Cigarette full-link digital intelligence cultivation method, platform and equipment and storage medium

Abstract

The invention discloses a cigarette full-link digital intelligence cultivation method, platform and equipment and a storage medium. The method comprises the following steps: acquiring member data and product positioning and breeding market data of cigarettes to be bred in a current breeding period; generating a putting instruction according to the breeding market data, so that the commercial subject performs putting according to the putting instruction and obtains sales data of the to-be-bred cigarettes of the retailers; screening core retailers according to the breeding market data so as to enable industrial subjects / commercial subjects to visit the core retailers, screening target consumers according to the member data so as to enable the commercial subjects to execute corresponding marketing schemes on the target consumers, and collecting feedback data of the core retailers and the target consumers on to-be-bred cigarettes; product positioning is optimized according to the feedback data, so that the industrial subject starts the next cultivation period. According to the technical scheme, a full-link closed-loop cultivation system is constructed by taking cooperation of four cultivation main bodies of industrial and commercial zero elimination as a core, and the accuracy and efficiency of cigarette cultivation are improved.

Description

Technical Field

[0001] This application relates to the field of brand cultivation technology, and in particular to a digital and intelligent cultivation method, platform, equipment and storage medium for the entire cigarette industry chain. Background Technology

[0002] Cultivating the market for premium cigarettes is a crucial step for the tobacco industry to enhance brand value and market competitiveness. Existing cultivation methods largely employ a linear model of "brand promotion - product launch - sales promotion," which has significant shortcomings: First, the chain is fragmented, with insufficient coordination between the industrial, commercial, retail, and consumer stages, resulting in a lack of support across the entire business flow for cultivation strategies. Second, accuracy is low, as target market identification, core retailer selection, and product launch rely heavily on experience-based judgment, failing to match actual needs. Third, feedback is delayed, with market feedback data collected in a fragmented manner, making it difficult to quickly inform strategy optimization. Fourth, the level of digitalization is low, lacking digital management tools throughout the process, leading to low cultivation efficiency and difficulty in quantifying results.

[0003] Therefore, how to provide a technical solution to improve the efficiency of cigarette brand cultivation is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This application provides a digital and intelligent cultivation method, equipment, media, and program product for the entire cigarette cultivation chain. It takes the collaboration of the four major cultivation entities (industrial and commercial, retail, and consumer goods) as the core to build a closed-loop cultivation system for the entire chain, thereby improving the accuracy and efficiency of cigarette cultivation.

[0005] According to one aspect of this application, a digital and intelligent cultivation method for the entire cigarette supply chain is provided, the method comprising: Acquire product positioning and market data of the cigarettes to be cultivated in the current cultivation cycle, as well as membership data of industrial entities and / or commercial entities; wherein, the market data includes basic data of each retailer in the cultivation market, demand for cigarettes to be cultivated, target value of cigarettes to be cultivated, and demand coefficients for peak and off-peak seasons, and the membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences; Based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficient for peak and off-peak seasons, the distribution instructions for the cigarettes to be cultivated are determined so that commercial entities can distribute the cigarettes to be cultivated to the corresponding retailers according to the distribution instructions, and obtain the sales data of each retailer for the cigarettes to be cultivated. Core retailers are identified based on the basic data of each retailer, and communication questions are generated based on the basic data of the core retailers and the sales data, so that industrial entities and / or commercial entities can visit the core retailers based on the communication questions and obtain first feedback data corresponding to the communication questions. Based on the membership data, target consumers and corresponding marketing plans are determined so that the business entity can implement the corresponding marketing plans for the target consumers in the current cultivation cycle and obtain the second feedback data of the target consumers on the cigarettes to be cultivated. Multi-dimensional feature extraction is performed on the first feedback data and the second feedback data to obtain feedback analysis results. The product positioning is optimized based on the feedback analysis results to determine the target positioning of the cigarette to be cultivated, so that the industrial entity can start the next cultivation cycle based on the target positioning.

[0006] According to another aspect of this application, a digital and intelligent cultivation platform for the entire cigarette supply chain is provided, characterized in that the platform includes a data acquisition module, a central database, a data analysis module, and a decision support module; wherein, The data acquisition module is used to collect multi-source data in the cigarette cultivation chain, as well as obtain market status data of the cultivation market, first feedback data of core retailers, second feedback data of target consumers, third feedback data of internet consumers, and marketing results of business entities after implementing marketing plans. The multi-source data includes product positioning and membership data from industrial entities, market cultivation data, marketing plans, and membership data from commercial entities, and sales data of cigarettes to be cultivated from various retailers in the cultivated market. The cultivated market data includes basic data of each retailer in the cultivated market, demand for cigarettes to be cultivated, target values ​​for cigarettes to be cultivated, and seasonal demand coefficients. The membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences. The market status data includes the ordering rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level of each retailer in the cultivated market for the cigarettes to be cultivated during the current marketing cycle. The marketing results include activity participation rate, potential consumer conversion rate, and marketing return on investment. The central database is used to store all data from the data acquisition module, the decision support module, and the data analysis module; wherein, the all data includes the business flow data of the cigarettes to be cultivated during the cultivation process; The data analysis module is used to call upon multi-source data stored in the central database to conduct targeted data analysis; wherein, the targeted data analysis includes: Based on preset weight values, the consumption level data, consumption capacity data, and market competition data of each candidate region are weighted to determine the potential score of each candidate region; the candidate regions with potential scores greater than preset scores are identified as the cultivation markets for the cigarettes to be cultivated. Core retailers are identified based on the basic data of each retailer. The distribution instructions for the cigarettes to be cultivated are determined based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficient for peak and off-peak seasons. Based on the membership data, target consumers and corresponding marketing plans are determined. The delivery instructions are adjusted based on the ordering rate, the order fulfillment rate, the actual sales volume, the sales turnover rate, and the inventory level to determine the delivery instructions for the cigarettes to be cultivated in the next delivery cycle. Based on the marketing results, the marketing plan is optimized to determine the target plan for the next development cycle; Multi-dimensional feature extraction is performed on the first feedback data, the second feedback data, and the third feedback data to obtain feedback analysis results; Based on the feedback analysis results, the product positioning is optimized to determine the target positioning of the cigarette to be developed; The decision support module receives targeted data analysis results from the data analysis module and drives decisions on target positioning optimization, market cultivation adjustment, core retailer selection, placement instruction adjustment, and marketing plan adjustment based on the targeted data analysis results; wherein, the targeted data analysis results include market cultivation, core retailers, placement instructions, target consumers, marketing plans corresponding to target consumers, feedback analysis results, and target positioning.

