Data processing system based on Internet

Abstract

The present invention discloses a data processing system based on the Internet, and specifically relates to the technical field of data processing. The present invention sets a target product audience information investigation module to investigate the active conditions of the target product audience in different APPs and calculate the registration rate, usage and creative user attention. The present invention sets an advertising marketing fund allocation module to calculate the activity index of the target product audience in different software according to the APP registration rate and the APP usage, calculate the influence index of the creative user on the target product audience according to the APP registration rate and the creative user attention, and calculate the matching degree between the creative user's work and the target product at the same time, determine the advertising marketing funds invested in different APPs according to the APP activity index, and allocate the promotion marketing funds of different creative users according to the influence index and matching degree of the creative user. The advertising marketing fund allocation method can not only improve the accuracy of advertising marketing investment, but also improve the attractiveness of advertising marketing.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and more specifically, to an Internet-based data processing system. Background Art

[0002] With the vigorous development of new media technologies in my country, Internet advertising has gradually become the main force in the current advertising industry and the mainstream direction of brand promotion.

[0003] The existing Internet-based advertising and marketing data processing system allows brand marketers to purchase advertising qualifications on the recommendation pages and entry interfaces of different platforms to achieve all-round advertising. The audience and advertising coverage have been expanded. Users who browse advertisements are listed as potential users of the product, and one-to-one consulting services are provided after user information is collected to improve consumer satisfaction. The sales volume before and after advertising and marketing is calculated based on the sales situation before and after advertising and marketing, and the marketing benefits are calculated accordingly. The advertising and marketing quality is calculated based on the marketing benefits and consumer satisfaction. The higher the advertising and marketing quality, the higher the advertising and marketing effect.

[0004] However, the existing system still has some problems. The cost of conducting all-round advertising on the recommendation interface and entry interface of different platforms is relatively high, and the accuracy of advertising needs to be improved. In the actual software usage process, users often ignore sudden advertisements. How to increase the attractiveness of advertisements is also a problem that needs to be solved. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an Internet-based data processing system to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an Internet-based data processing system, comprising:

[0007] Target product audience determination module: used to determine the corresponding target product audience according to the function, price and product concept of the target product, and estimate the total number of target product audiences through the Internet;

[0008] Target product audience information investigation module; used to investigate the activeness of confirmed target product audiences in different apps, including app registration information collection unit, app usage time information statistics unit, app usage status recording unit, app registration rate calculation unit, app usage calculation unit, creation work browsing popularity calculation unit and creation user attention calculation unit;

[0009] Advertising and marketing fund allocation module: used to allocate advertising and marketing funds according to the activity of target audiences in different apps, including target product audience activity calculation unit, creator user influence calculation unit, app marketing fund allocation unit and creator user promotion fund allocation unit;

[0010] Marketing benefit improvement prediction module: used to predict the marketing benefits brought by different creative users in different APPs based on the activity index of different APPs, the influence index of different creative users in the APPs and the matching degree of corresponding creative styles, and calculate the expected marketing benefit improvement index;

[0011] Advertising and marketing data collection module: used to collect the target product's page views, add-to-cart quantity, purchase price and actual order quantity before and after advertising and marketing;

[0012] Advertising and marketing data processing module: used to process the collected product purchase data information, including data source tracing unit, data summary statistics unit and benefit improvement index calculation unit;

[0013] Marketing benefit judgment module: used to compare the marketing benefit improvement index with the expected marketing benefit improvement index to determine whether the corresponding marketing means meet the marketing purpose, and screen out the corresponding marketing plans that do not meet the marketing purpose;

[0014] Marketing benefit data processing module: used to calculate marketing cost performance based on the marketing benefits and costs of different marketing channels that meet marketing objectives;

[0015] Marketing plan selection module: used to rank the marketing cost-effectiveness and select the top three marketing plans for further execution;

[0016] Database: used to store all data information in the system.

[0017] Preferably, the APP registration information collection unit in the target product audience information investigation module is used to collect the real-name registration information of the target product audience in different APPs; the APP usage time information statistics unit is used to count the time spent by the target product audience on different APPs each month; the APP usage statistics unit is used to count the historical browsing records, historical comment records, historical like records, historical attention records, historical collection records and historical forwarding records of the target product audience on different APPs in the past month; the APP registration rate calculation unit is used to calculate the APP registration rate according to the proportion of the number of registrations of the target product audience in the APP registration information to the total target product audience; the APP usage calculation unit is used to calculate the APP usage according to the proportion of the time spent by the target audience on each APP each month; the creative work browsing heat calculation unit is used to calculate the browsing rate, comment heat, like rate, forwarding rate and collection rate of a certain creative work by the target product audience in different APPs, and thereby calculate the creative work browsing heat; the creative user attention calculation unit is used to calculate the attention rate of the target product audience in different APPs to a certain creative user and the average browsing heat of the creative content, and thereby calculate the creative user attention.

