Marketing scheme self-adaptive generation method and system

Abstract

The invention provides a marketing scheme adaptive generation method and system, and belongs to the technical field of data processing, and the method specifically comprises the steps: carrying out the determination of data processing methods of platform data of different marketing management commodities through the user portrait data of the marketing management commodities based on the association condition of user portraits between different marketing management commodities, and carrying out the generation of the marketing scheme. According to the method, the data processing results of the platform data of different marketing management commodities and the self-adaptive generation data of the marketing schemes of different commodities are determined through the data processing method, the self-adaptive generation object of the marketing schemes in the marketing management commodities is determined, and self-adaptive generation processing of the marketing schemes is achieved.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, and in particular relates to a method and system for adaptive generation of marketing plans. Background Technology

[0002] Existing marketing plans are often updated based on manual identification. Generally, marketing plans are generated when there are abnormal sales of a product or when a new product is launched, which inevitably results in a certain degree of delay.

[0003] To address the aforementioned technical problems, existing solutions often generate personalized marketing plans based on the analysis results of user data. Specifically, similar technical solutions are presented in invention patent applications CN202510769723.6 "A Big Data-Based Tobacco Enterprise Marketing Plan Assistance and Optimization System" and CN202510514495.8 "An Intelligent System and Method for E-commerce Precision Marketing Integrating AI Personalized Recommendation." However, these solutions suffer from the following technical problems: Existing marketing plans are often generated based on user instructions and needs, and therefore generally rely on fixed cycles or user demands. For example, when product sales drop abnormally, this inevitably slows down the efficiency of marketing plan generation. Therefore, how to determine the products for adaptive marketing plan generation based on the number of user profiles and the overlap between user profiles and those of other products, so as to ensure the timeliness of updating marketing plans for products with a large number of user profiles or a high degree of overlap with user profiles of other products, has become an urgent technical problem to be solved.

[0004] Therefore, there is an urgent need for an adaptive marketing plan generation method and system. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted: Specifically, this application provides a method for adaptively generating marketing plans, which includes: S1 obtains product listing data from the e-commerce platform. Based on the listing data and the correlation between user profiles of different products, when it is determined that adaptive generation of marketing plans is required, the product is identified based on the user profile data of the product and the overlap with other products in different user profiles, and is designated as the marketing management product. S2 determines the data processing method for platform data of different marketing management products based on the association between user profiles and user profiles of the marketing management products. S3 uses the data processing method to determine the data processing results of platform data for different marketing management products, as well as the adaptive generation data of marketing plans for different products, and determines the adaptive generation object of marketing plans in the marketing management products.

[0006] The beneficial effects of this invention are as follows: By analyzing user profile data of the base product and the overlap between different user profiles and other products, the system identifies products for which marketing plans are adaptively generated. This ensures the efficiency of marketing plan generation for products with a large number of user profiles. Furthermore, by considering the correlation between user profiles and those of other products, the system ensures the adaptability of the user profile analysis data to other products. This further guarantees the timeliness of user profile analysis for products with high adaptability and lays the foundation for further improving the efficiency of marketing plan generation.

[0007] By analyzing the data processing results from different marketing management product platforms and the adaptive generation data of marketing plans for different products, the adaptive generation objects of marketing plans in marketing management products are determined. This fully considers the overlap of user needs in different user profiles, as well as the delay in updating marketing plans for products in user profiles with high overlap of user needs. This ensures that when the overlap of user needs is high and the delay in updating products in user profiles with high overlap of user needs is severe, timely and effective unified parsing and analysis of user data can be performed, i.e., adaptive marketing plan generation can be carried out, thereby improving the timeliness and matching degree of marketing plan update processing.

[0008] Furthermore, the product listing data on the e-commerce platform includes the type and quantity of products listed on the e-commerce platform.

[0009] Furthermore, it was determined that adaptive generation of marketing plans was necessary, specifically including: Based on the aforementioned product listing data and the correlation between user profiles for different products, it was determined that adaptive generation of marketing plans was required. Based on the aforementioned listing data, determine the number of products listed on the e-commerce platform; Based on the correlation between user profiles of different products, identify the overlapping products of the product in different user profiles; Based on the number of products listed on the e-commerce platform and the overlapping products of the products in different user profiles, it is determined whether adaptive generation of marketing plans is required.