[0007] According to another aspect of this application, an electronic device is provided, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the cigarette end-to-end digital cultivation method according to any embodiment of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the intelligent digital cultivation method for the entire cigarette supply chain as described in any embodiment of this application.

[0009] According to another aspect of this application, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the intelligent digital cultivation method for the entire cigarette supply chain as described in any embodiment of this application.

[0010] The technical solution provided in this application includes: acquiring member data and product positioning and market data of the cigarettes to be cultivated in the current cultivation cycle; generating distribution instructions based on the market data to enable commercial entities to distribute the cigarettes according to the instructions and acquire sales data of retailers for the cigarettes to be cultivated; screening core retailers based on the market data to enable industrial / commercial entities to visit them, and screening target consumers based on member data to enable commercial entities to implement corresponding marketing plans for them, and collecting feedback data from core retailers and target consumers on the cigarettes to be cultivated; optimizing product positioning based on feedback data to enable industrial entities to start the next cultivation cycle. This technical solution, with the collaboration of the four major cultivation entities (industrial and commercial, retail and consumer) as its core, constructs a closed-loop cultivation system across the entire chain, improving the accuracy and efficiency of cigarette cultivation.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart of a digital and intelligent cultivation method for the entire cigarette supply chain provided in Embodiment 1 of this application.

[0014] Figure 2 This is a flowchart of a digital and intelligent cultivation method for the entire cigarette supply chain, provided in Embodiment 2 of this application.

[0015] Figure 3 This is a schematic diagram of a high-end cigarette end-to-end digital and intelligent cultivation process provided in Embodiment 2 of this application.

[0016] Figure 4 This is a schematic diagram of the structure of a digital and intelligent cultivation platform for the entire cigarette supply chain provided in Embodiment 3 of this application.

[0017] Figure 5 This is a schematic diagram of the structure of a device for implementing a digital and intelligent cigarette cultivation method according to an embodiment of this application. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0019] It should be noted that the terms "first," "second," "current," "next," "target," "candidate," etc., used in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] Example 1 Figure 1 This is a flowchart illustrating a digital and intelligent cigarette brand cultivation method provided in Embodiment 1 of this application. This embodiment is applicable to the cultivation of cigarette brands. The method can be executed by a digital and intelligent cigarette brand cultivation platform, which can be implemented in hardware and / or software and configured in a device with data processing capabilities. Figure 1 As shown, the method includes the following steps.

[0021] S110. Obtain product positioning and market data for the cigarettes to be cultivated in the current cultivation cycle, as well as membership data of industrial entities and / or commercial entities.

[0022] The market cultivation data includes basic data of each retailer in the market cultivation area, the demand for cigarettes to be cultivated, the target value of cigarettes to be cultivated, and the demand coefficients for peak and off-peak seasons. The membership data includes consumers' historical consumption data, interactive behavior, and purchase preferences.

[0023] The development of the cigarette market involves four main entities: tobacco industrial enterprises, tobacco commercial enterprises, cigarette retailers, and consumers. Tobacco industrial enterprises are the manufacturers, responsible for product research and development and manufacturing of cigarettes; tobacco commercial enterprises are the sole legal wholesale channel connecting industrial enterprises and retailers, responsible for distributing cigarettes to retailers according to plan; cigarette retailers are the terminal retailers, responsible for selling cigarettes to consumers; and consumers are the purchasers and users of cigarettes.

[0024] Product positioning refers to defining the core value, unique selling points, and target consumer profile of the cigarette product to be developed. Specifically, industrial entities can utilize market research data provided by commercial entities, such as sales trends of high-end cigarettes, target consumer group research, and competitor analysis data. Combined with their own product process data, they can use digital analytics tools and clustering algorithms to stratify the target consumer group, extracting core characteristics such as consumption scenarios and purchasing decisions to determine the target consumer profile. Furthermore, they can combine competitor analysis data with their own product process data to determine the core value and unique selling points of the cigarette product to be developed. Finally, a product positioning report is generated based on the product's core value, unique selling points, and target consumer profile.

[0025] For example, the core value of a certain cigarette product under development is "high-end business appreciation, unique cigarette making process", and its unique selling points are "imported tobacco leaves, low tar and high aroma, customized gift box". The target consumer group profile is "business people aged 30-35 (65%), people with gift consumption needs (25%), and people with high-end consumption preferences (10%)".

[0026] The data for the cultivated market refers to sales data, survey data, etc., of the cultivated market for the cigarettes to be cultivated. Specifically, commercial entities can conduct surveys and statistics on the cultivated market for the cigarettes to be cultivated and then upload the cultivated market data. In this application, the cultivated market data includes basic data of each retailer in the cultivated market, the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficients for peak and off-peak seasons.

[0027] Retailer basic data refers to the static attributes and operating characteristics of retailers, including their geographical location, business type, type of business district, operating area, and historical cooperation with new product promotions. The demand for cigarettes to be cultivated refers to the demand for cigarettes reported by each retailer in the cultivated market to the commercial entity. For example, retailer A reports a demand of 100 cartons of cigarettes to be cultivated. The target value for cigarettes to be cultivated refers to the sales target that the commercial / industrial entity plans to achieve in the cultivated market, such as total sales volume and average daily sales volume. This can be predicted based on historical sales data, market research, and consumer preference analysis. The peak and off-peak season demand coefficient is an adjustment coefficient for fluctuations in cigarette market demand due to seasonality, holidays, and other factors. It is typically set at a baseline demand of 1, with the off-peak coefficient less than 1 and the peak season coefficient greater than 1. The supply can be flexibly adjusted according to the market cycle to match actual consumption patterns.

[0028] In some embodiments, optionally, before obtaining cultivation market data for the cigarette to be cultivated, the method further includes: determining the cultivation market for the cigarette to be cultivated; Accordingly, determining the cultivation market for the cigarettes to be cultivated includes: acquiring consumption level data, consumption capacity data, and market competition data for each candidate region; for each candidate region, weighting the consumption level data, consumption capacity data, and market competition data based on a preset weight value to determine the potential score of the candidate region; and identifying the candidate regions whose potential scores are greater than the preset scores as the cultivation market for the cigarettes to be cultivated.

