Brand marketing full-link intelligent optimization system based on multi-modal AI drive

Abstract

The invention relates to the technical field of data processing, in particular to a brand marketing full-link intelligent optimization system based on multi-mode AI driving. The system comprises a data acquisition and preprocessing module, an AI core engine, an intelligent strategy generation module, a marketing activity management and execution interface and a real-time monitoring and feedback module. The AI core engine integrates machine learning, natural language processing and an optimization algorithm, and performs deep analysis on multi-source data to predict customer behaviors, understand market emotions, generate personalized contents and optimize marketing parameters; the intelligent strategy generation module formulates an integrated marketing strategy based on AI insight; the system realizes dynamic, adaptive and intelligent optimization of brand marketing by monitoring marketing activity performance in real time and continuously adjusting an AI model and a marketing strategy by using a feedback loop. The invention aims to improve the accuracy, efficiency and return on investment of brand marketing by realizing dynamic, data-driven and continuous self-improvement optimization of brand marketing strategies and execution.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and specifically to a full-link intelligent optimization system for brand marketing driven by multimodal AI. Background Art

[0002] Current brand marketing approaches often rely on traditional strategies, which have inherent limitations in data analysis, speed to insight, targeting accuracy, and return on investment (ROI) measurement. Personalization at scale is particularly challenging. The rise of artificial intelligence in marketing has significantly improved tasks such as data collection, analysis, and automation. AI-powered marketing leverages data-driven analytics, natural language processing (NLP), and machine learning (ML) to provide customer insights and automate key decision-making.

[0003] However, despite the application of existing AI tools in marketing, marketers still face challenges in achieving truly dynamic and holistic optimization across all brand touchpoints. Seamlessly integrating insights from different AI modules (e.g., sentiment analysis, predictive models, content generators) into a coherent, continuously evolving brand strategy remains a challenge. In addition, proactively adapting to rapidly changing market conditions and consumer behavior in real time with high granularity, as well as accurately quantifying the impact of brand marketing activities on overall business goals, are also urgent issues that need to be addressed. Summary of the Invention

[0004] The present invention provides a full-link intelligent optimization system for brand marketing based on multimodal AI and related methods, which overcome the limitations of existing technologies by realizing dynamic, data-driven and continuous self-improvement optimization of brand marketing strategies and execution.

[0005] A multimodal AI-driven, full-link intelligent optimization system for brand marketing, including:

[0006] A data acquisition and preprocessing module configured to collect and process raw data related to brands, markets, customers, and marketing activities from multiple heterogeneous data sources to generate a unified analytical data set;

[0007] an artificial intelligence core engine, connected to the data acquisition and preprocessing module, configured to utilize the unified analytical data set, through a built-in machine learning unit, natural language processing unit, and optimization algorithm unit, to perform at least one or more of the following operations: predicting customer behavior, segmenting target audiences, analyzing text sentiment and intent, generating personalized marketing content, and determining optimization recommendations for marketing campaign parameters;

[0008] an intelligent strategy generation module, connected to the AI ​​core engine and configured to automatically generate or assist in generating a multi-channel brand marketing integration strategy based on the insights and optimization suggestions output by the AI ​​core engine and in combination with preset brand goals and business rules;

[0009] A marketing campaign management and execution interface, connected to the intelligent strategy generation module, configured to configure, initiate, and manage marketing campaigns on various marketing platforms based on the generated brand marketing integration strategy;

[0010] The real-time monitoring and feedback module is connected to the marketing campaign management and execution interface and the AI ​​core engine, and is configured to continuously monitor the real-time performance data of executed marketing campaigns and feed back the performance data and analysis results to the AI ​​core engine to drive the AI ​​core engine to adaptively adjust and optimize its internal models and algorithms, thereby guiding the intelligent strategy generation module to dynamically update the brand marketing integration strategy.

[0011] Preferably, the machine learning unit is configured to adopt at least one machine learning model selected from a supervised learning algorithm group, an unsupervised learning algorithm group, a reinforcement learning algorithm group or a deep learning algorithm group, the supervised learning algorithm group includes a classification algorithm and a regression algorithm, and the unsupervised learning algorithm group includes a clustering algorithm and an association rule mining algorithm.

