Method and system for enhancing digital marketing based on AI large model retrieval
By analyzing the conversion income and income trend impact data of the digital marketing platform and determining a reasonable cost allocation method, it solves the problem of increasing cost allocation in digital marketing and improves product sales volume and customer conversion rate.
Patent Information
- Application Number
- CN202510066149.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-16
- Publication Date
- 2025-02-18
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
In digital marketing, it is difficult for merchants to reasonably allocate increased advertising costs to various digital channel platforms to ensure further improvement in product sales and customer conversion rates.
By obtaining planned investment cost data and platform marketing data, analyzing the platform conversion income data and income trend impact data of each digital marketing platform, determining the comprehensive benefit data, and determining the method of increasing cost allocation based on this.
It improves the merchant’s product sales and customer conversion rate, ensures the reasonable allocation of increased advertising costs, and optimizes the digital marketing effect.
Smart Images
Figure CN119477438A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of digital marketing, and in particular to a method and system for enhancing digital marketing based on AI large model retrieval. Background Art
[0002] Digital marketing is a marketing method that uses digital technology and platforms to promote products, services or concepts to potential customers. It covers a variety of marketing techniques aimed at increasing brand awareness, driving website traffic, generating potential customers and improving customer conversion rates. Generally speaking, merchants increase their product sales and customer conversion rates by placing digital marketing ads at a fixed cost on various digital channel platforms. Merchants will regularly analyze the cost-effectiveness of digital marketing ads. When they find that product sales have increased, they will choose to increase advertising costs to further increase product sales. In this case, it is very important to reasonably allocate the increased costs to various digital channel platforms to ensure further increases in product sales and customer conversion rates. Summary of the invention
[0003] The present application provides a method for enhancing digital marketing based on AI large model retrieval, which can increase merchants' product sales and customer conversion rates.
[0004] In a first aspect, the present application provides a method for enhancing digital marketing based on AI large model retrieval. The method comprises: Obtaining planned investment cost data and platform marketing data; the planned investment cost data includes advertising cost data for each digital marketing platform; the platform marketing data includes customer quantity data and customer conversion rate data for each digital marketing platform within a preset time period; Analyzing platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data; Acquire historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform based on the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform in a preset number of historical time periods other than a preset time period; the revenue trend impact data is associated with the historical conversion rate data; The comprehensive benefit data of each digital marketing platform is determined according to the platform conversion revenue data and the revenue trend impact data, and the increased cost allocation method is determined based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
[0005] Furthermore, the platform conversion revenue data of each digital marketing platform is analyzed according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data and includes: Determine quantity conversion revenue data based on the customer quantity data within a preset time period; Determine conversion rate revenue data based on the customer conversion rate data within a preset time period; Determine platform trend impact data based on the customer quantity data and the customer conversion rate data; The platform conversion revenue data of each digital marketing platform is analyzed based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
[0006] Further, the determining of platform trend impact data according to the customer quantity data and the customer conversion rate data includes: Assume that there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and the n customer quantity data are assigned serial numbers 1-n, where the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and the m customer conversion rate data are assigned serial numbers 1-m, where the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One to one correspondence; Determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group and the adjacent conversion rate data group have the same serial number as the customer quantity data; Calculate the difference in quantity between each adjacent quantity data set ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value and a fourth quantity value based on the customer quantity data; the first quantity value is the quantity of customer quantity data not less than the customer quantity data with a sequence number of n; the second quantity value is the quantity of customer conversion rate data not less than the customer conversion rate data with a sequence number of m; the third quantity value is the quantity of customer quantity data less than the customer quantity data with a sequence number of n; the fourth quantity value is the quantity of customer conversion rate data less than the customer conversion rate data with a sequence number of m; The fifth quantity value, the sixth quantity value, the seventh quantity value and the eighth quantity value are determined based on the quantity difference value and the conversion rate difference value; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data sets; The calculation method of the platform trend impact data is as follows: ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
[0007] Furthermore, the calculation method of the platform conversion income data is: ; In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively.
[0008] Further, .