[0029] Taking the cultivation of high-end cigarettes as an example, consumption level data can be the proportion of high-income people and the distribution density of high-end restaurants / hotels in each candidate region; consumption capacity data can be the per capita disposable income and cigarette consumption expenditure in each candidate region; and market competition data can be the sales share and terminal coverage density of competing products in each candidate region.

[0030] The potential score can be used to evaluate the potential of a candidate region to become a cultivation market. Specifically, in this application, the potential score of each candidate region can be calculated using a weighted summation algorithm based on the target cultivation market potential assessment model. Candidate regions with potential scores greater than a preset score are selected as cultivation markets for cigarettes, or the potential scores are sorted in descending order, and a preset number of candidate regions are selected as cultivation markets based on the sorting results.

[0031] Membership data can be data registered by consumers through information registration channels provided by industrial entities, commercial entities, or retailers. Membership data may include consumers' historical consumption data, interaction records, and purchase preferences. It should be noted that the acquisition, storage, use, and processing of membership data in this application's technical solution all comply with relevant national laws and regulations.

[0032] S120. Based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the peak and off-peak season demand coefficients, determine the delivery instructions for the cigarettes to be cultivated, so that the commercial entity delivers the cigarettes to be cultivated to the corresponding retailers according to the delivery instructions, and obtains the sales data of the cigarettes to be cultivated from each of the retailers.

[0033] The delivery instructions refer to the specific instructions given by commercial entities to retailers regarding the cigarettes to be cultivated, which may include the delivery quantity, delivery frequency, delivery time, core retailer code, price requirements, etc. Sales data refers to the quantitative records of retailers' historical sales of cigarettes to be cultivated or the quantitative records of sales of cigarettes to be cultivated in the previous cultivation cycle, which may include sales volume data, inventory data, sales characteristics, and competitor comparison data.

[0034] In this application, a machine learning model can be used to generate delivery instructions. Based on the target value of cigarettes to be cultivated, the expected demand of retailers is obtained by multiplying it by the seasonal demand coefficient. Under inventory constraints and in combination with the demand for cigarettes to be cultivated reported by retailers, an optimization model is established with the goal of maximizing the coverage of expected demand or maximizing retailer satisfaction, and the optimal delivery quantity is solved.

[0035] It also allows the creation of a rule base, including inventory rules (such as minimum / maximum inventory), demand rules (such as peak season markup), and retailer tiering rules. Input demand, inventory, peak / off-peak season coefficients, and other market parameters (such as competitor activities), and the rule engine will automatically generate deployment instructions. For example, regarding inventory rules: if inventory < demand × peak / off-peak season coefficient, then deploy at 100% of demand; otherwise, deploy at 80% of inventory, reserving some inventory for emergencies.

[0036] In some embodiments, optionally, the delivery instruction includes at least the delivery quantity, delivery range, and delivery cycle of the cigarettes to be cultivated; correspondingly, after the cigarettes to be cultivated are delivered to the corresponding retailers according to the delivery instruction, the method further includes: acquiring market status data of the cultivation market; wherein, the market status data includes the order rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory of the cigarettes to be cultivated for each retailer in the cultivation market in the current delivery cycle; adjusting the delivery instruction based on the order rate, the order fulfillment rate, the actual sales volume, the sales turnover rate, and the inventory to determine the delivery instruction for the cigarettes to be cultivated in the next delivery cycle.

[0037] The term "distribution volume" refers to the specific quantity of cigarettes to be cultivated that are planned to be distributed to one or a group of core retailers within a specific distribution period, usually measured in "cartons" or "boxes". The distribution scope refers to the range of retailers and geographical area from which the cigarettes to be cultivated will be distributed; for example, the distribution scope could be to designated retailers or to a designated business district.

[0038] The campaign cycle refers to the time frame covered by a single campaign instruction, clearly defining the period from its effective date to its end. It specifies the pace of the campaign and the timeframe for evaluating its effectiveness. Common campaign cycles include: weekly, bi-weekly, monthly, or fixed periods aligned with specific marketing campaigns, such as during the National Day Golden Week. It's understood that a nurturing cycle can include multiple campaign cycles.

[0039] Specifically, commercial entities issue distribution instructions through the system, and retailers confirm receipt via a mobile app. The system monitors the market status of the cigarettes to be cultivated in real time, including order rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory. The order rate refers to the proportion of retailers who have actually placed orders for the cigarettes to be cultivated within the current cultivation period, relative to the total number of eligible target retailers. The order fulfillment rate refers to the proportion of the actual quantity of cigarettes ordered by retailers within the current cultivation period, relative to the maximum orderable quantity set by the commercial company. Actual sales volume refers to the total quantity of cigarettes to be cultivated that retailers actually sell to consumers within the current cultivation period. The sales turnover rate refers to the proportion of the actual sales volume of cigarettes to be cultivated within the current distribution period to the total initial inventory. A higher sales turnover rate indicates that the cigarettes to be cultivated are selling better at the retail level, and the risk of inventory backlog is lower. Inventory refers to the quantity of cigarettes to be cultivated that are held and awaiting sale. In addition, market status data may also include inventory turnover cycle, which refers to the time it takes for cigarettes to be cultivated from entering the warehouse to being sold. The shorter the inventory turnover cycle, the higher the operating efficiency.

[0040] Specifically, healthy ranges can be predefined for indicators such as order rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level, and their weights can be assigned in the decision-making process for distribution instructions. Based on each indicator and its corresponding weight, a comprehensive health score for each retailer can be calculated; a higher score indicates a stronger sales capacity for the cultivated cigarettes. Finally, a differentiated distribution strategy can be generated based on the comprehensive health score and a combination of key indicators. Priorities for indicators such as order rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level can also be predefined, allowing for rapid adjustments based on rules and signal priorities.

