AI-based full-marketing scene, full-process and full-automatic system and generation method

Abstract

The invention relates to an AI-based full-marketing scene, full-process and full-automatic system and a generation method. The system comprises a marketing demand confirmation module which is used for obtaining and analyzing marketing target parameters and confirming marketing demands; the activity scheme generation module is used for generating a complete activity scheme according to the marketing demand; the material design generation module is used for designing and generating omni-channel marketing materials according to the activity scheme; the activity promotion module is used for executing a multi-channel promotion strategy according to the activity scheme and the marketing materials; and the evaluation feedback module is used for modifying and optimizing the activity scheme in real time according to the analysis result. According to the scheme provided by the invention, full-process automation from marketing library construction, scheme generation, execution to effect feedback can be realized by means of an AI technology, a self-evolution closed-loop link of'insight-planning-execution-iteration 'is formed, the method has cross-industry and multi-scene universality, manpower cost and subjective decision deviation are reduced, and efficient and accurate marketing requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and marketing technology, and in particular to an AI-based full-marketing scenario, full-process, fully automated system and generation method. Background Art

[0002] The entire marketing process refers to a complete closed-loop system from market research, strategy planning, program design, activity execution to effect feedback. It is the core link for enterprises to achieve their business goals. Its value runs through the entire process of product development, user reach, sales conversion and brand building.

[0003] Traditional marketing models are prevalent across various industries, including retail, catering, services, and education. With intensifying market competition, increasing information diversity, and increasingly complex consumer demands, these models face challenges such as a disconnect between planning and execution, reliance on manual experience, and low efficiency. For example, traditional marketing plans often focus on macro-level theoretical planning, lacking in-depth consideration of specific implementation details, market dynamics, and personalized user needs, resulting in a disconnect between the plans and actual implementation scenarios. Furthermore, most marketing plans only progress beyond the planning stage, failing to integrate operational processes such as campaign execution, performance monitoring, and feedback. This creates a "plan-without-action" disconnect, making it difficult to dynamically adjust and optimize based on market changes and user feedback, significantly compromising marketing effectiveness. Furthermore, traditional models rely heavily on manual processes, lacking automated and intelligent technology support from plan development to campaign execution to data collection. This makes them inefficient and susceptible to the limitations of subjective experience, making them difficult to adapt to the rapidly evolving market environment and unable to meet the demands of efficient and targeted marketing.

[0004] Therefore, there is an urgent need to design an AI-based full-marketing scenario, full-process, fully automated system and generation method that can break the barriers that separate traditional marketing plans from activity execution. By leveraging AI technology, the entire process from marketing library construction, plan generation, activity execution to effect feedback can be automated, forming a self-evolving closed-loop chain of "insight-planning-execution-iteration". It has cross-industry and multi-scenario versatility, reduces labor costs and subjective decision-making deviations, improves marketing efficiency and resource utilization, and achieves the goal of accurately reaching target users and efficiently driving commercial conversions. It greatly improves practicality and competitiveness, and effectively meets the needs of efficient and precise marketing. Summary of the Invention

[0005] In order to overcome the problems existing in related technologies, the present application provides an AI-based full marketing scenario, full process, fully automated system and generation method. This AI-based full marketing scenario, full process, fully automated system and generation method can break the barriers that separate traditional marketing plans from activity execution. By leveraging AI technology, the entire process from marketing library construction, plan generation, activity execution to effect feedback can be automated, forming a self-evolving closed-loop link of "insight-planning-execution-iteration". It has cross-industry and multi-scenario versatility, reduces labor costs and subjective decision-making deviations, improves marketing efficiency and resource utilization, and achieves the goal of accurately reaching target users and efficiently driving commercial conversions, greatly improving practicality and competitiveness, and effectively meeting the needs of efficient and precise marketing.

[0006] The first aspect of this application is to provide an AI-based full-marketing scenario, full-process, fully automated system, including a marketing demand confirmation module, an activity plan generation module, a material design generation module, an activity promotion module, and an evaluation feedback module; The marketing demand confirmation module is used to obtain and analyze marketing target parameters and confirm marketing needs; The activity plan generating module is used to generate a complete activity plan according to the marketing needs; The material design and generation module is used to design and generate omni-channel marketing materials based on the content of the activity plan; The activity promotion module is used to implement a multi-channel promotion strategy based on the activity plan and the marketing materials to maximize activity exposure and participation; The evaluation and feedback module is used to obtain multi-dimensional data generated after the activity promotion, perform real-time data analysis, and then evaluate and feed back the analysis results to the activity plan generation module to modify and optimize the activity plan in real time.