[0009] In a second aspect, the present application provides a system for enhancing digital marketing based on AI large model retrieval, the system comprising: An acquisition module is used to acquire planned investment cost data and platform marketing data; the planned investment cost data includes advertising cost data for each digital marketing platform; the platform marketing data includes customer quantity data and customer conversion rate data for each digital marketing platform within a preset time period; An analysis module, configured to analyze platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data; A calculation module, used to obtain historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform based on the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform in a preset number of historical time periods other than the preset time period; the revenue trend impact data is associated with the historical conversion rate data; A determination module is used to determine the comprehensive benefit data of each digital marketing platform based on the platform conversion revenue data and the revenue trend impact data, and to determine an increased cost allocation method based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
[0010] Furthermore, the analysis module is further configured to analyze the platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data and includes: Determine quantity conversion revenue data based on the customer quantity data within a preset time period; Determine conversion rate revenue data based on the customer conversion rate data within a preset time period; Determine platform trend impact data based on the customer quantity data and the customer conversion rate data; The platform conversion revenue data of each digital marketing platform is analyzed based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
[0011] Furthermore, the analysis module is further configured such that the determining of the platform trend impact data according to the customer quantity data and the customer conversion rate data comprises: Assume that there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and the n customer quantity data are assigned serial numbers 1-n, where the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and the m customer conversion rate data are assigned serial numbers 1-m, where the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One to one correspondence; Determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group and the adjacent conversion rate data group have the same serial number as the customer quantity data; Calculate the difference in quantity between each adjacent quantity data set ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value and a fourth quantity value based on the customer quantity data; the first quantity value is the quantity of customer quantity data not less than the customer quantity data with a sequence number of n; the second quantity value is the quantity of customer conversion rate data not less than the customer conversion rate data with a sequence number of m; the third quantity value is the quantity of customer quantity data less than the customer quantity data with a sequence number of n; the fourth quantity value is the quantity of customer conversion rate data less than the customer conversion rate data with a sequence number of m; The fifth quantity value, the sixth quantity value, the seventh quantity value and the eighth quantity value are determined based on the quantity difference value and the conversion rate difference value; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data sets; The calculation method of the platform trend impact data is as follows: ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
[0012] Furthermore, the analysis module is further configured to calculate the platform conversion revenue data in the following manner: ; In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively.
[0013] Furthermore, the analysis module is further configured to: .
[0014] 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
[0015] 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 flowchart of a method for enhancing digital marketing based on AI big model retrieval in an embodiment of the present application is shown; Figure 2 A block diagram of a system for enhancing digital marketing based on AI big model retrieval in an embodiment of the present application is shown. DETAILED DESCRIPTION
[0016] 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.
[0017] 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.
[0018] The present application provides a method and system for enhancing digital marketing based on AI large model retrieval, which can improve customer conversion rate.
[0019] In the first aspect, the present application provides a method for enhancing digital marketing based on AI large model retrieval. Figure 1 The specific steps included in the method are as follows.
[0020] Step S110: Acquire planned investment cost data and platform marketing data; the planned investment cost data includes advertising cost data for each digital marketing platform; the platform marketing data includes customer quantity data and customer conversion rate data for each digital marketing platform within a preset time period.
[0021] In an embodiment of the present application, a merchant places advertisements through a digital marketing platform. For example, a plan is made at the beginning of the year to invest costs and a certain proportion of the cost of advertisements is placed on each digital marketing platform. However, in the middle of the year, it is found that the cost-effectiveness of the return on advertising is high, so a decision is made to invest more costs. In this solution, based on the above situation, the customer quantity data and customer conversion rate data based on the advertising return of each digital marketing platform in the current year, that is, within a preset time period, are analyzed to determine the allocation method of the increased advertising cost to each digital marketing platform. It can be understood that the customer conversion rate data here can be obtained based on the conventional conversion rate calculation method. The specific calculation method of the customer conversion rate is not limited here, and only the obtained customer conversion rate data is used for analysis and calculation.
[0022] Step S120: analyzing the platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data.