[0041] For example, retailers can be categorized based on their sales rate and inventory turnover cycle. The first category consists of high-sales-rate, low-inventory-turnover-cycle retailers (high-quality retailers); the second category consists of high-sales-rate, high-inventory-turnover-cycle retailers (potential retailers); the third category consists of low-sales-rate, low-inventory-turnover-cycle retailers (problem retailers); and the fourth category consists of low-sales-rate, high-inventory-turnover-cycle retailers (inefficient retailers). For the first category, the distribution volume can be increased and the distribution cycle shortened; for the second category, a small-batch, frequent distribution strategy can be adopted; for the third category, the distribution volume can be reduced and the distribution cycle lengthened, or even distribution can be suspended; for the fourth category, the distribution volume can be reduced and the distribution cycle lengthened.

[0042] For example, if the sales rate in the core business district is 95% and the sales rate in the high-end community is 80% in the latter half of the first month, the system will automatically generate an optimization suggestion: "Increase the amount of retail products delivered to the core community by 10% in the following month."

[0043] The advantages of the above technical solution are that by optimizing the process of determining the delivery instructions, the accuracy of cigarette delivery is improved, sales opportunities are avoided due to stockouts and channel blockages caused by unsold goods, thereby improving the efficiency of cigarette cultivation and optimizing inventory efficiency.

[0044] S130. Determine core retailers based on the basic data of each retailer, and generate communication questions based on the basic data of the core retailers and the sales data, so that industrial entities and / or commercial entities can visit the core retailers based on the communication questions and obtain first feedback data corresponding to the communication questions.

[0045] Core retailers can be those that demonstrate outstanding performance in terms of operational capabilities and cooperation. In this application, core retailers can be selected based on a retailer potential assessment model.

[0046] For example, taking high-end cigarettes as an example, the basic data for screening core retailers may include the retailer's sales percentage of high-end cigarettes over the past 12 months, terminal display compliance rate, consumer satisfaction rate, the geographical location of the terminal, and the surrounding consumption capacity. The geographical location of the terminal can be divided into core business districts, high-end communities, and rural areas, and the surrounding consumption capacity can be divided into high, medium, and low. Based on a pre-trained retailer potential assessment model, core retailers are screened according to the above basic data. For example, the retailer potential assessment model can quantify and score each retailer's indicators; for example, a high-end cigarette sales percentage ≥30% earns 10 points, 20%-30% earns 8 points, and so on. Then, a weighted summation is used to calculate the retailer's potential score, and retailers with a score ≥8 points are designated as core retailers.

[0047] The first feedback data refers to market performance information on cigarettes to be cultivated, provided by core retailers and consumers. This information may include sales data, inventory turnover, consumer reviews, purchase frequency, price stability, and repurchase rate. Specifically, communication questions can be generated based on sales anomaly detection. For example, the sales fluctuation coefficient, deviation from the regional average sales, and number of consecutive days with zero sales are calculated for each retailer. If sales have decreased by more than 30% in the last 7 days, the question generated is: "Sales of XX specification have decreased significantly in the last week. Have you encountered the following issues: ① Insufficient inventory ② Reduced consumer inquiries ③ Impact of competitor promotions?" Otherwise, the question generated is: "Should we adjust the stocking schedule?"

[0048] It can also generate communication questions based on the differentiated stratification of core retailers. For example, for Category A core retailers, namely the top 20% of core retailers with stable sales, the questions focus on experience mining, such as "Share methods to attract consumers"; for Category B core retailers, namely core retailers with moderate but fast-growing sales, the questions focus on improvement suggestions, such as "If you increase product appreciation activities, which form do you think would be more effective"; for Category C core retailers, namely those with consistently low sales, the questions focus on obstacle diagnosis, such as "What do you think is the main reason why this size sells slowly in your store?"

[0049] In this application, representative retailers can be selected from core retailers as key retailers, allowing industrial entities to visit these key retailers and commercial entities to conduct comprehensive visits to core retailers. Visitors can record communication content via a mobile app, such as retailer feedback like "the gift box packaging is inconvenient to open" or "some consumers feel the price is too high."

[0050] S150. Based on the member data, determine the target consumers and the corresponding marketing plans for the target consumers, so that the business entity can implement the corresponding marketing plans for the target consumers in the current cultivation cycle, and obtain the second feedback data of the target consumers on the cigarettes to be cultivated.

[0051] The second feedback data refers to data provided by target consumers after participating in marketing activities executed by the business entity. Specifically, this second feedback data can be obtained through surveys completed by consumers after the marketing campaign, or through data recorded by retailers during the sale of cigarettes to be marketed. The marketing plan can be a targeted marketing strategy predetermined by the business entity.

[0052] In this application, industrial and commercial entities can collaborate to develop marketing strategies, identify target consumers from the enterprise's membership data, and then the commercial entities can conduct marketing activities at the retail end based on the identification results to attract and convert target consumers, thereby improving the cultivation efficiency of cigarettes.

[0053] Specifically, core characteristic dimensions can be extracted based on membership data and preset rules, then target consumers can be identified based on these core characteristic dimensions, and finally, marketing plans can be matched with target consumers. Taking the cultivation of a high-end cigarette brand as an example, the core characteristic dimensions could be: whether the frequency of purchasing high-end cigarettes is greater than or equal to 3 times per year, whether the average spending per customer is greater than or equal to 5,000 yuan per year, whether the customer has participated in high-end activities more than or equal to 2 times in the past 6 months, and the customer's preference for purchasing high-end products.

[0054] S160. Multi-dimensional feature extraction is performed on the first feedback data and the second feedback data to obtain feedback analysis results. The product positioning is optimized based on the feedback analysis results to determine the target positioning of the cigarette to be cultivated, so that the industrial entity can start the next cultivation cycle based on the target positioning.

[0055] Specifically, for the first and second feedback data, sentiment analysis and keyword extraction can be performed based on artificial intelligence analysis algorithms to obtain core feedback points, i.e., feedback analysis results. These results are then fed back to the product positioning stage in step S110 to generate optimization suggestions. For example, if the core feedback points are "strong flavor" (32%), "inconvenient gift box packaging" (25%), and "high price" (18%), feeding them back to the product positioning stage can generate optimization suggestions such as "adjusting the product formula to reduce tar content, optimizing the gift box opening and closing design, and launching a simplified version to lower the entry-level price." The industry entity can then optimize the product positioning of the cigarettes to be cultivated based on these optimization suggestions to initiate the next cultivation cycle.

