Marketing scenario intelligent training management method, device and storage medium

Abstract

The present invention provides a marketing scenario intelligent training management method, device, and storage medium. The present invention relates to the technical field of marketing scenarios. The user's consumption scenario is determined based on the consumption profile and corresponding consumption parameters, and the corresponding marketing scenario is determined based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map, thereby ensuring the accuracy of the marketing scenario. The user's consumption route is determined based on the user's current location, corresponding demand information, and the current time, and corresponding marketing information is matched according to multiple consumption nodes and marketing scenarios in the user's consumption route. Each piece of marketing information is presented to the user in sequence. Therefore, a training set is determined based on the content of each piece of marketing information, the level coefficient of each piece of marketing information, and the user's consumption profile in the city. Intelligent training of the marketing scenario is triggered based on the training set and the marketing scenario, thereby ensuring the applicability of the marketing scenario in various cities.

Description

Technical Field

[0001] The present invention relates to the technical field of marketing scenarios, and in particular to a marketing scenario intelligent training management method, device and storage medium. Background Art

[0002] With the development of science and technology, marketing scenarios are gradually applied to life and are applicable to users' consumption in various cities at various promotion levels. At this time, the user's residence and the corresponding store distribution map are introduced, and the user's residence and the corresponding store distribution map are interacted and the corresponding marketing scenarios are output. However, there is no compatibility with the user's consumption scenario, which affects the accuracy of the marketing scenario and reduces the applicability of the marketing scenario in various cities. Summary of the Invention

[0003] Based on this, it is necessary to provide a marketing scenario intelligent training management method, equipment and storage medium to address the above technical problems.

[0004] A method for intelligent training and management of marketing scenarios, comprising: collecting consumption data sets of users in different cities; determining the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city; determining the consumption parameters of the user in the city based on the user's income level, the corresponding consumption time and the user's preferences in the city; determining the user's consumption scenario based on the consumption profile and the corresponding consumption parameters, and determining the corresponding marketing scenario based on the user's consumption scenario, the user's residence and the corresponding store distribution map; in the marketing scenario, determining the user's consumption route based on the user's current location, corresponding demand information and the current time, matching corresponding marketing information according to multiple consumption nodes in the user's consumption route and the marketing scenario, and presenting each marketing information to the user in sequence; determining the rank coefficient of each marketing information according to each marketing information, the user's viewing time and the user's click count, determining a training set according to the content of each marketing information, the rank coefficient of each marketing information and the user's consumption profile in the city, and triggering intelligent training of the marketing scenario according to the training set and the marketing scenario.

[0005] A marketing scenario intelligent training management device is applied to the above-mentioned marketing scenario intelligent training management method, and the marketing scenario intelligent training management device includes:

[0006] The collection module is used to collect consumption data sets of users in different cities;

[0007] The consumption profile module is used to determine the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city;

[0008] A consumption parameter module is used to determine the consumption parameters of a user in a city based on the user's income level, corresponding consumption time, and the user's preferences in the city;

[0009] The marketing scenario module is used to determine the user's consumption scenario based on the consumption profile and corresponding consumption parameters, and to determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map;

[0010] The marketing information module is used to determine the user's consumption route based on the user's current location, corresponding demand information, and the current time in this marketing scenario, and match corresponding marketing information according to multiple consumption nodes in the user's consumption route and marketing scenarios, and present each marketing information to the user in sequence;

[0011] The intelligent training module is used to determine the grade coefficient of each marketing message based on the marketing message, the user's viewing time and the user's click times, determine the training set based on the content of each marketing message, the grade coefficient of each marketing message and the user's consumption profile in the city, and trigger intelligent training of the marketing scenario based on the training set and the marketing scenario.

[0012] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned marketing scenario intelligent training management method.

[0013] The above-mentioned intelligent training management method, equipment and storage medium for marketing scenarios collect consumption data sets of users in different cities; determine the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city; determine the consumption parameters of the user in the city based on the user's income level, the corresponding consumption time and the user's preferences in the city; determine the user's consumption scenario based on the consumption portrait and the corresponding consumption parameters, and determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence and the corresponding store distribution map, which is compatible with the overall consideration of the user's consumption scenario, the user's place of residence and the corresponding store distribution map, and ensures the accuracy of the marketing scenario.

[0014] Furthermore, in this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information and current time, and corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and marketing scenarios. Each piece of marketing information is presented to the user in turn, thereby presenting marketing information of various aspects according to the consumption route, so as to facilitate subsequent management and control of each piece of marketing information.

[0015] Therefore, the rank coefficient of each marketing message is determined based on the marketing message, the user's viewing time and the user's click times; the training set is determined based on the content of each marketing message, the rank coefficient of each marketing message and the user's consumption profile in the city; the intelligent training of the marketing scenario is triggered based on the training set and the marketing scenario, so as to be compatible with the user's considerations in various cities and ensure the applicability of the marketing scenario in various cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of an application scenario of a marketing scenario intelligent training management method in one embodiment;

[0017] Figure 2 A flowchart of a marketing scenario intelligent training management method according to an embodiment;

[0018] Figure 3 This is a structural block diagram of a marketing scenario intelligent training management device in one embodiment;

[0019] Figure 4 This is a diagram of the internal structure of a marketing scenario intelligent training management device in one embodiment. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application. Example 1

[0021] The marketing scenario intelligent training management method provided in this application can be applied to Figure 1 In the application environment shown, the terminal 102 communicates with the server 104 via a network. The terminal 102 can be, but is not limited to, various personal computers, servers, and marketing scenarios. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers. Example 2

[0022] In this embodiment, please refer to Figures 2 to 4 , a marketing scenario intelligent training management method, applied to marketing scenario intelligent training management scenarios; the marketing scenario intelligent training management method includes:

[0023] Step S11: Collecting consumption data sets of users in different cities;

[0024] Step S12: Determine the consumption profile of the user in the city based on the consumption data set, the user's life information, and the corresponding city;

[0025] Step S13: Determine the user's consumption parameters in the city based on the user's income level, corresponding consumption time, and the user's preferences in the city;

[0026] Step S14: Determine the user's consumption scenario based on the consumption profile and the corresponding consumption parameters, and determine the corresponding marketing scenario based on the user's consumption scenario, the user's residence, and the corresponding store distribution map;

[0027] Step S15: In this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information, and current time, and corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and the marketing scenario. Each marketing information is presented to the user in sequence;

[0028] Step S16: Determine the grade coefficient of each marketing message based on each marketing message, the user's viewing time, and the user's click times; determine the training set based on the content of each marketing message, the grade coefficient of each marketing message, and the user's consumption profile in the city; and trigger intelligent training of the marketing scenario based on the training set and the marketing scenario.

[0029] In step S11, a collection of consumption data of users in different cities is collected;

[0030] In the specific implementation process of the present invention, the specific steps may be:

[0031] S111: Mark the user's payment platform;

[0032] S112: Export the user's previous payment data based on the user's payment platform;

[0033] S113: Determine the data set for each city based on the user's previous payment data and the matching of the corresponding payment location;

[0034] S114: Determine the consumption data of the user in different cities based on the data sets of each city and the consumption types of the user.

[0035] In an embodiment of the present application, the user's payment platform is marked; the user's previous payment data is exported according to the user's payment platform, and further management and control are performed based on the user's payment platform, thereby ensuring the export of the user's previous payment data to facilitate subsequent management and control of the user's previous payment data.

[0036] At this time, the purpose of marking the user's payment platform is to distinguish which payment platform the user is using (such as Alipay, WeChat Pay, UnionPay Pay, etc.) to facilitate subsequent data export and processing; usually, the payment platform will require the user to authenticate and bind the account when the user registers or uses its services; through this process, the payment platform can obtain the user's payment information and store it in the database; in order to mark the user's payment platform, a specific identifier or label can be assigned to the user in the database, which is associated with the user's payment platform.

[0037] Furthermore, the purpose of exporting the user's previous payment data is to analyze the user's payment behavior, consumption habits, etc., to provide a basis for subsequent decision-making or optimization; according to the payment platform marked by the user in step S111, the corresponding payment platform database can be connected to query and export the user's payment data; payment data usually includes transaction time, transaction amount, transaction type (such as transfer, payment, refund, etc.), transaction counterparty information, etc.; optionally, according to the user ID and payment platform name, the corresponding payment platform database can be located; all payment records of the user are queried in the database; the query results are exported to a specified file format (such as Excel, CSV, PDF, etc.) for subsequent analysis and processing.