[0007] In the preferred technical solution of the present application, the marketing demand confirmation module includes a store information collection unit and a customer demand confirmation unit; the store information collection unit is used to collect basic information, configuration information and service business information of the store through multiple channels online and offline, and provide original data support for subsequent precise analysis and strategy formulation; the customer demand confirmation unit is used to confirm the customer's marketing needs based on the data provided by the store information collection unit, and convert the customer's marketing needs into a clear and executable marketing plan.

[0008] In a preferred technical solution of the present application, the activity plan generation module includes a plan content generation unit and a plan confirmation unit; the plan content generation module is used to automatically generate a complete activity plan containing elements such as activity theme, activity practice, activity content design, execution details and risk plan according to the marketing plan; the plan confirmation unit is used to systematically review and optimize the complete activity plan output by the activity content generation module, and confirm and lock the execution plan version.

[0009] In a preferred technical solution of the present application, the material design and generation module includes a material design unit and a material generation unit; the material design unit is used to conceive and design omni-channel marketing materials based on the complete activity plan, wherein the conception and design content include but are not limited to determining the style of the material and the type of material to be generated; the material generation unit is used to convert the omni-channel marketing material design draft produced by the material design unit into a physical material or digital content that can be delivered.

[0010] In the preferred technical solution of the present application, the activity promotion module includes an execution schedule unit and a multi-channel publicity unit; the execution schedule unit is used to clarify the key nodes of each stage according to the complete activity plan and material preparation progress, and formulate a refined promotion plan covering the entire cycle; the multi-channel publicity unit is used to accurately reach the target through one or more online channels and / or offline channels according to the promotion plan formulated by the execution schedule unit, targeting different user groups and scenario needs.

[0011] In the preferred technical solution of the present application, the evaluation and feedback module includes a data analysis unit, an effect evaluation and feedback unit and a summary and optimization unit; the data analysis unit is used to collect and analyze the data of the entire activity cycle, deeply explore the rules and trends behind the data, and send the analysis data to the effect evaluation and feedback unit; the effect evaluation and feedback unit is used to compare the actual data of the activity with the preset goals based on the analysis data, comprehensively evaluate the effect of the activity and feedback to the summary and optimization unit; the summary and optimization unit is used to extract the successful experiences and failed lessons in the execution of the activity based on the effect of the activity, and convert them into reusable optimization strategies, and synchronize them to the activity plan generation module.

[0012] In the preferred technical solution of the present application, a marketing database construction module is also included; the marketing database construction module is used to build a high-quality, structured marketing database.

[0013] In the preferred technical solution of the present application, the marketing library construction module includes an AI learning unit, a tag classification unit and a template library creation unit; the AI ​​learning unit is used to deeply learn and analyze massive marketing activity cases and extract pure text data; the tag classification unit is used to perform structured tagging and classification on the data based on the analysis results of the AI ​​learning unit, and provide standardized input for creating a template library; the template library creation unit is used to refine the marked and classified data and extract structured marketing components, each marketing component represents a reusable and combinable atomic marketing strategy or logic, and the marketing component can be dynamically called and combined by the activity plan generation module.

[0014] The second aspect of this application is to provide an AI-based marketing full-process automated generation method, which is implemented based on the above-mentioned AI-based full marketing scenario, full process, and fully automated system, and specifically includes the following steps: Build a high-quality, structured marketing database; Obtain and analyze marketing target parameters and confirm marketing needs; Generate a complete activity plan based on the marketing needs and in combination with the marketing database; Design and generate omni-channel marketing materials based on the content of the activity plan; Execute multi-channel promotion strategies based on the activity plan and marketing materials to maximize activity exposure and participation; Acquire and analyze campaign promotion data in real time, then evaluate and update the AI ​​learning unit and template library creation unit in the marketing library building module based on the analysis results to modify and optimize campaign plans in real time.