[0056] In some embodiments, optionally, after generating a communication question based on the core retailer's basic data and the sales data, so that industrial entities and / or commercial entities visit the core retailer based on the communication question and obtain first feedback data corresponding to the communication question, the method further includes: obtaining third feedback data from internet consumers on the cigarette to be cultivated. Accordingly, the step of optimizing the product positioning based on the first feedback data and the second feedback data to determine the target positioning of the cigarette to be cultivated includes: performing multi-dimensional feature extraction on the first feedback data, the second feedback data, and the third feedback data to obtain feedback analysis results; and optimizing the product positioning based on the feedback analysis results to determine the target positioning of the cigarette to be cultivated.

[0057] Third-party feedback data refers to spontaneous, diverse consumer evaluations of cigarettes to be developed, obtained from uncontrolled and unofficial channels. Data sources for third-party feedback data can include public online spaces such as social media, e-commerce platforms, vertical communities, and content platforms.

[0058] Specifically, structured data from social media and e-commerce platforms can be obtained through API interfaces; third-party feedback data can also be obtained based on third-party data services.

[0059] Since the data sources of the third feedback data are diverse, after obtaining the third feedback data, data cleaning and noise reduction can be performed on the third feedback data. Then, the processed third feedback data can be segmented, sentiment labeled, entity recognized, and structured stored. This data can be combined with the first and second feedback data to obtain core feedback points, so as to optimize product positioning and determine the target positioning of the cigarettes to be cultivated.

[0060] The advantage of the above technical solution is that by acquiring and analyzing third-party feedback data from internet consumers regarding cultivated cigarettes, it is possible to understand consumers' views on cultivated cigarettes more realistically, comprehensively, and promptly, which is beneficial for optimizing product positioning and improving the accuracy of cultivation.

[0061] This application provides a fully digitalized cigarette cultivation method. This method acquires product positioning, cultivation market data, and member data for the cigarette to be cultivated in the current cultivation cycle; selects core retailers and generates distribution instructions based on the cultivation market data, enabling businesses to distribute the cigarette to be cultivated to these core retailers; identifies target consumers and corresponding marketing plans based on member data, allowing businesses to implement these plans; collects feedback data from core retailers and target consumers in the cultivation market; and optimizes product positioning based on the feedback data to obtain target positioning, enabling industrial entities to initiate the next cultivation cycle based on this target positioning. This technical solution, with the collaboration of the four major cultivation entities (industrial and commercial, retail, and consumer) as its core, constructs a closed-loop cultivation system across the entire chain, improving the accuracy and efficiency of cigarette cultivation.

[0062] Example 2 Figure 2 This is a flowchart of a fully digitalized cigarette cultivation method provided in Embodiment 2 of this application. This embodiment is based on the above embodiment and optimized, specifically optimizing the marketing process of the cigarettes to be cultivated. Figure 2 As shown, the method in this embodiment specifically includes the following steps.

[0063] S210. Obtain product positioning and market data for the cigarettes to be cultivated during the current cultivation cycle, as well as membership data of industrial entities and / or commercial entities. The market data includes basic data of each retailer in the cultivation market, the demand for cigarettes to be cultivated, the target value of cigarettes to be cultivated, and the demand coefficients for peak and off-peak seasons. The membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences.

[0064] S220. Based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the peak and off-peak season demand coefficients, determine the delivery instructions for the cigarettes to be cultivated, so that the commercial entity delivers the cigarettes to be cultivated to the corresponding retailers according to the delivery instructions, and obtains the sales data of the cigarettes to be cultivated from each of the retailers.

[0065] S230. Determine core retailers based on the basic data of each retailer; generate communication questions based on the basic data of the core retailers and the sales data, so that industrial entities and / or commercial entities can visit the core retailers based on the communication questions and obtain first feedback data corresponding to the communication questions.

[0066] S240. Determine the target consumer and the feature tags corresponding to the target consumer based on the membership data.

[0067] The core feature dimensions are input into a pre-trained target consumer identification model, which outputs a list of target consumers and their corresponding feature labels. These feature labels can be categorized based on consumption preferences and customer value.

[0068] For example, based on a company's membership data, a list of 2,000 target consumers is output using a target consumer identification model. From the perspective of consumption preferences, the target consumer characteristic tags are divided into three categories: "Business entertainment needs," "Gift consumption," and "High-end appreciation." From the perspective of customer value, the target consumer characteristic tags are divided into three categories: "Key retention," "Key development," and "Key retention."

[0069] S250. Determine the marketing plan corresponding to each of the aforementioned feature tags, so that the business entity can implement the corresponding marketing plan for the target consumers in the current cultivation cycle, and obtain the second feedback data of the target consumers on the cigarettes to be cultivated.

[0070] The marketing plan can be a targeted marketing strategy pre-determined by the business entity. For example, for consumers with "business banquet needs," the marketing plan might be to invite them to participate in a "business appreciation salon"; for consumers with "gift consumption needs," the marketing plan might be to offer customized gift box engraving services; for consumers with "high-end appreciation needs," the marketing plan might be to give away limited-edition trial products; for consumers with "key retention needs," the marketing plan might be to provide exclusive sales channels and personalized annual benefits; for consumers with "key development needs," the marketing plan might be to build a growth-level incentive system; and for consumers with "key retention needs," the marketing plan might be to provide compensatory incentives.

[0071] In some embodiments, optionally, after the business entity executes the corresponding marketing plan for the target consumer, the method further includes: obtaining marketing results, optimizing the marketing plan based on the marketing results, and determining the target plan for the next nurturing cycle; wherein the marketing results include activity participation rate, potential consumer conversion rate, and marketing return on investment.

[0072] Among them, the activity participation rate refers to the proportion of consumers who actually participate in the marketing activity to the target consumers; the potential consumer conversion rate refers to the proportion of consumers who actually purchase cigarettes to be cultivated after participating in the activity to the target consumers; and the marketing return on investment refers to the ratio of the net revenue generated by the marketing activity to the marketing investment cost.

[0073] Specifically, marketing results can be obtained based on business system data, retail terminal data, consumer surveys, and third-party platforms. Examples include sales data systems, customer relationship management systems, marketing campaign management systems, retail POS data, retailer feedback, online questionnaires, social media, and e-commerce platforms.

