogenized conversion rate data of label data in the same month as the mutation sample data according to the historical platform marketing data, and determining mutation influence data according to the homogenized conversion rate data and the historical platform marketing data; the calculation module is used for calculating platform marketing data of an undetermined time period according to the trend influence data and the mutation influence data, and determining cost adjustment data based on the platform marketing data and historical advertisement input cost data; by adopting the system, the benefits of digital marketing can be improved.

Description

Technical Field

[0001] The present application relates to the field of marketing technology, and in particular to a generative AI marketing system. Background Art

[0002] With the development of technology, digital marketing methods have become more diverse. Generally speaking, businesses place advertisements on various digital marketing platforms to increase sales of their products and improve customer conversion rates. As time goes by, it is difficult to distinguish the benefits brought by different digital marketing platforms simply by customer conversion rates, and it is also difficult to determine the next cost investment for digital marketing platforms. Therefore, it is very important to further analyze the marketing data of digital marketing platforms based on customer conversion rates. Summary of the invention

[0003] This application provides a generative AI marketing system that is conducive to improving the benefits of digital marketing.

[0004] The present application provides a generative AI marketing system. The system includes an acquisition module, a determination module, an analysis module and a calculation module: The acquisition module is used to acquire historical advertising investment cost data and historical platform marketing data, wherein the historical advertising investment cost data is the cost data of the last advertisement delivery at the current moment; the historical platform marketing data includes historical user conversion rate data; the historical user conversion rate data includes month label data; The determination module is used to determine the normal sample data and the mutant sample data within a preset time period according to the historical platform marketing data, and calculate the trend impact data based on the normal sample data; the trend impact data is associated with the normal sample data; the preset time period is a time period of the same year; The analysis module is used to analyze a preset number of homogeneous conversion rate data of the label data in the same month as the mutation sample data according to the historical platform marketing data, and determine the mutation impact data according to the homogeneous conversion rate data and the historical platform marketing data; the mutation impact data is associated with the historical user conversion rate data; The calculation module is used to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and to determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data.

[0005] Furthermore, the determination module is further configured to determine the normal sample data and the sudden change sample data within a preset time period according to the historical platform marketing data, and calculate the trend impact data based on the normal sample data; the trend impact data associated with the normal sample data includes: Assume that the preset time period includes n sample time nodes, and each sample time node includes a historical user conversion rate data; then the historical user conversion rate data of the i-th sample time node is ; Among them, the first sample time node is the time node farthest from the current moment, and the nth sample time node is the time node closest to the current moment; Calculate n-1 conversion rate difference data based on historical user conversion rate data at n sample time nodes ;in, ; Filter by , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as the first mutation data; screening meets , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as second mutation data; combining the first mutation data and the second mutation data to obtain mutation sample data; Removing the mutation sample data from all historical user conversion rate data within a preset time period to obtain normal sample data; Determine the quantity value of historical user conversion rate data in the normal sample data, which is the sample quantity value; Assuming the sample size is m, the historical user conversion rate at the jth sample time node is ; The first sample time node is the time node farthest from the current moment, and the mth sample time node is the time node closest to the current moment; The trend impact data is calculated as , where The trend affects the data.

[0006] Furthermore, the analysis module is further configured to analyze a preset number of homogeneous conversion rate data of the label data of the same month as the mutation sample data according to the historical platform marketing data, and determine mutation impact data according to the homogeneous conversion rate data and the historical platform marketing data; the mutation impact data associated with the historical user conversion rate data includes: Determine, according to the mutation sample data, the month label data of each historical user conversion rate data contained in the mutation sample data; Based on the historical platform marketing data, homogeneous conversion rate data with label data of the same month but different years from the year to which the historical user conversion rate data belongs are screened out; Determine the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month of the homogeneous conversion rate data based on the historical platform marketing data; Determine that the homogeneous conversion rate data are all greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, or are not greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, and the quantity value is the mutation quantity value; Calculate the mutation probability value according to the mutation quantity value and the total quantity of the homogenization conversion rate data; Assume that the mutation sample data always includes q historical user conversion rate data, and the preset number of homogeneous conversion rate data of the pth historical user conversion rate data is , the mutation number value is , then the mutation probability value of the historical user conversion rate data is ; The calculation method of mutation impact data is In the formula, The mutation affects the data.

[0007] Furthermore, the calculation module is further configured to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data, including: The calculation method of the platform marketing data is as follows: ; In the formula, Marketing data for the platform, To preset basic conversion rate data, For trend impact data, The mutation affects the data.