[0015] The AI-based full marketing scenario, full process, fully automated system and generation method provided by the present invention have applications including but not limited to enterprises or merchants in various industries such as retail, catering, automotive services, education and training, health and beauty that require marketing, and are not restricted here.

[0016] The technical solution provided by this application includes the following beneficial effects: the AI-based full marketing scenario, full process, fully automated system of this application includes a marketing library construction module, a marketing demand confirmation module, an activity plan generation module, a material design generation module, an activity promotion module and an evaluation feedback module. Through the marketing library construction module, the existing massive marketing activity cases are learned and purified, and then classified to build a structured marketing component template library, which effectively solves the problem that the existing marketing plans are large and empty, and are disconnected from execution, resulting in poor practicality. Through the marketing demand confirmation module, the customer's marketing needs are converted into a clear and executable marketing plan, and then the activity plan generation module automatically generates a complete activity plan and locks the execution plan version. Through the material design generation module, abstract information is converted into concrete visual and textual content to ensure that the materials meet the activity execution requirements and facilitate precise delivery in the predetermined channels. Then, through the activity promotion module, multi-dimensional reach and precise delivery are carried out to improve user participation and conversion rate. Finally, through the evaluation feedback module, the activity effect is comprehensively evaluated and fed back to the marketing library construction module and / or the activity plan generation module, thereby continuously optimizing the entire marketing process and providing higher quality support for subsequent activities. Compared with existing technologies, the solution provided in this application can break the barriers that separate traditional marketing plans from activity execution. By leveraging AI technology, it can automate the entire process from marketing library construction, plan generation, activity execution to effect feedback, forming a self-evolving closed-loop link of "insight-planning-execution-iteration". It has cross-industry and multi-scenario versatility, reduces labor costs and subjective decision-making deviations, improves marketing efficiency and resource utilization, and achieves the goal of accurately reaching target users and efficiently driving commercial conversions. It greatly improves practicality and competitiveness, and effectively meets the needs of efficient and precise marketing.

[0017] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other objects, features and advantages of the present application will become more apparent through a more detailed description of exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0019] Figure 1 This is a schematic diagram of an AI-based full marketing scenario, full process, and fully automated system shown in an embodiment of the present application; Figure 2 It is a flow chart of the AI-based full marketing scenario, full process, and fully automated generation method shown in the embodiment of this application. DETAILED DESCRIPTION

[0020] The preferred embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0021] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0022] With intensifying market competition, information diversification, and increasingly complex consumer demands, traditional marketing plans often focus on macro-level theoretical planning, lacking in-depth consideration of specific implementation details, market dynamics, and personalized user needs. This leads to a disconnect between the plans and actual implementation scenarios. Furthermore, most marketing plans only progress past the planning stage, failing to integrate implementation, performance monitoring, and feedback into the system. This creates a "plan-without-action" disconnect, making it difficult to dynamically adjust and optimize based on market changes and user feedback, significantly compromising marketing effectiveness. Furthermore, traditional models rely heavily on manual processes, lacking automated and intelligent technology support from plan development and campaign execution to data collection. This makes them inefficient and susceptible to the limitations of subjective experience, making them difficult to adapt to the rapidly iterating market environment and unable to meet the needs of companies for efficient and precise marketing.

[0023] In response to the above problems, the embodiments of the present application provide an AI-based full-process marketing automation system and generation method, which can break the barriers that separate traditional marketing plans from activity execution. By leveraging AI technology, the entire process from marketing library construction, plan generation, activity execution to effect feedback can be automated, forming a self-evolving closed-loop link of "insight-planning-execution-iteration". It has cross-industry and multi-scenario versatility, reduces labor costs and subjective decision-making deviations, improves marketing efficiency and resource utilization, and achieves the goal of accurately reaching target users and efficiently driving commercial conversions, greatly improving practicality and competitiveness, and effectively meeting the needs of efficient and precise marketing.