[0074] In this application, an effectiveness evaluation report can be generated based on marketing results, and optimization suggestions can be proposed. This allows businesses to use the optimization suggestions to determine the optimization measures for their marketing plans and to identify the target plan for the next development cycle. For example, if the activity participation rate is low, the corresponding optimization suggestions could be "simplify the activity participation process, lower the activity participation threshold, and increase incentive mechanisms."

[0075] S260. Optimize the product positioning based on the first feedback data and the second feedback data to determine the target positioning of the cigarette to be cultivated, so that the industrial entity can start the next cultivation cycle based on the target positioning.

[0076] This invention provides a digital and intelligent cigarette cultivation method that improves the cultivation efficiency, terminal conversion effect, and market acceptance of cigarettes by accurately identifying potential consumers.

[0077] Based on the above embodiments, optionally, after obtaining the product positioning and cultivation market data of the cigarette to be cultivated in the current cultivation cycle, the method further includes: generating a first training course and a second training course for the cigarette to be cultivated according to the product positioning, so that business entities can be trained based on the first training course and retailers can be trained based on the second training course; wherein, the first training course includes product process videos, core selling point interpretation documents, target consumer group profile presentation slides and competitor comparison manuals, and the second training course includes product knowledge, marketing scripts and personalized display tutorials.

[0078] The first training course involves industrial entities providing training to commercial entities on the product value and market positioning of the cigarettes to be developed, in order to promote a comprehensive understanding of the cigarettes to be developed by commercial entities.

[0079] Specifically, business account managers can access the first training course via a mobile app to complete both online and offline learning. After completing the first training course, they can also evaluate the effectiveness of their learning. For example, the pass rate of account managers must reach over 90%, and those who fail must attend supplementary training. The system generates a training effectiveness report, recording the learning time and assessment scores of each account manager, and provides feedback to industrial and commercial entities.

[0080] The second training course involves business entities providing retailers with training on product knowledge, marketing techniques, and personalized display skills for the cigarettes to be developed.

[0081] For example, online, the system can push video courses such as "product knowledge" (raw materials, processes, selling points), "opening sales pitches" (for business clients: "This is made with unique cigarette fibers, low tar and high aroma, perfect for high-class business banquets"), and "personalized display tutorials" (display techniques for high-end gift boxes and lighting matching). Offline, businesses can organize hands-on training to guide retailers in setting up gift box display areas and practicing sales pitches; an online assessment module can be set up, requiring retailers to upload display practice videos and pass a quiz to complete the training.

[0082] In addition, supplementary training suggestions can be generated based on the assessment results. For example, targeted tutorials can be pushed to retailers who fail the "display practice test", and a dedicated person can be arranged to provide one-on-one guidance.

[0083] The advantage of the above technical solution is that it can automatically generate training courses for business entities and retailers based on product positioning, which is highly targeted and efficient, and greatly improves the efficiency of cigarette cultivation.

[0084] The technical solution described in this application will be explained using a specific example of the digital and intelligent development process across the entire high-end cigarette supply chain. Figure 3 As shown. The data processing operations of industrial enterprises, commercial enterprises, retailers, and consumers during the cultivation process are all based on the cigarette end-to-end digital intelligence platform. This platform can execute the cigarette end-to-end digital intelligence cultivation method provided in any embodiment of this application, and possesses the corresponding functional modules and beneficial effects for executing the method.

[0085] S301. Industrial enterprises conduct product positioning based on market research, and generate first and second training courses based on product positioning. S302. Industrial enterprises identify targets and cultivate markets through data analysis.

[0086] S303, empowerment training for industrial enterprises to conduct the first training course on product value and market positioning for business account managers.

[0087] S304. Industrial and commercial enterprises use data analysis to screen core retail customers.

[0088] S305. Industrial and commercial enterprises reach retailers through online promotions and target consumers through offline activities.

[0089] Online promotion includes, but is not limited to, digital channels such as live streaming by private domain retailers, short video distribution, and WeChat official account pushes; offline consumer activities include, but are not limited to, gift box delivery, tasting events, and themed meetings. Online promotion and offline activities need to differentiate between the primary needs of retailers and consumers: for retailers, the focus should be on promoting "product sales techniques and support policies," while for consumers, the focus should be on promoting "brand culture and product selling points," and content should be precisely matched based on the tag data of the two types of entities.

[0090] S306. Industrial enterprises and commercial enterprises shall collaborate to formulate or optimize product launch strategies, with commercial enterprises carrying out product launches and conducting real-time assessments of the product market status to adjust and optimize the launch strategies.

[0091] S307: Industrial and commercial enterprises respectively visit core retail customers to collect market feedback.

[0092] S308, based on the second training course, empowers core retailers through a digital training system that combines online courses and offline practice, providing retailers with terminal skills such as product knowledge, open marketing, and personalized display.

[0093] S309. Industrial and commercial enterprises should collaborate to develop or optimize marketing strategies, identify target consumers from enterprise member data, and commercial enterprises should conduct marketing activities at the retail end based on the identification results to attract and convert potential consumers, and evaluate the effectiveness of the activities in real time, and adjust and optimize the marketing strategies accordingly.

[0094] S310 Retailers use training content and digital recommendation tools to provide product recommendations and sales services to consumers.

[0095] For example, by inputting basic consumer information (such as "35-year-old male, business person, purchasing gifts"), the system automatically matches feature tags and recommends corresponding marketing messages ("This product uses a unique cigarette-making process and is packaged in a customized gift box, making it a very prestigious business gift") and product combinations (gift box, high-end tote bag).

[0096] S311. Collect and analyze consumer feedback data in a timely manner, and feed the analysis results back to step S301 to optimize product positioning and form a closed-loop cultivation chain; wherein, the data generated in steps S306, S307, S309, and S311 are collected in real time and integrated into the cigarette full-chain digital intelligent cultivation platform, and optimization suggestions are generated through preset data analysis models to drive the strategy formulation and dynamic adjustment in steps S301, S302, S304, S306, and S309.