[0018] Preferably, the specific calculation process in the target product audience information survey module is as follows:

[0019] A1. Calculate the APP registration rate α c , the specific calculation formula is: where α a is the number of target audience registrations in the APP registration information, and α0 is the number of all target audiences;

[0020] A2. Calculate APP usage β c , the specific calculation formula is: where β a is the foreground occupancy time of each software each month, β0 is the total foreground occupancy time of different software each month;

[0021] A3. Calculate the browsing rate of a certain creative work by the target product audience. c 、Comment popularity b c 、Like ratec a , forwarding rate c and collection rate f c The specific calculation formula is as follows: View rate where a i The number of views and comment popularity of each target product audience who has viewed the same creative work where b a The number of reviews published for the target product audience, a e The number of target product audiences who have viewed a certain creative work, the like rate where c e The number of likes and forwarding rates of the target audience for the same creative work where e a The number of times the same creative work is forwarded, e b The number of target product audiences for forwarding works, collection rate where f a The number of collections of the same creative work by the target product audience;

[0022] A4. Calculate the browsing popularity of creative works γ c , the specific calculation formula is: Among them, x1, x2, x3, x4, and x5 are the proportional coefficients of the corresponding factors. The browsing popularity of all works of the creator of the corresponding work is calculated and summarized. After filtering out the data with large deviations, the average value γ is calculated. e , the specific calculation formula is:

[0023] A5. Calculate the attention rate d of the target product audience in different apps to a certain creative user c , the specific calculation formula is: where d a is the number of target audiences who follow the content creator, db is the total number of target audiences who have viewed the content creator’s works;

[0024] A6. Calculate the creation user attention δ c , the specific calculation formula is: Among them, y1 and y2 are corresponding constant coefficients, y1>0, y2>0.

[0025] Preferably, the specific process of fund allocation in the advertising and marketing fund allocation module is as follows:

[0026] Target product audience activity calculation unit: Calculate the target product audience activity index X in different software based on APP registration rate and APP usage a , the specific calculation formula is: X a =a e1 *(α c ) 3 +a e2 *β c , where a e1 、a e2 is the corresponding constant coefficient;

[0027] Creator user influence calculation unit: Calculate the influence index Y of the creator user on the target product audience based on the APP registration rate and the creator user's attention a , the specific calculation formula is: where be1 、b e2 is the index adjustment factor of the corresponding factor;

[0028] APP marketing fund allocation unit: calculate the activity index of different APP target product audiences and rank them, calculate the difference between the corresponding activity values ​​of adjacent rankings and the closeness of the activity value of the lower ranked software h a , the specific calculation formula is: Where X ai , X aj is the activity of the adjacent ranking APPs, ij = 1, and the first ranking APP invests advertising marketing funds W ta 30%, the second-ranked APP invested in advertising and marketing funds t2 =W t1 *(1-h a1 ), according to this method, the advertising and marketing funds invested by the APP ranked j are W tj =W ti *(1-h ai ), where ij = 1;

[0029] Creative user promotion fund allocation unit: calculate the influence index of different creative users in the APP and rank them, classify the user's creative content and set keywords of different levels and categories, match them with the target product, count the keyword overlaps and calculate the matching degree Z a The specific calculation formula is: Z a =c b1 *ξ1+c b2 *ξ2+......+c bn *ξ n , where c bi is the proportional coefficient of each level, ξ i is the ratio of the number of overlapping keywords at each level to the number of keywords. i When it is 0, the calculation result is the target matching degree, and the difference ε between the two rankings is calculated. The specific calculation formula is: Among them, F i S is the influence index ranking of the creator user. j To rank the overlap between creative user works and target products, φ is a constant adjustment parameter. The corresponding creative users are ranked according to the degree of difference and are allocated 30%, 25%, 20%, 15%, and 10% of the APP advertising marketing funds respectively.

[0030] Preferably, the marketing benefit improvement prediction module calculates the expected marketing benefit improvement index Q according to the activity index of different APPs, the influence index of different creative users in the APPs, and the creative style matching degree. cThe specific calculation formula is: Among them, j1, j2, and j3 are proportional factors corresponding to different factors. The expected marketing benefit improvement indexes corresponding to different creative users in the selected APP are summarized to calculate the overall expected marketing benefit improvement index Q e , the specific calculation formula is: Q e =∑Q ci .

