Marketing effect evaluation and optimization system for Internet marketing
Through the marketing effect evaluation and optimization system for Internet marketing, the digital twin model and optimization module are used to solve the problem that enterprises find it difficult to evaluate and optimize marketing effects in Internet marketing, and the dynamic and precise optimization of Internet marketing solutions is achieved.
Patent Information
- Application Number
- CN202411213886.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-31
- Publication Date
- 2024-12-10
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
It is difficult for enterprises to effectively predict, evaluate and optimize marketing effects in Internet marketing. The existing manual methods are time-consuming and labor-intensive and difficult to ensure the effectiveness of marketing plans.
Develop a marketing effect evaluation and optimization system for Internet marketing, including marketing plan setting module, evaluation module and optimization module. By building a digital twin model, generating simulation scenarios, obtaining simulation recommendation values, setting optimization thresholds, and timely optimizing marketing plans, we can achieve Dynamic and precise optimization.
It has achieved comprehensive and three-dimensional simulation analysis and evaluation of Internet marketing plans, timely optimized marketing effects, and ensured the dynamic and precise optimization of Internet marketing.
Smart Images
Figure CN119107123A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of Internet marketing, and more specifically, to a marketing effect evaluation and optimization system for Internet marketing. Background Art
[0002] With the rapid development of Internet technology, Internet marketing has become an important channel for enterprises to promote their products and services. However, how to predict, evaluate and optimize the marketing effect of Internet marketing has become a major challenge facing enterprises. At present, enterprises will arrange marketing managers to formulate, evaluate and optimize marketing plans. This manual marketing plan processing method is not only time-consuming and labor-intensive, but also difficult to guarantee the true marketing effect of the marketing plan. Therefore, it is particularly important to develop a marketing effect evaluation and optimization system for Internet marketing. Summary of the invention
[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a marketing effect evaluation and optimization system for Internet marketing.
[0004] To achieve the above object, the present invention provides the following technical solutions:
[0005] Marketing effect evaluation and optimization system for Internet marketing, including marketing plan setting module, marketing plan evaluation module, and marketing plan optimization module;
[0006] The marketing plan setting module is used to build a digital twin model based on current marketing data, market competition data and marketing target data, generate a marketing simulation scenario, generate multiple marketing plans based on the marketing simulation scenario, obtain the marketing simulation recommendation value of each marketing plan, and mark the marketing plan with the largest marketing simulation recommendation value as the marketing operation plan;
[0007] The marketing program evaluation module performs Internet marketing based on the marketing operation program, sets a plurality of equally spaced time nodes in the Internet marketing process, generates a marketing program operation log of the marketing operation program at each time node, and determines whether the marketing operation program needs to be optimized based on the comparison result of the marketing program optimization value in the marketing program operation log and the marketing program optimization threshold;
[0008] When the marketing plan optimization module determines that the marketing operation plan needs to be optimized, it simultaneously marks the current marketing data of the marketing operation plan as optimized marketing data, rebuilds the digital twin model according to the optimized marketing data market competition data and marketing target data, generates an optimized marketing simulation scenario, generates multiple optimized marketing plans based on the optimized marketing simulation scenario, marks the optimized marketing plan with the largest marketing simulation recommendation value as the optimized operation plan, and conducts Internet marketing based on the optimized operation plan.
[0009] Furthermore, the marketing simulation recommendation value of the marketing plan is obtained by the following method:
[0010] Conduct marketing simulation for the marketing plan. Set multiple equally spaced time nodes during the simulation marketing process. Generate a marketing plan simulation log for the marketing plan at each time node. The marketing plan simulation log includes the time node number, plan evaluation value, and node time. Mark the plan evaluation value of the marketing plan simulation log as HKr, set the plan evaluation value coefficient as SYm, sort all marketing plan simulation logs in the order of node time, calculate the difference between the plan evaluation values of the two adjacent marketing plan simulation logs after sorting, and obtain the plan simulation change value. Set the plan simulation change threshold. When the plan simulation change value is greater than or equal to the plan simulation change threshold, no processing is performed. When the plan simulation change value is less than the plan simulation change threshold, increase the plan simulation stability times by one, sum the simulation stability times, and obtain the total simulation stability times Fws. Use the formula Get the marketing simulation recommendation value JZz of the marketing plan.