[0010] Furthermore, the method for determining the adaptive generation object of the marketing plan in the marketing management product is as follows: Based on the data processing results of platform data for different marketing management products, the analysis results of user needs for the marketing management products in different user profiles are determined. Users with the same user needs are grouped into the same group. Based on the combination data of the marketing management products in different user profiles, the purchasing user data of the combination in different user profiles is determined. Products containing the user profile are designated as user profile-associated products. Based on the adaptive generation data of the marketing plan for the user profile-associated products, the adaptive generation time of the marketing plan for the user profile-associated products is determined. Based on the adaptive generation time of the marketing plan associated with the user profile in the user profile and the combined purchasing user data in the user profile, determine whether the marketing management product is an adaptive generation object of the marketing plan.

[0011] In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described marketing scheme adaptive generation method when running the computer program.

[0012] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0014] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0015] Figure 1 This is a flowchart of a marketing plan adaptive generation method; Figure 2 This is a flowchart that determines whether an adaptive generation process for a marketing plan is needed; Figure 3 It is a flowchart of the method for determining the products in marketing management; Figure 4 This is a flowchart illustrating the methods for determining the data processing procedures for platform data related to marketing management of goods. Detailed Implementation

[0016] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0017] Example 1 like Figure 1 As shown, this application provides an adaptive marketing plan generation method, specifically including: S1 obtains product listing data from the e-commerce platform. Based on the listing data and the correlation between user profiles of different products, when it is determined that adaptive generation of marketing plans is required, the product is identified based on the user profile data of the product and the overlap with other products in different user profiles, and is designated as the marketing management product. Furthermore, the product listing data on the e-commerce platform includes the type and quantity of products listed on the e-commerce platform.

[0018] Specifically, such as Figure 2 As shown, it is determined that adaptive generation of marketing plans is required, specifically including: S11 determines the number of products listed on the e-commerce platform based on the aforementioned listing data; The total number of products is tallied, and the number of "overlapping products" for each product is calculated. The number of overlapping products for a product reflects its "breadth of association" within the platform's product ecosystem. The higher the number, the more marketing scenarios it can be integrated into.

[0019] The total number of products listed on the e-commerce platform is 800. Analysis of overlapping products (assuming the following): Product P (Smartphone): Target audience = College students, urban white-collar workers. Number of overlapping products = 600 (overlaps with most products); Product Q (Professional Protein Powder): Target audience = Fitness enthusiasts. Number of overlapping products = 80 (only associated with fitness products); Product R (Niche Philosophy Books): Target audience = College students. Number of overlapping products = 35 (niche even among college students); Product S (High-end Commercial Coffee Machine): Target audience = Urban white-collar workers. Number of overlapping products = 250.

[0020] S12 Based on the association between different user profiles of different products, determine the overlapping products of the product in different user profiles; Overlapping products: These are other products that share at least one of the same user profiles as the target product. Example: Product A (laptop) is marketed as targeting university students and urban white-collar workers. Product B (textbooks) is marketed as targeting university students. Product C (suits) is marketed as targeting urban white-collar workers. Therefore, products B and C are both "overlapping products" of product A.

[0021] S13 determines whether adaptive generation of marketing plans is needed based on the number of products listed on the e-commerce platform and the overlapping products of the products in different user profiles.

[0022] It is understood that the overlapping products in the user profile are all products that are included in the user profile, that is, the user profile also belongs to the user profile of the overlapping products.

[0023] Specifically, based on the number of products listed on the e-commerce platform and the overlapping products of those products across different user profiles, it is determined whether adaptive generation of marketing plans is necessary. This includes: S131 Obtain the number of products listed on the e-commerce platform and determine whether the number of products listed on the e-commerce platform is less than the product quantity threshold. If so, the amount of effective data of the marketing target is small, so it is determined that adaptive generation of marketing plan is required. If not, proceed to the next step. In the above steps, the sufficiency of the total number of goods is judged. If the number of goods listed (800) is not less than T_total_sku (500), the goods library is rich, and the process enters S132.

[0024] S132 determines the number of overlapping products of the product in different user profiles, and determines whether there are products with a number of overlapping products less than a preset threshold for the number of overlapping products. If yes, proceed to the next step; otherwise, if there are no overlapping products, it is determined that there are too many overlapping products and therefore no adaptive generation of marketing plans is required. In the above steps, determine whether there are any overlapping products with a quantity less than 100. If yes (product Q=80, product R=35), there are "poorly overlapping products". Proceed to S133.

[0025] S133 identifies products with fewer overlapping products than a preset threshold for the number of overlapping products as poorly overlapping products. It then determines whether the proportion of poorly overlapping products in the listed products is greater than a preset threshold for the proportion of such products. If so, it is determined that an adaptive generation of a marketing plan is required. If not, it proceeds to the next step. In the above steps, the universality of low-overlap products is judged. Suppose, after statistics, among 800 products on the platform, the overlap quantity of 120 products < 100, and the proportion of poor-overlap products = 120 / 800 = 15%. Judgment: 15% > T_bad_ratio (10%), so poor-overlap products are widespread. The process ends here, and it is determined that adaptive generation processing of the marketing plan needs to be carried out.