[0008] Furthermore, the calculation module is further configured to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data, including: Comparing the platform marketing data with the historical conversion rate user data of the previous month at the current moment; If the platform marketing data is not less than the historical conversion rate user data of the previous month at the current moment, the preset advertising delivery cost is increased based on the historical advertising investment cost data; If the platform marketing data is less than the historical conversion rate user data of the previous month at the current moment, the preset advertising delivery cost is reduced based on the historical advertising investment cost data.

[0009] Furthermore, the calculation module is further configured such that the calculation method of the preset basic conversion rate data includes calculating an average value based on all historical user conversion rate data within a preset time period to obtain the preset basic conversion rate data.

[0010] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein: Figure 1 A block diagram of a generative AI marketing system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0012] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0013] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0014] The present application provides a generative AI marketing system that can increase the benefits of digital marketing.

[0015] like Figure 1As shown, the system includes an acquisition module 110, a determination module 120, an analysis module 130 and a calculation module 140, wherein the acquisition module 110 is used to acquire historical advertising investment cost data and historical platform marketing data, wherein the historical advertising investment cost data is the cost data of the last advertisement at the current moment; the historical platform marketing data includes historical user conversion rate data; the historical user conversion rate data includes month label data; the determination module 120 is used to determine the normal sample data and the mutation sample data within a preset time period according to the historical platform marketing data, and calculate the trend impact data based on the normal sample data; the trend impact data The preset time period is the time period of the same year; the analysis module 130 is used to analyze a preset number of homogeneous conversion rate data of the label data in the same month as the mutation sample data according to the historical platform marketing data, and determine the mutation impact data according to the homogeneous conversion rate data and the historical platform marketing data; the mutation impact data is associated with the historical user conversion rate data; the calculation module 140 is used to calculate the platform marketing data of the to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data.

[0016] The determination module 120 is further configured to determine the normal sample data and the sudden change sample data within a preset time period according to the historical platform marketing data, and calculate the trend impact data based on the normal sample data; the trend impact data is associated with the normal sample data, including: assuming that the preset time period contains n sample time nodes, each of the sample time nodes includes a historical user conversion rate data; then the historical user conversion rate data of the i-th sample time node is ; Among them, the first sample time node is the time node farthest from the current moment, and the nth sample time node is the time node closest to the current moment; Calculate n-1 conversion rate difference data based on historical user conversion rate data at n sample time nodes ;in, ; Filter by , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as the first mutation data; screening meets , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as the second mutation data; combining the first mutation data and the second mutation data to obtain mutation sample data; removing the mutation sample data from all historical user conversion rate data within a preset time period to obtain normal sample data; determining the number of historical user conversion rate data in the normal sample data, which is the sample number value; assuming that the sample number value is m, the historical user conversion rate at the jth sample time node is ; The first sample time node is the time node farthest from the current moment, and the mth sample time node is the time node closest to the current moment; then the calculation method of trend impact data is , where Influencing data for trends.

[0017] The analysis module 130 is further configured to analyze a preset number of homogenized conversion rate data of the same month label data as the mutation sample data according to the historical platform marketing data, and determine mutation impact data according to the homogenized conversion rate data and the historical platform marketing data; the mutation impact data is associated with the historical user conversion rate data, including determining the month label data of each historical user conversion rate data contained in the mutation sample data according to the mutation sample data; based on the historical platform marketing data, filter out homogenized conversion rate data of different years but with the same month label data as the year to which the historical user conversion rate data belongs; based on the historical platform marketing data, filter out homogenized conversion rate data of different years but with the same month label data; based on the historical platform The marketing data of the platform determines the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month of the homogeneous conversion rate data; determines that the homogeneous conversion rate data are all greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, or are not greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, and the quantity value is the mutation quantity value; calculates the mutation probability value according to the mutation quantity value and the total quantity of the homogeneous conversion rate data; assuming that the mutation sample data always includes q historical user conversion rate data, the preset number of homogeneous conversion rate data of the pth historical user conversion rate data is , the mutation number value is , then the mutation probability value of the historical user conversion rate data is ; The calculation method of mutation impact data is In the formula, The mutation affects the data.

[0018] The calculation module 140 is further configured to calculate the platform marketing data of the to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data, including: the calculation method of the platform marketing data is: ; In the formula, Marketing data for the platform, To preset basic conversion rate data, For trend impact data, is the mutation impact data; the calculation method of the preset basic conversion rate data here includes calculating the average value based on all historical user conversion rate data within a preset time period to obtain the preset basic conversion rate data.

[0019] Among them, determining the cost adjustment data based on the platform marketing data and the historical advertising investment cost data includes comparing the platform marketing data with the historical conversion rate user data of the previous month at the current moment; if the platform marketing data is not less than the historical conversion rate user data of the previous month at the current moment, then increasing the preset advertising delivery cost based on the historical advertising investment cost data; if the platform marketing data is less than the historical conversion rate user data of the previous month at the current moment, then reducing the preset advertising delivery cost based on the historical advertising investment cost data.

