Internet-based cross-platform consumption discount system
By designing a cross-platform consumption discount system based on the Internet, using personalized analysis and discount adjustment modules, we provide users with personalized discount sorting and adjust discount activities according to sales results, the problem of lack of personalized services and discount activities in the existing technology is solved, and the effect of improving user experience and merchant sales is achieved.
Patent Information
- Application Number
- CN202411611602.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-12
- Publication Date
- 2025-03-18
AI Technical Summary
The existing technology lacks personalized services when sorting product discounts, which cannot reflect user preferences, reduce user experience and desire to purchase. At the same time, merchants cannot adjust discount activities based on the sales situation of the sales platform, resulting in poor results.
Design a cross-platform consumption discount system based on the Internet, including user capture module, personalized analysis unit, discount recommendation module and discount adjustment module. By analyzing users’ purchase records and preferences, the discount recommendation module provides users with personalized discount sorting, and adjusts discount activities based on sales results through the discount adjustment module.
It has achieved a personalized and targeted discount platform for users, which can improve users' purchasing experience and convenience, stimulate users' desire to purchase, expand merchant customer base, increase sales, and improve the promotional effect of merchant discount activities.
Smart Images
Figure CN119648288A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of Internet technology, and in particular to a cross-platform consumption preferential system based on the Internet. Background Art
[0002] With the continuous development of the Internet and e-commerce industries, merchants are provided with a variety of commodity sales platforms. However, different sales platforms are independent of each other and have different target audiences. Therefore, when commodities are on sale, the discounts set for different sales platforms are different. In order to expand the merchant customer base and increase sales, it is necessary to break down platform barriers, integrate consumption resources from multiple different platforms, and provide consumers with a system of preferential information and consumption convenience.
[0003] In the prior art, when a user browses a discount system for a product, the discounts on each sales platform are sorted mainly by price, and then the user selects a sales platform. However, this search and sorting method has at least the following deficiencies: 1. Different users have different preferences for each sales platform, and when sorting the sales platforms in the prior art, there is a lack of reference to the user's preference for each sales platform, thereby failing to provide personalized services to users, and also reducing the user's experience with the discount platform, failing to increase the user's desire to buy.
[0004] 2. The discounts and styles of different sales platforms have different impacts on the shopping experience of different types of user groups. However, in the prior art, when sorting the discounts of each sales platform, the users are not classified and then the operational service effects of different sales platforms on user groups are not analyzed, thereby failing to improve the user's subsequent purchase experience, and failing to improve the image of the sales platform and the merchant stores on the sales platform in the eyes of users.
[0005] 3. Different users have different preferences for discount amounts and gifts, and the existing technology sorts by amount, but lacks the ability to sort the discounts of various sales platforms based on the user's preferences for discount amounts and gifts. It is unable to provide users with a personalized and targeted sales platform, and is unable to effectively stimulate users' desire to buy, thereby failing to expand the merchant's customer base and increase sales.
[0006] 4. In the prior art, the discount platform mainly provides users with discount query services, but merchants cannot analyze the sales of merchants on various sales platforms based on the data in the discount platform, and thus cannot make targeted adjustments to discount activities based on the sales of each sales platform, cannot increase the sales of goods, and cannot effectively expand the merchant's customer base, thereby reducing the effectiveness of discount activities. Summary of the invention
[0007] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a cross-platform consumption discount system based on the Internet.
[0008] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an Internet-based cross-platform consumer discount system, including: a user capture module, a personalized analysis unit, a discount recommendation module and a discount adjustment module.
[0009] The user capture module is used to obtain the user's purchase record when the user searches for discounts for target products in the discount system.
[0010] The personalized analysis unit includes a platform preference analysis module and a platform discount analysis module.
[0011] The platform preference analysis module is used to use the user's purchase records to analyze the user's preference weights for each sales platform, and set the user's tags in the target stores of each sales platform for the target product, and then analyze the operational effect value of the target product on the user tag group in the target stores of each sales platform.
[0012] The platform discount analysis module is used to obtain discount data of the target product on each sales platform, and analyze the attractiveness of the discount of the target product on each sales platform to the user by using the user's purchase record.
[0013] The discount recommendation module is used to sort the discounts of each sales platform by using the user's preference weight for each sales platform, the operation effect value of the target product in the target store of each sales platform for the user tag group, and the attractiveness of the discounts of the target product on each sales platform to the user.
[0014] The discount adjustment module is used to obtain the consumption data of the target product on each sales platform after a preset sales cycle, analyze the sales effect of the target product on each sales platform, and then adjust the discounts on each sales platform.