[0038] Specifically, suppose there is a user A who uses Alipay and WeChat Pay at the same time; when user A registers or uses the services of these two payment platforms, Alipay and WeChat Pay will respectively assign a unique user ID to user A in their respective databases and associate the ID with the user's payment information; in order to mark user A's payment platform, a record can be added for user A in a unified database, which contains user A's name, the user IDs of the two payment platforms, and the corresponding payment platform names (such as "Alipay" and "WeChat Pay").

[0039] Suppose we need to export all payment data of user A on Alipay and WeChat Pay. First, we connect to the databases of these two payment platforms based on the payment platform names marked by user A in step S111 (i.e., "Alipay" and "WeChat Pay"). Then, we query all payment records of user A in the database. Finally, we export the query results as an Excel file. In the Excel file, we can clearly see all transaction records of user A on Alipay and WeChat Pay, including transaction time, transaction amount, transaction type, and transaction counterparty information.

[0040] Therefore, the data set of each city is determined based on the user's previous payment data and the corresponding payment location; the user's consumption data in different cities is determined based on the data set of each city and the user's consumption types, which is compatible with the overall consideration of the data set of each city and the user's consumption types, and ensures the accuracy of the user's consumption data in different cities.

[0041] At this time, the payment data is classified into various cities through the user's payment data and payment location information, thereby obtaining a data set for each city; first, it is necessary to integrate the user's previous payment data, which usually includes fields such as transaction time, transaction amount, transaction type, and transaction location; then, the transaction location information in the payment data is parsed, which usually involves geocoding technology, that is, converting the address into longitude and latitude coordinates, or matching the address with a preset city list through address matching technology; finally, based on the parsed location information, the payment data is classified into the corresponding city to form a data set for each city.

[0042] At the same time, through the data sets of each city and the consumption type information of users, the consumption habits and preferences of users in different cities are analyzed. First, the consumption types of users need to be divided. This can be done according to dimensions such as the category of goods and the type of service. Then, the payment data of the corresponding consumption types are filtered out from the data sets of each city. Finally, the filtered payment data is analyzed, including calculating indicators such as consumption amount and consumption frequency, to reveal the consumption habits and preferences of users in different cities.

[0043] In step S12, the consumption profile of the user in the city is determined based on the consumption data set, the user's life information, and the corresponding city;

[0044] In the specific implementation process of the present invention, the specific steps may be:

[0045] S121: Obtain consumption data set;

[0046] S122: Collecting the user's life information based on traversing the user's life database;

[0047] S123: Associating the consumption data set, the user's life information, and the corresponding city;

[0048] S124: Determine a first consumption feature based on the consumption data set and the user's life information;

[0049] S125: Determine a second consumption characteristic based on the consumption data set and the corresponding city;

[0050] S126: Determine the consumption profile of the user in the city based on the first consumption characteristic, the second consumption characteristic, and the length of stay of the user in the city.

[0051] In an embodiment of the present application, a consumption data set is obtained; the user's life information is collected based on the traversal of the user's life database, which realizes the traversal of the user's life database and ensures the accuracy of the user's life information.

[0052] At this time, the user's consumption data at different times and places is collected, including but not limited to transaction amount, transaction time, transaction product / service type, transaction merchant, etc.; optionally, the user's purchase record on the payment platform, including product name, purchase time, price, payment method, etc.; the user's payment record records every transaction of the user, including transaction time, transaction amount, transaction object, etc.; the user's consumption record in the physical store can be obtained through the membership system, POS machine, etc.; provide a consumption data set that integrates multiple data sources to facilitate enterprises to quickly obtain comprehensive user consumption data.

[0053] Specifically, connect with payment platforms, payment platforms and other third-party service providers through API interfaces to obtain users' consumption data in real time; crawl users' consumption information from public websites; and purchase integrated consumption data sets from third-party data providers.

[0054] At the same time, we collect users' basic information, interests, hobbies, social behaviors and other life information in order to gain a deeper understanding of users' consumption motivations and behavior patterns; at this time, the personal information filled in by users when registering on the platform, such as name, age, gender, occupation, etc.; user behavior records on social media, including posts, comments, likes, people followed, etc.; user behavior records on the platform, such as browsing records, search records, click records, etc.; optionally, obtain relevant information through user authorization to access their social media accounts or behavior logs; design questionnaires to collect users' basic information and interests; use data mining technology to extract useful life information from user behavior logs.

[0055] Furthermore, the consumption data set, the user's life information and the corresponding city are associated; the first consumption feature is determined based on the consumption data set and the user's life information; and the second consumption feature is determined based on the consumption data set and the corresponding city. This is compatible with the multi-dimensional control of the consumption data set, the user's life information and the corresponding city, and ensures the accuracy of the first consumption feature and the second consumption feature.

[0056] At this time, the user's consumption data and life information are associated with specific cities in order to analyze the user's consumption habits and lifestyle in different cities; the consumption data collection and the user's life information database are merged to ensure that each user's data is complete; according to the user's consumption record or geographic location information in life information, a corresponding city label is added for each user; the integrated data is cleaned to remove duplicate, erroneous or invalid data to ensure the accuracy and reliability of the data.

[0057] Analyze the user's consumption habits, preferences and needs to form the first consumption characteristics; at this time, analyze the user's consumption data, including consumption amount, consumption frequency, consumption type (such as catering, shopping, entertainment, etc.), consumption brand, etc.; associate the user's consumption data with their life information, and analyze the impact of life information on consumption habits; extract the user's consumption characteristics based on the analysis results, such as consumption capacity, consumption preferences, brand loyalty, etc.

[0058] Analyze the differences and characteristics of users' consumption in different cities to form the second consumption characteristics; at this time, analyze the consumption data of users in different cities, including consumption amount, consumption type, consumption frequency, etc.; compare the consumption data of users in different cities to find out consumption trends and differences; extract the consumption characteristics of users in specific cities based on the analysis results, such as urban consumption hotspots, differences in consumption habits, etc.

[0059] Specifically, suppose a payment platform wants to analyze the consumption differences of users in different cities; first, the platform integrates the user's consumption data set and the user's life information database; then, based on the delivery address information in the user's purchase record, it adds a corresponding city tag for each user; for example, user A's purchase record shows that his delivery address is mainly in Beijing, so the city tag "Beijing" is added to user A; after data cleaning, the payment platform obtains a complete data set containing user consumption data, life information and city tags.

[0060] By analyzing User A's consumption data, the payment platform discovered that User A spent a large amount of money on shopping and mainly purchased high-end brand clothing and electronic products. At the same time, combined with User A's life information, the payment platform learned that User A is a 35-year-old professional who pays attention to personal image and quality of life. Therefore, the payment platform extracted the first consumption characteristic of User A: high spending power, focus on quality, and preference for high-end brands.

[0061] The payment platform further analyzed User A's consumption data in Beijing and Shanghai. Through comparison, it was found that User A's consumption in Beijing was mainly concentrated in high-end shopping malls and brand stores, and he purchased clothing, electronic products, and luxury goods. In Shanghai, User A preferred to visit trendy and fashionable streets and buy fashionable clothing and accessories. Therefore, the payment platform extracted User A's second consumption characteristic: in Beijing, he pays attention to quality and brand, and the amount of consumption is relatively high; in Shanghai, he pays more attention to fashion and trends, with a higher consumption frequency but a relatively low single consumption amount.

[0062] Therefore, the consumption profile of the user in the city is determined based on the first consumption characteristic, the second consumption characteristic and the length of time the user stays in the city, which is compatible with the overall consideration of the first consumption characteristic, the second consumption characteristic and the length of time the user stays in the city, and realizes the multi-dimensional control of the first consumption characteristic, the second consumption characteristic and the length of time the user stays in the city, thereby ensuring the accuracy of the consumption profile of the user in the city.

[0063] At this time, the first consumption characteristics (characteristics based on the user's personal life information and general consumption habits), the second consumption characteristics (the user's specific consumption habits in different cities) and the length of time the user stays in the city are combined to comprehensively portray the user's consumption portrait in the city; this portrait helps companies to have a deeper understanding of the user's consumption behavior in the city, thereby formulating more accurate marketing strategies.

[0064] Integrate the first consumption feature, the second consumption feature, and the length of stay of the user in the city to form a comprehensive data set; assign appropriate weights to each feature based on its importance and relevance; for example, for users who stay for a long time, their second consumption feature has a higher weight; while for users who stay for a short time, their length of stay and the first consumption feature are more important; based on the integrated data set and weight assignment, use data analysis tools or algorithms to construct a consumption profile of the user in the city; the consumption profile includes information such as the user's consumption preferences, consumption frequency, consumption amount, and consumption hotspots; verify the accuracy of the consumption profile by comparing it with actual consumption data, and optimize it as needed.