[0026] S134 is based on the quantity of overlapping products among different poor-overlap products to determine the overlapping products of poor-overlap products in different user portraits. The user portrait with the quantity of overlapping products less than the preset product quantity threshold is used as the overlapping deviation user portrait. It is judged whether there are poor-overlap products with overlapping deviation user portraits. If so, enter the next step; if not, it is determined that adaptive generation processing of the marketing plan is not required. Furthermore, suppose there are only 40 poor-overlap products, and the proportion of poor-overlap products = 40 / 800 = 5%. Enter S134.

[0027] Explanation of the step: Analyze these poor-overlap products to see if there is a certain user portrait with a small number of products it covers.

[0028] Overlapping deviation user portrait: For a certain poor-overlap product, if the quantity of its overlapping products under a certain user portrait < T_sku_per_profile, then this user portrait is its "overlapping deviation user portrait". Here, the "quantity of overlapping products" specifically refers to the quantity under this single user portrait, and find out the possible "structural shortcoming" on the platform, that is, which type of user portrait corresponds to an unsound product ecosystem.

[0029] Analyze poor-overlap products. Take product R (niche philosophy books) as an example. Its user portrait is only college students. Under the portrait of college students, the quantity of overlapping products of product R = 35. Judgment: 35 < T_sku_per_profile (50). For product R, college students are one of its "overlapping deviation user portraits".

[0030] S135 is based on the quantity of overlapping deviation user portraits of poor-overlap products. It is judged whether the quantity of overlapping deviation user portraits of poor-overlap products is greater than the preset deviation user portrait quantity threshold. If so, it is determined that adaptive generation processing of the marketing plan needs to be carried out; if not, it is determined that adaptive generation processing of the marketing plan is not required.

[0031] In the steps described above, it's crucial to determine if a sufficient number of products exhibit this "overlap deviation" issue. If only one product displays this characteristic, it might be an exception. However, if many products exhibit this characteristic, it indicates a problem with the overall product ecosystem within the user profile.

[0032] Suppose that among the 40 products with poor overlap, 20 products have at least one "overlapping deviation user profile". If 20 > T_bad_profile (10), then it is determined that adaptive generation of marketing plans is required.

[0033] By adaptively generating marketing plans for the aforementioned products, the efficiency of marketing plan generation can be improved.

[0034] Specifically, such as Figure 3 As shown, the method for determining the marketing management products is as follows: S21 determines the user profile of the product based on the user profile data of the product; User Profile: A tagged model abstracted from user demographic attributes, behavioral characteristics, interests, and preferences, used to represent a user group with specific needs. In the example: college students, urban white-collar workers, and fitness enthusiasts. These profiles segment the platform's users into different target markets. User profiles are the foundation of precision marketing and product recommendations, helping the platform understand "who should buy the product."

[0035] S22 determines the overlapping products in different user profiles based on the overlap between the product and other products in different user profiles; Overlapping products are other products that share at least one user profile with the target product. For example, product P (smartphone) and product X (laptop) both share a college student profile, therefore they are overlapping products. Similarly, product P (smartphone) and product Y (coffee machine) both share an urban white-collar worker profile, therefore they are also overlapping products.

[0036] Overlapping products constitute the "marketing ecosystem" of a target product. The more overlapping products a product has, the more marketing scenarios it can be embedded in (such as bundled sales and related recommendations), and the greater its exposure opportunities.

[0037] S23 determines whether the product is a marketing management product based on overlapping product data in different user profiles.

[0038] It should be noted that determining whether a product is a marketing management product involves using overlapping product data across different user profiles. Specifically, this includes: When S231 determines that the product does not belong to a poorly overlapping product based on the overlapping product data of the product, it is determined that the product does not belong to the marketing management product; Poorly Overlapping Product refers to a product whose global overlapping product quantity is lower than a preset threshold, that is, the overlapping product quantity < the preset overlapping product quantity threshold (T_overlap_sku). In the embodiment, the threshold T_overlap_sku = 100. The global quantities of products Q(80), R(35), and S(95) are all less than 100, so they are determined to be poorly overlapping products.

[0039] When S232 determines that the product belongs to a poorly overlapping product, it judges whether there is a deviation user profile for the overlapping of the product. If so, it is determined that the product is a marketing management product; if not, it is determined that the product does not belong to the marketing management product.

