Network marketing system based on user matching

Abstract

The invention relates to the technical field of marketing, in particular to a network marketing system based on user matching, which comprises a user group analysis module, a network marketing implementation module, a marketing effect analysis module and a marketing investment analysis module. According to the method, by considering the relevance between the user features and product positioning, the cost input of advertisement pushing is optimized, the conversion rate and efficiency of advertisements are improved, the matching degree of various features and products is judged by analyzing the user groups, and the marketing input is adjusted on the basis, so that marketing resources are distributed more reasonably, and the marketing efficiency is improved. The method has the advantages that the marketing efficiency is improved, the advertisement cost is reduced, the market response can be quickly fed back through real-time marketing effect tracking, the marketing decision is more data-driven, the flexibility and response speed of the strategy are enhanced, the continuous optimization of the marketing activity is ensured according to the feedback adjustment of the effect, and the power and direction for continuous improvement are provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of marketing, and in particular to a network marketing system based on user matching. Background Art

[0002] The field of marketing technology focuses on the promotion of goods and services through various media, including consumer behavior analysis, market segmentation and target market positioning. By collecting a large amount of data on consumer behavior, preferences and interactions, these data are used for precision marketing and market segmentation to improve the personalization and efficiency of advertising. The main goal of the field of marketing technology is to optimize the cost-effectiveness of marketing activities, improve customer satisfaction and loyalty, and increase the company's brand awareness and market share.

[0003] Among them, the online marketing system is a system that executes marketing activities through Internet channels. Its main purpose is to promote products or services through the Internet. The system uses network platforms such as websites, social media or e-commerce platforms, combined with user data analysis, to achieve highly personalized and targeted advertising content. The online marketing system has a wide range of uses, including increasing brand visibility, promoting product sales, collecting market feedback and improving customer service, while tracking the effectiveness of marketing activities in real time to provide data support for corporate decision-making.

[0004] Traditional marketing systems lack the ability to evaluate the matching degree between data and products, which results in the inability to accurately match marketing investment to target users. This results in widespread advertising but poor results, which easily leads to a waste of resources. Traditional systems lack flexibility and real-time feedback mechanisms in the implementation and evaluation of marketing activities, which makes it difficult to maximize marketing results, and also leads to a waste of resources, which reduces marketing results and affects brand promotion and user satisfaction. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose an Internet marketing system based on user matching.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an online marketing system based on user matching, the system comprising: The user group analysis module is based on the target online marketing product. Through market research, it extracts the user characteristics information of the target user group, including the user's age, hobbies, gender and income information, determines the matching degree between each characteristic and the product positioning, and evaluates the matching degree between the user group and the product based on the user proportion of each matching characteristic, and obtains the user group matching analysis results; The network marketing implementation module is based on the user group matching analysis results, and according to the matching, size, inclusiveness and popularity of the product in the user group, analyzes the recommended advertising costs and implements network marketing, including product advertising push through social media and network platforms, and adjusts the push frequency of marketing content according to the unit advertising push cost, to obtain product network marketing implementation information; The marketing effect analysis module is based on the product network marketing implementation information. Through data statistics, it analyzes the changes in product purchase volume, purchase rate of user groups, product reputation and product popularity before and after marketing, quantitatively evaluates the marketing effect, analyzes the changes in marketing effect over a period of time, evaluates the changing trend of marketing effect, and obtains marketing effect trend analysis information; The marketing investment analysis module evaluates the deviation between the current marketing effect and the target marketing effect based on the marketing effect trend analysis information, compares it with the preset deviation threshold, and determines whether marketing needs to be stopped. If marketing needs to continue, the subsequent marketing investment is adjusted according to the size of the marketing deviation and the changing trend of the marketing effect to obtain marketing investment optimization information.

[0007] The present invention is improved in that the steps of obtaining the user group matching analysis result are: Based on the target online marketing product, extract the user characteristics information of the target user group, including age, hobbies, gender and income information, and count the proportion of users with each characteristic to obtain the user characteristics analysis results; Based on the user feature analysis results, according to the product positioning, the fit between each user feature and the product positioning is analyzed to obtain a product fit analysis result; Based on the product fit analysis results and user feature analysis results, the formula is: ; Calculate the matching scores between user groups and products to obtain the matching analysis results of user groups; in, represents the total number of features, Score the matching degree between user groups and products. is the scaling factor, Indicates The weight of the feature, Indicates The fit score of the feature, Indicates The proportion of each feature in the user group.