[0024] The technical solutions of the embodiments of the present application are described in detail below with reference to the accompanying drawings. Example 1

[0025] See also Figure 1The AI-based full-marketing scenario, full-process, fully automated system of the present application includes a marketing library construction module, a marketing demand confirmation module, an activity plan generation module, a material design generation module, an activity promotion module, and an evaluation feedback module. The marketing library construction module is used to build a high-quality, structured marketing database to provide data and resource support for subsequent links. The marketing demand confirmation module is used to obtain and parse marketing target parameters to confirm marketing needs, wherein the marketing target parameters include but are not limited to customer type, budget, time, target KPI, and clarify the core objectives, budget range, time period and expected effect of the marketing activities, converting fuzzy needs into an executable target framework to avoid deviations in the direction of subsequent work. The activity plan generation module is used to generate a complete activity plan based on the marketing needs and the marketing database, so that the marketing activities can be executed according to the activity plan and ensure the orderly progress of the marketing activities. The material design generation module is used to design and generate omni-channel marketing materials that can attract users based on the activity plan, converting abstract marketing concepts into concrete content that attracts users. The activity promotion module is used to implement a multi-channel promotion strategy based on the activity plan and the marketing materials to maximize the exposure and participation of the activity. The evaluation feedback module is used to obtain multi-dimensional data generated after the promotion of the activity, perform real-time data analysis, and then evaluate and update the AI ​​learning unit and template library creation unit in the marketing library construction module and / or the activity plan generation module based on the analysis results, so as to modify and optimize the activity plan in real time, further improve the practicality and success rate of the marketing plan, and thereby enhance customer practical satisfaction.

[0026] Specifically, the marketing library construction module includes an AI learning unit, a tag classification unit, and a template library creation unit. The AI ​​learning unit is used to deeply learn and analyze massive marketing activity cases and extract pure text data. The sources of the massive marketing activity cases include but are not limited to the Internet, internal materials, and successful case collections. Through powerful data processing and algorithm iteration capabilities, it removes irrelevant information such as headers, footers, and advertisements, retains the main text, and outputs pure text data. The tag classification unit is used to perform structured tagging and classification on the data based on the analysis results of the AI ​​learning unit, providing standardized input for creating a template library. For example, taking the marketing activities of 4S stores as an example, the text data can be divided into activity types and related businesses, where activity types can be divided into traffic diversion, activation of old users, etc., and the related businesses include but are not limited to car washing, maintenance, film application, repair, etc. The template library creation unit is used to refine the tagged and classified data and extract structured marketing components. Each marketing component represents a reusable and combinable atomic marketing strategy or logic, and the marketing components can be dynamically called and combined by the activity plan generation module. For example, each marketing component includes a traffic hook, a package design, a promotion channel strategy, and other content, along with its applicable scenarios, target customers, and budget range. These applicable scenarios include car washing, beauty, maintenance, and repair, among others. This makes the elements of the marketing components in the template library more comprehensive and refined, facilitating the subsequent generation of more complete and accurate marketing plans. By building a marketing library module that learns from and purifies the vast number of existing marketing campaign cases, and then categorizes them to construct a structured marketing component template library, it can effectively improve the feasibility and implementability of subsequent campaign plans, effectively resolving the problem of existing marketing plans being large and empty, disconnected from execution, and therefore lacking practicality.

[0027] The marketing demand confirmation module includes a store information collection unit and a customer demand confirmation unit. The store information collection unit is used to collect basic information, configuration information and service business information of stores through multiple channels online and offline, and provide original data support for subsequent precise analysis and strategy formulation, wherein the basic information includes but is not limited to specific store types, such as car wash and repair shops, quick repair and maintenance shops, specialized repair shops, etc., the configuration information includes but is not limited to the number of employees, parking space conditions, etc., and the service industry information includes but is not limited to car wash and beauty, maintenance and repair, sheet metal painting, etc. The customer demand confirmation unit is used to confirm the customer's marketing needs based on the data provided by the store information collection unit, and convert the customer's marketing needs into a clear and executable marketing plan, wherein the marketing plan includes but is not limited to sections such as activity purpose, activity time, activity scope, marketing tools, target customers, goals and budget, activity form and cooperation resources.