[0040] Deviation User Profile: For a specific product, if the quantity of overlapping products under a certain user profile is too low, then this user profile is called its "deviation user profile for overlapping".

[0041] Judgment criterion: The quantity of overlapping products in the sub-profile under a certain user profile < the preset product quantity threshold (T_sku_per_profile). In the embodiment, the threshold T_sku_per_profile = 50. The quantity of product R under the college student profile is 35 (<50), so the college student is the deviation user profile for overlapping of product R. The quantities of product S under the college student and urban white-collar profiles are both less than 50, so both of these profiles are its deviation user profiles for overlapping.

[0042] Case 1: Product P (a best-selling smart phone), user profiles: college students, urban white-collar workers; Overlapping product data: Global overlapping product quantity: 600 (that is, there are 600 products on the platform that share at least the college student or urban white-collar worker profile with product P) Quantity of overlapping products in the sub-profile: Under the college student profile: 400, under the urban white-collar worker profile: 450; S231: Whether it belongs to a poorly overlapping product, judgment: Global overlapping quantity (600) < T_overlap_sku (100)? No, product P does not belong to a poorly overlapping product, and product P does not belong to the marketing management product.

[0043] Product P is associated with a large number of products on the platform, has a wide audience, and is the platform's "traffic star." It can gain sufficient exposure through regular recommendation algorithms and marketing campaigns, without requiring special marketing management.

[0044] Case Study 2: Product Q (Professional Yoga Mat), User Profile: Fitness Enthusiasts; Overlapping product data: Global overlapping product count: 80, overlapping product count by profile: Under the Fitness Guru profile: 80 (because there is only one profile for it).

[0045] S231: Is it a poorly overlapping product? Judgment: Global overlap (80) < 100? Yes, Decision: Product Q is a poorly overlapping product. Final conclusion: Product Q is a marketing management product.

[0046] Case Study 3: Product R (Niche Philosophy Books) User Profile: University Students; Overlapping product data: Global overlapping product count: 35; Overlapping product count by user profile: Under the university student profile: 35. S231: Is it a poorly overlapping product? Judgment: Global overlap (35) < 100? Yes. Decision: Product R is a poorly overlapping product, and product R is a marketing management product.

[0047] The brilliance of this method lies in its ability to precisely identify the products—products R and Q—that most require assistance through cross-analysis of both global and local dimensions. These products are not inherently bad, but rather chosen because they have limited overlap with other user profiles, resulting in less data to reference when generating marketing plans. Therefore, they are selected as marketing management products.

[0048] S2 determines the data processing method for platform data of different marketing management products based on the association between user profiles and user profiles of the marketing management products. Specifically, such as Figure 4 As shown, the method for determining the data processing method for the platform data of the marketing management products is as follows: S31 determines the number of user profiles for the marketing management products based on the user profile data of the marketing management products; The number of user profiles refers to the number of different user profiles associated with a marketing management product. In the example: Product S is associated with college students and urban white-collar workers, so its number is 2. Product R is only associated with college students, so its number is 1. The more profiles, the wider the customer base that the product intends to cover, and the more complex the sources and needs of user feedback may be, resulting in a higher processing priority.

[0049] S32 determines the overlapping user profiles with different marketing management products based on the correlation between user profiles and the different marketing management products, and uses these as overlapping user profiles. Overlapping User Profile: User profiles shared by two different marketing management products.

[0050] S33 determines the data processing method for the platform data of the marketing management product based on the number of user profiles of the marketing management product and the overlapping user profiles between different marketing management products.

[0051] Platform Data: Specifically refers to the inquiry and review data of users who purchased the marketing management products. This is first-hand information for understanding users' real needs and pain points. The platform data refers to the inquiry and review data of users who purchased the marketing management products.

[0052] In this example, user questions about product S (portable coffee mug), such as "How long does this mug keep drinks hot?", and comments like "Leaky, poor quality," all fall under the category of platform data. This data is the most direct and valuable raw information for understanding users' real needs, product defects, and marketing pain points. The purpose of processing this data is to extract "real needs" to guide the generation of marketing plans.

[0053] Specifically, based on the number of user profiles for the marketing management products and the overlapping user profiles with different marketing management products, the data processing method for the platform data of the marketing management products is determined, including: S331 Obtain the number of user profiles of the marketing management product, and determine whether the number of user profiles of the marketing management product is greater than the preset user profile number threshold. If so, determine that the data processing method of the platform data of the marketing management product is to perform real-time data processing, thereby determining the real needs of the purchasing users of the marketing management product, and then generating a targeted marketing plan. If not, proceed to the next step. In the above steps, the user profile breadth priority determination first checks whether the number of user profiles for a product exceeds a threshold. If it does, it is directly determined as a high priority product.