[0023] Specifically, analyzing the platform conversion revenue data of each digital marketing platform based on the platform marketing data includes: determining the quantity conversion revenue data based on the customer quantity data within a preset time period; determining the conversion rate revenue data based on the customer conversion rate data within a preset time period; determining the platform trend impact data based on the customer quantity data and the customer conversion rate data; and analyzing the platform conversion revenue data of each digital marketing platform based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
[0024] Wherein, determining the platform trend influencing data according to the customer quantity data and the customer conversion rate data includes: assuming that there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and assigning serial numbers 1-n to the n customer quantity data respectively, wherein the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and assigning serial numbers 1-m to the m customer conversion rate data respectively, wherein the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One-to-one correspondence; determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group containing the customer quantity data and the adjacent conversion rate data group having the same serial number as the customer conversion rate data have a one-to-one correspondence; calculate the quantity difference of each adjacent quantity data group ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value, and a fourth quantity value based on the customer quantity data; the first quantity value is the number of customer quantity data that is not less than the number of customer quantity data with a sequence number of n; the second quantity value is the number of customer conversion rate data that is not less than the number of customer conversion rate data with a sequence number of m; the third quantity value is the number of customer quantity data that is less than the number of customer quantity data with a sequence number of n; the fourth quantity value is the number of customer conversion rate data that is less than the number of customer conversion rate data with a sequence number of m; determine a fifth quantity value, a sixth quantity value, a seventh quantity value, and an eighth quantity value based on the quantity difference and the conversion rate difference; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data groups; the calculation method of the platform trend impact data is, ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
[0025] The calculation method of platform conversion revenue data is: ; In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively; .
[0026] It can be understood that the quantity conversion revenue data here is the sum of all customer quantity data within the preset time period, and the conversion rate revenue data here is the average of all customer conversion rate data within the preset time period; it should be noted that in this scheme, the preset time period includes multiple customer conversion rate data and customer quantity data; specifically, the preset time period can be mentioned in this scheme, when the merchant gives costs to multiple digital marketing platforms, and ends at the current moment; in the preset time period, the customer quantity data and customer conversion rate data at the preset time nodes are used for calculation; if the preset time period is from January to June of this year, then in the analysis and calculation, the customer quantity data and customer conversion rate data of January, February, March, April, May and June are used for calculation; specifically, the customer quantity data is the total number of customers for each month; the customer conversion rate data is calculated by dividing the total number of transaction customers by the total number of visiting customers for each month.
[0027] Step S130: Acquire historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform based on the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform in a preset number of historical time periods in addition to the preset time period; the revenue trend impact data is associated with the historical conversion rate data.
[0028] In this scheme, there are p historical time periods, and the historical conversion rate data of the qth historical time period is ; Among them, the first historical time period is the historical time period closest to the current moment, and the pth historical time period is the historical time period farthest from the current moment; and each historical time period has only one historical conversion rate data; specifically, when selecting the historical time period, refer to the next calculation time period in each established cycle that is equivalent to the preset time period mentioned above; for example, the preset time period mentioned above is January to June of this year, then the historical time period is July to August of the previous year, July to August of the year before last, etc.; there is no limitation on the specific time length of the historical time period, and only the overall customer conversion rate data in the historical time period is determined based on the historical marketing data as the historical conversion rate data to participate in the analysis and calculation.
[0029] Calculate p-1 historical conversion rate differences ,in, ; Determine whether all historical conversion rate differences are greater than 0 or not greater than 0; if so, the calculation method of the revenue trend impact data is If not, the calculation method of the income trend impact data is ;in, is the number of historical conversion rate differences greater than 0, The number of historical conversion rate differences that are not greater than 0.
[0030] Step S140: Determine the comprehensive benefit data of each digital marketing platform according to the platform conversion revenue data and the revenue trend impact data, and determine the increased cost allocation method based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
[0031] Specifically, the calculation method of comprehensive benefit data is: ; In the formula, For comprehensive benefit data, Transform revenue data for the platform, Data affecting revenue trends; it is understandable that based on the planned input cost data, the total input cost of the merchant and the input cost ratio data of each digital marketing platform can be determined; based on the comprehensive benefit data, the benefit ratio data of each digital marketing platform can be determined, and based on the benefit ratio data, the input cost ratio data can be replaced, and combined with the preset increased costs, the corresponding proportion of increased costs can be allocated to each digital marketing platform.
[0032] 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.