[0011] Furthermore, the program evaluation value of the marketing program simulation log is obtained in the following manner: counting the cost data and effect data of each type of marketing indicator between the current time node and the previous time node, obtaining the marketing evaluation index of each type of marketing indicator, setting the marketing evaluation upper limit index and the marketing evaluation lower limit index, when the marketing evaluation index is greater than or equal to the marketing evaluation upper limit index, marking this type of marketing indicator as an ideal marketing indicator, when the marketing evaluation index is less than or equal to the marketing evaluation lower limit index, marking this type of marketing indicator as a disappointing marketing indicator, when the marketing evaluation index is between the marketing evaluation upper limit index and the marketing evaluation lower limit index, marking this type of marketing indicator as a normal marketing indicator, and setting the total number of ideal marketing indicators. The quantity is marked as EGC, the total number of disappointment marketing indicators is marked as PBJ, all disappointment marketing indicators are divided into groups, the marketing evaluation indexes of the two disappointment marketing indicators in the same group are summed and averaged to obtain the average disappointment marketing indicator, the average disappointment marketing threshold indicator is set, when the average disappointment marketing indicator is greater than or equal to the average disappointment marketing threshold indicator, the number of marketing disappointment superpositions is increased once, when the average disappointment marketing indicator is less than the average disappointment marketing threshold indicator, no processing is performed, the number of marketing disappointment superpositions is summed to obtain the total number of marketing disappointment superpositions LSP, the marketing evaluation indexes of all normal marketing indicators are summed and averaged to obtain the average normal marketing evaluation index HRG, and the formula is used. The program evaluation value Tds of the marketing program simulation log is obtained, where t1 is the total number coefficient of ideal marketing indicators, t2 is the total number coefficient of disappointed marketing indicators, t3 is the total number coefficient of marketing disappointment superposition, and t4 is the average normal marketing evaluation index coefficient.
[0012] Furthermore, the marketing evaluation index of the marketing indicator is obtained in the following way: mark the cost data and effect data of the same type of marketing indicators as indicator data sets [A, B], where A is the cost data of the marketing indicator and B is the effect data of the marketing indicator, obtain a marketing indicator evaluation model for this type of marketing indicator, input the indicator data set [A, B] into the marketing indicator evaluation model, and the marketing indicator evaluation model outputs the marketing evaluation index.
[0013] Furthermore, the marketing plan operation log includes the time node number, plan evaluation value, and node moment.
[0014] Furthermore, each time a marketing plan operation log is generated, a marketing plan optimization value of the marketing operation plan is obtained, and a marketing plan optimization threshold is set. When the marketing plan optimization value is greater than or equal to the marketing plan optimization threshold, it is determined that the marketing operation plan needs to be optimized;
[0015] When the marketing plan optimization value is less than the marketing plan optimization threshold, it is determined that there is no need to optimize the marketing operation plan.
[0016] Furthermore, the marketing plan optimization value of the marketing operation plan is obtained in the following manner: obtain all the marketing plan operation logs of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan operation logs, and obtain the operation evaluation mean Se, obtain the time node number of the operation plan operation log, obtain the marketing plan simulation log with the same time node number in the simulated marketing of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan simulation logs, and obtain the simulation evaluation mean Ew, calculate the difference between the plan evaluation value of the marketing plan operation log with the same time node number and the plan evaluation value of the marketing plan simulation log and take the absolute value to obtain the plan gap value, set the plan gap threshold, when the plan gap value is greater than or equal to the plan gap threshold, increase the plan gap times by one, when the plan gap value is less than the plan gap threshold, do not process it, sum up the plan gap times to obtain the total plan gap times UDg, and use the formula The marketing operation plan optimization value ASb is obtained, where p1 is the operation evaluation mean coefficient and p2 is the simulation evaluation mean coefficient.