[0054] Business Implications: This is a "VIP channel" reserved for the most complex products. Products covering multiple user profiles have a wide-ranging impact if problems arise. Real-time processing allows for rapid insight into the differentiated needs and dissatisfaction of different customer groups, quickly generating targeted marketing messages or solutions, preventing reputational damage, and maximizing sales opportunities.

[0055] Real-time Data Processing: A high-priority data processing method. Once new platform data (inquiries / reviews) is generated, the system immediately or near real-time parses, analyzes, and responds to it. In this example: if product S is determined to use this method, any new negative review or inquiry about it will immediately trigger analysis, potentially generating a customer service script or adjusting advertising copy to handle urgent and complex issues. This allows for rapid response to market changes, curbing the spread of negative reviews, and seizing fleeting marketing opportunities.

[0056] S332 Based on the overlapping user profile data between the marketing management product and different marketing management products, determine the marketing management products that have overlapping user profiles with the marketing management product. When the number of marketing management products that have overlapping user profiles with the marketing management product is greater than a preset threshold for the number of overlapping management products, then determine that the data processing method for the platform data of the marketing management product is to perform real-time data processing, thereby determining the real needs of the users who purchase the marketing management product, and then generating a targeted marketing plan. Otherwise, proceed to the next step. In the above steps, the breadth of product association is prioritized for judgment. If the product itself has few user profiles, but it is associated with many other marketing management products, it indicates that it is in a "high-problem area" and should be given high attention.

[0057] In this process, associated managed products specifically refer to other marketing managed products that share at least one overlapping user profile with the target marketing managed product. This identifies the product's "problem allies." If a product has a large number of associated managed products, it means it's at the core of a common problem. Real-time processing of its data can yield insights that can be helpful in solving the problems of an entire product group, resulting in a very high return on investment.

[0058] S333 identifies marketing management products with overlapping user profiles with the marketing management products as associated management products, determines whether the marketing management products have associated management products, and if so, proceeds to the next step; otherwise, it determines that the data processing method for the platform data of the marketing management products is to determine the data processing method based on the number of user profiles of the marketing management products, thereby determining the real needs of the purchasing users of the marketing management products, and then generating targeted marketing plans. The above steps include an existence check, explained as follows: Check if the product has at least one "related managed product," which is a distribution point. If not (i.e., the product is completely isolated), its problem is extremely unique, requiring reliance on its own data; therefore, a standardized, case-specific processing method is used. If it does exist, then a more complex association analysis is performed.

[0059] S334 Based on the overlapping user profiles between the marketing management product and the associated management product, and the proportion of user profiles in the associated management product, determine the association factor between the marketing management product and different associated management products. Determine whether there are any associated management products with an association factor greater than a preset association factor. If yes, proceed to the next step. If no, determine that the data processing method for the platform data of the marketing management product is to determine the data processing method based on the number of user profiles of the marketing management product, thereby determining the real needs of the purchasing users of the marketing management product, and then generating a targeted marketing plan. In the above steps, in-depth association analysis is performed to quantify the similarity between the product and the user group of one of the "related managed products".

[0060] Key Term: Association Factor: A metric measuring the similarity of user groups between two marketing-managed products. Calculation Formula: Association Factor = Number of overlapping user profiles / Total number of user profiles for the associated products. Example: Product C (yoga mat) has two user profiles: "Fitness Enthusiast" and "White-collar Worker." Product D (sports wristband) only has one user profile: "Fitness Enthusiast." Their association factor = 1 / 2 = 0.5.

[0061] Business Implications: A higher correlation factor (closer to 1) indicates a greater consistency in the customer base of the two products, suggesting that their problems and needs are more likely to be similar. Conversely, a lower correlation factor indicates that while there is a correlation, their respective focuses differ. This determines the value of insights gained from "allies."

[0062] S335 determines the processing requirement factor of the marketing management product based on the association factors between different associated management products and the proportion of products in which the number of user profiles in associated user products is greater than a preset threshold for the number of user profiles, and determines the data processing method of the platform data of the marketing management product based on the processing requirement factor.

[0063] S335: Comprehensive demand assessment, taking into account the relationship with all related managed goods, to calculate a final comprehensive score.

[0064] Key term: Processing demand factor, a comprehensive quantitative indicator used to ultimately determine the urgency of data processing for a product platform. Design logic: The larger the correlation factor (higher reference value), and the lower the proportion of complex products among the related products (more insights to draw upon), the higher the data processing demand for that product itself. The formula is: Processing demand factor = (Average correlation factor) * (1 - Proportion of complex products).