[0012] Preferably, the NLP unit is configured to perform at least one processing selected from sentiment analysis, topic modeling, named entity recognition, natural language understanding or natural language generation.

[0013] Preferably, the optimization algorithm unit is configured to adopt at least one optimization technology selected from linear programming, nonlinear programming, heuristic algorithm, multi-objective optimization algorithm or real-time bidding algorithm to optimize marketing budget allocation, bidding strategy, marketing campaign parameter combination or personalized offer recommendation.

[0014] Preferably, the intelligent strategy generation module is further configured to adopt explainable AI technology to provide human-understandable decision-making basis for the brand marketing integration strategy it generates.

[0015] Preferably, the real-time monitoring and feedback module is further configured to automatically trigger the suspension of poorly performing marketing activities, the expansion of high-performing marketing activities, or the initiation of new A / B tests based on preset thresholds or rules.

[0016] The multimodal AI-driven full-link intelligent optimization method for brand marketing includes the following steps:

[0017] The data acquisition and preprocessing module collects and processes raw data related to brands, markets, customers, and marketing activities from multiple heterogeneous data sources to generate a unified analytical data set;

[0018] The artificial intelligence core engine utilizes the unified analytical data set to perform in-depth analysis through its built-in machine learning unit, natural language processing unit, and optimization algorithm unit to generate customer insights, content recommendations, and marketing parameter optimization solutions;

[0019] The intelligent strategy generation module develops a multi-channel brand marketing integration strategy based on the insights, suggestions and optimization plans generated by the AI ​​core engine and combined with brand goals and business rules;

[0020] Deploy and execute marketing activities on the marketing platform according to the formulated brand marketing integration strategy through the marketing activity management and execution interface;

[0021] The real-time monitoring and feedback module tracks the real-time performance indicators of the executed marketing activities, and feeds back the tracked real-time performance indicators and analysis results to the AI ​​core engine. The AI ​​core engine adaptively learns and adjusts its internal models and algorithms, and based on the adjusted results, iteratively optimizes and dynamically updates the brand marketing integration strategy through the intelligent strategy generation module, forming a closed-loop intelligent optimization process.

[0022] Preferably, the in-depth analysis to generate customer insights, content recommendations and marketing parameter optimization solutions includes: using machine learning models to predict customers' future behavior or the probability of responding to marketing activities; and using NLP technology to analyze sentiment tendencies and key topics in text data such as social media and customer reviews.

[0023] Preferably, the formulation of a multi-channel brand marketing integration strategy includes: determining target audience segmentation, personalized messaging themes, content creative direction, optimal channel combination, budget allocation plan among channels, and key performance indicators.

[0024] Preferably, the brand marketing integration strategy is iteratively optimized and dynamically updated by the intelligent strategy generation module, including automatically adjusting bids, budget allocations, target audience positioning or advertising creative elements of ongoing marketing activities based on real-time performance feedback.

[0025] Compared with the prior art, the advantages of the present invention are:

[0026] Personalization and Precision Reach: Leveraging AI data analysis, machine learning, and natural language processing capabilities, the system can achieve precise customer insights, conduct refined audience segmentation and micro-targeting, and generate and push highly personalized marketing content on a large scale, ensuring that marketing information can accurately reach target users and significantly improve user engagement and conversion rates.

[0027] Improved marketing effectiveness and returns: Through AI-driven real-time data analysis, predictive modeling, and dynamic optimization of marketing activities (such as budget allocation, ad bidding, and channel selection), the system can continuously improve the effectiveness of marketing activities and maximize return on investment (ROI).