[0015] The beneficial effects of the present invention are as follows: the present invention provides a cross-platform consumer discount system based on the Internet, which integrates multiple sales platforms. When a user searches for a target product in the discount system, the discount platform analyzes the user's preference weights for each sales platform based on the user's historical purchase records, sets labels for the user, analyzes the operating effect values of each sales platform for the user's corresponding label group, and analyzes the user's preference weights for the discount amount and gift data in the discount, and then comprehensively sorts the discounts of each sales platform to provide users with a targeted and personalized discount platform, increase the user's purchasing experience and convenience, and at the same time stimulate the user's purchasing experience, expand the merchant customer base, and increase sales. In addition, the sales situation of the merchant on each sales platform is analyzed, and then the sales platform with poor sales is provided with adjustment directions, thereby improving the promotion effect of the merchant's discount activities, thereby increasing the number of users and merchants on the discount platform, improving the reputation of the discount platform, and increasing the competitiveness of the discount platform in the market. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.
[0017] Figure 1 It is a schematic diagram of the system structure connection of the present invention. DETAILED DESCRIPTION
[0018] 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.
[0019] See also Figure 1 As shown, a cross-platform consumer discount system based on the Internet includes: a user capture module, a personalized analysis unit, a discount recommendation module and a discount adjustment module.
[0020] The user capture module is used to obtain the user's purchase record when the user searches for discounts for target products in the discount system.
[0021] Among them, the user's purchase record is obtained from the background of the discount system.
[0022] The personalized analysis unit includes a platform preference analysis module and a platform discount analysis module.
[0023] The platform preference analysis module is used to use the user's purchase records to analyze the user's preference weights for each sales platform, and set the user's tags in the target stores of each sales platform for the target product, and then analyze the operational effect value of the target product on the user tag group in the target stores of each sales platform.
[0024] The target stores of each sales platform of the target product are stores opened by merchants of the target product on each sales platform.
[0025] In a specific embodiment, the analysis of the user's preference weights for each sales platform is specifically performed as follows: the sales platform selected by the user when purchasing each historical product and the preferential ranking of each sales platform are extracted from the user's purchase record; when the sales platform selected for purchasing a certain historical product is the same as the sales platform ranked first in preferential ranking, the number of times the first category of use of the selected sales platform is increased by 1; when the sales platform selected for purchasing a certain historical product is different from the sales platform ranked first in preferential ranking, the number of times the second category of use of the selected sales platform is increased, thereby counting the total number of times the first category of use and the total number of times the second category of use of each sales platform are calculated, and are marked as N1 respectively. g and N2 g , where g represents the number of each sales platform and g is a positive integer.
[0026] The preference weight analysis formula is: In the formula, κ g represents the user’s preference weight for the g-th sales platform, and w represents the total number of sales platforms.
[0027] In another specific embodiment, the user's label is set in the target store of each sales platform of the target product, and the specific setting process is as follows: the type of the target product is obtained. If there is no historical purchase product of the same type as the target product in the user's purchase record, the user's label in the target store of each sales platform of the target product is set to develop a new user; otherwise, each historical purchase product of the same type as the target product is extracted from the user's purchase record, and the sales platform and store name corresponding to each historical purchase product are obtained.
[0028] The sales platforms corresponding to the purchases of each historically purchased commodity are compared with each sales platform. If the sales platforms corresponding to the purchases of each historically purchased commodity are different from a certain sales platform, the label of the user in the target store of the sales platform of the target commodity is set as an off-platform conversion user.
[0029] If the sales platform corresponding to a historically purchased product is the same as a certain sales platform, then the historically purchased products in the sales platform and the store name in each historically purchased product are obtained. If the store name in each historically purchased product in the sales platform is different from the name of the target store in the sales platform, then the label of the user in the target store of the sales platform of the target product is set to be an in-platform conversion user.
[0030] On the contrary, if the store name in a historical purchase product on the sales platform is the same as the name of the target store on the sales platform, setting the user's tag in the target store on the sales platform of the target product is to maintain the old user.
[0031] In a specific embodiment, the operation effect value of the target product on the user tag group in the target store of each sales platform is analyzed, and the specific process is as follows: the number of users with the same user tag as the target product in the target store of each sales platform within a preset period and the total number of users with the same user tag who purchased the target product are obtained, which are respectively recorded as M1 g and M2 g , g represents the number of each sales platform, and g is a positive integer.