[0033] In the second aspect, the present application provides a system for enhancing digital marketing based on AI large model retrieval. Figure 2 As shown, the system includes an acquisition module 210, which is used to acquire planned investment cost data and platform marketing data; the planned investment cost data includes advertising cost data for each digital marketing platform; the platform marketing data includes customer quantity data and customer conversion rate data of each digital marketing platform within a preset time period; an analysis module 220, which is used to analyze platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data; a calculation module 230, which is used to acquire historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform according to the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform within a preset number of historical time periods other than the preset time period; the revenue trend impact data is associated with the historical conversion rate data; a determination module 240, which is used to determine the comprehensive benefit data of each digital marketing platform according to the platform conversion revenue data and the revenue trend impact data, and determine the increased cost allocation method based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
[0034] The analysis module 220 is further configured to analyze the platform conversion revenue data of each digital marketing platform based on the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data, including: determining the quantity conversion revenue data based on the customer quantity data within a preset time period; determining the conversion rate revenue data based on the customer conversion rate data within a preset time period; determining the platform trend impact data based on the customer quantity data and the customer conversion rate data; analyzing the platform conversion revenue data of each digital marketing platform based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
[0035] Furthermore, suppose there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and the n customer quantity data are assigned serial numbers 1-n, wherein the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and the m customer conversion rate data are assigned serial numbers 1-m, wherein the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One-to-one correspondence; determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group containing the customer quantity data and the adjacent conversion rate data group having the same serial number as the customer conversion rate data have a one-to-one correspondence; calculate the quantity difference of each adjacent quantity data group ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value, and a fourth quantity value based on the customer quantity data; the first quantity value is the number of customer quantity data that is not less than the number of customer quantity data with a sequence number of n; the second quantity value is the number of customer conversion rate data that is not less than the number of customer conversion rate data with a sequence number of m; the third quantity value is the number of customer quantity data that is less than the number of customer quantity data with a sequence number of n; the fourth quantity value is the number of customer conversion rate data that is less than the number of customer conversion rate data with a sequence number of m; determine a fifth quantity value, a sixth quantity value, a seventh quantity value, and an eighth quantity value based on the quantity difference and the conversion rate difference; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data groups; the calculation method of the platform trend impact data is, ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
[0036] Furthermore, the calculation method of platform conversion revenue data is: ; In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively; .
[0037] 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.
[0038] 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 method for enhancing digital marketing based on AI big model retrieval, characterized in that: include: Obtaining planned investment cost data and platform marketing data; the planned investment cost data includes advertising cost data for each digital marketing platform; The platform marketing data includes customer quantity data and customer conversion rate data of each digital marketing platform within a preset time period; Analyzing platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data; Acquire historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform based on the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform in a preset number of historical time periods other than a preset time period; the revenue trend impact data is associated with the historical conversion rate data; The comprehensive benefit data of each digital marketing platform is determined according to the platform conversion revenue data and the revenue trend impact data, and the increased cost allocation method is determined based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
2. The method according to claim 1, characterized in that The platform conversion revenue data of each digital marketing platform is analyzed according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data and includes: Determine quantity conversion revenue data based on the customer quantity data within a preset time period; Determine conversion rate revenue data based on the customer conversion rate data within a preset time period; Determine platform trend impact data based on the customer quantity data and the customer conversion rate data; The platform conversion revenue data of each digital marketing platform is analyzed based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
3. The method according to claim 2, characterized in that Determining the platform trend impact data according to the customer quantity data and the customer conversion rate data includes: Assume that there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and the n customer quantity data are assigned serial numbers 1-n, where the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and the m customer conversion rate data are assigned serial numbers 1-m, where the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One to one correspondence; Determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group and the adjacent conversion rate data group have the same serial number as the customer quantity data; Calculate the difference in quantity between each adjacent quantity data set ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value and a fourth quantity value based on the customer quantity data; the first quantity value is the quantity of customer quantity data not less than the customer quantity data with a sequence number of n; the second quantity value is the quantity of customer conversion rate data not less than the customer conversion rate data with a sequence number of m; the third quantity value is the quantity of customer quantity data less than the customer quantity data with a sequence number of n; the fourth quantity value is the quantity of customer conversion rate data less than the customer conversion rate data with a sequence number of m; The fifth quantity value, the sixth quantity value, the seventh quantity value and the eighth quantity value are determined based on the quantity difference value and the conversion rate difference value; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data sets; The calculation method of the platform trend impact data is as follows: ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
4. The method according to claim 3, characterized in that The calculation method of the platform conversion revenue data is as follows: ； In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively.