[0028] The activity plan generation module includes a plan content generation unit and a plan confirmation unit. The plan content generation unit is used to automatically generate a complete activity plan containing elements such as activity theme, activity practice, activity content design, execution details and risk plans based on the combination of deep learning models and strategy algorithms, autonomous reasoning and decision-making according to the marketing plan. Among them, the activity content design includes ultra-low-price experience and other popular products, maintenance package and other conversion activities, community lottery and other fission mechanisms; the execution details include but are not limited to publicity channels, event promotion schedules and data tracking, and the risk plans include but are not limited to dealing with explosive orders and handling customer complaints. The plan confirmation unit is used to systematically review and optimize the complete activity plan output by the activity content generation module, and confirm and lock the execution plan version.

[0029] The material design and generation module includes a material design unit and a material generation unit. The material design unit is used to invoke an intermodal content generation model (AIGC) and, based on the semantic information of the complete campaign plan, automatically conceive and design multiple omnichannel marketing material design schemes with different visual styles (e.g., color schemes, fonts, and icon combinations). This conceiving and design process includes, but is not limited to, determining the material style and the type of material to be generated. The material style includes at least two or three sets of visual elements with different color schemes, icons, and other elements for customers to choose from. Material types include, but are not limited to, posters, promotional H5 pages, DM flyers, table cards, X-stands, banners, and banna carousels. The material generation unit is used to seamlessly convert the omnichannel marketing material designs determined by the material design unit into ready-to-deliver physical materials or digital content, including, but not limited to, high-definition promotional images, mini-programs, and QR codes. The material design generation module not only transforms abstract information such as the campaign theme and gameplay rules into concrete visual and textual content, providing an executable design solution for material production, but also ensures that the materials meet campaign execution requirements, facilitating precise delivery through pre-determined channels.

[0030] The activity promotion module includes an execution schedule unit and a multi-channel publicity unit; the execution schedule unit is used to clarify the key nodes of each stage according to the complete activity plan and material preparation progress, and formulate a refined promotion plan covering the entire cycle. Specifically, a full-cycle promotion plan can be formulated by formulating a schedule, wherein the content of the schedule includes but is not limited to budget investment, activity layout, personnel division of labor, channel communication and activity blasting, etc. By clarifying the time nodes and task priorities, it is ensured that the promotion activities of each platform are connected in an orderly manner and promoted efficiently, avoiding resource mismatch or promotion gaps. The multi-channel publicity unit is used to accurately reach the target through one or more online channels and / or offline channels according to the promotion plan formulated by the execution schedule unit, targeting different user groups and scenario needs, such as through WeChat, telephone, short video, SMS and public accounts. Through multi-dimensional reach and precise delivery, the coverage of the activity is expanded, user participation and conversion rate are improved, and the activity goals are maximized. For example, when inviting through the telephone or WeChat channel, employee invitation or AI invitation can be used. When the customer agrees to add WeChat, the link appointment is completed. If the customer does not agree to add WeChat, it is converted into SMS push, thereby achieving the goal of reaching customers in multiple dimensions through multi-channel collaboration.

[0031] The evaluation and feedback module includes a data analysis unit, an effect evaluation and feedback unit, and a summary and optimization unit. The data analysis unit is used to collect and analyze data from the entire activity cycle, deeply explore the rules and trends behind the data, and send the analysis data to the effect evaluation and feedback unit; the effect evaluation and feedback unit is used to compare the actual activity data with the preset goals based on the analysis data, comprehensively evaluate the activity effect and feedback to the summary and optimization unit, wherein the actual activity data content includes but is not limited to page views, click-through rate, transaction rate, write-off rate, conversion rate, etc. The summary and optimization unit is used to extract successful experiences and failed lessons from the execution of the activity based on the activity effect, and convert them into reusable optimization strategies, and at the same time update them to the AI ​​learning unit and template library creation unit in the marketing library construction module to achieve self-iteration and evolution of the system model, so that the entire marketing activity can continuously evolve in sync with market changes, thereby generating more efficient solutions in future marketing activities.

[0032] In practical applications: For example, in the catering industry, a restaurant might want to promote a new seasonal dish. This system automatically analyzes the restaurant's customer profile (e.g., frequency of consumption, average customer spending), combines it with successful case studies from its marketing database, and generates a "New Taste, New Experience" themed campaign plan. This includes designing compelling food posters and WeChat Moments content, pushing limited-time discount coupons to high-value customers through its official account, and promoting content through short video platforms to drive traffic to the restaurant.