[0032] At the same time, obtain the increase in the number of users in each tag in the target store of each sales platform for the target product within the specified period and the increase in the number of users in each tag who purchase the target product, which are recorded as M1 respectively. gy and M2 gy , y=1,2,3,4; 1,2,3,4 represent developing new users, converting users outside the platform, converting users within the platform, and maintaining old users respectively.
[0033] The expression of operation effect value is:
[0034] In the formula, α g It represents the operational effect value of the target product on the user tag group in the target store of the g-th sales platform. η1 and η2 are the proportional coefficients of the set increase in the number of users and the increase in the number of target product purchases, respectively.
[0035] It should be noted that the designated period may be one week or one month, and the increased number of users in each tag in the target store of each sales platform during the designated period and the increased number of users purchasing the target product in each tag indicate the increased number of users in each tag in the target store of each sales platform during the designated period and the increased number of users purchasing the target product in each tag.
[0036] Among them, η1 and η2 are set by merchants. After merchants carry out preferential activities on different sales platforms, the preferential system integrates various sales platforms. At the same time, merchants can use the preferential system to obtain the sales situation of each sales platform, and merchants can set the proportional coefficient of the increase in the number of users and the proportional coefficient of the increase in the number of target goods purchased in the preferential system according to the purpose of the preferential activities. For example: when the main purpose of the preferential activity is to expand new users and increase the number of users, η1 is greater than η2. At this time, η1 can be taken as 0.7 and η2 can be taken as 0.3.
[0037] The platform discount analysis module is used to obtain discount data of the target product on each sales platform, and analyze the attractiveness of the discount of the target product on each sales platform to the user by using the user's purchase record.
[0038] In a specific embodiment, the analysis of the attractiveness of the target product to the user on each sales platform is performed as follows: each historically purchased product of the same type as the target product is obtained from the user's purchase record as each reference product, and the discount data of the sales platform corresponding to the purchase of each reference product and the discount data of each reference product on each sales platform are extracted, wherein the discount data includes the discount amount and gift data.
[0039] The gift data includes gift data and gift prices, etc.
[0040] Compare the discount amount in the discount data of the sales platform where each reference product was purchased with the discount amount in the discount data of each sales platform, analyze the user's weight on the discount amount, and record it as a1; compare the gift data in the discount data of the sales platform where each reference product was purchased with the gift data in the discount data of each sales platform, analyze the user's weight on the gift data, and record it as a2.
[0041] Preferably, the analysis process of the user's weight of the discount amount and the analysis process of the user's weight of the gift data are as follows: subtract the discount amount in the discount data of the corresponding sales platform from the discount amount in the discount data of each reference product corresponding to each sales platform, to obtain the discount amount difference between the sales platform corresponding to the purchase of each reference product and each sales platform, and subtract the discount amount in the discount data of each reference product corresponding to each sales platform, and select the maximum difference as the maximum discount amount difference of each reference product; subtract the discount amount difference between the sales platform corresponding to the purchase of each reference product and each sales platform from the maximum discount amount difference of each reference product, and then divide it by the maximum discount amount difference of each reference product, and then perform the mean calculation to obtain the user's discount amount difference rate, recorded as b1.
[0042] Analyze the gift data in the discount data of the sales platform corresponding to each reference product and the gift data in the discount data of each sales platform according to the difference rate of the user's discount amount to obtain the user's gift data difference rate, which is recorded as b2;
[0043] The discount amount and gift data of the target product in the discount data of each sales platform are recorded as YH g and ZP g , g represents the number of each sales platform, and g is a positive integer.
[0044] Using analytical formula In the formula, δ g represents the attractiveness of the target product to users on the g-th sales platform, YH max , ZP max They are respectively the maximum discount amount and the maximum gift data of the target product in the discount data of each sales platform.
[0045] The discount recommendation module is used to sort the discounts of each sales platform by using the user's preference weight for each sales platform, the operation effect value of the target product in the target store of each sales platform for the user tag group, and the attractiveness of the discounts of the target product on each sales platform to the user.
[0046] In a specific embodiment, the specific process of ranking the sales platforms by preferential treatment is as follows: the user's preference weights κ for each sales platform are g , the operational effect value α of the target product on the user tag group in the target store of each sales platform g And the attractiveness of the target product to users on various sales platforms δ g Substitute into the analysis formula Get the preferential sorting priority value of the g-th sales platform
[0047] Sort the preferential ranking priority values of each sales platform in descending order, that is, perform preferential ranking for each sales platform.