[0065] It should be noted that the processing demand factor of the marketing management product is determined based on the correlation factor between different associated management products and the proportion of products in which the number of user profiles in associated user products exceeds a preset user profile number threshold. The larger the correlation factor between different associated management products and the lower the proportion of products in which the number of user profiles in associated user products exceeds the preset user profile number threshold, the larger the processing demand factor of the marketing management product.

[0066] Specifically, the data processing method for determining the platform data of the marketing management products based on the aforementioned demand factors includes: When the processing demand factor is greater than a preset processing demand factor threshold, the data processing method for the platform data of the marketing management product is determined to be real-time data processing, thereby determining the real needs of the purchasing users of the marketing management product, and then generating a targeted marketing plan. In other cases, the data processing method for the platform data of the marketing management product is determined based on the number of user profiles of the marketing management product, thereby determining the real needs of the purchasing users of the marketing management product, and then generating a targeted marketing plan.

[0067] It is understandable that the data processing method is determined based on the number of user profiles for the marketing management products, specifically including: Based on the number of user profiles of the marketing management product, the product of the number of user profiles and a preset ratio factor is used as the analysis quantity threshold. Whenever the number of new purchasing users of the marketing management product in the time period after the last data processing is not less than the analysis quantity threshold, the data processing of the user profiles of the marketing management product is performed.

[0068] In the above steps, the calculated processing demand factor is compared with the final threshold to determine whether to use real-time processing or a standard method. This is the final output node of the entire decision tree, which transforms all the analysis into a clear and executable instruction.

[0069] Key term: Analysis quantity threshold, which, under this standard method, is the number of new purchasing users accumulated to trigger one data processing step. Calculation formula: Analysis quantity threshold = Number of user profiles * Preset scaling factor.

[0070] Example: For a niche product with 1 user profile and a scaling factor of 1, the analysis threshold is 1, meaning it is analyzed once for each new customer.

[0071] Commercial Implications: This is a highly intelligent and cost-effective batch processing strategy. It "tailor-makes" the processing frequency for each product: a lower threshold for niche products (fewer profiles) to ensure timely feedback; and a higher threshold for products with a wider audience (more profiles) to avoid wasting computing resources. This achieves effective coverage of long-tail problems with limited resources.

[0072] Case 1: Product S (Portable Coffee Cup) – Determined to be "Processed in Real Time"; S331: User profile breadth judgment, judgment: if the number of user profiles of product S (3) > T_profile_num (2), determine the data processing method of product S platform data as [perform real-time data processing].

[0073] Product S covers three different user groups, meaning its user feedback may come from diverse sources and have complex needs. Real-time processing of its inquiry and review data can quickly identify the differences and commonalities in the needs of two groups, thus providing a reference for generating marketing plans for other products.

[0074] The essence of this methodology is a resource intelligent allocation system. Within the high-priority pool of "marketing management products," it performs a secondary, precise sorting: Real-time data processing channels (S331 / S332): These are assigned to products with a wide impact (multiple user profiles) or high commonality of problems (many related products). They address issues on a broader scale.

[0075] Refined decision-making channels (S333-S335): Used for trade-offs. Ultimately, goods with high demand factors are qualified for real-time processing because their problems are "unique and critical".

[0076] The volume-based processing channel (final rejection): This channel is allocated to products with a single problem, low sales volume, and limited reference value. It serves as a cost-effective "safety net," ensuring that all issues receive attention while minimizing resource consumption.

[0077] S3 uses the data processing method to determine the data processing results of platform data for different marketing management products, as well as the adaptive generation data of marketing plans for different products, and determines the adaptive generation object of marketing plans in the marketing management products.

[0078] Specifically, the adaptive generation object of marketing plans in the marketing management product. S41 uses the data processing results of platform data for different marketing management products to determine the analysis results of user needs for the marketing management products in different user profiles, divides users with the same user needs into the same group, and determines the purchasing user data of the combination in different user profiles based on the combination data of the marketing management products in different user profiles. S41: Demand Clustering and User Grouping, Step Explanation: Cluster the platform data (reviews / consultations) of product Z, grouping users who express the same core needs into the same group.

[0079] Key terms and their meanings: User needs analysis results: The core pain points or interests of users extracted from platform data. For example, "Need AI personalized guidance", "Worried about children getting hurt", "Hope for entertainment functions".

[0080] Combination: A group of users with similar user needs. A single user profile can contain multiple combinations. Combination purchasing user data refers to the number of users within each "combination." This reflects the prevalence of the need and the market size.