[0097] Example 3 Figure 4 This is a schematic diagram of the structure of a digital and intelligent cultivation platform for the entire cigarette supply chain provided in Embodiment 3 of this application. Figure 4 As shown, the platform 400 includes a data acquisition module 410, a central database 420, a data analysis module 430, and a decision support module 440. Among them, The data acquisition module 410 is used to collect multi-source data in the cigarette cultivation chain, as well as obtain market status data of the cultivation market, first feedback data of core retailers, second feedback data of target consumers, third feedback data of internet consumers, and marketing results of business entities after implementing marketing plans. The multi-source data includes product positioning and membership data from industrial entities, market cultivation data, marketing plans, and membership data from commercial entities, and sales data of cigarettes to be cultivated from various retailers in the cultivated market. The cultivated market data includes basic data of each retailer in the cultivated market, demand for cigarettes to be cultivated, target values ​​for cigarettes to be cultivated, and seasonal demand coefficients. The membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences. The market status data includes the ordering rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level of each retailer in the cultivated market for the cigarettes to be cultivated during the current marketing cycle. The marketing results include activity participation rate, potential consumer conversion rate, and marketing return on investment. The central database 420 is used to store all the data from the data acquisition module, the decision support module, and the data analysis module; wherein, the all the data includes the business flow data of the cigarettes to be cultivated during the cultivation process; The data analysis module 430 is used to call upon multi-source data stored in the central database, as well as pre-trained retailer potential assessment models and target consumer identification models, to conduct targeted data analysis; wherein, the targeted data analysis includes: Based on preset weight values, the consumption level data, consumption capacity data, and market competition data of each candidate region are weighted to determine the potential score of each candidate region; the candidate regions with potential scores greater than preset scores are identified as the cultivation markets for the cigarettes to be cultivated. Core retailers are identified based on the basic data of each retailer. The distribution instructions for the cigarettes to be cultivated are determined based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficient for peak and off-peak seasons. Based on the membership data, target consumers and corresponding marketing plans are determined. The delivery instructions are adjusted based on the order rate, the order fulfillment rate, the actual sales volume, the sales turnover rate, and the inventory level to determine the delivery instructions for the cigarettes to be cultivated in the next delivery cycle. Based on the marketing results, the marketing plan is optimized to determine the target plan for the next development cycle; Multi-dimensional feature extraction is performed on the first feedback data, the second feedback data, and the third feedback data to obtain feedback analysis results; Based on the feedback analysis results, the product positioning is optimized to determine the target positioning of the cigarette to be developed; The decision support module 440 is used to receive the targeted data analysis results from the data analysis module, and drive decisions on target positioning optimization, market cultivation adjustment, core retailer selection, delivery instruction adjustment, and marketing plan adjustment based on the targeted data analysis results; wherein, the targeted data analysis results include market cultivation, core retailers, delivery instructions, target consumers, marketing plans corresponding to target consumers, feedback analysis results, and target positioning.

[0098] This application provides a digital and intelligent cigarette cultivation platform covering the entire supply chain. The platform includes a data acquisition module, a decision support module, a data analysis module, and a central database. Centered on the collaboration of the four major cultivation entities (industrial and commercial, retail, and consumer goods), it constructs a closed-loop cultivation system across the entire supply chain. Through digital and intelligent means, it connects data and business flows at each stage, significantly improving the accuracy and efficiency of high-end cigarette cultivation. The real-time data acquisition and integration mechanism across the entire supply chain breaks the traditional linear model of the cultivation process. It intelligently analyzes data such as placement effectiveness, marketing results, and consumer feedback, quickly outputting optimization suggestions and feeding back to each front-end stage, enabling dynamic iteration of cultivation strategies. This solves the problems of broken supply chains, low accuracy, delayed feedback, and low digitalization in traditional cigarette cultivation. It reduces reliance on manual labor and trial-and-error costs in the cultivation process, and achieves precision and intelligence throughout the entire cultivation process, significantly improving cigarette cultivation efficiency, terminal conversion effects, and market acceptance, thus facilitating the efficient conversion of brand value into market benefits.

[0099] The cigarette end-to-end digital intelligent cultivation platform provided in this application embodiment can execute the cigarette end-to-end digital intelligent cultivation method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0100] Example 4 Figure 5 A schematic diagram of the structure of a device 10 that can be used to implement embodiments of this application is shown. The device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0101] like Figure 5 As shown, device 10 includes at least one processor 11 and a memory, such as read-only memory (ROM) 12, random access memory (RAM) 13, etc., communicatively connected to at least one processor 11. The memory stores computer programs executable by at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 may also store various programs and data required for the operation of device 10. The processor 11, ROM 12, and RAM 13 are interconnected via bus 14. Input / output (I / O) interface 15 is also connected to bus 14.

[0102] Multiple components in device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of monitors, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0103] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the digital and intelligent cultivation method for the entire cigarette manufacturing chain.

[0104] In some embodiments, the cigarette end-to-end digital intelligent cultivation method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the cigarette end-to-end digital intelligent cultivation method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the cigarette end-to-end digital intelligent cultivation method by any other suitable means (e.g., by means of firmware).

[0105] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0106] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0107] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0108] To provide interaction with a user, the systems and techniques described herein can be implemented on a device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0109] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0110] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0111] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0112] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0113] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A digital and intelligent cultivation method for the entire cigarette supply chain, characterized in that, The method includes: Acquire product positioning and market data of the cigarettes to be cultivated in the current cultivation cycle, as well as membership data of industrial entities and / or commercial entities; wherein, the market data includes basic data of each retailer in the cultivation market, demand for cigarettes to be cultivated, target value of cigarettes to be cultivated, and demand coefficients for peak and off-peak seasons, and the membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences; Based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficient for peak and off-peak seasons, the distribution instructions for the cigarettes to be cultivated are determined so that commercial entities can distribute the cigarettes to be cultivated to the corresponding retailers according to the distribution instructions, and obtain the sales data of each retailer for the cigarettes to be cultivated. Core retailers are identified based on the basic data of each retailer, and communication questions are generated based on the basic data and sales data of the core retailers, so that industrial entities and / or commercial entities can visit the core retailers based on the communication questions and obtain first feedback data corresponding to the communication questions. Based on the membership data, target consumers and corresponding marketing plans are determined so that the business entity can implement the corresponding marketing plans for the target consumers in the current cultivation cycle and obtain the second feedback data of the target consumers on the cigarettes to be cultivated. Multi-dimensional feature extraction is performed on the first feedback data and the second feedback data to obtain feedback analysis results. The product positioning is optimized based on the feedback analysis results to determine the target positioning of the cigarette to be cultivated, so that the industrial entity can start the next cultivation cycle based on the target positioning.