[0028] Market adaptation and proactive decision-making: The system has the ability to monitor market dynamics, consumer sentiment, and the competitive landscape in real time, and quickly analyze these changes through AI to proactively adjust marketing strategies and implementation plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is the system architecture diagram of the multimodal AI-driven full-link intelligent optimization system for brand marketing proposed in this invention. DETAILED DESCRIPTION

[0030] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0031] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0032] Example 1:

[0033] Reference Figure 1 This embodiment provides a multimodal AI-driven, full-chain intelligent optimization system for brand marketing, comprising a data acquisition and preprocessing module, an AI core engine, an intelligent strategy generation module, a marketing campaign management and execution interface, and a real-time monitoring and feedback module. These modules collaborate to achieve intelligent and optimized brand marketing activities. The following details the implementation of each module.

[0034] Data acquisition and preprocessing module: This module is responsible for collecting, aggregating, cleaning, transforming and unifying data from various heterogeneous data sources.

[0035] It should be noted that the heterogeneous data sources include:

[0036] Customer data: This includes customer relationship management (CRM) system data, transaction history, website / app behavior data, demographic data, loyalty program data, etc.

[0037] Marketing campaign data: performance indicators from advertising platforms (such as impressions, clicks, conversions, cost per click (CPC), cost per action (CPA), email marketing system data, social media marketing tool data, etc.

[0038] Market data: including competitor activities, industry trends, economic indicators, third-party market research reports, etc.

[0039] Brand data: including brand guidelines, product information, existing marketing materials, brand sentiment data, etc.

[0040] Social media data: including posts, comments, shares, mentions, sentiment, etc.

[0041] Unstructured data: including images, videos, open-ended survey responses, etc.

[0042] The data preprocessing of this module includes: data cleaning (handling missing values ​​and outliers), data normalization, feature engineering (creating new relevant features from raw data, such as input features for customer lifetime value prediction), and data integration (creating a unified customer view or market view).

[0043] Fusing and contextualizing these diverse data types can produce richer features for AI models than if they were processed in isolation.

[0044] AI Core Engine: Responsible for performing complex intelligent computing and analysis, including machine learning units, natural language processing units, and optimization algorithm units.

[0045] The machine learning unit applies a variety of machine learning algorithms for predictive analysis, customer segmentation, scoring, and pattern recognition, including:

[0046] Predict customer behavior: predict future purchasing behavior, churn likelihood, engagement probability, etc.

[0047] Audience segmentation and micro-targeting: Intelligently divide customers into granular groups based on multiple attributes (demographics, behaviors, psychographics, predicted intent).

[0048] Lead Scoring and Prioritization: Identify and rank the leads most likely to convert.

[0049] Offer / Product Recommendation Engine: Personalized recommendations based on user activity and predictive models.

[0050] The machine learning algorithms used by the machine learning unit include:

[0051] Supervised learning: Classification algorithms for churn prediction, lead qualification, and sentiment classification, and regression algorithms for sales forecasting and optimal bidding.

[0052] Unsupervised learning: Clustering algorithms (such as K-means) for identifying different customer segments based on behavior or preferences, and association rule mining algorithms for market basket analysis.

[0053] Deep learning: Neural networks used for complex pattern recognition, image analysis, or advanced NLP tasks in large datasets.

[0054] It’s important to note that these machine learning models are not static, but are continuously updated and adjusted based on real-time feedback from ongoing marketing campaigns, forming a system that is capable of self-improvement.

[0055] The natural language processing unit enables the system to understand, interpret, and generate human language from various text-based data sources.

[0056] The technologies used by the natural language processing unit include:

[0057] Sentiment analysis: Determine positive, negative, or neutral sentiment from customer reviews, social media comments, and survey responses, including aspect-based sentiment analysis.

[0058] Topic Modeling / Text Classification: Identifying key topics in text data (e.g., customer feedback, articles).

[0059] Named Entity Recognition (NER): Identifying key entities in text, such as brand names, products, and places.

[0060] Natural Language Understanding (NLU): interpreting the intent behind user queries or statements (e.g., for chatbots or analyzing customer support interactions).

[0061] Natural Language Generation (NLG): Creating human-like text for use in marketing copy, personalized emails, social media posts, chatbot responses, ad creatives, and more.

[0062] Applies to:

[0063] Brand perception monitoring: Analyze social media and reviews to understand public sentiment toward your brand.

[0064] Personalized content creation: Generate customized marketing messages, email subject lines, advertising copy, etc.