[0031] Preferably, the data source tracing stage in the advertising and marketing data processing module is used to trace the purchase information of the target product after marketing to confirm the purchase link reprinting platform and reprinting user; the data aggregation and statistical unit is used to aggregate the purchase information from the same reprinting platform and the same reprinting user; the benefit improvement index calculation unit is used to calculate the normal sales benefit and marketing benefit based on the number of views, added purchases, purchase price and actual order volume of the target product before and after marketing, and thereby calculate the marketing benefit improvement index.

[0032] Preferably, the specific calculation process in the advertising and marketing data processing module is as follows:

[0033] B1. Count the number of product views in the previous month of marketing a 、Product purchase quantity g a 、Products with different selling prices a The actual order quantity of the corresponding product v a , calculate product sales revenue W a , the specific calculation formula is:

[0034] B2. Count the number of product views generated by different marketing sources within one month after marketing. b 、Product purchase quantity g b , product selling price p b The actual order quantity of the corresponding product v b , calculate product marketing revenue W b , the specific calculation formula is:

[0035] B3. Calculate sales benefit W ac and marketing benefits bc , the specific calculation formula is: W ac =W a -W a0 , where W a0 is the total cost of products sold, W bc =W b -W t -W b0 , where W t is the corresponding advertising and marketing funds, W b0 Cost of products sold for marketing;

[0036] B4. Calculate the marketing benefit improvement index Q corresponding to each marketing source a , the specific calculation formula is:

[0037] B5. Calculate the marketing benefit improvement index Q corresponding to all marketing sources t , the specific calculation formula is: Q t =∑Q ai .

[0038] Preferably, the marketing benefit judgment module first judges the overall marketing benefit improvement index. If the actual marketing benefit improvement index is greater than the expected marketing benefit improvement index, it means that the corresponding marketing plan meets the current marketing purpose. If the actual marketing benefit improvement index is less than the expected marketing benefit improvement index, it means that the corresponding marketing plan does not meet the current marketing purpose. The specific situation is analyzed, and the marketing benefit improvement index brought by the corresponding creative user in each selected APP is compared with the corresponding expected marketing benefit improvement index to determine whether there is a marketing plan that does not meet the current marketing purpose, and adjust the subsequent marketing fund allocation method.

[0039] Preferably, the marketing benefit data processing module calculates the marketing cost performance w according to the marketing benefits and costs corresponding to different marketing sources. v The specific formula is:

[0040] Technical effects and advantages of the present invention:

[0041] 1. The present invention sets a target product audience determination module to determine the target audience. The present invention sets a target product audience information investigation module to investigate the activity of the target product audience in different APPs, and respectively calculates the registration rate, usage and creative user attention of the target product audience in different APPs. The present invention sets an advertising marketing fund allocation module to calculate the activity index of the target product audience in different software according to the APP registration rate and the APP usage and rank them. At the same time, the advertising marketing investment funds corresponding to the APPs with different rankings are determined according to the difference between the corresponding activities of adjacent rankings and the degree of proximity to the activity value of the lower-ranked software, thereby avoiding additional economic losses caused by all-round advertising and realizing the centralized investment of advertising funds. The influence index of the creative user on the target product audience is calculated according to the APP registration rate and the creative user attention, reflecting the influence of the creative user on the target product audience. At the same time, the matching degree between the creative user's work and the target product is calculated and ranked. According to the difference between the two rankings, the selected creative user is invested with corresponding advertising marketing funds. According to the activity of the target product audience and the influence index of the creative user on the target product audience, the corresponding advertising marketing funds are invested, which not only improves the accuracy of advertising marketing investment, but also improves the attractiveness of advertising marketing.

[0042] 2. The present invention sets up a marketing benefit improvement prediction module, which predicts the marketing benefits brought by different creative users in different APPs according to the activity index of different APPs, the influence index of different creative users in the APPs and the corresponding creative style matching degree, and calculates the expected marketing benefit improvement index and the overall expected marketing benefit sign index corresponding to each advertising marketing channel. The present invention sets up an advertising marketing data collection module and an advertising marketing data processing module to collect and process the sales information of the target products before and after marketing, respectively calculates the marketing benefit improvement index corresponding to each marketing source and summarizes and calculates the marketing benefit improvement index. The present invention sets up a marketing benefit judgment module to judge whether the selected marketing plan meets the marketing purpose. The present invention sets up a marketing benefit data processing module to calculate the marketing cost-effectiveness of the marketing plan that meets the marketing purpose, and sets up a marketing plan selection module to select the marketing plans with the top three marketing cost-effectiveness values ​​to continue to execute, which further optimizes the marketing plan and helps to improve the marketing benefit. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a system structure block diagram of the present invention.