[0065] Specifically, suppose a travel service platform wants to build a consumption profile for user B in Chengdu, the city he is about to visit. User B's first consumption characteristic shows that he is a 30-year-old young man who loves food and culture and has a high pursuit of high-quality life experience. The second consumption characteristic shows that when in Beijing, user B tends to dine in high-end restaurants, visit cultural attractions, and buy unique souvenirs; while in Shanghai, he prefers to try street food and visit fashionable and trendy neighborhoods. User B plans to stay in Chengdu for 5 days.

[0066] Based on this information, the travel service platform constructed a consumption profile of User B in Chengdu: Consumption preferences: love of food, especially Sichuan cuisine and local special snacks; strong interest in cultural attractions and museums; preference for high-quality life experiences, such as high-end accommodation and customized travel services; consumption frequency: due to the long stay (5 days), User B is expected to taste local food many times, visit multiple cultural attractions, and purchase some special souvenirs; consumption amount: given User B's pursuit of a high-quality life, it is expected that the amount of consumption in Chengdu will be relatively high, especially in terms of dining and accommodation; consumption hotspots: User B will go to Chengdu's famous food streets (such as Jinli, Kuanzhai Alley, etc.) to taste local snacks; visit cultural attractions such as Wuhou Temple and Du Fu Thatched Cottage; and choose to stay in high-end hotels located in the city center or scenic areas.

[0067] Through this consumption portrait, the travel service platform can provide user B with more personalized service recommendations, such as recommending local restaurants that suit his taste, customizing travel routes that suit his interests, etc., thereby improving user satisfaction and platform competitiveness.

[0068] In another embodiment of the present application, an example of a consumption profile matching table is used for illustration. The consumption profile matching table example is:

[0069]

[0070] In this consumer profile matching table, we determine the consumer profile description for the user in the city based on the user's primary consumer characteristics (such as food lovers, shopping experts, etc.), secondary consumer characteristics (such as liking to try local special snacks, preferring shopping in high-end shopping malls, etc.) and the user's length of stay in the city (such as 3 days, 7 days, etc.).

[0071] In addition, the first consumption characteristic: 0.4 (because the first consumption characteristic reflects the user's long-term consumption habits and has a greater impact on the user's consumption behavior in the city); the second consumption characteristic: 0.3 (because the second consumption characteristic reflects the user's specific consumption habits in the city and has a certain impact on the user's consumption behavior in the city); length of stay: 0.3 (because the length of stay determines the nature and time of the user's consumption in the city); assuming that a user's first consumption characteristic is "food lover" (score 8 points, full of 10 points), the second consumption characteristic is "like to try local special snacks" (score 7 points, full of 10 points), and the length of stay is 5 days (score 5 points, full of 10 points, scored according to the length of stay).

[0072] The user's consumption profile score in Chengdu is: 0.48 + 0.37 + 0.3*5 = 3.2 + 2.1 + 1.5 = 6.8 points. Based on the score, we can describe the user's consumption profile as follows: During their stay in Chengdu, the user tends to try local snacks and enjoy the fun brought by food. However, due to the low scores of the length of stay and certain consumption characteristics, the user will not consume too frequently or try too many different foods.

[0073] In step S13, the consumption parameters of the user in the city are determined based on the user's income level, corresponding consumption time, and the user's preferences in the city;

[0074] In the specific implementation process of the present invention, the specific steps may be:

[0075] S131: Collecting user income data based on the user's payment platform traversal;

[0076] S132: Determine the user's income level based on the user's income data, the user's job title in the company, and the user's place of residence;

[0077] S133: Determine multiple consumption categories and corresponding consumption frequencies based on the user's consumption data in the city;

[0078] S134: Determine the user's preference in the city based on the multiple consumption categories, corresponding consumption frequencies, and corresponding cities;

[0079] S135: interacting with the user's income level, corresponding consumption time, and the user's preferences in the city;

[0080] S136: Determine the consumption parameters of the user in the city according to the user's income level, corresponding consumption time, and the user's preferences in the city.

[0081] In an embodiment of the present application, the user's income data is collected based on the traversal of the user's payment platform; the user's income level is determined based on the user's income data, the user's job title in the enterprise, and the user's place of residence, which is compatible with the overall consideration of the user's income data, the user's job title in the enterprise, and the user's place of residence, thereby ensuring the accuracy of the user's income level.

[0082] At this point, the user's payment platforms are traversed. This step involves checking all payment platforms used by the user, including but not limited to Alipay, WeChat Pay, bank transfer applications, etc.; these platforms record all of the user's transaction activities, including income; transaction records are extracted from these payment platforms, especially those related to the user's income; this includes salary income, bonuses, investment income, etc.; the extracted data needs to be integrated to form a comprehensive view of the user's income; this helps analyze the user's income level and income source.

[0083] Based on the income data collected from the payment platform, the user's total income, income source and income stability are analyzed; combined with the user's job title in the company, their income level can be more accurately assessed; different titles often correspond to different salary ranges; the cost of living and overall salary level of the place of residence are also important factors in determining the user's income level; the cost of living in first-tier cities is higher, and the salary level is correspondingly higher; while in second- or third-tier cities, it is lower.

[0084] Let’s assume there is a user, Mr. Zhang, who uses Alipay and WeChat Pay as his primary payment tools. By traversing these two platforms, we can collect the following income data for Mr. Zhang:

[0085] Alipay: Monthly salary, year-end bonus, investment income, etc.; WeChat Pay: Occasional part-time job income, red envelope income, etc. After integrating these data, we can get Mr. Zhang's total income. For example, his monthly salary is 10,000 yuan, his year-end bonus is 30,000 yuan, and his investment income averages 2,000 yuan per month. Part-time job income and red envelope income are more random.

[0086] Assume he is a project manager at a technology company and lives in Beijing. Based on his income data and job title, we can make the following analysis:

[0087] Income data analysis: Mr. Zhang's total income includes fixed salary, year-end bonus and investment income, with an average monthly total income of about 14,000 yuan (fixed salary + investment income, year-end bonus is amortized annually); Job title considerations: As a project manager, Mr. Zhang's salary level is generally higher than that of ordinary employees; in first-tier cities like Beijing, the salary range of project managers is relatively wide, but combined with his total income, we can infer that he is at an upper-middle income level; Residence factors: As a first-tier city, Beijing has a high cost of living; however, Mr. Zhang's total income is sufficient to support his life in Beijing and has a certain savings capacity.

[0088] Based on the above factors, we can determine that Mr. Zhang's income level is above average. This analysis helps companies more accurately understand users' financial status and spending power, thereby formulating more appropriate marketing strategies.

[0089] At the same time, multiple consumption types and corresponding consumption frequencies are determined based on the user's consumption data in the city; the user's preferences in the city are determined based on the multiple consumption types and corresponding consumption frequencies in the city. The introduction of multiple consumption types and corresponding consumption frequencies in the city realizes multi-dimensional control of the user's preferences in the city.

[0090] At this point, a comprehensive check is conducted on all consumption data of the user in the city; this includes the user's transaction records on various payment platforms (such as Alipay, WeChat Pay), bank card transactions, and transaction records on the online and offline shopping platforms involved; different consumption categories are identified from the consumption data, such as catering, transportation, entertainment, shopping, housing (rent or mortgage), education, medical care, etc.; for each consumption category, the user's consumption frequency is calculated; this can be calculated on a daily, weekly, monthly or annual basis, depending on the availability of data and the purpose of analysis.

[0091] Based on the consumption types identified and the consumption frequency calculated in the previous step, analyze the user's investment and preferences in various types of consumption; combine the consumption environment and cultural characteristics of the city to further determine the user's consumption preferences; for example, some cities are famous for their food, and users in these cities tend to consume dining frequently; while other cities are known for their rich shopping or entertainment activities; comprehensively consider the consumption types, frequencies, and city characteristics to determine the user's preferences in the city; this helps companies understand the user's consumption tendencies and provides a basis for providing personalized services and products.

[0092] Specifically, suppose there is a user, Mr. Zhang, who recently moved to Shanghai. In order to understand her consumption habits in Shanghai, we traverse all her consumption data in Shanghai.

[0093] Dining: Ms. Zhang eats out at restaurants near her company five times a week on weekdays and tries different restaurants or orders takeout on weekends, for an average of ten dining out visits per week. Transportation: She commutes to get off work daily by subway and tops up her transportation card four times a month (once a week). She also occasionally uses Didi, but less frequently. Entertainment: Ms. Zhang goes to the cinema one to two times a month and attends friends' gatherings or social events two to three times a month. Shopping: She frequently shop online, purchasing daily necessities or clothing several times a week, but makes fewer large purchases (such as electronics and furniture).

[0094] Combining the types and frequency of her consumption in Shanghai, as well as Shanghai's consumption environment and cultural characteristics, we can draw the following conclusions:

[0095] Dining preferences: Ms. Zhang frequently eats out and has a strong desire to try different restaurants. Considering Shanghai's rich food culture, we can infer that she has a strong interest in Shanghai's dining culture.