[0017] Compared with the prior art, the present invention has the following beneficial effects:
[0018] 1. Set up a marketing plan setting module, which can simulate multiple marketing plans based on current marketing data, market competition data and marketing target data, and conduct comprehensive and three-dimensional simulation analysis and evaluation of each marketing plan, determine the marketing plan with the best marketing effect and carry out actual marketing;
[0019] 2. Setting up a marketing plan evaluation module and a marketing plan optimization module can accurately analyze the actual marketing effect of the plan during the actual marketing process of the marketing plan. When problems arise in the actual operation, the marketing plan can be optimized in a timely manner to ensure dynamic and accurate optimization of the Internet marketing plan. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 This is the operation flow chart of the marketing effect evaluation and optimization system for Internet marketing;
[0021] Figure 2 A flowchart for determining whether a marketing operation plan needs to be optimized. DETAILED DESCRIPTION
[0022] Reference Figure 1-Figure 2 , a marketing effectiveness evaluation and optimization system for Internet marketing, including a marketing plan setting module, a marketing plan evaluation module, and a marketing plan optimization module.
[0023] Marketing plan setting module: Build a digital twin model based on current marketing data, market competition data (market competition data includes but is not limited to market trend data and competitor data) and marketing target data (i.e. the target data that marketing ultimately hopes to achieve), generate marketing simulation scenarios, and generate multiple marketing plans based on the marketing simulation scenarios; obtain the marketing simulation recommendation value for each marketing plan, and mark the marketing plan with the largest marketing simulation recommendation value as the marketing operation plan.
[0024] The marketing simulation recommendation value of the marketing plan is obtained in the following way: simulate the marketing of the marketing plan, set multiple equally spaced time nodes in the process of simulated marketing, and generate a marketing plan simulation log of the marketing plan at each time node. The marketing plan simulation log includes the time node number (each time node corresponds to an independent number, the time node number is j, j=1, 2, ..., n, and the time nodes are numbered in sequence), the plan evaluation value, and the node time (Beijing time corresponding to the time node). The plan evaluation value of the marketing plan simulation log is marked as HKr, and the plan evaluation value coefficient is set to SYm. The range of each plan evaluation value HKr corresponds to a plan evaluation value coefficient SYm, m=1, 2, 3, ...m; SY1<SY2<SY3<...<SYm, each Each scheme evaluation value coefficient corresponds to a range of scheme evaluation values, including (0, HK1], (HK1, HK2], …, (HKR-1, HKR], when HKr∈(0, HK1], the scheme evaluation value coefficient is SY1, all marketing scheme simulation logs are sorted in the order of node time, the scheme evaluation values of two adjacent marketing scheme simulation logs after sorting are calculated to obtain the scheme simulation change value, and the scheme simulation change threshold is set (system threshold, which can be modified according to actual needs). When the scheme simulation change value is greater than or equal to the scheme simulation change threshold, no processing is performed. When the scheme simulation change value is less than the scheme simulation change threshold, the scheme simulation stability times are increased by one, and the simulation stability times are summed to obtain the total simulation stability times Fws. The formula is used Get the marketing simulation recommendation value JZz of the marketing plan.