5. The method according to claim 4, characterized in that 。 6. A system for enhancing digital marketing based on AI big model retrieval, characterized in that: include: An acquisition module (210), used to acquire planned input cost data and platform marketing data; The planned investment cost data includes advertising cost data for each digital marketing platform; the platform marketing data includes customer quantity data and customer conversion rate data for each digital marketing platform within a preset time period; An analysis module (220), configured to analyze platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data; A calculation module (230) is used to obtain historical marketing data of each digital marketing platform, and calculate revenue trend impact data of each digital marketing platform based on the historical marketing data; the historical marketing data includes historical conversion rate data of each digital marketing platform in a preset number of historical time periods other than the preset time period; the revenue trend impact data is associated with the historical conversion rate data; A determination module (240) is used to determine the comprehensive benefit data of each digital marketing platform based on the platform conversion revenue data and the revenue trend impact data, and determine the increased cost allocation method based on the comprehensive benefit data and the planned investment cost data; the increased cost allocation method is associated with the comprehensive benefit data and the advertising cost data.
7. The system according to claim 6, characterized in that The analysis module (220) is further configured to analyze the platform conversion revenue data of each digital marketing platform according to the platform marketing data; the platform conversion revenue data is associated with the customer quantity data and the customer conversion rate data and includes: Determine quantity conversion revenue data based on the customer quantity data within a preset time period; Determine conversion rate revenue data based on the customer conversion rate data within a preset time period; Determine platform trend impact data based on the customer quantity data and the customer conversion rate data; The platform conversion revenue data of each digital marketing platform is analyzed based on the quantity conversion revenue data, the conversion rate revenue data and the platform trend impact data.
8. The system according to claim 7, characterized in that The analysis module (220) is further configured to determine the platform trend impact data according to the customer quantity data and the customer conversion rate data, including: Assume that there are n customer quantity data and m customer conversion rate data in a preset time period, n=m; and the n customer quantity data are assigned serial numbers 1-n, where the customer quantity data with serial number 1 is the customer quantity data closest to the current moment; and the m customer conversion rate data are assigned serial numbers 1-m, where the customer conversion rate data with serial number 1 is the customer conversion rate data closest to the current moment; and the customer quantity data with the same serial number Customer conversion rate data One to one correspondence; Determine n-1 adjacent quantity data groups based on n customer quantity data ; The adjacent quantity data group contains two adjacent customer quantity data; based on the m customer quantity data, m-1 adjacent conversion rate data groups are determined ; The adjacent conversion rate data group contains two adjacent customer conversion rate data; and the adjacent quantity data group and the adjacent conversion rate data group have the same serial number as the customer quantity data; Calculate the quantity difference of each adjacent quantity data group ,in, ; Calculate the conversion rate difference of each adjacent conversion rate data group ,in, ; Determine a first quantity value, a second quantity value, a third quantity value and a fourth quantity value based on the customer quantity data; the first quantity value is the quantity of customer quantity data not less than the customer quantity data with a sequence number of n; the second quantity value is the quantity of customer conversion rate data not less than the customer conversion rate data with a sequence number of m; the third quantity value is the quantity of customer quantity data less than the customer quantity data with a sequence number of n; the fourth quantity value is the quantity of customer conversion rate data less than the customer conversion rate data with a sequence number of m; The fifth quantity value, the sixth quantity value, the seventh quantity value and the eighth quantity value are determined based on the quantity difference value and the conversion rate difference value; the fifth quantity value is and The number of data sets, is the ith quantity difference, is the jth conversion rate difference; and, , The sixth quantity value is and The seventh quantity value is and The eighth quantity value is and The number of data sets; The calculation method of the platform trend impact data is as follows: ; In the formula, Influencing data for platform trends, is the first quantity value, is the second quantity value, is the third quantity value, is the fourth quantity value, is the fifth quantity value, is the sixth quantity value, is the seventh quantity value, It is the eighth quantity value.
9. The system according to claim 8, characterized in that The analysis module (220) is further configured to calculate the platform conversion revenue data in the following manner: ； In the formula, Transform revenue data for the platform, To convert revenue data into quantity, For conversion rate revenue data, Provide data on platform trends; The first weight and the second weight are preset respectively.
10. The system according to claim 9, characterized in that The analysis module (220) is further configured to: .