[0096] Entertainment preferences: Although Ms. Zhang's frequency of moviegoing and social activities is not particularly high, she has a fixed monthly entertainment expenditure, indicating that she enjoys social and leisure activities. As an international metropolis, Shanghai provides a wealth of entertainment options, and Ms. Zhang likes to try different entertainment methods.

[0097] Shopping preferences: Ms. Zhang shopped online frequently, indicating that she preferred convenient shopping methods. Furthermore, as the fashion capital of Shanghai, there are numerous shopping venues. Although she did not make large purchases frequently, she did pay attention to fashion trends and new product releases.

[0098] Therefore, the user's income level, the corresponding consumption time and the user's preferences in the city are interacted; the user's consumption parameters in the city are determined based on the interaction of the user's income level, the corresponding consumption time and the user's preferences in the city, thereby realizing the interaction of the user's income level, the corresponding consumption time and the user's preferences in the city, ensuring the overall consideration of the user's income level, the corresponding consumption time and the user's preferences in the city, and realizing the precise control of the user's consumption parameters in the city.

[0099] In step S14, the user's consumption scenario is determined based on the consumption profile and the corresponding consumption parameters, and the corresponding marketing scenario is determined based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map;

[0100] In the specific implementation process of the present invention, the specific steps may be:

[0101] S141: Obtain the consumption profile and corresponding consumption parameters;

[0102] S142: Match the corresponding scenario weight to the consumption profile and the corresponding consumption parameters;

[0103] S143: Determine the user's consumption scenario based on the consumption profile, consumption parameters, and corresponding scenario weights;

[0104] S144: Determine the user's place of residence based on the user's residential data;

[0105] S145: Determine a corresponding store distribution map based on the user's residence, a map of the surrounding area of ​​the residence, and store marks;

[0106] S146: Determine the corresponding marketing scenario based on the user's consumption scenario, the user's residence, and the corresponding store distribution map.

[0107] In an embodiment of the present application, the consumption portrait and the corresponding consumption parameters are obtained; the corresponding scene weights are matched to the consumption portrait and the corresponding consumption parameters; the user's consumption scene is determined based on the consumption portrait, consumption parameters and the corresponding scene weights. The consumption portrait, consumption parameters and the corresponding scene weights are introduced, and the consumption portrait, consumption parameters and the corresponding scene weights are controlled as a whole to ensure the accuracy of the user's consumption scene.

[0108] At this point, we need to deeply understand the user's consumption profile and consumption parameters, and match this information with different consumption scenarios, thereby assigning a weight to each scenario. This weight reflects the user's consumption characteristics or preferences in different scenarios. First, we need to review the user's consumption profile, which usually includes information such as the user's age, gender, income level, occupation, interests and hobbies, and consumption habits. Next, we interpret specific parameters related to user consumption, such as consumption frequency, average consumption amount, consumption hotspots (i.e., the areas or products that users consume most frequently), and consumption elasticity (i.e., the sensitivity of consumption to changes in income level). Based on the user's consumption profile and consumption parameters, we match the user with different consumption scenarios. These scenarios include online shopping, offline shopping (such as supermarkets, shopping malls, specialty stores, etc.), leisure and entertainment (such as cinemas, gyms, spas, etc.), travel, etc. Finally, we assign a weight to each matched consumption scenario. The allocation of weights can be based on a variety of factors such as the user's historical consumption data in that scenario, consumption preferences, market trends, and the competitive environment. The weight value is usually a number between 0 and 1, indicating the user's consumption characteristics or preferences in that scenario.

[0109] We will combine the user's consumption profile, consumption parameters and previously assigned scenario weights to determine the user's optimal consumption scenario; this will help us more accurately understand the user's consumption behavior in different scenarios and provide a basis for subsequent marketing strategy formulation; first, we summarize the user's consumption profile, consumption parameters and scenario weights and other information; then, we analyze the weight value of each scenario and find the scenario with the highest weight; this scenario is most likely to become the user's main consumption scenario; while determining the main consumption scenario, we also need to consider other factors, such as the user's time schedule, geographical location restrictions, etc.; these factors will affect the user's actual consumption behavior in different scenarios; finally, we comprehensively consider all factors to determine the user's optimal consumption scenario.

[0110] Specifically, suppose there is a user named Mr. Li, whose consumption profile shows that he is a middle-aged, high-income male who likes outdoor activities and high-end shopping; his consumption parameters include an average monthly spending of 5,000 yuan in high-end shopping malls and an average monthly spending of 2,000 yuan on online shopping platforms, and he is less sensitive to prices.

[0111] Based on this information, we can match Mr. Li with the following consumption scenarios and assign corresponding weights:

[0112] Shopping in high-end malls: The weight is 0.6; Mr. Li likes shopping in high-end malls and his spending in this scenario is relatively high; Online shopping: The weight is 0.3; Although Mr. Li also shops online, his online spending is lower than that in high-end malls; Shopping in outdoor goods stores: The weight is 0.1; Mr. Li likes outdoor activities, so he occasionally shops in outdoor goods stores, but this is not his main consumption scenario.

[0113] Based on his consumption profile, consumption parameters and scenario weights, we can determine that his main consumption scenario is shopping in high-end shopping malls; this is because his average monthly consumption in high-end shopping malls is higher, and the weight value of this scenario is 0.6, which is the highest weight among all scenarios; at the same time, we also need to note that although Mr. Li also consumes on online shopping platforms with a weight value of 0.3, his online consumption amount is still lower than that in high-end shopping malls; therefore, when formulating marketing strategies, we can pay more attention to the high-end shopping mall scenario and provide Mr. Li with personalized shopping experiences and promotions.

[0114] Furthermore, the user's place of residence is determined based on the user's residential data; the corresponding store distribution map is determined based on the user's place of residence, the surrounding drawings of the place of residence, and the store marks. The user's place of residence, the surrounding drawings of the place of residence, and the store marks are introduced, and the user's place of residence, the surrounding drawings of the place of residence, and the store marks are considered as a whole to ensure the accuracy of the store distribution map.

[0115] At this point, the user's precise place of residence is determined based on the residential data provided by the user; this data includes specific information such as the user's home address, postal code, city, region or street; by analyzing and processing this data, we can clearly determine the user's approximate geographical location, which is crucial for subsequent market segmentation, marketing strategy formulation and store selection.

[0116] First, we need to collect users' residential data from various channels such as user registration information, purchase records or questionnaires; then, we need to clean the collected data to remove duplicate, erroneous or incomplete information to ensure the accuracy and reliability of the data; then, we use geographic information system (GIS) technology to convert users' residential data into specific geographic location information, such as latitude and longitude coordinates; finally, based on the geographic positioning results and combined with administrative division data, we determine the user's precise place of residence, such as city, region, street, etc.

[0117] Based on the user's place of residence, a geographical map of the place of residence, and the marked store information, a distribution map of stores around the user is drawn; this map will help us intuitively understand the business environment in the user's area, including key information such as the type, number, distribution location of stores, and their relative distance from the user's place of residence.

[0118] First, we need to collect geographic maps of the user's residence, which usually includes geographic information such as streets, buildings, and public facilities; at the same time, we also need to collect marked store information, such as the store's name, type, address, etc.; then, integrate the collected geographic maps and store information to ensure that the correspondence between them is accurate; then, use GIS technology or professional mapping software to draw the integrated data into a store distribution map; this map should clearly show the store types, quantity and distribution locations around the user's residence; finally, optimize and adjust the distribution map as needed, such as adding labels, marking distances, or providing other relevant information, to better meet subsequent analysis and marketing needs.

[0119] Specifically, assume that the residential data of user Ms. Wang includes her home address: "A certain community in Sanlitun Street, Chaoyang District, Beijing"; through data cleaning and geolocation, we can determine that Ms. Wang's residence is Sanlitun Street, Chaoyang District, Beijing; this information is crucial for subsequent analysis of Ms. Wang's consumption habits, formulating targeted marketing strategies, and selecting nearby stores for promotion.

[0120] After determining that she lived in Sanlitun Street, Chaoyang District, Beijing, we collected geographical maps and marked store information of the area. By integrating this data, we drew a distribution map of the stores around Ms. Wang. This map shows the distribution of various stores in Sanlitun Street, such as high-end shopping malls, fashion brand stores, gourmet restaurants, coffee shops, etc. Through this map, we can intuitively understand the business environment in Ms. Wang’s area and provide strong support for the subsequent formulation of marketing strategies. For example, based on Ms. Wang’s consumption profile and preferences, we can select nearby fashion brand stores or high-end shopping malls for targeted promotion and marketing.