[0081] In this example (product Z): In the fitness enthusiast profile: Combination 1 (requirement: AI personalized plan) -> number of purchasing users = 120, Combination 2 (requirement: professional data tracking) -> number of purchasing users = 80; In the profile of tech enthusiasts: Combination 3 (Requirement: Device interoperability) -> Number of purchasing users = 40; In the profile of new mothers: Combination 4 (needs: safety and child locks) -> number of purchasing users = 150, Combination 5 (needs: short and efficient courses) -> number of purchasing users = 90.

[0082] S42 takes the products with the user profile as the user profile associated products, and determines the adaptive generation time of the marketing plan for the user profile associated products based on the adaptive generation data of the marketing plan for the user profile associated products. In the above steps, analyze the timeliness of the related product's campaign, identify other products that share the same user profile as product Z, and check when they last generated a marketing campaign.

[0083] User profile associated products: Other products that share at least one user profile with the target product (product Z). They are competitors or complements.

[0084] The time of adaptive generation of the marketing campaign: The timestamp of the last time a marketing campaign was generated for the user profile shared by this associated product.

[0085] This example data shows the following related products sharing the "fitness enthusiast" profile with product Z: Fitness App-A, Protein Powder-B, Fitness App-A (generated 10 days ago), Protein Powder-B (generated 45 days ago). Also, the following related products sharing the "new mom" profile with product Z: Maternity & Baby Products-C, Maternity & Baby Products-C (generated 5 days ago).

[0086] S43 determines whether the marketing management product is an adaptive generation object of the marketing plan based on the adaptive generation time of the marketing plan associated with the user profile in the user profile and the combined purchasing user data in the user profile.

[0087] Furthermore, based on the adaptive generation time of the marketing plan for the product associated with the user profile in the user profile and the combined purchasing user data in the user profile, it is determined whether the marketing management product is an adaptive generation object of the marketing plan, specifically including: S431 uses the combined purchasing user data in different user profiles to determine whether there is a combination of purchasing users in different user profiles where the number of purchasing users is greater than a preset threshold. If yes, proceed to the next step; otherwise, determine that the marketing management product does not belong to the adaptive generation object of the marketing plan. S431: Identify high-potential demand combinations and determine: Check whether there is a combination of product Z with more than 50 purchasing users under each user profile.

[0088] Results: Fitness enthusiast: Combination 1 (120) > 50, Combination 2 (80) > 50 -> Exist; Technology enthusiast: Combination 3 (40) < 50 -> Does not exist; New mom: Combination 4 (150) > 50, Combination 5 (90) > 50 -> Exist; Decision: Yes, proceed to S432.

[0089] S432 takes the user profile of the combination where the number of purchasing users is greater than the preset threshold for the number of purchasing users as the marketing plan construction demand profile, and determines whether the number of marketing plan construction demand schemes for the marketing management product is greater than the preset threshold for the number of demand schemes. If yes, it is determined that the marketing management product belongs to the adaptive generation object of the marketing plan; otherwise, it proceeds to the next step. In the above steps, the breadth of demand is determined, and the marketing plan constructs demand profiles: These refer to user profiles identified in S431 that have high-potential demand combinations.

[0090] Judgment: The marketing plan construction demand profile for product Z includes fitness enthusiasts and new mothers, with a quantity of 2. Judgment: 2>T_demand_profile (1)? Yes (usually "greater than" includes equal to). Decision: Yes, product Z belongs to the adaptive generation object of the marketing plan.

[0091] S433 Based on the adaptive generation time of the marketing plan associated with the user profile in the demand profile of the marketing plan construction, determine the user profile associated with the product whose interval from the current time is greater than the preset interval length threshold, and treat it as the update delay product. Determine whether there is an update delay product. If yes, proceed to the next step. If no, determine that the marketing management product does not belong to the adaptive generation object of the marketing plan. "Delayed update products" refers to user profile-related products where, under a specific "marketing plan building demand profile," the interval between the adaptive generation time of the marketing plan and the current time is greater than a preset interval threshold.

[0092] Related products sharing the same fitness influencer profile as product Z: Fitness App-A, Protein Powder-B. Fitness App-A's profile creation time: 10 days ago; Protein Powder-B's profile creation time: 45 days ago. Fitness App-A and Protein Powder-B are products with delayed updates.

[0093] Based on the update delay product data in different construction requirement profiles, S434 determines the number of construction requirement profiles with update delay products. When the number of construction requirement profiles with update delay products is greater than the preset threshold for the number of requirement profiles, it is determined that the marketing management product belongs to the adaptive generation object of the marketing plan. Otherwise, it is determined that the marketing management product does not belong to the adaptive generation object of the marketing plan.