[0025] The program evaluation value of the marketing program simulation log is obtained in the following way: count the cost data and effect data of various types of marketing indicators between the current time node and the previous time node (there are various types of marketing indicators, including but not limited to website indicators, sales indicators, and interactive indicators. Each type of marketing indicator includes cost data and effect data. Taking website indicators as an example, cost data refers to the overall investment data of the website, including but not limited to website advertising costs and website operation and maintenance costs. Effect data refers to the marketing effect data of the website, including but not limited to website traffic growth rate and search engine ranking improvement data), obtain the marketing evaluation index of each type of marketing indicator, set the marketing evaluation upper limit index and the marketing evaluation lower limit index (the marketing evaluation upper limit index is greater than the marketing evaluation lower limit index, both are system thresholds and can be modified according to actual needs). When the marketing evaluation index is greater than or equal to the marketing evaluation upper limit index, this type of marketing indicator is marked as an ideal marketing indicator. When the marketing evaluation index is less than or equal to the marketing evaluation lower limit index When , mark this type of marketing indicator as a disappointment marketing indicator. When the marketing evaluation index is between the marketing evaluation upper limit index and the marketing evaluation lower limit index, mark this type of marketing indicator as a normal marketing indicator. Mark the total number of ideal marketing indicators as EGC, and the total number of disappointment marketing indicators as PBJ. Divide all disappointment marketing indicators into groups of two by two. Sum and average the marketing evaluation indexes of the two disappointment marketing indicators in the same group to obtain the average disappointment marketing indicator. Set the average disappointment marketing threshold indicator (system threshold, which can be modified according to actual needs). When the average disappointment marketing indicator is greater than or equal to the average disappointment marketing threshold indicator, increase the number of marketing disappointment superpositions by one. When the average disappointment marketing indicator is less than the average disappointment marketing threshold indicator, no processing is performed. Sum the number of marketing disappointment superpositions to obtain the total number of marketing disappointment superpositions LSP. Sum and average the marketing evaluation indexes of all normal marketing indicators to obtain the average normal marketing evaluation index HRG. Use the formula The program evaluation value Tds of the marketing program simulation log is obtained, where t1 is the total number coefficient of ideal marketing indicators, t2 is the total number coefficient of disappointed marketing indicators, t3 is the total number coefficient of marketing disappointment superposition, and t4 is the average normal marketing evaluation index coefficient. The value of t1 is 1.59, the value of t2 is 1.83, the value of t3 is 1.28, and the value of t4 is 1.02.
[0026] The marketing evaluation index of the marketing indicator is obtained in the following way: mark the cost data and effect data of the same type of marketing indicators as indicator data sets [A, B], where A is the cost data of the marketing indicator and B is the effect data of the marketing indicator, obtain the marketing indicator evaluation model of this type of marketing indicator, input the indicator data set [A, B] into the marketing indicator evaluation model, and the marketing indicator evaluation model outputs the marketing evaluation index.
[0027] Different types of marketing indicators correspond to different marketing indicator evaluation models. The construction methods of different marketing indicator evaluation models are the same. It is only necessary to replace the training data of the model accordingly. The value ranges of the marketing evaluation indexes assigned by different models are the same. This embodiment takes the website indicator as an example to introduce the construction method of the marketing indicator evaluation model: collect the indicator data sets [A, B] of N groups of website indicators, use the indicator data sets [A, B] of N groups of website indicators as the training data of the deep learning model, assign the marketing evaluation index to the training data, divide the training data into a training set and a validation set at a ratio of 1:3, iterate the training set and the validation set to obtain the marketing indicator evaluation model. Among them, the value range of the marketing evaluation index is (1~2), the larger the value of the marketing evaluation index, the more it meets the website indicator, and the smaller the value of the marketing evaluation index, the less it meets the website indicator.
[0028] By setting up a marketing plan setting module, you can simulate multiple marketing plans based on current marketing data, market competition data, and marketing target data, and conduct comprehensive and three-dimensional simulation analysis and evaluation on each marketing plan to determine the marketing plan with the best marketing effect and carry out actual marketing.
[0029] Marketing plan evaluation module: Internet marketing is carried out based on the marketing operation plan. Multiple equally spaced time nodes are set in the Internet marketing process (the intervals between time nodes in the simulation marketing process and the Internet marketing process are the same). At each time node, a marketing plan operation log is generated for the marketing operation plan. The marketing plan operation log includes the time node number, plan evaluation value, and node time.
[0030] Each time a marketing plan operation log is generated, the marketing plan optimization value of the marketing operation plan is obtained, and a marketing plan optimization threshold is set (system threshold, which can be modified according to actual needs). When the marketing plan optimization value is greater than or equal to the marketing plan optimization threshold, it is determined that the marketing operation plan needs to be optimized.
[0031] When the marketing plan optimization value is less than the marketing plan optimization threshold, it is determined that there is no need to optimize the marketing operation plan.