[0008] The present invention is improved in that the step of analyzing the recommended advertising cost is: Based on the user group matching analysis results, through user surveys, statistics are collected on the size and inclusiveness of the user group and the popularity of the product in the user group, and industry standard benchmark advertising cost data is collected to obtain advertising cost correlation information; Based on the advertising cost association information, the formula is: ; Calculate the advertising cost recommendation value and obtain the advertising cost recommendation information; in, Recommended value for advertising cost, Benchmark advertising costs for the industry standard, Score the matching degree between user groups and products. is the matching threshold, , and is the adjustment factor, is the size of the user group, is the standard user group size, is an indicator of the inclusiveness of the user group, is the inclusive standard value, To improve the popularity of the product among the user groups, is the standard value of popularity.

[0009] The present invention is improved in that the steps of obtaining the product network marketing implementation information are as follows: Based on the advertising cost recommendation information, the advertising cost set by the enterprise is received, and the cost required for each push is extracted through the target network marketing channel to obtain push frequency related data; Based on the push frequency correlation data, by formula: ; Calculate the adjusted push frequency; in, Indicates the adjusted push frequency. is the adjustment factor, It is the advertising cost set by the enterprise. It is the unit advertising push fee. It is the target marketing days; Based on the adjusted push frequency, online marketing is implemented, including product advertising push through social media and online platforms, to obtain product online marketing implementation information.

[0010] The present invention is improved in that the steps of obtaining the quantitative evaluation of marketing effect are: Based on the product online marketing implementation information, obtain the product purchase volume before and after the marketing, user group purchase rate, product word-of-mouth score and product popularity information through data statistics to obtain data statistical results; Based on the statistical results, analyze the purchase volume change ratio, purchase rate change ratio, word-of-mouth score change ratio, and product popularity change ratio before and after marketing to obtain effect-related data; Based on the effect correlation data, by the formula: ; Calculate the marketing effect score and obtain marketing effect evaluation information; in, Score marketing effectiveness. , , and is the weight coefficient, is the scaling factor, is the purchase volume change ratio, is the purchase rate change ratio, is the change ratio of word-of-mouth rating, is the product awareness change ratio.

[0011] The present invention is improved in that the step of obtaining the marketing effect trend analysis information is as follows: Based on the marketing effect evaluation information, collect marketing effect data within a period of time, extract corresponding time points, and obtain marketing trend related data; Based on the marketing trend correlation data, through the formula: ; Calculate the rate of change of marketing effectiveness; in, Represents the rate of change of marketing effectiveness, For time Marketing effectiveness rating, For time Marketing effectiveness rating, is the time interval; Based on the changing rate of the marketing effect, and according to the size and positive and negative of the changing rate of the marketing effect, the changing trend of the marketing effect is evaluated to obtain marketing effect trend analysis information.

[0012] The present invention is improved in that the step of determining whether it is necessary to stop marketing is: Based on the marketing effect trend analysis information, the current marketing effect score and the preset target marketing effect score are extracted, and the formula is: ; Calculate the deviation index of marketing effectiveness; in, is the deviation index of marketing effect, Score marketing effectiveness. Score the target marketing effectiveness; The deviation index based on the marketing effect is compared with the preset deviation tolerance threshold. If the deviation index exceeds the deviation tolerance threshold, it is judged that there is a problem with the marketing and the marketing needs to be stopped. An abnormal marketing effect prompt is immediately sent to the marketing management personnel to obtain marketing abnormality judgment information.