[0020] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0021] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0022] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A generative AI marketing system, characterized in that: It includes an acquisition module (110), a determination module (120), an analysis module (130) and a calculation module (140): The acquisition module (110) is used to acquire historical advertising investment cost data and historical platform marketing data, wherein the historical advertising investment cost data is the cost data of the last advertisement placed at the current moment; the historical platform marketing data includes historical user conversion rate data; and the historical user conversion rate data includes month label data; The determination module (120) is used to determine normal sample data and sudden change sample data within a preset time period according to the historical platform marketing data, and calculate trend impact data based on the normal sample data; The trend impact data is associated with the normal sample data; the preset time period is a time period of the same year; The analysis module (130) is used to analyze a preset number of homogeneous conversion rate data of label data in the same month as the mutation sample data according to the historical platform marketing data, and determine mutation impact data according to the homogeneous conversion rate data and the historical platform marketing data; the mutation impact data is associated with the historical user conversion rate data; The calculation module (140) is used to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and to determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data.

2. The generative AI marketing system according to claim 1, characterized in that: The determination module (120) is further configured to determine the normal sample data and the sudden change sample data within a preset time period according to the historical platform marketing data, and calculate the trend impact data based on the normal sample data; The trend impact data associated with the conformal sample data includes: Assume that the preset time period includes n sample time nodes, and each sample time node includes a historical user conversion rate data; then the historical user conversion rate data of the i-th sample time node is ; Among them, the first sample time node is the time node farthest from the current moment, and the nth sample time node is the time node closest to the current moment; Calculate n-1 conversion rate difference data based on historical user conversion rate data at n sample time nodes ;in, ; Filter by , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as the first mutation data; screening meets , and Conversion rate difference data , and the conversion rate difference data Corresponding historical user conversion rate data Named as second mutation data; combining the first mutation data and the second mutation data to obtain mutation sample data; Removing the mutation sample data from all historical user conversion rate data within a preset time period to obtain normal sample data; Determine the quantity value of historical user conversion rate data in the normal sample data, which is the sample quantity value; Assuming the sample size is m, the historical user conversion rate at the jth sample time node is ; The first sample time node is the time node farthest from the current moment, and the mth sample time node is the time node closest to the current moment; The trend impact data is calculated as , where The trend affects the data.

3. The generative AI marketing system according to claim 2, characterized in that: The analysis module (130) is further configured to analyze a preset number of homogeneous conversion rate data of label data in the same month as the mutation sample data according to the historical platform marketing data, and determine mutation impact data according to the homogeneous conversion rate data and the historical platform marketing data; The mutation impact data associated with the historical user conversion rate data includes: Determine, according to the mutation sample data, the month label data of each historical user conversion rate data contained in the mutation sample data; Based on the historical platform marketing data, homogeneous conversion rate data with label data of the same month but different years from the year to which the historical user conversion rate data belongs are screened out; Determine the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month of the homogeneous conversion rate data based on the historical platform marketing data; Determine that the homogeneous conversion rate data are all greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, or are not greater than the historical user conversion rate data of the previous month and the historical user conversion rate data of the next month, and the quantity value is the mutation quantity value; Calculate the mutation probability value according to the mutation quantity value and the total quantity of the homogenization conversion rate data; Assume that the mutation sample data always includes q historical user conversion rate data, and the preset number of homogeneous conversion rate data of the pth historical user conversion rate data is , the mutation number value is , then the mutation probability value of the historical user conversion rate data is ; The calculation method of mutation impact data is , where The mutation affects the data.

4. The generative AI marketing system according to claim 3, characterized in that: The calculation module (140) is further configured to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data, including: The calculation method of the platform marketing data is as follows: ； In the formula, Marketing data for the platform, To preset basic conversion rate data, For trend impact data, The mutation affects the data.

5. The generative AI marketing system according to claim 4, characterized in that: The calculation module (140) is further configured to calculate the platform marketing data of a to-be-determined time period according to the trend impact data and the mutation impact data, and determine the cost adjustment data based on the platform marketing data and the historical advertising investment cost data, including: Comparing the platform marketing data with the historical conversion rate user data of the previous month at the current moment; If the platform marketing data is not less than the historical conversion rate user data of the previous month at the current moment, the preset advertising delivery cost is increased based on the historical advertising investment cost data; If the platform marketing data is less than the historical conversion rate user data of the previous month at the current moment, the preset advertising delivery cost is reduced based on the historical advertising investment cost data.

6. The generative AI marketing system according to claim 4, characterized in that: The calculation module (140) is further configured such that the calculation method of the preset basic conversion rate data includes calculating an average value based on all historical user conversion rate data within a preset time period to obtain the preset basic conversion rate data.

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