[0094] In the above steps, the demand profile for updating delayed products is fitness enthusiasts, and the number of demand profiles is 1. Therefore, it is determined that the marketing management product does not belong to the adaptive generation object of the marketing plan.

[0095] Furthermore, when the marketing management product is an adaptive generation object of the marketing plan, the user profile of the marketing management product is used as the target user profile. In the e-commerce platform, the platform data of the purchasing users with the target user profile in different products are all processed to adaptively generate a marketing plan based on the real needs of the purchasing users with the target user profile.

[0096] Example 2 In a second aspect, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described marketing scheme adaptive generation method when running the computer program.

[0097] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0098] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0099] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for adaptively generating marketing plans, characterized in that, Specifically, it includes: Obtain product listing data from e-commerce platforms. Based on the listing data and the correlation between user profiles of different products, determine the products that require adaptive generation of marketing plans. Then, based on the user profile data of the products and the overlap between the products and other products in different user profiles, identify the products that require adaptive generation of marketing plans and designate them as marketing management products. Based on the correlation between user profiles and different marketing management products, the data processing method for platform data of different marketing management products is determined using the user profile data of the marketing management products. The data processing results of platform data for different marketing management products are determined using the data processing method, as well as the adaptive generation data of marketing plans for different products, and the adaptive generation object of marketing plans in the marketing management products is determined.

2. The adaptive marketing plan generation method as described in claim 1, characterized in that, The product listing data on the e-commerce platform includes the types and quantities of products listed on the platform.

3. The adaptive marketing plan generation method as described in claim 1, characterized in that, The need for adaptive marketing plan generation has been identified, specifically including: Based on the aforementioned listing data, determine the number of products listed on the e-commerce platform; Based on the correlation between user profiles of different products, identify the overlapping products of the product in different user profiles; Based on the number of products listed on the e-commerce platform and the overlapping products of the products in different user profiles, it is determined whether adaptive generation of marketing plans is required.

4. The adaptive marketing plan generation method as described in claim 3, characterized in that, The overlapping products in the user profile are those products that are included in all user profiles, meaning that the user profile also belongs to the user profile of the overlapping products.

5. The adaptive marketing plan generation method as described in claim 1, characterized in that, The method for determining the marketing management products is as follows: Based on the user profile data of the product, determine the user profile of the product; Based on the overlap between the product and other products in different user profiles, identify the overlapping products in different user profiles; By using overlapping product data across different user profiles, it can be determined whether the product is a marketing management product.

6. The adaptive marketing plan generation method as described in claim 5, characterized in that, Determining whether a product is a marketing management product based on overlapping product data across different user profiles includes: If, based on the overlapping product data of the product, it is determined that the product does not belong to the category of poorly overlapping products, then the product is determined not to be a marketing management product. If the product is a poorly overlapping product, determine whether the product has an overlapping user profile. If yes, determine that the product is a marketing management product; otherwise, determine that the product is not a marketing management product.

7. The adaptive marketing plan generation method as described in claim 1, characterized in that, The method for determining the adaptive generation object of the marketing plan in the marketing management product is as follows: Based on the data processing results of platform data for different marketing management products, the analysis results of user needs for the marketing management products in different user profiles are determined. Users with the same user needs are grouped into the same group. Based on the combination data of the marketing management products in different user profiles, the purchasing user data of the combination in different user profiles is determined. Products containing the user profile are designated as user profile-associated products. Based on the adaptive generation data of the marketing plan for the user profile-associated products, the adaptive generation time of the marketing plan for the user profile-associated products is determined. Based on the adaptive generation time of the marketing plan associated with the user profile in the user profile and the combined purchasing user data in the user profile, determine whether the marketing management product is an adaptive generation object of the marketing plan.

8. The adaptive marketing plan generation method as described in claim 7, characterized in that, Based on the adaptive generation time of the marketing plan for the product associated with the user profile in the user profile and the combined purchasing user data in the user profile, it is determined whether the marketing management product is an adaptive generation object of the marketing plan, specifically including: If, based on the combination of purchasing user data in different user profiles, it is determined that the marketing management product does not belong to the adaptive generation object of the marketing plan when there is no combination in different user profiles where the number of purchasing users is greater than a preset threshold for the number of purchasing users.

9. The adaptive marketing plan generation method as described in claim 8, characterized in that, When the marketing management product is an adaptive generation object of the marketing plan, the user profile of the marketing management product is used as the target user profile. The platform data of the purchasing users of the target user profile in different products on the e-commerce platform are processed to adaptively generate a marketing plan based on the real needs of the purchasing users of the target user profile.

10. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a marketing scheme adaptive generation method according to any one of claims 1-9.

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