[0013] The present invention is improved in that the step of obtaining the marketing investment optimization information is: Based on the marketing abnormality judgment information, if the deviation index of the marketing effect is within the deviation tolerance threshold, and the marketing manager indicates that the network marketing needs to be continued, the deviation index of the marketing effect and the change rate information of the marketing effect are collected to obtain the input-related data; Based on the investment-related data and according to the current marketing investment, the formula is: ; Calculate the adjusted marketing investment and obtain marketing investment optimization information; in, For the adjusted marketing investment, For current marketing investment, is the deviation index of marketing effect, is the rate of change of marketing effect, and is the adjustment factor.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by considering the correlation between user characteristics and product positioning, the cost investment of advertising push is optimized, the conversion rate and efficiency of advertising are improved, and by analyzing the user groups, the degree of matching between various characteristics and products is judged, and the marketing investment is adjusted on this basis, so that marketing resources are more reasonably allocated and advertising costs are reduced. Through real-time marketing effect tracking, market reactions can be quickly fed back, making marketing decisions more data-driven, enhancing the flexibility and response speed of the strategy, and adjusting according to effect feedback to ensure the continuous optimization of marketing activities, providing enterprises with the motivation and direction for continuous improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 A flowchart of obtaining the user group matching analysis result of the present invention; Figure 3 A flow chart for analyzing the recommended advertising costs for the present invention; Figure 4 A flowchart for obtaining product network marketing implementation information for the present invention; Figure 5 A flowchart for quantitatively evaluating the marketing effect of the present invention; Figure 6 A flow chart for obtaining marketing effect trend analysis information for the present invention; Figure 7 A flow chart for determining whether marketing needs to be stopped in the present invention; Figure 8 A flow chart for obtaining marketing investment optimization information for the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0018] See also Figure 1 The present invention provides a technical solution: an online marketing system based on user matching, the system comprising: The user group analysis module is based on the target online marketing product. Through market research, it extracts the user characteristics information of the target user group, including the user's age, hobbies, gender and income information, determines the matching degree between each characteristic and the product positioning, and evaluates the matching degree between the user group and the product based on the user proportion of each matching characteristic, and obtains the user group matching analysis results; The network marketing implementation module is based on the user group matching analysis results, and according to the matching, size, inclusiveness and popularity of the product in the user group, analyzes the recommended advertising costs and implements network marketing, including product advertising push through social media and network platforms, and adjusts the push frequency of marketing content according to the unit advertising push cost, to obtain product network marketing implementation information; The marketing effect analysis module is based on the product network marketing implementation information. Through data statistics, it analyzes the changes in product purchase volume, purchase rate of user groups, product reputation and product popularity before and after marketing, quantitatively evaluates the marketing effect, analyzes the changes in marketing effect over a period of time, evaluates the changing trend of marketing effect, and obtains marketing effect trend analysis information; The marketing investment analysis module evaluates the deviation between the current marketing effect and the target marketing effect based on the marketing effect trend analysis information, compares it with the preset deviation threshold, and determines whether marketing needs to be stopped. If marketing needs to continue, the subsequent marketing investment is adjusted according to the size of the marketing deviation and the changing trend of the marketing effect to obtain marketing investment optimization information.

[0019] The user group matching analysis results include age distribution matching, interest and hobby correlation, gender ratio adaptation and income level comparison information; product online marketing implementation information includes advertising push frequency, target social platform selection and main advertising content types; marketing effect trend analysis information includes purchase volume growth rate, user engagement improvement ratio and brand reputation improvement index; marketing investment optimization information includes budget adjustment ratio, marketing channel optimization and target user segmentation adjustment.

[0020] See also Figure 2 , the steps to obtain the user group matching analysis results are: Based on the target online marketing product, extract the user characteristics information of the target user group, including age, hobbies, gender and income information, and count the proportion of users with each characteristic to obtain the user characteristics analysis results; Based on the target online marketing products, users’ age, interests, gender and income information are obtained from social media and online shopping behaviors. According to the frequency of users’ online interactions and content selectivity, users’ interests are classified and labeled using natural language processing and image recognition technologies. Gender information is verified through users’ public profiles, and income information is estimated through users’ purchasing behaviors and economic data of their regions. Time series analysis is performed based on users’ active time and frequency of participation in activities to obtain the proportion of users with each characteristic.

[0021] Based on the user feature analysis results and product positioning, analyze the fit between each user feature and product positioning to obtain the product fit analysis results; Based on the results of user feature analysis, product positioning is matched with user data. Through machine learning model training, a prediction model for the fit between user features and products is established. Using a decision tree algorithm, users’ preferences for products are predicted based on their age, interests, gender, and income information. For each feature, the fit score between the product and the feature is calculated. The cross-validation method is used to ensure the accuracy of the model. Finally, the fit scores of each feature output by the model are combined to generate product fit analysis results to provide quantitative data support for marketing strategies.

[0022] Based on the product fit analysis results and user feature analysis results, the formula is: ; Calculate the matching scores between user groups and products to obtain the matching analysis results of user groups; in, represents the total number of features, Score the matching degree between user groups and products. is the scaling factor, Indicates The weight of the feature, Indicates The fit score of the feature, Indicates The proportion of each feature in the user group.

[0023] formula: ; The benefit of the formula is that it provides a method to quantify the match between user groups and products, which can quantify the fit between user characteristics and product features, thereby optimizing product design and market positioning.