[0121] Therefore, the corresponding marketing scenario is determined based on the user's consumption scenario, the user's place of residence and the corresponding store distribution map, and the user's consumption scenario, the user's place of residence and the corresponding store distribution map are controlled in multiple dimensions to ensure the accuracy of the marketing scenario.

[0122] A consumption scenario refers to the specific environment and context in which consumers purchase or use products or services. Different consumption scenarios will stimulate different consumer needs, thus influencing their purchasing decisions. Analyze the daily habits, interests, hobbies, and potential needs of the target user group. Determine the applicable consumption scenario based on the characteristics of the product or service. Verify and optimize the positioning of consumption scenarios through market research, user interviews, and other methods.

[0123] The user's place of residence is one of the important factors that need to be considered when formulating marketing strategies. By understanding the geographical distribution of users, companies can carry out marketing and resource allocation more targetedly. Collect and analyze users' geographic location data, including cities, regions, streets, etc. Use tools such as geographic information systems (GIS) to visualize user data and form user distribution maps. Based on the user distribution map, identify key areas and potential markets.

[0124] The store distribution map shows the layout of a company's physical stores in different regions. By analyzing the store distribution map, a company can understand its own market coverage and potential market gaps. It collects and organizes the company's store information, including store location, area, operating conditions, etc. It uses map software or GIS tools to visualize store information and form a store distribution map. Combined with the user distribution map, it analyzes the rationality of the store layout and potential market gaps.

[0125] After determining the user's consumption scenarios, residence, and store distribution map, the company needs to combine this information to determine the corresponding marketing scenarios; marketing scenarios refer to the marketing activities conducted by the company for a specific user group within a specific time and space; comprehensively analyze the user's consumption scenarios, residence, and store distribution map to identify potential marketing opportunities; based on marketing opportunities, formulate specific marketing activity plans, including activity themes, time, location, participation methods, etc.; and promote and publicize activities through a combination of online and offline methods to attract user participation.

[0126] Taking the smart home company as an example, we opened a new physical store in a large shopping mall in the central urban area of ​​a first-tier city. There are many large residential communities around the shopping mall, and users have a high demand for our smart home products. In combination with users' home entertainment, home office and other consumption scenarios, we plan to hold a "Smart Home Experience Day" event during the opening of the new physical store. The content of the event includes smart product display, on-site experience, expert explanations, discount promotions, etc.

[0127] We publicized and promoted the event through various channels including social media, offline advertising, and partnerships, attracting a large number of users to participate. During the event, our smart home products received widespread attention and recognition, achieving good marketing results. By deeply analyzing users' consumption scenarios, places of residence, and store distribution maps, and combining specific product characteristics with market demand, companies can identify corresponding marketing scenarios and formulate corresponding marketing strategies and activity plans. This not only helps to enhance the company's brand awareness and market share, but also better meet user needs and expectations.

[0128] Specifically, a marketing scenario matching table is designed to associate the user's consumption scenario, residence, store distribution, and corresponding marketing scenario. The following is a simplified example of a marketing scenario matching table:

[0129]

[0130] User ID: the user's unique identifier; consumption scenario: the consumption situation generated by the user in daily life; residence: the specific geographical location of the user; store distribution: the number and distribution of stores near the user's residence; marketing scenario: targeted marketing activities determined based on the user's consumption scenario and residence, combined with the store distribution.

[0131] In step S15, in this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information, and current time, and corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and the marketing scenario. Each marketing information is presented to the user in sequence;

[0132] In the specific implementation process of the present invention, the specific steps may be:

[0133] S151: In this marketing scenario, user demand information is collected;

[0134] S152: Determine the user's current location based on the user's location data;

[0135] S153: Determine the user's consumption route based on the user's current location, corresponding demand information, and current time;

[0136] S154: Determine multiple consumption nodes in the user's consumption route based on the user's consumption route, the store distribution map, and the user's consumption budget;

[0137] S155: Associating multiple consumption nodes and marketing scenarios in the user's consumption route;

[0138] S156: Based on the multiple consumption nodes in the user's consumption route and the marketing scenarios, the corresponding marketing information is matched and each piece of marketing information is presented to the user in sequence.

[0139] In an embodiment of the present application, in this marketing scenario, user demand information is collected; the user's current location is determined based on the user's location data; and the user's consumption route is determined according to the user's current location, the corresponding demand information, and the current time. This takes into account the user's current location, the corresponding demand information, and the current time as a whole, ensuring the accuracy of the user's consumption route.

[0140] At this time, in this marketing scenario, user demand information is collected. Optionally, user demand information in a specific marketing scenario can be obtained through questionnaires, user interviews, online behavior tracking (such as browsing history, search keywords, etc.) or smart device collection (such as usage habits of smart home systems); demand information includes user product preferences, price sensitivity, purchase frequency, specific functional requirements, etc.

[0141] It is necessary to collect location data from users' devices (such as smartphones, car navigation systems, etc.); this data includes GPS data, WiFi hotspot information, mobile device signals, etc. The collected location data needs to be processed and analyzed to determine the user's precise location; when processing user location data, privacy protection regulations must be strictly complied with to ensure the security and privacy of user data.

[0142] The user's consumption route is determined based on the user's current location, corresponding demand information and current time, which introduces the user's current location, corresponding demand information and current time; demand information includes the user's explicitly expressed needs (such as finding nearby restaurants, shopping malls, etc.) and potential needs obtained through data analysis (such as the user's favorite product types, consumption habits, etc.).

[0143] The current time has a significant impact on consumption routes. For example, users are more likely to look for restaurants during lunch time, while they are more likely to look for entertainment venues at night. By combining the user's current location, demand information, and current time, users can plan the optimal consumption route.

[0144] Specifically, suppose a user is using a navigation app; the app collects GPS data through the user's smartphone and uploads it to the server in real time; after receiving the data, the server uses map matching technology to determine the user's current location and displays it on a map; in this way, the user can clearly know where he is, as well as information such as surrounding roads and buildings; suppose a user opens the app at lunch time and expresses the need to find a nearby restaurant; the app first filters out a list of nearby restaurants based on the user's current location and lunch time; then, it uses path search technology to plan the best route for the user to the nearest restaurant; at the same time, the app can also consider traffic conditions (such as congestion, road construction, etc.) to provide users with more accurate arrival time estimates; we can determine the user's current location based on the user's location data, and plan the best consumption route for them based on their demand information and current time; this not only improves user convenience, but also provides merchants with more marketing opportunities.

[0145] Furthermore, multiple consumption nodes in the user's consumption route are determined based on the user's consumption route, store distribution map and the user's consumption budget. The interaction of the user's consumption route, store distribution map and the user's consumption budget is introduced, and the division of multiple consumption nodes in the consumption route is realized. It is compatible with the overall consideration of the user's consumption route, store distribution map and the user's consumption budget, and ensures the accuracy of multiple consumption nodes in the user's consumption route.

[0146] For consumption routes, first, based on the user's starting location, target location, and stopover points, combined with map data and traffic information, one or more feasible consumption routes are planned; the user's travel time periods, such as morning rush hour, lunch break, evening shopping, etc., as well as the business hours of each consumption node, are taken into consideration to ensure that the nodes on the route are within the user's available time.

[0147] For the store distribution map, the planned consumption route is superimposed on the store distribution map to identify the store locations around the route; based on the user's consumption preferences (such as dining, shopping, entertainment, etc.), the store types that meet the user's needs are screened out.

[0148] Regarding consumption budget, understand the user's consumption budget, including the overall budget and individual budget (such as dining budget, shopping budget, etc.); based on the store's price information (such as per capita consumption, product price range, etc.), filter out stores that meet the user's budget.

[0149] For consumption nodes, the selected stores are prioritized based on factors such as user preferences, store ratings, promotional activities, etc.; based on route planning, time nodes, budget constraints and priority sorting, multiple consumption nodes in the user's consumption route are determined.

[0150] Specifically, suppose a user plans a day of shopping and entertainment on the weekend, starting from home and ending at the cinema, with a spending budget of 500 yuan. The following is a specific example:

[0151] The user starts from home and plans to go to the shopping mall first, then to the food court, and finally to the cinema; taking into account the traffic conditions and store opening hours, the user plans an optimal consumption route; around the shopping mall, the user selects fashion clothing, electronic products, beauty and other stores; around the food court, the user selects various restaurants and snack stalls; the user sets an overall budget of 500 yuan, of which the shopping budget is 300 yuan and the dining budget is 200 yuan; based on the store's price information, the user selects stores that meet the budget, such as restaurants with an average consumption of less than 100 yuan per person.