[0048] The discount adjustment module is used to obtain the consumption data of the target product on each sales platform after a preset sales cycle, analyze the sales effect of the target product on each sales platform, and then adjust the discounts on each sales platform.
[0049] In a specific embodiment, the sales effect of the target product on each sales platform is analyzed. The specific analysis process is: the total number of users in each tag and the total number of users in each tag who purchased the target product are obtained from the consumption data of the target product on each sales platform, which are respectively recorded as M1′ gy and M2′ gy , and then input it into the sales analysis model to output the analysis results of the target product’s sales performance on each sales platform.
[0050] Preferably, the sales analysis model is expressed as:
[0051] In the formula, represents the analysis result of the sales effect of the target product on the g-th sales platform, M1′ gymax , M2′ gymax are the maximum total number of users in the yth tag of the target product in the gth sales platform and the maximum total number of users who purchased the target product in the historical sales records. They represent the average total number of users with the y-th tag and the average total number of target products purchased by users, respectively. e represents a natural constant. μ1 and μ2 represent the important weights of the total number of users and the important weights of the total number of target products purchased by users, respectively. θ is the set sales effect coefficient threshold.
[0052] in, The average is calculated by the total number of users in each tag of the target product in each sales platform and the total number of users in each tag who purchased the target product.
[0053] The setting process of μ1 and μ2 is the same as that of η1 and η2, and will not be repeated here.
[0054] The sales effect coefficient threshold is the critical value for judging whether the sales effect is good or not. It is set by the engineers of the discount system. When the calculated data is greater than or equal to the sales effect coefficient threshold, it indicates that the sales effect is good. Otherwise, it indicates that the sales effect is poor. For example, when the sales effect coefficient threshold is 5,
[0055] The calculated data is 4, and 4<5, which indicates that the sales effect of the target product on the g-th sales platform is poor.
[0056] The analysis result of the sales effect includes values of 1 and 0. When the analysis result of the sales effect of the target product on a certain sales platform is 1, it indicates that the sales effect of the target product on the sales platform is good. Otherwise, it indicates that the sales effect of the target product on the sales platform is poor.
[0057] In another specific embodiment, the discounts of each sales platform are adjusted, and the specific process is as follows: the discounts of each sales platform whose sales effect analysis result is 0 need to be adjusted, and the sales platforms that need to be adjusted are recorded as marked platforms, and the discounts of each sales platform whose sales effect analysis result is 1 do not need to be adjusted.
[0058] By using the total number of users in each tag of the target product in each sales platform and the total number of users in each tag who purchase the target product, the target tags in each target platform are screened out, and the weights of the discount amount and the weights of the gift data of each user in each target tag in each target platform are extracted. Then, the mean is calculated to obtain the weights of the discount amount and the weights of the gift data of the users in the target tags in each target platform. If the weight of the discount amount of the users in the target tag in a target platform is greater than the preset weight threshold, it indicates that the target platform needs to adjust the discount amount. If the weight of the gift data of the users in the target tag in a target platform is greater than the preset weight threshold, it indicates that the target platform needs to adjust the gift data. In this way, the adjustment targets of each target platform are obtained and sent to the merchant terminal of the target product.
[0059] Preferably, the target tags in each target platform are screened out, and the specific screening process is as follows: extract the total number of users in each tag of the target product in each target platform and the total number of users who purchased the target product in each tag, and record them as M1′ respectively. fy and M2′ fy, f represents the number of each target platform, and f is a positive integer.
[0060] According to the analysis formula Get the user consumption analysis result of the target product in the yth tag on the fth target platformξ fy , where ψ is the set user consumption effect coefficient threshold.
[0061] The labels whose user consumption analysis results of the target products in each target platform are 0 are used as the target labels.
[0062] The embodiment of the present invention integrates multiple sales platforms. When a user searches for a target product in the discount system, the discount platform analyzes the user's preference weight for each sales platform based on the user's historical purchase record, sets a label for the user, analyzes the operation effect value of each sales platform for the user's corresponding label group, and analyzes the user's preference weight for the discount amount and gift data in the discount, and then comprehensively sorts the discounts of each sales platform to provide users with a targeted and personalized discount platform, increase the user's purchasing experience and convenience, and at the same time stimulate the user's purchasing experience, expand the merchant customer base, and increase sales. In addition, the sales situation of the merchant on each sales platform is analyzed, and then the sales platform with poor sales is provided with adjustment directions, thereby improving the promotion effect of the merchant's discount activities, thereby increasing the number of users and merchants on the discount platform, improving the reputation of the discount platform, and increasing the competitiveness of the discount platform in the market.