[0024] Detailed explanation of the formula and the process of formula calculation: Define parameter N as the total number of user characteristics, such as age, interests, etc. is the scaling factor used to normalize the scores, determined from the survey data, is the weight of the i-th feature, and the weight data is obtained through market analysis. is the feature fit score, obtained through user surveys and feedback, is the proportion of the feature in the user group, calculated through data analysis, setting N=4, , , , .

[0025] The calculation process is as follows: ; The results show that in the adjusted and normalized scoring model, the matching score between the user group and the product is 18.15. This score can be used in marketing strategies to determine the market acceptance of the product and adjust the marketing strategy for specific user groups.

[0026] See also Figure 3 , the steps to analyze the recommended advertising costs are: Based on the results of the user group matching analysis, through user surveys, statistics are collected on the size and inclusiveness of the user group and the popularity of the product among the user group, and industry standard benchmark advertising cost data is collected to obtain advertising cost correlation information; Based on the results of user group matching analysis, data on user groups are collected through online surveys and on-site interviews, including the size and diversity of the user groups and their awareness of the products. Through online questionnaires, face-to-face interviews and telephone surveys, the questionnaire design takes into account the basic information of users, frequency of product use and overall satisfaction with the products. Through data cleaning and preprocessing, effective survey results are screened out, and then statistical analysis software is used to conduct a comprehensive analysis of the data to ensure the accuracy and representativeness of the collected data. The collected user data not only reflects the structure of the user group, but also provides a basis for the positioning of the product in the market, provides data support for the formulation of subsequent marketing promotion strategies, and obtains advertising cost-related information.

[0027] Based on the advertising cost correlation information, through the formula: ; Calculate the advertising cost recommendation value and obtain the advertising cost recommendation information; in, Recommended value for advertising cost, Benchmark advertising costs for the industry standard, Score the matching degree between user groups and products. is the matching threshold, , and is the adjustment factor, is the size of the user group, is the standard user group size, is an indicator of the inclusiveness of the user group, is the inclusive standard value, To improve the popularity of the product among the user groups, is the standard value of popularity.

[0028] formula: ; The benefit of the formula is that it can dynamically adjust advertising costs based on market and user data to achieve cost optimization and rational allocation of resources.

[0029] Detailed explanation of the formula and the process of formula calculation and derivation: The benchmark advertising cost for the industry standard is obtained through market research and industry data and is set to 1000 units of currency. The matching score between the user group and the product calculated above is 18.15. The matching threshold is set to 20, and the adjustment coefficient is , , They are 1.2, 1.1, and 0.9 respectively, and are adjusted dynamically according to market conditions and strategy needs. is the user group size, set to 5000 people, is the standard user group size, set to 10,000 people, is the inclusiveness index of the user group, set to 0.8, is the standard inclusive value, set to 1, is the popularity of the product among the user group, set to 0.5, is the standard value of popularity, set to 1, and the calculation process is as follows: ; The results show that under the current market and user conditions, the recommended advertising cost is 435.78 units of currency. This recommended value is lower than the benchmark advertising cost, indicating that the current product has a high degree of match with the user group. Therefore, marketing resources can be optimized to achieve cost savings while ensuring the effectiveness of the marketing strategy.

[0030] See also Figure 4 , the steps to obtain product network marketing implementation information are: Based on the advertising cost recommendation information, the advertising cost set by the enterprise is received, and the cost required for each push is extracted through the target network marketing channel to obtain the push frequency correlation data; Based on the advertising cost recommendation information, we receive the set advertising costs from the enterprise, and measure the specific costs required for each promotion activity through the target online marketing channels. The measurement process includes rate surveys on major social media platforms and online advertising services, and the use of API to obtain real-time cost data. The average cost of each promotion method is integrated and calculated through data analysis software. Data collection involves multiple marketing platforms to ensure the breadth and representativeness of the data. By comparing the feedback data of different time periods and different user groups, the average cost of each push is obtained through statistical analysis, and the corresponding push frequency-related data is deduced based on this, and comprehensive push frequency-related information is obtained, which provides a basis for subsequent marketing strategy adjustments.

[0031] Based on the push frequency correlation data, through the formula: ; Calculate the adjusted push frequency; in, Indicates the adjusted push frequency. is the adjustment factor, It is the advertising cost set by the enterprise. It is the unit advertising push fee. It is the target marketing days; formula: ; The benefit of the formula is that by adjusting the parameters, you can flexibly respond to market changes and budget adjustments to ensure that the efficiency of your marketing campaigns is maximized.