[0033] For example, in the retail industry, a clothing store needs to clear out its inventory for the end of the season. Based on inventory levels and target customer groups, this system automatically plans a limited-time flash sale with additional member discounts. It also generates corresponding online H5 promotional materials and offline X-shaped display racks, precisely reaching target customers through SMS and WeChat groups.

[0034] In the first embodiment of the present invention, the AI-based full marketing scenario, full process, and fully automated system of the present application includes a marketing library construction module, a marketing demand confirmation module, an activity plan generation module, a material design generation module, an activity promotion module, and an evaluation feedback module. Through the marketing library construction module, the existing massive marketing activity cases are learned and purified, and then classified to build a structured marketing component template library, which effectively solves the problem that the existing marketing plans are large and empty, and are disconnected from execution, resulting in poor practicality. Through the marketing demand confirmation module, the customer's marketing needs are converted into a clear and executable marketing plan, and then the activity plan generation module automatically generates a complete activity plan and locks the execution plan version. Through the material design generation module, abstract information is converted into concrete visual and textual content to ensure that the materials meet the activity execution requirements and facilitate precise delivery in the predetermined channels. Then, through the activity promotion module, multi-dimensional reach and precise delivery are carried out to improve user participation and conversion rate. Finally, through the evaluation feedback module, the activity effect is comprehensively evaluated and fed back to the marketing library construction module and / or the activity plan generation module, thereby continuously optimizing the entire marketing process and providing higher quality support for subsequent activities. Compared with existing technologies, the solution provided in this application can break the barriers that separate traditional marketing plans from activity execution. By leveraging AI technology, it can automate the entire process from marketing library construction, plan generation, activity execution to effect feedback, forming a self-evolving closed-loop link of "insight-planning-execution-iteration". It has cross-industry and multi-scenario versatility, reduces labor costs and subjective decision-making deviations, improves marketing efficiency and resource utilization, and achieves the goal of accurately reaching target users and efficiently driving commercial conversions. It greatly improves practicality and competitiveness, and effectively meets the needs of efficient and precise marketing. Example 2

[0035] Corresponding to the above embodiment 1, this application also proposes an AI-based full marketing scenario, full process, and fully automated generation method, please refer to Figure 1-Figure 2 , specifically: Based on the structure of the above-mentioned embodiment 1, the second embodiment of the present application provides an AI-based marketing full-process automated generation method, which is implemented based on the above-mentioned AI-based full marketing scenario, full process, and fully automated system, and specifically includes the following steps: S1. Build a high-quality, structured marketing database; S2. Obtain and analyze marketing target parameters and confirm marketing needs; S3. Generate a complete activity plan based on the marketing needs and in combination with the marketing database; S4. Design and generate omni-channel marketing materials based on the content of the activity plan; S5. Execute a multi-channel promotion strategy based on the activity plan and marketing materials to maximize activity exposure and participation; S6. Acquire and analyze campaign promotion data in real time, then evaluate and update the AI ​​learning unit and template library creation unit in the marketing library building module based on the analysis results to modify and optimize the campaign plan in real time.

[0036] The effects of the second embodiment of the present application are detailed in the first embodiment above and will not be repeated here.

[0037] Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the application herein may be implemented as electronic hardware, computer software, or combinations of both.

[0038] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems and methods according to multiple embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or code, and the part of the module, program segment or code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0039] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. An AI-based full-marketing scenario, full-process, fully automated system, characterized by: It includes marketing demand confirmation module, activity plan generation module, material design generation module, activity promotion module and evaluation feedback module; The marketing demand confirmation module is used to obtain and analyze marketing target parameters and confirm marketing needs; The activity plan generating module is used to generate a complete activity plan according to the marketing needs; The material design and generation module is used to design and generate omni-channel marketing materials based on the activity plan; The activity promotion module is used to implement a multi-channel promotion strategy based on the activity plan and the marketing materials to maximize activity exposure and participation; The evaluation and feedback module is used to obtain multi-dimensional data generated after the activity promotion, perform real-time data analysis, and then evaluate and feed back the analysis results to the activity plan generation module to modify and optimize the activity plan in real time.