[0044] Figure 2 It is an operation flow chart of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] like Figure 1 The embodiment shown provides an Internet-based data processing system, including a target product audience determination module, a target product audience information investigation module, an advertising and marketing fund allocation module, a marketing benefit improvement prediction module, an advertising and marketing data collection module, an advertising and marketing data processing module, a marketing benefit judgment module, a marketing benefit data processing module, a marketing plan selection module and a database.

[0047] The target product audience determination module, the target product audience information investigation module, and the advertising and marketing fund allocation module are connected in sequence. The advertising and marketing fund allocation module is respectively connected to the marketing benefit improvement prediction module and the advertising and marketing data collection module. The advertising and marketing data collection module is connected to the advertising and marketing data processing module. The marketing benefit improvement prediction module and the advertising and marketing data processing module are respectively connected to the marketing benefit judgment module. The marketing benefit judgment module, the marketing benefit data processing module and the marketing plan selection module are connected in sequence. The database is connected to all modules in the system.

[0048] The target product audience determination module is used to determine the corresponding target product audience according to the function, price and product concept of the target product, and estimate the total number of the target product audience through the Internet.

[0049] The target product audience information investigation module is used to investigate the activeness of the confirmed target product audience in different APPs, and includes an APP registration information collection unit, an APP usage time information statistics unit, an APP usage status recording unit, an APP registration rate calculation unit, an APP usage calculation unit, a creation work browsing popularity calculation unit and a creation user attention calculation unit.

[0050] Furthermore, the APP registration information collection unit in the target product audience information investigation module is used to collect the real-name registration information of the target product audience in different APPs; the APP usage time information statistics unit is used to count the time spent by the target product audience on different APPs each month; the APP usage statistics unit is used to count the historical browsing records, historical comment records, historical like records, historical attention records, historical collection records and historical forwarding records of the target product audience on different APPs in the past month; the APP registration rate calculation unit is used to calculate the APP registration rate according to the proportion of the number of registrations of the target product audience in the APP registration information to the total target product audience; the APP usage calculation unit is used to calculate the APP usage according to the proportion of the time spent by the target audience on each APP each month; the creative work browsing heat calculation unit is used to calculate the browsing rate, comment heat, like rate, forwarding rate and collection rate of a certain creative work by the target product audience in different APPs, and thereby calculate the creative work browsing heat; the creative user attention calculation unit is used to calculate the attention rate of the target product audience in different APPs to a certain creative user and the average browsing heat of the creative content, and thereby calculate the creative user attention.

[0051] Furthermore, the specific calculation process in the target product audience information survey module is as follows:

[0052] A1. Calculate the APP registration rate α c , the specific calculation formula is: where α a is the number of target audience registrations in the APP registration information, and α0 is the number of all target audiences;

[0053] A2. Calculate APP usage β c , the specific calculation formula is: where β a is the foreground occupancy time of each software each month, β0 is the total foreground occupancy time of different software each month;

[0054] A3. Calculate the browsing rate of a certain creative work by the target product audience. c 、Comment popularity b c 、Like ratec a , forwarding rate c and collection rate f c The specific calculation formula is as follows: View rate where a i The number of views and comment popularity of each target product audience who has viewed the same creative work where b a The number of reviews published for the target product audience, a e The number of target product audiences who have viewed a certain creative work, the like rate where c e The number of likes and forwarding rates of the target audience for the same creative work where e a The number of times the same creative work is forwarded, e b The number of target product audiences for forwarding works, collection rate where f a The number of collections of the same creative work by the target product audience;

[0055] A4. Calculate the browsing popularity of creative works γ c , the specific calculation formula is: Among them, x1, x2, x3, x4, and x5 are the proportional coefficients of the corresponding factors. The browsing popularity of all works of the creator of the corresponding work is calculated and summarized. After filtering out the data with large deviations, the average value γ is calculated. e , the specific calculation formula is:

[0056] A5. Calculate the attention rate d of the target product audience in different apps to a certain creative user c , the specific calculation formula is: where d a is the number of target audiences who follow the content creator, db is the total number of target audiences who have viewed the content creator’s works;

[0057] A6. Calculate the creation user attention δ c , the specific calculation formula is: Among them, y1 and y2 are corresponding constant coefficients, y1>0, y2>0.

[0058] The advertising and marketing fund allocation module is used to allocate advertising and marketing funds according to the activity of the target audience in different APPs, including a target product audience activity calculation unit, a creative user influence calculation unit, an APP marketing fund allocation unit and a creative user promotion fund allocation unit.