[0032] Detailed explanation of the formula and the process of formula calculation and derivation: As the adjustment coefficient, it is set to 1.1 to adapt to market demand fluctuations. The advertising cost set for the enterprise is set to 20,000 units of currency. is the average cost of each push, which is set to 200 units of currency based on market research data. The target marketing days are set to 30 days.

[0033] The calculation process is as follows: ; The results show that under given budget and market conditions, the ideal push frequency is 3.67 times a day. This frequency can maximize the efficiency of resource utilization while meeting market coverage needs, ensuring that advertising push can cover enough potential customers, thereby improving the overall effectiveness of the marketing campaign.

[0034] Based on the adjusted push frequency, implement online marketing, including product advertising push through social media and online platforms, and obtain product online marketing implementation information; Based on the adjusted push frequency, online marketing is implemented. The implementation process covers the entire process from content creativity to actual delivery, starting with market analysis, understanding the preferences of target users, customizing appropriate advertising content, and then selecting appropriate online platforms. The selection of each platform is based on data analysis of user activity and advertising effectiveness. At the same time, the real-time effect of advertising is monitored, and user interaction data, such as click-through rate and conversion rate, is collected to adjust future advertising strategies. The advertising campaign generates detailed product online marketing implementation information, effectively enhancing the product's market exposure and ensuring the optimization of advertising delivery.

[0035] See also Figure 5 , the steps to obtain quantitative evaluation of marketing effectiveness are: Based on the product network marketing implementation information, through data statistics, obtain the product purchase volume before and after marketing, user group purchase rate, product word-of-mouth score and product popularity information, and obtain data statistical results; Based on the product network marketing implementation information, key marketing indicators are obtained through data statistical methods, including product purchase volume, user group purchase rate, product word-of-mouth rating and product awareness information. The process covers data collection, processing and analysis, and uses database management systems and statistical analysis software for data integration. User behavior data and feedback are collected through network tracking tools and customer relationship management systems. Data cleaning technology is used to remove invalid or duplicate data. Statistical methods are used to calculate changes in purchase rate and purchase volume. Word-of-mouth ratings are aggregated from user ratings on major evaluation platforms. Product awareness is tracked and evaluated through social media analysis tools, including keyword mention frequency and user interaction data. Statistical data are integrated into detailed statistical results, providing a scientific basis for the adjustment of subsequent marketing strategies.

[0036] Based on the statistical results, analyze the change ratio of purchase volume, purchase rate, word-of-mouth rating, and product awareness before and after marketing to obtain effect-related data; Based on the statistical results, we analyze the changes in various indicators before and after marketing, including purchase volume, purchase rate, word-of-mouth rating and product awareness. The change ratio of indicators is obtained through data comparison and analysis. The data of purchase volume and purchase rate are derived from the sales records of the e-commerce platform. By comparing the sales data before and after the marketing activities, the purchase volume change ratio and the purchase rate change ratio are calculated. The word-of-mouth rating change ratio is determined by collecting the evaluation scores of users on the online platform before and after marketing, and performing simple arithmetic averaging and comparison. The change ratio of product awareness is determined by analyzing social media data, such as the growth rate of the number of fans and the increase in the number of interactions. All these analyses are combined into effect-related data. The data provides a quantitative basis for evaluating the effectiveness of marketing activities, ensuring the accuracy of marketing decisions and the measurability of effects.

[0037] Based on the effect correlation data, through the formula: ; Calculate the marketing effect score and obtain marketing effect evaluation information; in, Score marketing effectiveness. , , and is the weight coefficient, is the scaling factor, is the purchase volume change ratio, is the purchase rate change ratio, is the change ratio of word-of-mouth rating, is the product awareness change ratio.

[0038] formula: ; The benefit of the formula is that it provides a quantitative method for comprehensively evaluating marketing effectiveness, which can reflect the effectiveness of the overall marketing activities based on the weights of different indicators.