[0063] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.
Claims
1. A cross-platform consumer discount system based on the Internet, characterized in that: include: User capture module, personalized analysis unit, discount recommendation module and discount adjustment module; A user capture module is used to obtain the user's purchase record when the user searches for discounts of target products in the discount system; The personalized analysis unit includes a platform preference analysis module and a platform discount analysis module; The platform preference analysis module is used to analyze the user's preference weights for each sales platform using the user's purchase records, and to set the user's tags in the target stores of each sales platform of the target product, and then to analyze the operation effect value of the target product in the target stores of each sales platform for the user tag group; The platform discount analysis module is used to obtain discount data of the target product on each sales platform, and analyze the attractiveness of the discount of the target product on each sales platform to the user by using the user's purchase record; The discount recommendation module is used to sort the discounts of various sales platforms by using the user's preference weights for each sales platform, the operation effect value of the target product in the target store on each sales platform for the user tag group, and the attractiveness of the discounts of the target product on each sales platform to the user; The discount adjustment module is used to obtain the consumption data of the target product on each sales platform after a preset sales cycle, analyze the sales effect of the target product on each sales platform, and then adjust the discounts on each sales platform.
2. According to claim 1, a cross-platform consumption preferential system based on the Internet is characterized in that: The analysis of the user's preference weights for each sales platform is specifically performed as follows: Extract the sales platform selected by the user when purchasing each historical product and the preferential ranking of each sales platform from the user's purchase record. When the sales platform selected when purchasing a historical product is the same as the sales platform with the first preferential ranking, the number of times the first category of use of the selected sales platform is increased by 1. When the sales platform selected when purchasing a historical product is different from the sales platform with the first preferential ranking, the number of times the second category of use of the selected sales platform is increased. In this way, the total number of times the first category of use and the total number of times the second category of use of each sales platform are counted and marked as N1 respectively. g and N2 g , where g represents the number of each sales platform, and g is a positive integer; The preference weight analysis formula is: In the formula, κ g represents the user’s preference weight for the g-th sales platform, and w represents the total number of sales platforms.
3. According to claim 2, a cross-platform consumption preferential system based on the Internet is characterized in that: The user's tag is set in the target store of each sales platform of the target product. The specific setting process is as follows: Get the type of the target product. If there is no historical purchase product of the same type as the target product in the user's purchase record, set the user's label in the target store on each sales platform of the target product to develop a new user. Otherwise, extract the historical purchase products of the same type as the target product from the user's purchase record, and obtain the sales platform and store name corresponding to each historical purchase product. Compare the sales platform corresponding to each historically purchased product with each sales platform. If the sales platform corresponding to each historically purchased product is different from a certain sales platform, then the user's label in the target store of the sales platform of the target product is set as an off-platform conversion user; If the sales platform corresponding to a certain historically purchased product is the same as a certain sales platform, then obtain the historically purchased products in the sales platform and the store name in each historically purchased product. If the store name in each historically purchased product in the sales platform is different from the name of the target store in the sales platform, then set the user's label in the target store of the sales platform of the target product to be an internal conversion user; On the contrary, if the store name in a historical purchase product on the sales platform is the same as the name of the target store on the sales platform, setting the user's tag in the target store on the sales platform of the target product is to maintain the old user.
4. The cross-platform consumption preferential system based on the Internet according to claim 3 is characterized in that: The specific process of analyzing the operation effect value of the target product on the user tag group in the target store of each sales platform is as follows: Get the number of users with the same user tag who purchased the target product in the target store on each sales platform within the preset period and the total number of users with the same user tag who purchased the target product, which are recorded as M1 and M2 respectively. g and M2 g , g represents the number of each sales platform, and g is a positive integer; At the same time, obtain the increase in the number of users in each tag in the target store of each sales platform for the target product within the specified period and the increase in the number of users in each tag who purchase the target product, which are recorded as M1 respectively. gy and M2 gy , y=1,2,3,4; 1,2,3,4 represent developing new users, converting users outside the platform, converting users within the platform, and maintaining old users respectively; The expression of operation effect value is: In the formula, α g It represents the operational effect value of the target product on the user tag group in the target store of the g-th sales platform. η1 and η2 are the proportional coefficients of the set increase in the number of users and the increase in the number of target product purchases, respectively.