[0032] The marketing plan optimization value of the marketing operation plan is obtained in the following way: obtain all the marketing plan operation logs of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan operation logs, and obtain the operation evaluation mean Se, obtain the time node number of the operation log of the operation plan, obtain the marketing plan simulation log with the same time node number in the simulated marketing of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan simulation logs, and obtain the simulation evaluation mean Ew, calculate the difference between the plan evaluation value of the marketing plan operation log with the same time node number and the plan evaluation value of the marketing plan simulation log and take the absolute value to obtain the plan gap value, set the plan gap threshold (system threshold, which can be modified according to actual needs), when the plan gap value is greater than or equal to the plan gap threshold, increase the plan gap times by one, when the plan gap value is less than the plan gap threshold, do not process it, sum up the plan gap times to obtain the total plan gap times UDg, and use the formula The marketing operation plan optimization value ASb is obtained, where p1 is the mean coefficient of operation evaluation, p2 is the mean coefficient of simulation evaluation, the value of p1 is 1.52, and the value of p2 is 0.72.
[0033] Marketing plan optimization module: When it is determined that the marketing operation plan needs to be optimized, the current marketing data of the marketing operation plan will be simultaneously marked as optimized marketing data, and the digital twin model will be rebuilt according to the optimized marketing data market competition data and marketing target data to generate an optimized marketing simulation scenario. Based on the optimized marketing simulation scenario, multiple optimized marketing plans are generated, and the optimized marketing plan with the largest marketing simulation recommendation value is marked as the optimized operation plan, and Internet marketing is carried out based on the optimized operation plan.
[0034] By setting up a marketing plan evaluation module and a marketing plan optimization module, we can accurately analyze the actual marketing effect of the plan during the actual marketing process of the marketing plan. When problems arise in the actual operation, the marketing plan can be optimized in a timely manner to ensure dynamic and accurate optimization of the Internet marketing plan.
[0035] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.
[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.
[0037] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
[0038] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0039] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.
[0040] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0041] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.
[0042] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
Claims
1. A marketing effect evaluation and optimization system for Internet marketing, characterized by: Including marketing plan setting module, marketing plan evaluation module, and marketing plan optimization module; The marketing plan setting module is used to build a digital twin model based on current marketing data, market competition data and marketing target data, generate a marketing simulation scenario, generate multiple marketing plans based on the marketing simulation scenario, obtain the marketing simulation recommendation value of each marketing plan, and mark the marketing plan with the largest marketing simulation recommendation value as the marketing operation plan; The marketing program evaluation module performs Internet marketing based on the marketing operation program, sets a plurality of equally spaced time nodes in the Internet marketing process, generates a marketing program operation log of the marketing operation program at each time node, and determines whether the marketing operation program needs to be optimized based on the comparison result of the marketing program optimization value in the marketing program operation log and the marketing program optimization threshold; When the marketing plan optimization module determines that the marketing operation plan needs to be optimized, it simultaneously marks the current marketing data of the marketing operation plan as optimized marketing data, rebuilds the digital twin model according to the optimized marketing data market competition data and marketing target data, generates an optimized marketing simulation scenario, generates multiple optimized marketing plans based on the optimized marketing simulation scenario, marks the optimized marketing plan with the largest marketing simulation recommendation value as the optimized operation plan, and conducts Internet marketing based on the optimized operation plan.
2. The marketing effect evaluation and optimization system for Internet marketing according to claim 1, characterized in that: The marketing simulation recommendation value of the marketing plan is obtained in the following way: Conduct marketing simulation for the marketing plan. Set multiple equally spaced time nodes during the simulation marketing process. Generate a marketing plan simulation log for the marketing plan at each time node. The marketing plan simulation log includes the time node number, plan evaluation value, and node time. Mark the plan evaluation value of the marketing plan simulation log as HKr, set the plan evaluation value coefficient as SYm, sort all marketing plan simulation logs in the order of node time, calculate the difference between the plan evaluation values of the two adjacent marketing plan simulation logs after sorting, and obtain the plan simulation change value. Set the plan simulation change threshold. When the plan simulation change value is greater than or equal to the plan simulation change threshold, no processing is performed. When the plan simulation change value is less than the plan simulation change threshold, increase the plan simulation stability times by one, sum the simulation stability times, and obtain the total simulation stability times Fws. Use the formula Get the marketing simulation recommendation value JZz of the marketing plan.