[0152] In the shopping mall, users gave priority to fashion clothing stores and electronics stores with high ratings and discounts; in the food court, users chose a restaurant with a good reputation and reasonable prices; finally, users went to the cinema to watch a movie, completing a day of shopping and entertainment activities.

[0153] In another embodiment of the present application, a consumption node matching table is constructed. The consumption node matching table is used to record potential consumption nodes in the user's consumption route and perform preliminary screening based on factors such as the user's consumption budget, store type, and location. The following is an example of a consumption node matching table:

[0154]

[0155] In this consumer node matching table:

[0156] The consumption node number is used to uniquely identify each consumption node; the store type indicates the type of store, such as a cafe or bookstore; the location information describes the store's location relative to the user's consumption route; the estimated consumption amount is the user's expected consumption amount at the store; whether it meets the budget is a Boolean value indicating whether the consumption node is within the user's consumption budget; the user preference indicates the user's preference for the store type, which can be high, medium, or low.

[0157] Through preliminary screening, we can exclude consumption nodes that do not meet the budget or have low user preference, such as the "high-end restaurant" in the above consumption node matching table.

[0158] Therefore, multiple consumption nodes and marketing scenarios in the user's consumption route are associated; corresponding marketing information is matched based on the multiple consumption nodes and marketing scenarios in the user's consumption route, and each piece of marketing information is presented to the user in turn, thereby achieving the matching of multiple consumption nodes and marketing scenarios in the user's consumption route, ensuring the presentation of marketing information, and improving the accuracy of marketing information.

[0159] At this point, first, based on the user's consumption route and multiple previously determined consumption nodes (such as stores, restaurants, cinemas, etc.), clarify the location and type of each node; for each consumption node, define the related marketing scenarios; marketing scenarios are based on various factors such as festivals (such as Christmas promotions), events (such as new product launches), user behavior (such as first-time shopping discounts), etc.; associate each consumption node with the corresponding marketing scenario, which usually involves data analysis to understand the user's consumption behavior and historical preferences in specific scenarios; based on the user's real-time feedback and location changes, dynamically adjust the association between marketing scenarios and consumption nodes to ensure the timeliness and relevance of marketing activities.

[0160] Establish a database containing various marketing information, including coupons, discounts, gifts, event details, etc.; match the marketing information related to each consumption node from the marketing information database according to the user's consumption route and related marketing scenarios; formulate a presentation strategy for marketing information, considering the time point (such as before the user arrives at the node, when consuming within the node, etc.), method (such as SMS, APP push, on-site display, etc.) and frequency (such as one-time presentation or multiple reminders); personalize the presented marketing information according to the user's consumption history, preferences and real-time feedback to improve user participation and satisfaction; monitor the presentation effect of marketing information and collect user feedback data to optimize future marketing activities.

[0161] Specifically, suppose a user plans to start from home, go to the shopping mall first, then go to the restaurant for dinner, and finally go to the cinema to watch a movie; in this scenario: the shopping mall node is associated with the "Winter Sale" marketing scenario because multiple merchants are promoting winter clothing and household items.

[0162] The restaurant node is associated with the "Food Festival" marketing scenario because the restaurant participates in the local food festival and offers special dishes and discounted packages; the cinema node is associated with the "New Movie Release" marketing scenario because a new movie that the user is interested in is being released.

[0163] Suppose a user has planned a shopping route from a shopping mall to a restaurant and then to a movie theater. As the user approaches the mall, a "Winter Sale" coupon message is pushed through the app to encourage them to shop there. Before the user dine, a detailed "Food Festival" message, including special dishes and discounted packages, is sent via SMS to entice them to dine at the designated restaurant. When the user arrives at the movie theater, a promotional message about a new movie release, such as free popcorn or a small gift with ticket purchase, is displayed on the self-service ticket machine, enhancing the user's viewing experience.

[0164] In another embodiment of the present application, in order to clearly illustrate how to match marketing information based on the user's consumption node and marketing scenario, we can construct a marketing information matching table; the following is an example of a marketing information matching table:

[0165]

[0166] In this marketing information matching table: the consumption node number corresponds to the store selected by the user in the consumption route; the store type describes the specific type of each consumption node; the marketing scenario is the marketing theme set according to factors such as store type, season, holidays, etc.; the marketing information is the specific discount activities or promotional information provided to the user.

[0167] In step S16, the rank coefficient of each marketing message is determined based on the marketing message, the user's viewing time, and the number of clicks by the user. A training set is determined based on the content of each marketing message, the rank coefficient of each marketing message, and the user's consumption profile in the city. Intelligent training of the marketing scenario is triggered based on the training set and the marketing scenario.

[0168] In the specific implementation process of the present invention, the specific steps may be:

[0169] S161: Obtain various marketing information, and present the various marketing information to the user based on the mobile device;

[0170] S162: Collecting the user's viewing time and the number of clicks based on the presentation of each marketing information;

[0171] S163: Perform multiple interactions on each marketing message, the user's viewing time, and the number of clicks by the user, and determine a rank coefficient for each marketing message based on the multiple interactions of each marketing message, the user's viewing time, and the number of clicks by the user;

[0172] S164: Determine the content of each marketing information according to the traversal of each marketing information;

[0173] S165: Interacting the content of each marketing information, the rank coefficient of each marketing information, and the consumption profile of the user in the city; determining a training set based on the interaction of the content of each marketing information, the rank coefficient of each marketing information, and the consumption profile of the user in the city;

[0174] S166: Determine multiple training dimensions based on the matching of the training set and the marketing scenario, and mark the corresponding training data in the multiple training dimensions; trigger intelligent training of the marketing scenario according to the multiple training dimensions, the corresponding training data and the corresponding marketing scenario to output the optimized marketing scenario to achieve optimized management of the marketing scenario.

[0175] During the specific implementation of the present invention, various marketing information is obtained and presented to users based on mobile devices; the user's viewing time and the number of clicks are collected based on the presentation of each marketing information; multiple interactions are performed on each marketing information, the user's viewing time and the user's clicks, and the grade coefficient of each marketing information is determined based on the multiple interactions of each marketing information, the user's viewing time and the user's clicks, thereby achieving multiple interactions of each marketing information, the user's viewing time and the user's clicks, and ensuring the accuracy of the grade coefficient of each marketing information.

[0176] At this time, various marketing information is obtained and presented to users based on mobile devices, further controlling the interaction between mobile devices and users. At the same time, the company displays marketing content to users on various marketing channels (such as social media, search engines, emails, APP push, etc.), including advertisements, coupons, event information, etc.; the time users spend browsing or viewing a certain marketing information; this reflects the user's interest and attention in the information; the number of times users click on a certain marketing information, which usually indicates the user's interest in the information or willingness to act; in actual operations, companies can collect this data through data analysis tools or platforms; for example, by embedding tracking codes in web pages or APPs, users' click behavior and browsing time can be recorded.

[0177] The multiple interactions of various marketing messages, user viewing time and user click counts refer to a comprehensive analysis of the three variables of marketing messages, user viewing time and click counts, taking into account the mutual influence and correlation between them; for example, if a marketing message has a high number of clicks but a short viewing time, it means that the message attracts users to click, but the content is not attractive enough; conversely, if the viewing time is long but the number of clicks is small, it means that the information content is attractive but lacks elements that prompt users to take action; based on the results of the multiple interaction analysis, a grade coefficient is assigned to each marketing message to evaluate the attractiveness and effectiveness of the information; the grade coefficient can be a numerical value or a grade classification (such as A, B, C, etc.), depending on the enterprise's evaluation standards and needs; optionally, in actual operations, the enterprise can use data analysis models or algorithms to perform multiple interaction analysis and grade coefficient determination; for example, a machine learning algorithm can be used to train a large amount of historical data to identify the attractiveness and effect patterns of different marketing messages, and assign grade coefficients to new information accordingly.

[0178] Specifically, suppose an e-commerce website displays an advertisement for a new mobile phone on its homepage. Using data analysis tools, the website records that user A viewed the ad for 30 seconds and clicked it twice, while user B only viewed it for 5 seconds and did not click it. These data provide the basis for subsequent analysis. Based on the data of user A and user B, as well as data from more similar users, the e-commerce website uses a data analysis model for analysis. Suppose the data analysis model finds that marketing information with a viewing time of 15-30 seconds and more than 1 click usually has a higher conversion rate. Therefore, for ads viewed and clicked by user A, the data analysis model will assign a higher grade coefficient (such as grade A), indicating that the information has high appeal and effectiveness. For information that user B only briefly viewed and did not click, the data analysis model will assign a lower grade coefficient (such as grade C), indicating that the information needs to be optimized or adjusted.