5. The cross-platform consumption preferential system based on the Internet according to claim 4 is characterized in that: The analysis of the attractiveness of the target product to users on various sales platforms is specifically conducted as follows: Obtaining historically purchased products of the same type as the target product from the user's purchase records as reference products, and extracting the discount data of the sales platform where each reference product was purchased and the discount data of each reference product on each sales platform, wherein the discount data includes the discount amount and gift data; Compare the discount amount in the discount data of the sales platform where each reference product is purchased with the discount amount in the discount data of each sales platform, analyze the user's weight on the discount amount, and record it as a1; compare the gift data in the discount data of the sales platform where each reference product is purchased with the gift data in the discount data of each sales platform, analyze the user's weight on the gift data, and record it as a2; The discount amount and gift data of the target product in the discount data of each sales platform are recorded as YH g and ZP g , g represents the number of each sales platform, and g is a positive integer; Using analytical formula In the formula, δ g represents the attractiveness of the target product to users on the g-th sales platform, YH max , ZP max They are respectively the maximum discount amount and the maximum gift data of the target product in the discount data of each sales platform.
6. The cross-platform consumption preferential system based on the Internet according to claim 5, characterized in that: The specific process of sorting the preferential prices of various sales platforms is as follows: The user's preference weight for each sales platform κ g , the operational effect value α of the target product on the user tag group in the target store of each sales platform g And the attractiveness of the target product to users on various sales platforms δ g Substitute into the analysis formula Get the preferential sorting priority value of the g-th sales platform Sort the preferential ranking priority values of each sales platform in descending order, that is, perform preferential ranking for each sales platform.
7. The Internet-based cross-platform consumption preferential system according to claim 4, characterized in that: The sales effect of the target product on each sales platform is analyzed, and the specific analysis process is as follows: From the consumption data of the target product on each sales platform, the total number of users in each tag and the total number of users in each tag who purchased the target product are obtained, which are recorded as M1′ gy and M2′ gy , and then input it into the sales analysis model to output the analysis results of the sales effect of the target product on each sales platform; The analysis result of the sales effect includes values of 1 and 0. When the analysis result of the sales effect of the target product on a certain sales platform is 1, it indicates that the sales effect of the target product on the sales platform is good. Otherwise, it indicates that the sales effect of the target product on the sales platform is poor.
8. The cross-platform consumption preferential system based on the Internet according to claim 7, characterized in that: The sales analysis model is expressed as: In the formula, M1′ represents the analysis result of the sales effect of the target product on the g-th sales platform. gymax , M2′ gymax are the maximum total number of users in the yth tag of the target product in the gth sales platform and the maximum total number of users who purchased the target product in the historical sales records. They represent the average total number of users with the y-th tag and the average total number of target products purchased by users, respectively. e represents a natural constant. μ1 and μ2 represent the important weights of the total number of users and the important weights of the total number of target products purchased by users, respectively. θ is the set sales effect coefficient threshold.
9. The Internet-based cross-platform consumption preferential system according to claim 7, characterized in that: The specific process of adjusting the discounts on each sales platform is as follows: The discounts of each sales platform whose sales effect analysis result is 0 need to be adjusted, and each sales platform that needs to be adjusted is recorded as each marked platform. The discounts of each sales platform whose sales effect analysis result is 1 do not need to be adjusted; By using the total number of users in each tag of the target product in each sales platform and the total number of users in each tag who purchase the target product, the target tags in each target platform are screened out, and the weights of the discount amount and the weights of the gift data of each user in each target tag in each target platform are extracted. Then, the mean is calculated to obtain the weights of the discount amount and the weights of the gift data of the users in the target tags in each target platform. If the weight of the discount amount of the users in the target tag in a target platform is greater than the preset weight threshold, it indicates that the target platform needs to adjust the discount amount. If the weight of the gift data of the users in the target tag in a target platform is greater than the preset weight threshold, it indicates that the target platform needs to adjust the gift data. In this way, the adjustment targets of each target platform are obtained and sent to the merchant terminal of the target product.
10. The cross-platform consumption preferential system based on the Internet according to claim 9, characterized in that: The target tags in each target platform are screened out, and the specific screening process is as follows: Extract the total number of users in each tag of the target product in each target platform and the total number of users in each tag who purchased the target product, and record them as M1′ fy and M2′ fy , f represents the number of each target platform, and f is a positive integer; According to the analysis formula Get the user consumption analysis result of the target product in the yth tag on the fth target platformξ fy , where ψ is the set user consumption effect coefficient threshold; The labels whose user consumption analysis results of the target products in each target platform are 0 are used as the target labels.