3. The marketing effect evaluation and optimization system for Internet marketing according to claim 2 is characterized in that: The program evaluation value of the marketing program simulation log is obtained in the following way: count the cost data and effect data of each type of marketing indicators between the current time node and the previous time node, obtain the marketing evaluation index of each type of marketing indicator, set the marketing evaluation upper limit index and the marketing evaluation lower limit index, when the marketing evaluation index is greater than or equal to the marketing evaluation upper limit index, mark this type of marketing indicator as an ideal marketing indicator, when the marketing evaluation index is less than or equal to the marketing evaluation lower limit index, mark this type of marketing indicator as a disappointing marketing indicator, when the marketing evaluation index is between the marketing evaluation upper limit index and the marketing evaluation lower limit index, mark this type of marketing indicator as a normal marketing indicator, and mark the total number of ideal marketing indicators as Denoted as EGC, the total number of disappointment marketing indicators is marked as PBJ, all disappointment marketing indicators are divided into a group, the marketing evaluation indexes of the two disappointment marketing indicators in the same group are summed and averaged to obtain the average disappointment marketing indicator, and the average disappointment marketing threshold indicator is set. When the average disappointment marketing indicator is greater than or equal to the average disappointment marketing threshold indicator, the number of marketing disappointment superpositions is increased once. When the average disappointment marketing indicator is less than the average disappointment marketing threshold indicator, no processing is performed. The number of marketing disappointment superpositions is summed to obtain the total number of marketing disappointment superpositions LSP. The marketing evaluation indexes of all normal marketing indicators are summed and averaged to obtain the average normal marketing evaluation index HRG. The formula is used The program evaluation value Tds of the marketing program simulation log is obtained, where t1 is the total number coefficient of ideal marketing indicators, t2 is the total number coefficient of disappointed marketing indicators, t3 is the total number coefficient of marketing disappointment superposition, and t4 is the average normal marketing evaluation index coefficient.
4. The marketing effect evaluation and optimization system for Internet marketing according to claim 3 is characterized in that: The marketing evaluation index of the marketing indicator is obtained in the following way: mark the cost data and effect data of the same type of marketing indicators as indicator data sets [A, B], where A is the cost data of the marketing indicator and B is the effect data of the marketing indicator, obtain the marketing indicator evaluation model of this type of marketing indicator, input the indicator data set [A, B] into the marketing indicator evaluation model, and the marketing indicator evaluation model outputs the marketing evaluation index.
5. The marketing effect evaluation and optimization system for Internet marketing according to claim 1 is characterized in that: The marketing plan operation log includes the time node number, plan evaluation value, and node time.
6. The marketing effect evaluation and optimization system for Internet marketing according to claim 1 is characterized in that: Each time a marketing plan operation log is generated, the marketing plan optimization value of the marketing operation plan is obtained, and a marketing plan optimization threshold is set. When the marketing plan optimization value is greater than or equal to the marketing plan optimization threshold, it is determined that the marketing operation plan needs to be optimized; When the marketing plan optimization value is less than the marketing plan optimization threshold, it is determined that there is no need to optimize the marketing operation plan.
7. The marketing effect evaluation and optimization system for Internet marketing according to claim 6, characterized in that: The marketing plan optimization value of the marketing operation plan is obtained in the following way: obtain all the marketing plan operation logs of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan operation logs, and obtain the operation evaluation mean Se, obtain the time node number of the operation plan operation log, obtain the marketing plan simulation log with the same time node number in the simulated marketing of the marketing operation plan, sum up and average the plan evaluation values of all the marketing plan simulation logs, and obtain the simulation evaluation mean Ew, calculate the difference between the plan evaluation value of the marketing plan operation log with the same time node number and the plan evaluation value of the marketing plan simulation log and take the absolute value to obtain the plan gap value, set the plan gap threshold, when the plan gap value is greater than or equal to the plan gap threshold, increase the plan gap times by one, when the plan gap value is less than the plan gap threshold, do not process it, sum up the plan gap times to obtain the total plan gap times UDg, and use the formula The marketing operation plan optimization value ASb is obtained, where p1 is the operation evaluation mean coefficient and p2 is the simulation evaluation mean coefficient.