[0059] Furthermore, the specific process of fund allocation in the advertising and marketing fund allocation module is as follows:

[0060] Target product audience activity calculation unit: Calculate the target product audience activity index X in different software based on APP registration rate and APP usage a , the specific calculation formula is: X a =a e1 *(α c ) 3 +a e2 *β c , where a e1 、a e2is the corresponding constant coefficient;

[0061] Creator user influence calculation unit: Calculate the influence index Y of the creator user on the target product audience based on the APP registration rate and the creator user's attention a , the specific calculation formula is: where b e1 、b e2 is the index adjustment factor of the corresponding factor;

[0062] APP marketing fund allocation unit: calculate the activity index of different APP target product audiences and rank them, calculate the difference between the corresponding activity values ​​of adjacent rankings and the closeness of the activity value of the lower ranked software h a , the specific calculation formula is: Where X ai , X aj is the activity of the adjacent ranking APPs, ij = 1, and the first ranking APP invests advertising marketing funds W ta 30%, the second-ranked APP invested in advertising and marketing funds t2 =W t1 *(1-h a1 ), according to this method, the advertising and marketing funds invested by the APP ranked j are W tj =W ti *(1-h ai ), where ij = 1;

[0063] Creative user promotion fund allocation unit: calculate the influence index of different creative users in the APP and rank them, classify the user's creative content and set keywords of different levels and categories, match them with the target product, count the keyword overlaps and calculate the matching degree Z a The specific calculation formula is: Z a =c b1 *ξ1+c b2 *ξ2+......+c bn *ξ n , where c bi is the proportional coefficient of each level, ξ i is the ratio of the number of overlapping keywords at each level to the number of keywords. i When it is 0, the calculation result is the target matching degree, and the difference degree ε between the two rankings is calculated. The specific calculation formula is: where F i S is the influence index ranking of the creator user. jTo rank the overlap between creative user works and target products, φ is a constant adjustment parameter. The corresponding creative users are ranked according to the degree of difference and are allocated 30%, 25%, 20%, 15%, and 10% of the APP advertising marketing funds respectively.

[0064] The marketing benefit improvement prediction module is used to predict the marketing benefits brought by different creative users in different APPs based on the activity index of different APPs, the influence index of different creative users in the APPs and the corresponding creative style matching, and calculate the expected marketing benefit improvement index.

[0065] Furthermore, the marketing benefit improvement prediction module calculates the expected marketing benefit improvement index Q according to the activity index of different APPs, the influence index of different creative users in the APPs, and the creative style matching degree. c The specific calculation formula is: Among them, j1, j2, and j3 are proportional factors corresponding to different factors. The expected marketing benefit improvement indexes corresponding to different creative users in the selected APP are summarized to calculate the overall expected marketing benefit improvement index Q e , the specific calculation formula is: Q e =∑Q ci .

[0066] The advertising and marketing data collection module is used to collect the page views, add-to-cart quantity, purchase price and actual order quantity of the target product before and after the advertising and marketing.

[0067] The advertising and marketing data processing module is used to process the collected product purchase data information, including a data source tracing unit, a data summary statistics unit and a benefit improvement index calculation unit.

[0068] Furthermore, the data source tracing stage in the advertising and marketing data processing module is used to trace the purchase information of the target product after marketing to confirm the purchase link reprinting platform and reprinting user; the data aggregation and statistical unit is used to aggregate the purchase information from the same reprinting platform and the same reprinting user; the benefit improvement index calculation unit is used to calculate the normal sales benefit and marketing benefit based on the number of views, added purchases, purchase price and actual order volume of the target product before and after marketing, and thereby calculate the marketing benefit improvement index.

[0069] Furthermore, the specific calculation process in the advertising and marketing data processing module is as follows:

[0070] B1. Count the number of product views in the previous month of marketing a 、Product purchase quantity g a 、Products with different selling prices a The actual order quantity of the corresponding product v a , calculate product sales revenue Wa , the specific calculation formula is:

[0071] B2. Count the number of product views generated by different marketing sources within one month after marketing. b 、Product purchase quantity g b , product selling price p b The actual order quantity of the corresponding product v b , calculate product marketing revenue W b , the specific calculation formula is:

[0072] B3. Calculate sales benefit W ac and marketing benefits bc , the specific calculation formula is: W ac =W a -W a0 , where W a0 is the total cost of products sold, W bc =W b -W t -W b0 , where W t is the corresponding advertising and marketing funds, W b0 Cost of products sold for marketing;

[0073] B4. Calculate the marketing benefit improvement index Q corresponding to each marketing source a , the specific calculation formula is:

[0074] B5. Calculate the marketing benefit improvement index Q corresponding to all marketing sources t , the specific calculation formula is: Q t =∑Q ai .