2. The AI-based full marketing scenario, full process, and fully automated system according to claim 1 is characterized in that: The marketing demand confirmation module includes a store information collection unit and a customer demand confirmation unit; The store information collection unit is used to collect basic information, configuration information, and service business information of stores through multiple online and offline channels, providing raw data support for subsequent precise analysis and strategy formulation; The customer demand confirmation unit is used to confirm the customer's marketing needs based on the data provided by the store information collection unit, and convert the customer's marketing needs into a clear and executable marketing plan.

3. The AI-based full marketing scenario, full process, and fully automated system according to claim 2 is characterized in that: The activity plan generation module includes a plan content generation unit and a plan confirmation unit; The program content generation module is used to automatically generate a complete activity program containing elements such as activity theme, activity practice, activity content design, execution details and risk plan based on the marketing plan; The plan confirmation unit is used to systematically review and optimize the complete activity plan output by the activity content generation module, and confirm and lock the execution plan version.

4. The AI-based full marketing scenario, full process, and fully automated system according to claim 3 is characterized in that: The material design and generation module includes a material design unit and a material generation unit; The material design unit is used to conceive and design omni-channel marketing materials based on the complete campaign plan, wherein the conception and design content includes but is not limited to determining the style of the materials and the type of materials to be generated; The material generation unit is used to convert the omni-channel marketing material design draft produced by the material design unit into a physical material or digital content that can be delivered.

5. The AI-based full marketing scenario, full process, and fully automated system according to claim 4 is characterized in that: The activity promotion module includes an execution schedule unit and a multi-channel publicity unit; The execution timetable unit is used to identify key nodes in each stage and formulate a detailed promotion plan covering the entire cycle based on the complete activity plan and material preparation progress; The multi-channel promotion unit is used to accurately reach the target through one or more online channels and / or offline channels according to the promotion plan formulated by the execution schedule unit, targeting different user groups and scenario needs.

6. The AI-based full marketing scenario, full process, and fully automated system according to claim 5 is characterized in that: The evaluation and feedback module includes a data analysis unit, an effect evaluation and feedback unit, and a summary and optimization unit; The data analysis unit is used to collect and analyze data from the entire activity cycle, deeply explore the patterns and trends behind the data, and send the analysis data to the effect evaluation feedback unit; The effect evaluation and feedback unit is used to compare the actual activity data with the preset target based on the analysis data, comprehensively evaluate the activity effect and feed back the result to the summary and optimization unit; The summary optimization unit is used to extract successful experiences and failed lessons from the execution of the activity according to the activity results, and convert them into reusable optimization strategies, and synchronize them to the activity plan generation module.

7. The AI-based full marketing scenario, full process, and fully automated system according to claim 1 is characterized in that: Also included are the Marketing Library building blocks; The marketing database building module is used to build a high-quality, structured marketing database.

8. The AI-based full marketing scenario, full process, and fully automated system according to claim 7 is characterized in that: The marketing library construction module includes an AI learning unit, a tag classification unit, and a template library creation unit; The AI ​​learning unit is used to deeply learn and analyze massive marketing campaign cases and extract clean text data; The labeling and classification unit is used to perform structured labeling and classification on the data based on the analysis results of the AI ​​learning unit, providing standardized input for creating a template library; The template library creation unit is used to refine the marked and classified data and extract structured marketing components. Each marketing component represents a reusable and combinable atomic marketing strategy or logic, and the marketing component can be dynamically called and combined by the activity plan generation module.

9. An AI-based full marketing scenario, full process, fully automated generation method, characterized by: The AI-based full marketing scenario, full process, and fully automated system according to any one of claims 1 to 8 is implemented, specifically comprising the following steps: Build a high-quality, structured marketing database; Obtain and analyze marketing target parameters and confirm marketing needs; Generate a complete activity plan based on the marketing needs and in combination with the marketing database; Design and generate omni-channel marketing materials based on the content of the activity plan; Execute multi-channel promotion strategies based on the activity plan and marketing materials to maximize activity exposure and participation; Acquire and analyze campaign promotion data in real time, then evaluate and update the AI ​​learning unit and template library creation unit in the marketing library building module based on the analysis results to modify and optimize campaign plans in real time.

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