[0039] Detailed explanation of the formula and the process of formula calculation and derivation: Weight coefficient , , , They represent the importance of purchase volume, purchase rate, word-of-mouth score and popularity, and are set to 0.4, 0.3, 0.2 and 0.1, respectively. is the scaling factor, set to 1 to simplify the calculation, , , , They are the change ratios of various indicators respectively. Assuming the purchase volume increases by 20%, the purchase rate increases by 10%, the word-of-mouth score increases by 5%, and the product awareness increases by 15%, the calculation process is as follows: ; The results show that under the set weights and market responses, the comprehensive effect score of the marketing campaign is ,The score reflects that the marketing activities are more effective in terms of purchase volume and popularity, but relatively low in terms of word-of-mouth improvement. ,The correlation between the numerical results and the step results lies in that through ,the comprehensive evaluation of various indicators, it provides a quantitative understanding of the ,overall effect of the

[0040] See also Figure 6 , the steps to obtain marketing effect trend analysis information are: Based on the marketing effect evaluation information, collect the marketing effect data within a period of time, extract the corresponding time points, and obtain the marketing trend related data; A method of obtaining quantitative and qualitative data from various marketing channels based on marketing effectiveness evaluation information, including the number of interactions on social media, website traffic statistics, and response rates to email marketing. Each data point is automatically recorded at a preset time point through a data collection system to ensure the accuracy and continuity of the time series. The data collection cycle is usually once a day, but may be increased to once an hour during a specific marketing campaign to capture rapidly changing market reactions. Through the data, companies can track the immediate effects of marketing activities and adjust strategies accordingly to optimize resource allocation and improve the overall effectiveness of marketing activities, thereby obtaining marketing trend-related data.

[0041] Based on marketing trend correlation data, through the formula: ; Calculate the rate of change of marketing effectiveness; in, Represents the rate of change of marketing effectiveness, For time Marketing effectiveness rating, For time Marketing effectiveness rating, is the time interval; formula: ; The benefit of the formula is that it can quantify the dynamic changes in the effectiveness of marketing activities and provide fast-response data support so that companies can adjust their marketing strategies instantly.

[0042] Detailed explanation of the formula and the process of formula calculation and derivation: and Two consecutive time points and Marketing effectiveness rating, The interval between these two time points is set to 1 day, and the time is set to The marketing effectiveness score is 0.105, and in time The score is 0.095, and the calculation process is as follows: ; The results show that the marketing effect has increased within one day, with a growth rate of 0.01. This rate of change reflects the change in the effect of marketing activities in a short period of time, helping the marketing team to evaluate the immediate impact of its strategy and make necessary adjustments to improve the efficiency and effectiveness of future marketing activities.

[0043] Based on the change rate of marketing effect, the change trend of marketing effect is evaluated according to the size and positive and negative of the change rate of marketing effect, and the marketing effect trend analysis information is obtained; Based on the rate of change of marketing effectiveness, the change rates of several consecutive cycles are compared and trend line analyzed. Regression analysis is performed using statistical software to determine the growth or decline trend of marketing effectiveness and analyze the relationship between these trends and the specific content of marketing activities, such as the impact of specific advertising or social media activities. Through this analysis, the marketing team can identify which strategies are most effective and which need to be improved, and ultimately obtain marketing effectiveness trend analysis information. This information is crucial for formulating future marketing plans, ensuring the effective use of marketing resources and maximizing return on investment.

[0044] See also Figure 7 The steps to determine whether marketing needs to be stopped are: Based on the marketing effect trend analysis information, extract the current marketing effect score and the preset target marketing effect score, through the formula: ; Calculate the deviation index of marketing effectiveness; in, is the deviation index of marketing effect, Score marketing effectiveness. Score the target marketing effectiveness; formula: ; The benefit of the formula is that it can quantify the degree to which marketing activities deviate from their goals, thus providing precise data support for adjusting strategies.

[0045] Detailed explanation of the formula and the process of formula calculation: Definition is the deviation index of marketing effect, Score the actual marketing effectiveness. Score the target marketing effect. Assuming the target score is 0.2 and the current score is 0.18, the calculation process is as follows: ; The results show that the current marketing effectiveness score is lower than the target score, and the deviation index is , providing the marketing team with a clear target gap, indicating that measures need to be taken to improve marketing effectiveness or adjust target setting to make it more in line with the market and actual conditions.