[0075] The marketing benefit judgment module is used to compare the marketing benefit improvement index with the expected marketing benefit improvement index to judge whether the corresponding marketing means meet the marketing purpose, and screen out the corresponding marketing plans that do not meet the marketing purpose.

[0076] Furthermore, the marketing benefit judgment module first judges the overall marketing benefit improvement index. If the actual marketing benefit improvement index is greater than the expected marketing benefit improvement index, it means that the corresponding marketing plan meets the current marketing purpose. If the actual marketing benefit improvement index is less than the expected marketing benefit improvement index, it means that the corresponding marketing plan does not meet the current marketing purpose. The specific situation is analyzed, and the marketing benefit improvement index brought by the corresponding creative user in each selected APP is compared with the corresponding expected marketing benefit improvement index to determine whether there is a marketing plan that does not meet the current marketing purpose, and adjust the subsequent marketing fund allocation method.

[0077] The marketing benefit data processing module is used to calculate the marketing cost performance ratio according to the marketing benefits and costs of different marketing channels that meet the marketing objectives.

[0078] Furthermore, the marketing benefit data processing module calculates the marketing cost performance w according to the marketing benefits and costs corresponding to different marketing sources. v The specific formula is:

[0079] The marketing scheme selection module is used to rank the marketing cost performance ratios and select the top three marketing schemes with the highest cost performance ratios for further execution.

[0080] The database is used to store all data information in the system.

[0081] like Figure 2 This embodiment provides an operation flow of an Internet-based data processing system, including the following steps:

[0082] S1: Determine the target product audience based on the function, price and product concept of the target product, and estimate the total number of target product audiences through the Internet;

[0083] S2: Investigate the activeness of the confirmed target product audience in different apps, calculate the app registration rate based on the real-name registration information of the target product audience in different apps, calculate the app usage based on the time the target product audience spends on different apps each month, calculate the target product audience's browsing popularity for a certain creative work based on the target product audience's historical browsing records, historical comment records, historical like records, historical collection records, and historical forwarding records on different apps in the past month, calculate the attention rate based on the target product audience's historical attention records on different apps in the past month, and calculate the creative user attention based on the attention rate and the average browsing popularity of the creative content;

[0084] S3: Calculate the activity index of the target product audience in different softwares based on the APP registration rate and APP usage. Determine the advertising and marketing funds invested in different APPs based on the APP activity ranking and the difference between the corresponding activities of adjacent rankings and the closeness of the activity values ​​of the lower-ranked software. Calculate the influence index of the creator user on the target product audience based on the APP registration rate and the creator user's attention. At the same time, calculate the matching degree between the creator user's work style and the target product. Select the creator user for product promotion and allocate product promotion funds based on the difference between the influence ranking and the matching ranking.

[0085] S4: Predict the marketing benefits brought by different creative users in different APPs based on the activity index of different APPs, the influence index of different creative users in the APPs and the matching degree of corresponding creative styles, and calculate the expected marketing benefit improvement index;

[0086] S5: Collect the page views, added-to-cart quantity, purchase price and actual order quantity of the target product before the advertising marketing, and the page views, added-to-cart quantity, purchase price and actual order quantity of the target product corresponding to different marketing sources after the marketing;

[0087] S6: trace the purchase information of the target product after the marketing to confirm the purchase link reprinting platform and reprinting user, aggregate the purchase information from the same reprinting platform and the same reprinting user, calculate the normal sales benefit and marketing benefit based on the target product's page views, add-to-cart quantity, purchase price and actual order quantity before and after the marketing, and calculate the marketing benefit improvement index accordingly;

[0088] S7: First, judge the overall marketing benefit improvement index. If the actual marketing benefit improvement index is greater than the expected marketing benefit improvement index, it means that the corresponding marketing plan meets the current marketing purpose. If the actual marketing benefit improvement index is less than the expected marketing benefit improvement index, it means that the corresponding marketing plan does not meet the current marketing purpose. Analyze the specific situation and compare the marketing benefit improvement index brought by the corresponding creative users in each selected APP with the corresponding expected marketing benefit improvement index to determine whether there is a marketing plan that does not meet the current marketing purpose and adjust the subsequent marketing fund allocation method.