[0179] Furthermore, the content of each marketing message is determined based on the traversal of each marketing message; the content of each marketing message, the rank coefficient of each marketing message, and the consumption profile of the user in the city are interacted; and the training set is determined based on the interaction of the content of each marketing message, the rank coefficient of each marketing message, and the consumption profile of the user in the city. This achieves the interaction of the content of each marketing message, the rank coefficient of each marketing message, and the consumption profile of the user in the city, ensuring the richness of the training set.

[0180] At this point, all available marketing information is traversed, that is, each piece of information is checked one by one; the purpose of the traversal is to understand the content of each marketing information in detail, which includes text descriptions, pictures, videos, linked product or service details, etc.; the determination of content is crucial for subsequent steps because it will directly affect the matching degree between marketing information and user portraits, as well as the formulation of the final marketing strategy.

[0181] The marketing information content determined in the previous step is interactively analyzed with the marketing information's rank coefficient (reflecting the information's appeal and effectiveness) and the user's consumption profile in the city (reflecting the user's consumption habits, preferences, purchasing power, and other information). The purpose of this interactive analysis is to identify the relationship between marketing information and user profiles, and to identify which information is more likely to attract specific types of users.

[0182] Based on this interactive analysis, the system or analyst will construct a training set; the training set is a data set containing multiple data points, each of which contains the content of the marketing information, the grade coefficient, and the user profile features that may match the information; this training set will be used for subsequent machine learning model training or strategy formulation.

[0183] Specifically, suppose an e-commerce platform is preparing a marketing campaign for summer promotions. In step S164, the analyst will traverse all marketing information related to summer promotions, such as sunscreen discount information, summer clothing recommendations, and beach vacation package advertisements. For each piece of information, the analyst will record its specific content, such as the discount amount of sunscreen, the style description of summer clothing, the price of the beach vacation package and the included services.

[0184] In the example of summer promotions on e-commerce platforms, analysts will interactively analyze each marketing message (such as a sunscreen discount) with its ranking coefficient (such as an evaluation based on user click and purchase data) and the user's consumption profile in the city (such as whether the user frequently purchases sunscreen products, whether they prefer high-end brands, whether they frequently participate in promotional activities, etc.); through this analysis, analysts may find that sunscreen discount information is particularly effective for user groups who frequently purchase sunscreen products and are sensitive to price.

[0185] Based on this discovery, analysts will construct a training set containing multiple data points such as "sunscreen discount information + high rating coefficient + user profiles that frequently purchase sunscreen products and are price-sensitive." This training set can then be used to train a machine learning model, which can predict which marketing messages are most likely to attract which types of users, thereby helping e-commerce platforms develop more precise marketing strategies.

[0186] In another embodiment of the present application, a relationship matching table is established to record the corresponding relationship between the content of marketing information, the grade coefficient and the user consumption profile; this relationship matching table can be used to construct a training set. Example of relationship matching table:

[0187]

[0188] In this relationship matching table, each row represents a training data point; for example, the first data point represents a marketing information about summer clothing discounts, with a grade coefficient of A, which is suitable for the user group who likes summer clothing and has a medium consumption level. This data point is classified into training set 1.

[0189] Therefore, based on the matching of the training set and the marketing scenario, multiple training dimensions are determined, and corresponding training data are marked in the multiple training dimensions; intelligent training of the marketing scenario is triggered according to the multiple training dimensions, the corresponding training data and the corresponding marketing scenario to output the optimized marketing scenario, so as to realize the optimized management of the marketing scenario, realize the interaction of multiple training dimensions, the corresponding training data and the corresponding marketing scenario, ensure the accurate triggering of the intelligent training of the marketing scenario, and further control the optimized management of the marketing scenario.

[0190] At this point, the training set (including user consumption profiles, marketing information and other data) is matched with specific marketing scenarios to identify multiple key training dimensions; these dimensions may include user interests, consumption behaviors, product characteristics, marketing channels, etc.; by analyzing the data in the training set and combining the specific characteristics of the marketing scenario, factors that have a significant impact on marketing effectiveness are extracted as training dimensions.

[0191] After determining the training dimensions, the data under each dimension needs to be labeled so that subsequent intelligent training can accurately identify and utilize this data; assign labels or codes to the data under each training dimension to ensure the accuracy and traceability of the data; for example, for the dimension of user interest, the user's preference for different types of products can be encoded and labeled; further, use machine learning or deep learning algorithms, combine multiple training dimensions and corresponding training data, and simulate and optimize marketing scenarios; select a suitable machine learning model, and input the labeled training data into the model for training; by adjusting model parameters and optimizing algorithms, improve the model's prediction and adaptability to marketing scenarios.

[0192] After intelligent training, the model will output optimized marketing scenario plans; these plans are designed to improve marketing effectiveness, meet user needs, and reduce marketing costs; the results of intelligent training are applied to actual marketing scenarios, and the plans are continuously adjusted and optimized through A / B testing, user feedback, etc., ultimately achieving optimized management of marketing scenarios.

[0193] Specifically, let’s assume we have an online retail platform that wants to optimize its email marketing campaigns. Here’s a concrete example of how to do it following the steps above:

[0194] Multiple training dimensions are determined based on the matching of training sets and marketing scenarios: the training set contains data such as user purchase history, browsing behavior, interests and hobbies; marketing scenarios: email marketing activities aimed at promoting new products on the platform; training dimensions: based on the characteristics of the training set and marketing scenarios, the following training dimensions are determined: user purchase preferences (such as product type, price sensitivity), browsing behavior characteristics (such as page dwell time, click-through rate), interests and hobbies (such as sports, fashion, etc.).

[0195] Encode and mark user purchasing preferences, such as marking users who have purchased sports products as "sports preferences"; quantify browsing behavior characteristics, such as calculating each user's average page dwell time and click-through rate; and categorize and mark interests and hobbies, such as marking users who like fashion content as "fashion lovers."

[0196] Select a suitable machine learning model (such as logistic regression, random forest, etc.) for training; input labeled training data into the model for training, and adjust the model parameters to optimize the ability to predict the effectiveness of email marketing campaigns; after intelligent training, the model outputs an optimized email marketing campaign plan; for example, send emails containing the latest sports product promotion information to user groups with "sports preferences" and "long page dwell time"; collect data through A / B testing and user feedback, and adjust and optimize the optimized marketing scenarios; for example, when it is found that a certain type of user does not respond well to a specific type of product promotion, promptly adjust the promotion strategy or replace the promoted product.

[0197] The above-mentioned intelligent training management method, equipment and storage medium for marketing scenarios collect consumption data sets of users in different cities; determine the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city; determine the consumption parameters of the user in the city based on the user's income level, the corresponding consumption time and the user's preferences in the city; determine the user's consumption scenario based on the consumption portrait and the corresponding consumption parameters, and determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence and the corresponding store distribution map, which is compatible with the overall consideration of the user's consumption scenario, the user's place of residence and the corresponding store distribution map, and ensures the accuracy of the marketing scenario.

[0198] Furthermore, in this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information and current time, and corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and marketing scenarios. Each piece of marketing information is presented to the user in turn, thereby presenting marketing information of various aspects according to the consumption route, so as to facilitate subsequent management and control of each piece of marketing information.

[0199] Therefore, the rank coefficient of each marketing message is determined based on the marketing message, the user's viewing time and the user's click times; the training set is determined based on the content of each marketing message, the rank coefficient of each marketing message and the user's consumption profile in the city; the intelligent training of the marketing scenario is triggered based on the training set and the marketing scenario, so as to be compatible with the user's considerations in various cities and ensure the applicability of the marketing scenario in various cities. Example 3

[0200] In this embodiment, Figure 3 As shown, a marketing scenario intelligent training management device is provided, including:

[0201] The collection module is used to collect consumption data sets of users in different cities;

[0202] The consumption profile module is used to determine the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city;

[0203] A consumption parameter module is used to determine the consumption parameters of a user in a city based on the user's income level, corresponding consumption time, and the user's preferences in the city;

[0204] The marketing scenario module is used to determine the user's consumption scenario based on the consumption profile and corresponding consumption parameters, and to determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map;

[0205] The marketing information module is used to determine the user's consumption route based on the user's current location, corresponding demand information, and the current time in this marketing scenario, and match corresponding marketing information according to multiple consumption nodes in the user's consumption route and marketing scenarios, and present each marketing information to the user in sequence;

[0206] The intelligent training module is used to determine the grade coefficient of each marketing message based on the marketing message, the user's viewing time and the user's click times, determine the training set based on the content of each marketing message, the grade coefficient of each marketing message and the user's consumption profile in the city, and trigger intelligent training of the marketing scenario based on the training set and the marketing scenario. Example 4

[0207] In this embodiment, a marketing scenario intelligent training management device is provided; its internal structure diagram can be shown as follows: Figure 4 As shown; the marketing scenario intelligent training management device includes a processor, memory, network interface, display screen and input device connected through a system bus; wherein, the processor of the marketing scenario intelligent training management device is used to provide computing and control capabilities; the memory of the marketing scenario intelligent training management device includes a non-volatile storage medium and an internal memory; the non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database, which is used to store user behavior data and user portraits; the internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium; the network interface of the marketing scenario intelligent training management device is used to communicate with other devices deployed with application software; when the computer program is executed by the processor, a marketing scenario intelligent training management method is implemented; the display screen of the marketing scenario intelligent training management device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the marketing scenario intelligent training management device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the device casing, or an external keyboard, touchpad or mouse, etc.