[0046] The deviation index based on marketing effect is compared with the preset deviation tolerance threshold. If the deviation index exceeds the deviation tolerance threshold, it is judged that there is a problem with marketing and marketing needs to be stopped. The abnormal marketing effect prompt is immediately sent to the marketing management personnel to obtain marketing abnormality judgment information; The deviation index based on marketing effect is compared with the preset deviation tolerance threshold. The judgment process includes the calculation of the deviation index and the setting of the threshold. The deviation tolerance threshold is set according to the enterprise's risk management strategy, for example, it is set to 15%, and compared with the actual calculated deviation index. If the actual deviation exceeds this threshold, an abnormal marketing effect prompt is automatically sent to the marketing management personnel to ensure that marketing activities can be adjusted in time to prevent waste of resources and obtain marketing abnormality judgment information. This information is crucial for timely adjustment of marketing strategies and prevention of marketing risks.

[0047] See also Figure 8 , the steps to obtain marketing investment optimization information are: Based on the marketing abnormality judgment information, if the deviation index of the marketing effect is within the deviation tolerance threshold, and the marketing manager indicates that the network marketing needs to be continued, the deviation index of the marketing effect and the change rate information of the marketing effect are collected to obtain the input-related data; If the deviation index of the marketing effect is within the deviation tolerance threshold and the marketing manager indicates that online marketing needs to continue, data collection and analysis will be carried out. The process includes collecting details about the current marketing activities, such as advertising frequency, audience response and interaction data, and cost-benefit analysis. The team will also monitor changes in all marketing-related indicators, such as click-through rate, conversion rate and activity on social media. The data will be comprehensively analyzed to evaluate the rationality of the continued implementation of the marketing strategy and form detailed input-related data.

[0048] Based on the input-related data and the current marketing investment, the formula is: ; Calculate the adjusted marketing investment and obtain marketing investment optimization information; in, For the adjusted marketing investment, For current marketing investment, is the deviation index of marketing effect, is the rate of change of marketing effect, and is the adjustment factor.

[0049] formula: ; The benefit of the formula is that it provides a dynamic adjustment strategy for more effectively allocating marketing resources by combining the deviation index and the rate of change of marketing effectiveness to adjust current marketing investment.

[0050] Detailed explanation of the formula and the process of formula calculation and derivation: Set current marketing investment is 100,000 yuan, the deviation index is 0.2133, the rate of change The adjustment coefficient is 0.08. and They are 0.5 and 0.3 respectively. Calculate the adjusted marketing investment according to the formula:

[0051]

[0052] ; The adjusted marketing investment is RMB 113,065, an increase of approximately 13.065% from the current level, which shows that adjusting investment based on real-time data on marketing effectiveness can help to utilize resources more effectively to achieve or exceed preset marketing effectiveness scoring targets.

[0053] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. The network marketing system based on user matching is characterized by: The system comprises: The user group analysis module is based on the target online marketing product. Through market research, it extracts the user characteristics information of the target user group, including the user's age, hobbies, gender and income information, determines the matching degree between each characteristic and the product positioning, and evaluates the matching degree between the user group and the product based on the user proportion of each matching characteristic, and obtains the user group matching analysis results; The network marketing implementation module is based on the user group matching analysis results, and according to the matching, size, inclusiveness and popularity of the product in the user group, analyzes the recommended advertising costs and implements network marketing, including product advertising push through social media and network platforms, and adjusts the push frequency of marketing content according to the unit advertising push cost, to obtain product network marketing implementation information; The marketing effect analysis module is based on the product network marketing implementation information. Through data statistics, it analyzes the changes in product purchase volume, purchase rate of user groups, product reputation and product popularity before and after marketing, quantitatively evaluates the marketing effect, analyzes the changes in marketing effect over a period of time, evaluates the changing trend of marketing effect, and obtains marketing effect trend analysis information; The marketing investment analysis module evaluates the deviation between the current marketing effect and the target marketing effect based on the marketing effect trend analysis information, compares it with the preset deviation threshold, and determines whether marketing needs to be stopped. If marketing needs to continue, the subsequent marketing investment is adjusted according to the size of the marketing deviation and the changing trend of the marketing effect to obtain marketing investment optimization information.

2. The network marketing system based on user matching according to claim 1, characterized in that: The steps for obtaining the user group matching analysis result are: Based on the target online marketing product, extract the user characteristics information of the target user group, including age, hobbies, gender and income information, and count the proportion of users with each characteristic to obtain the user characteristics analysis results; Based on the user feature analysis results, according to the product positioning, the fit between each user feature and the product positioning is analyzed to obtain a product fit analysis result; Based on the product fit analysis results and user feature analysis results, the formula is: ； Calculate the matching scores between user groups and products to obtain the matching analysis results of user groups; in, represents the total number of features, Score the matching degree between user groups and products. is the scaling factor, Indicates The weight of the feature, Indicates The fit score of the feature, Indicates The proportion of each feature in the user group.