2. The method according to claim 1, characterized in that, Prior to obtaining the cultivation market data for the cigarettes to be cultivated, the method further includes: Identify the target market for the cigarettes to be developed; Accordingly, determining the cultivation market for the cigarettes to be cultivated includes: Obtain consumption level data, consumption capacity data, and market competition data for each candidate region; For each candidate region, the consumption level data, the consumption capacity data, and the market competition data are weighted based on preset weight values ​​to determine the potential score of the candidate region; Candidate regions with potential scores greater than preset scores are identified as the cultivation markets for the cigarettes to be cultivated.

3. The method according to claim 1, characterized in that, After obtaining the product positioning and cultivation market data of the cigarette to be cultivated in the current cultivation cycle, the method further includes: Based on the product positioning, a first training course and a second training course are generated for the cigarettes to be cultivated, so that business entities can be trained based on the first training course and retailers can be trained based on the second training course; wherein, the first training course includes product process videos, core selling point interpretation documents, target consumer group profile presentation slides and competitor comparison manuals, and the second training course includes product knowledge, marketing scripts and personalized display tutorials.

4. The method according to claim 1, characterized in that, The delivery instruction includes at least the delivery quantity, delivery range, and delivery cycle of the cigarettes to be cultivated; Accordingly, after the cigarettes to be cultivated are delivered to the corresponding retailers according to the delivery instruction, the method further includes: Obtain market status data for the cultivated market; wherein, the market status data includes the order rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level of each retailer in the cultivated market for the cigarettes to be cultivated in the current distribution cycle; The delivery instructions are adjusted based on the order rate, the order fulfillment rate, the actual sales volume, the sales turnover rate, and the inventory level to determine the delivery instructions for the cigarettes to be cultivated in the next delivery cycle.

5. The method according to claim 1, characterized in that, The step of determining target consumers and corresponding marketing plans based on the membership data includes: Based on the membership data, target consumers and corresponding feature tags are determined; Determine the marketing plan corresponding to each of the aforementioned feature tags.

6. The method according to claim 5, characterized in that, After instructing the business entity to implement the corresponding marketing plan for the target consumers, the method further includes: Obtain marketing results; wherein, the marketing results include campaign participation rate, potential customer conversion rate, and marketing return on investment; Based on the marketing results, the marketing plan is optimized to determine the target plan for the next development cycle.

7. The method according to claim 1, characterized in that, After generating communication questions based on the core retailer's basic data and the sales data, enabling industrial entities and / or commercial entities to visit the core retailer based on the communication questions and obtain first feedback data corresponding to the communication questions, the method further includes: Obtain third-party feedback data from internet consumers regarding the cigarettes to be cultivated; Accordingly, the step of optimizing the product positioning based on the first feedback data and the second feedback data to determine the target positioning of the cigarette to be cultivated includes: Multi-dimensional feature extraction is performed on the first feedback data, the second feedback data, and the third feedback data to obtain feedback analysis results; Based on the feedback analysis results, the product positioning is optimized to determine the target positioning of the cigarette to be developed.

8. A digital and intelligent cultivation platform for the entire cigarette supply chain, characterized in that, The platform includes a data acquisition module, a central database, a data analysis module, and a decision support module; wherein, The data acquisition module is used to collect multi-source data in the cigarette cultivation chain, as well as obtain market status data of the cultivation market, first feedback data of core retailers, second feedback data of target consumers, third feedback data of internet consumers, and marketing results of business entities after implementing marketing plans. The multi-source data includes product positioning and membership data from industrial entities, market cultivation data, marketing plans, and membership data from commercial entities, and sales data of cigarettes to be cultivated from various retailers in the cultivated market. The cultivated market data includes basic data of each retailer in the cultivated market, demand for cigarettes to be cultivated, target values ​​for cigarettes to be cultivated, and seasonal demand coefficients. The membership data includes consumers' historical consumption data, interactive behavior, and purchasing preferences. The market status data includes the ordering rate, order fulfillment rate, actual sales volume, sales turnover rate, and inventory level of each retailer in the cultivated market for the cigarettes to be cultivated during the current marketing cycle. The marketing results include activity participation rate, potential consumer conversion rate, and marketing return on investment. The central database is used to store all data from the data acquisition module, the decision support module, and the data analysis module; wherein, the all data includes the business flow data of the cigarettes to be cultivated during the cultivation process; The data analysis module is used to call upon multi-source data stored in the central database to conduct targeted data analysis; wherein, the targeted data analysis includes: Based on preset weight values, the consumption level data, consumption capacity data, and market competition data of each candidate region are weighted to determine the potential score of each candidate region; the candidate regions with potential scores greater than preset scores are identified as the cultivation markets for the cigarettes to be cultivated. Core retailers are identified based on the basic data of each retailer. The distribution instructions for the cigarettes to be cultivated are determined based on the demand for the cigarettes to be cultivated, the target value of the cigarettes to be cultivated, and the demand coefficient for peak and off-peak seasons. Based on the membership data, target consumers and corresponding marketing plans are determined. The delivery instructions are adjusted based on the order rate, the order fulfillment rate, the actual sales volume, the sales turnover rate, and the inventory level to determine the delivery instructions for the cigarettes to be cultivated in the next delivery cycle. Based on the marketing results, the marketing plan is optimized to determine the target plan for the next development cycle; Multi-dimensional feature extraction is performed on the first feedback data, the second feedback data, and the third feedback data to obtain feedback analysis results; Based on the feedback analysis results, the product positioning is optimized to determine the target positioning of the cigarette to be developed; The decision support module is used to receive the targeted data analysis results from the data analysis module, and drive decisions on target positioning optimization, market cultivation adjustment, core retailer selection, placement instruction adjustment, and marketing plan adjustment based on the targeted data analysis results; wherein, the targeted data analysis results include market cultivation, core retailers, placement instructions, target consumers, marketing plans corresponding to target consumers, feedback analysis results, and target positioning.

9. An electronic device, characterized in that, The device includes: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the digital and intelligent cigarette cultivation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the intelligent digital cultivation method for the entire cigarette supply chain as described in any one of claims 1-7.

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