[0208] The above-mentioned embodiments only express several implementation methods of the present application. The description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the invention patent. It should be pointed out that for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present application, which all fall within the scope of protection of the present application. Therefore, the scope of protection of the patent of this application shall be based on the attached claims.

Claims

1. A marketing scenario intelligent training management method, characterized by: include: Collect consumption data of users in different cities; Determining a consumption profile of a user in a city based on a consumption data set, the user's life information, and a corresponding city, including: obtaining a consumption data set; collecting the user's life information based on traversing the user's life database; associating the consumption data set, the user's life information, and the corresponding city; determining a first consumption feature based on the consumption data set and the user's life information, associating the user's consumption data with his life information, and analyzing the impact of the life information on consumption habits; extracting the user's consumption feature based on the analysis result, determining a second consumption feature based on the consumption data set and the corresponding city, determining the user's consumption profile in the city based on the first consumption feature, the second consumption feature, and the length of stay of the user in the city, integrating the first consumption feature, the second consumption feature, and the length of stay of the user in the city to form a comprehensive data set; assigning appropriate weights to each feature based on the importance and relevance of the feature, and constructing a consumption profile of the user in the city; Determine the user's consumption parameters in a city based on the user's income level, corresponding consumption time, and the user's preferences in the city; Determine the user's consumption scenario based on the consumption profile and corresponding consumption parameters, and determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map; In this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information, and current time. The corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and marketing scenarios, and each piece of marketing information is presented to the user in sequence. The rank coefficient of each marketing message is determined based on the information, the user's viewing time, and the number of clicks. The training set is determined based on the content of each marketing message, the rank coefficient of each marketing message, and the user's consumption profile in the city. Intelligent training of the marketing scenario is triggered based on the training set and the marketing scenario.

2. The marketing scenario intelligent training management method according to claim 1, characterized in that: The collection of consumption data of users in different cities includes: Mark the user's payment platform; Export the user's previous payment data based on the user's payment platform; Determine the data set for each city based on the user's previous payment data and the matching of the corresponding payment location; The consumption data of users in different cities is determined based on the data sets of each city and the consumption types of users.

3. The marketing scenario intelligent training management method according to claim 1, characterized in that: Determining the user's consumption parameters in a city based on the user's income level, corresponding consumption time, and the user's preferences in the city includes: Collecting user income data based on the user's payment platform traversal; Determine the user's income level based on the user's income data, the user's job title at the company, and the user's place of residence; Determine multiple consumption categories and corresponding consumption frequencies based on the user's consumption data in the city; Determine the user's preferences in a city based on multiple consumption categories, corresponding consumption frequencies, and corresponding cities; Interact with the user's income level, corresponding consumption time, and the user's preferences in the city; The consumption parameters of the user in the city are determined based on the user's income level, corresponding consumption time, and the interaction of the user's preferences in the city.

4. The marketing scenario intelligent training management method according to claim 1, characterized in that: Determining the user's consumption scenario based on the consumption profile and the corresponding consumption parameters, and determining the corresponding marketing scenario based on the user's consumption scenario, the user's residence, and the corresponding store distribution map, includes: Get the consumption profile and corresponding consumption parameters; Match the consumption profile and corresponding consumption parameters with the corresponding scenario weight; Determine the user's consumption scenario based on the consumption profile, consumption parameters, and corresponding scenario weights; Determine the user's place of residence based on the user's residential data; Determine the corresponding store distribution map based on the user's residence, the surrounding drawings of the residence, and the store marks; Determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map.

5. The marketing scenario intelligent training management method according to claim 1, characterized in that: In this marketing scenario, the user's consumption route is determined based on the user's current location, corresponding demand information, and current time, and corresponding marketing information is matched according to multiple consumption nodes in the user's consumption route and marketing scenarios. Each marketing information is presented to the user in sequence, including: In this marketing scenario, user demand information is collected; determining a current location of the user based on the user's location data; Determine the user's consumption route based on the user's current location, corresponding demand information, and current time; Determine multiple consumption nodes in the user's consumption route based on the user's consumption route, store distribution map, and the user's consumption budget; Associate multiple consumption nodes and marketing scenarios in a user's consumption route; Based on multiple consumption nodes in the user's consumption route and the corresponding marketing information matching the marketing scenarios, each piece of marketing information is presented to the user in turn.

6. The marketing scenario intelligent training management method according to claim 1, characterized in that: The step of determining the grade coefficient of each marketing message based on each marketing message, the user's viewing time, and the user's click count, determining a training set based on the content of each marketing message, the grade coefficient of each marketing message, and the user's consumption profile in the city, and triggering intelligent training of the marketing scenario based on the training set and the marketing scenario includes: Obtain various marketing information and present it to users based on mobile devices; Collect user viewing time and number of clicks based on the presentation of each marketing message; Multiple interactions are performed on each marketing message, the viewing time of the user, and the number of clicks of the user, and a rank coefficient of each marketing message is determined based on the multiple interactions of each marketing message, the viewing time of the user, and the number of clicks of the user.

7. The marketing scenario intelligent training management method according to claim 6, characterized in that: The method further includes determining a grade coefficient of each marketing message based on each marketing message, the viewing time of the user, and the number of clicks of the user, determining a training set based on the content of each marketing message, the grade coefficient of each marketing message, and the consumption profile of the user in the city, and triggering intelligent training of the marketing scenario based on the training set and the marketing scenario. Determining the content of each marketing message based on the traversal of each marketing message; Interacting the content of each marketing message, the rank coefficient of each marketing message, and the consumer profile of the user in the city; determining a training set based on the interaction of the content of each marketing message, the rank coefficient of each marketing message, and the consumer profile of the user in the city; Based on the matching of the training set and the marketing scenario, multiple training dimensions are determined, and corresponding training data are marked in the multiple training dimensions; intelligent training of the marketing scenario is triggered according to the multiple training dimensions, the corresponding training data and the corresponding marketing scenario to output the optimized marketing scenario, so as to realize optimized management of the marketing scenario.

8. A marketing scenario intelligent training management device, characterized by: The marketing scenario intelligent training management device is applied to the marketing scenario intelligent training management method according to any one of claims 1 to 7, and the marketing scenario intelligent training management device includes: The collection module is used to collect consumption data sets of users in different cities; The consumption profile module is used to determine the consumption profile of the user in the city based on the consumption data set, the user's life information and the corresponding city, including: obtaining the consumption data set; collecting the user's life information based on the traversal of the user's life database; associating the consumption data set, the user's life information and the corresponding city; determining a first consumption feature based on the consumption data set and the user's life information, associating the user's consumption data with his life information, and analyzing the impact of the life information on consumption habits; extracting the user's consumption feature based on the analysis result, determining a second consumption feature based on the consumption data set and the corresponding city, determining the user's consumption profile in the city based on the first consumption feature, the second consumption feature and the length of stay of the user in the city, integrating the first consumption feature, the second consumption feature and the length of stay of the user in the city to form a comprehensive data set; assigning appropriate weights to each feature based on the importance and relevance of the feature, and constructing the user's consumption profile in the city; A consumption parameter module is used to determine the consumption parameters of a user in a city based on the user's income level, corresponding consumption time, and the user's preferences in the city; The marketing scenario module is used to determine the user's consumption scenario based on the consumption profile and corresponding consumption parameters, and to determine the corresponding marketing scenario based on the user's consumption scenario, the user's place of residence, and the corresponding store distribution map; The marketing information module is used to determine the user's consumption route based on the user's current location, corresponding demand information, and the current time in this marketing scenario, and match corresponding marketing information according to multiple consumption nodes in the user's consumption route and marketing scenarios, and present each marketing information to the user in sequence; The intelligent training module is used to determine the grade coefficient of each marketing message based on the marketing message, the user's viewing time and the user's click times, determine the training set based on the content of each marketing message, the grade coefficient of each marketing message and the user's consumption profile in the city, and trigger intelligent training of the marketing scenario based on the training set and the marketing scenario.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the marketing scenario intelligent training management method described in any one of claims 1 to 7 are implemented.

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