3. The network marketing system based on user matching according to claim 1, characterized in that: The steps of analyzing the recommended advertising costs are: Based on the user group matching analysis results, through user surveys, statistics are collected on the size and inclusiveness of the user group and the popularity of the product in the user group, and industry standard benchmark advertising cost data is collected to obtain advertising cost correlation information; Based on the advertising cost association information, the formula is: ； Calculate the advertising cost recommendation value and obtain the advertising cost recommendation information; in, Recommended value for advertising cost, Benchmark advertising costs for the industry standard, Score the matching degree between user groups and products. is the matching threshold, , and is the adjustment factor, is the size of the user group, is the standard user group size, is an indicator of the inclusiveness of the user group, is the inclusive standard value, To increase the popularity of the product among the user groups, is the standard value of popularity.

4. The network marketing system based on user matching according to claim 3 is characterized in that: The steps for obtaining the product network marketing implementation information are as follows: Based on the advertising cost recommendation information, the advertising cost set by the enterprise is received, and the cost required for each push is extracted through the target network marketing channel to obtain push frequency related data; Based on the push frequency correlation data, by formula: ； Calculate the adjusted push frequency; in, Indicates the adjusted push frequency. is the adjustment factor, It is the advertising cost set by the enterprise. It is the unit advertising push fee. It is the target marketing days; Based on the adjusted push frequency, online marketing is implemented, including product advertising push through social media and online platforms, to obtain product online marketing implementation information.

5. The network marketing system based on user matching according to claim 1, characterized in that: The steps for obtaining the quantitative evaluation of marketing effect are: Based on the product online marketing implementation information, obtain the product purchase volume before and after the marketing, user group purchase rate, product word-of-mouth score and product popularity information through data statistics to obtain data statistical results; Based on the statistical results, analyze the purchase volume change ratio, purchase rate change ratio, word-of-mouth score change ratio, and product popularity change ratio before and after marketing to obtain effect-related data; Based on the effect correlation data, by the formula: ； Calculate the marketing effect score and obtain marketing effect evaluation information; in, Score marketing effectiveness. , , and is the weight coefficient, is the scaling factor, is the purchase volume change ratio, is the purchase rate change ratio, is the change ratio of word-of-mouth rating, is the product awareness change ratio.

6. The network marketing system based on user matching according to claim 5, characterized in that: The steps for obtaining the marketing effect trend analysis information are as follows: Based on the marketing effect evaluation information, collect marketing effect data within a period of time, extract corresponding time points, and obtain marketing trend related data; Based on the marketing trend correlation data, through the formula: ； Calculate the rate of change of marketing effectiveness; in, Represents the rate of change of marketing effectiveness, For time Marketing effectiveness rating, For time Marketing effectiveness rating, is the time interval; Based on the changing rate of the marketing effect, and according to the size and positive and negative of the changing rate of the marketing effect, the changing trend of the marketing effect is evaluated to obtain marketing effect trend analysis information.

7. The network marketing system based on user matching according to claim 1, characterized in that: The steps for determining whether marketing needs to be stopped are: Based on the marketing effect trend analysis information, the current marketing effect score and the preset target marketing effect score are extracted, and the formula is: ； Calculate the deviation index of marketing effectiveness; in, is the deviation index of marketing effect, Score marketing effectiveness. Score the target marketing effectiveness; The deviation index based on the marketing effect is compared with the preset deviation tolerance threshold. If the deviation index exceeds the deviation tolerance threshold, it is judged that there is a problem with the marketing and the marketing needs to be stopped. An abnormal marketing effect prompt is immediately sent to the marketing management personnel to obtain marketing abnormality judgment information.

8. The network marketing system based on user matching according to claim 7 is characterized in that: The steps for obtaining the marketing investment optimization information are as follows: Based on the marketing abnormality judgment information, if the deviation index of the marketing effect is within the deviation tolerance threshold, and the marketing manager indicates that the network marketing needs to be continued, the deviation index of the marketing effect and the change rate information of the marketing effect are collected to obtain the input-related data; Based on the investment-related data and according to the current marketing investment, the formula is: ； Calculate the adjusted marketing investment and obtain marketing investment optimization information; in, For the adjusted marketing investment, For current marketing investment, is the deviation index of marketing effect, is the rate of change of marketing effect, and is the adjustment factor.

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