[0089] S8: Calculate the marketing cost-effectiveness based on the marketing benefits and costs of different marketing channels that meet the marketing objectives;

[0090] S9: Rank the marketing cost-effectiveness and select the top three marketing plans with the highest cost-effectiveness to continue implementing.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An Internet-based data processing system, characterized in that: include: Target product audience determination module: used to determine the corresponding target product audience according to the function, price and product concept of the target product, and estimate the total number of target product audiences through the Internet; Target product audience information investigation module; used to investigate the activeness of confirmed target product audiences in different apps, including app registration information collection unit, app usage time information statistics unit, app usage status recording unit, app registration rate calculation unit, app usage calculation unit, creation work browsing popularity calculation unit and creation user attention calculation unit; The APP registration information collection unit in the target product audience information investigation module is used to collect the real-name registration information of the target product audience in different APPs; the APP usage time information statistics unit is used to count the time spent by the target product audience on different APPs each month; the APP usage statistics unit is used to count the historical browsing records, historical comment records, historical like records, historical attention records, historical collection records and historical forwarding records of the target product audience on different APPs in the past month; the APP registration rate calculation unit is used to calculate the APP registration rate according to the proportion of the number of registrations of the target product audience in the APP registration information to the total target product audience; the APP usage calculation unit is used to calculate the APP usage according to the proportion of the time spent by the target audience on each APP each month; the creative work browsing heat calculation unit is used to calculate the browsing rate, comment heat, like rate, forwarding rate and collection rate of a certain creative work by the target product audience in different APPs, and thereby calculate the creative work browsing heat; The creator user attention calculation unit is used to calculate the attention rate of the target product audience in different apps to a certain creator user and the average browsing popularity of the created content, and thus calculate the creator user attention; The specific calculation process of the target product audience information survey module is as follows: A1. Calculate the APP registration rate α c , the specific calculation formula is: , where α a is the number of target audience registrations in the APP registration information, and α0 is the number of all target audiences; A2. Calculate APP usage β c , the specific calculation formula is: , where β a is the foreground occupancy time of each software each month, β0 is the total foreground occupancy time of different software each month; A3. Calculate the browsing rate of a certain creative work by the target product audience. c 、Comment popularity b c 、Like ratec a , forwarding rate c and collection rate f c The specific calculation formula is as follows: View rate , where a i The number of views and comment popularity of each target product audience who has viewed the same creative work , where b a The number of reviews published for the target product audience, a e The number of target product audiences who have viewed a certain creative work, the like rate , where c e The number of likes and forwarding rates of the target audience for the same creative work , where e a The number of times the same creative work is forwarded, e b The number of target product audiences for forwarding works, collection rate , where f a The number of collections of the same creative work by the target product audience; A4. Calculate the browsing popularity of creative works γ c , the specific calculation formula is: , where x1, x2, x3, x4, and x5 are the proportional coefficients of the corresponding factors. The browsing popularity of all works of the creator corresponding to the creation work is calculated and summarized. After filtering out the data with large deviations, the average value γ is calculated. e , the specific calculation formula is: ; A5. Calculate the attention rate d of the target product audience in different apps to a certain creative user c , the specific calculation formula is: , where d a To focus on the number of target audiences of the content creator, d b The total number of target audiences who have viewed the content creator's works; A6. Calculate the creation user attention δ c , the specific calculation formula is: , where y1 and y2 are corresponding constant coefficients, y1>0, y2>0; Advertising and marketing fund allocation module: used to allocate advertising and marketing funds according to the activity of target audiences in different apps, including target product audience activity calculation unit, creator user influence calculation unit, app marketing fund allocation unit and creator user promotion fund allocation unit; The specific process of fund allocation in the advertising and marketing fund allocation module is as follows: Target product audience activity calculation unit: Calculate the target product audience activity index X in different software based on APP registration rate and APP usage a , the specific calculation formula is: , where a e1 、a e2 is the corresponding constant coefficient; Creator user influence calculation unit: Calculate the influence index Y of the creator user on the target product audience based on the APP registration rate and the creator user's attention a , the specific calculation formula is: , where b e1 、b e2 is the index adjustment factor of the corresponding factor; APP marketing fund allocation unit: calculate the activity index of different APP target product audiences and rank them, calculate the difference between the corresponding activity values ​​of adjacent rankings and the closeness of the activity value of the lower ranked software h a , the specific calculation formula is: , where X ai , X aj is the activity of the adjacent ranking APPs, ji=1, the top ranking APP invests advertising marketing funds W ta 30% of the advertising and marketing funds invested by the second-ranked APP , according to this method, the funds are allocated, corresponding to the advertising and marketing funds invested by the APP ranked j , where ji=1; Creative user promotion fund allocation unit: calculate the influence index of different creative users in the APP and rank them, classify the user's creative content and set keywords of different levels and categories, match them with the target product, count the keyword overlaps and calculate the matching degree Z a The specific calculation formula for the latter ranking is: , where c bi is the proportional coefficient of each level, ξ i is the ratio of the number of overlapping keywords at each level to the number of keywords. i When it is 0, the calculation result is the target matching degree, and the difference degree ε between the two rankings is calculated. The specific calculation formula is: , where Fi is the influence index ranking of the creator, Sj is the ranking of the overlap between the creator's works and the target product, and φ is a constant adjustment parameter. The corresponding creators are ranked according to the degree of difference and are allocated 30%, 25%, 20%, 15%, and 10% of the APP advertising marketing funds respectively; Marketing benefit improvement prediction module: used to predict the marketing benefits brought by different creative users in different APPs based on the activity index of different APPs, the influence index of different creative users in the APPs and the matching degree of corresponding creative styles, and calculate the expected marketing benefit improvement index; Advertising and marketing data collection module: used to collect the target product's page views, add-to-cart quantity, purchase price and actual order quantity before and after advertising and marketing; Advertising and marketing data processing module: used to process the collected product purchase data information, including data source tracing unit, data summary statistics unit and benefit improvement index calculation unit; Marketing benefit judgment module: used to compare the marketing benefit improvement index with the expected marketing benefit improvement index to determine whether the corresponding marketing means meet the marketing purpose, and screen out the corresponding marketing plans that do not meet the marketing purpose; Marketing benefit data processing module: used to calculate marketing cost performance based on the marketing benefits and costs of different marketing channels that meet marketing objectives; Marketing plan selection module: used to rank the marketing cost-effectiveness and select the marketing plans with the top three cost-effectiveness values ​​for further execution.

2. The Internet-based data processing system according to claim 1, characterized in that: The marketing benefit improvement prediction module calculates the expected marketing benefit improvement index Q according to the activity index of different APPs, the influence index of different creative users in the APPs, and the matching degree of creative styles. c The specific calculation formula is: , where j1, j2, and j3 are proportional factors corresponding to different factors. The expected marketing benefit improvement indexes corresponding to different creators in the selected APP are summarized to calculate the overall expected marketing benefit improvement index Q e , the specific calculation formula is: .

3. The Internet-based data processing system according to claim 1, characterized in that: The data source tracing unit in the advertising and marketing data processing module is used to trace the purchase information of the target product after marketing to confirm the purchase link reprinting platform and reprinting user; the data summary and statistics unit is used to summarize the purchase information from the same reprinting platform and the same reprinting user; the benefit improvement index calculation unit is used to calculate the normal sales benefit and marketing benefit based on the number of views, added purchases, purchase price and actual order volume of the target product before and after marketing, and thereby calculate the marketing benefit improvement index.

4. The Internet-based data processing system according to claim 3, characterized in that: The specific calculation process of the advertising and marketing data processing module is as follows: B1. Count the number of product views in the previous month of marketing a 、Product purchase quantity g a 、Products with different selling prices ai The actual order quantity of the corresponding product v ai , calculate product sales revenue W a , the specific calculation formula is: ; B2. Count the number of product views generated by different marketing sources within one month after marketing. b 、Product purchase quantity g b , product selling price p b The actual order quantity of the corresponding product v b , calculate product marketing revenue W b , the specific calculation formula is: ; B3. Calculate sales benefit W ac and marketing benefits bc , the specific calculation formula is: , where W a0 is the total cost of products sold, , where W t is the corresponding advertising and marketing funds, W b0 Cost of products sold for marketing; B4. Calculate the marketing benefit improvement index Q corresponding to each marketing source a , the specific calculation formula is: ; B5. Calculate the marketing benefit improvement index Q corresponding to all marketing sources t , the specific calculation formula is: .

5. The Internet-based data processing system according to claim 1, characterized in that: In the marketing benefit judgment module, the overall marketing benefit improvement index is first judged. If the actual marketing benefit improvement index is greater than the expected marketing benefit improvement index, it means that the corresponding marketing plan meets the current marketing purpose. If the actual marketing benefit improvement index is less than the expected marketing benefit improvement index, it means that the corresponding marketing plan does not meet the current marketing purpose. The specific situation is analyzed, and the marketing benefit improvement index brought by the corresponding creative user in each selected APP is compared with the corresponding expected marketing benefit improvement index to determine whether there is a marketing plan that does not meet the current marketing purpose, and adjust the subsequent marketing fund allocation method.

6. The Internet-based data processing system according to claim 4, characterized in that: The marketing benefit data processing module calculates the marketing cost performance w according to the marketing benefits and costs corresponding to different marketing sources v The specific formula is: .

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