[0065] Intelligent Customer Support: Provide chatbot or virtual assistant support for customer service.

[0066] Market trend analysis: Extract insights from news articles and industry reports.

[0067] It should be noted that the natural language processing unit is specifically trained or fine-tuned to understand and generate content that strictly adheres to a specific brand voice and style guide, thereby addressing brand consistency. For example, this is achieved through few-shot learning using brand-specific examples or by employing a dedicated brand voice model to ensure that all NLG outputs are consistent with the brand image.

[0068] The optimization algorithm unit uses mathematical optimization techniques and algorithms to find the best solutions for various marketing decisions given constraints and goals.

[0069] The technologies used by the optimization algorithm unit include:

[0070] Linear / nonlinear programming: used for budget allocation and media mix modeling.

[0071] Multi-objective optimization: Balancing multiple, potentially conflicting marketing objectives (e.g., maximizing reach while minimizing cost, maximizing engagement and conversions).

[0072] Real-time bidding algorithms: used in programmatic advertising, adjust bids based on real-time data and predictive scores.

[0073] Applies to:

[0074] Marketing budget allocation: Optimize spending across channels, marketing campaigns, and target segments.

[0075] Bid Management: Dynamically adjust bids in programmatic ad auctions.

[0076] Adjust marketing campaign parameters: optimize advertising frequency, delivery time and location.

[0077] A / B / n testing strategy: Intelligently design and analyze experiments to find the best marketing campaign elements.

[0078] Offer optimization: Identify the best offer for each customer segment at the right time.

[0079] Intelligent Strategy Generation Module: This module transforms insights, predictions, and analysis from the AI ​​core engine into actionable, coherent, and optimized brand marketing strategies. Specifically, it includes:

[0080] Receives input from the machine learning unit (e.g., key customer segments, predicted behavior), the natural language processing unit (e.g., primary sentiment, key discussion topics, effective messaging style), and the optimization algorithm unit (e.g., optimal budget allocation, recommended bid range).

[0081] Apply a configurable set of business rules, brand objectives, and marketing principles.

[0082] Generate comprehensive strategic recommendations that may include: target audience definition and personas, key messaging themes and content pillars, recommended channel mix and budget allocation, personalization strategies for different segments, campaign scheduling and timing, KPIs and measurement framework.

[0083] It should be noted that to increase the credibility of the results, this module can integrate explainable AI (XAI) technology to explain why the AI ​​recommends certain strategies. For example, the system not only generates strategies but also provides human-understandable reasons.

[0084] Campaign Management and Execution Interface: Provides tools for setting up, launching, and managing marketing campaigns based on AI-generated strategies, including:

[0085] Integrate with major advertising platforms, email marketing tools, and CRM systems via API.

[0086] Workflow automation for campaign creation and deployment.

[0087] Centralized dashboard to oversee all event marketing activities.

[0088] Version control of strategy and campaign settings.

[0089] Real-time Monitoring and Feedback Module: Continuously monitors the performance of campaign marketing activities and feeds this data back to the AI ​​core engine for learning, adaptation, and re-optimization. Specifically:

[0090] Collect real-time performance data (KPIs such as impressions, clicks, conversions, engagement rates, spend, ROI) from various platforms.

[0091] Compare actual performance to forecasted performance and established targets.

[0092] Identify deviations, anomalies, or opportunities for improvement.

[0093] This processed feedback (e.g., performance data, error signals, reward signals for reinforcement learning) is fed back to the machine learning unit (for model retraining / fine-tuning), the natural language processing unit (e.g., if certain messages perform poorly), and the optimization algorithm unit (to adjust parameters).

[0094] Through real-time monitoring and feedback, the system:

[0095] Dynamically adjust bids, budgets, and targeting in real time.

[0096] Automatically pause underperforming ads or amplify successful ones.

[0097] Trigger A / B tests for elements that don’t perform as expected.

[0098] Optimize the predictive model based on new incoming data.

[0099] Example 2:

[0100] Based on Example 1, this embodiment proposes a full-link intelligent optimization method for brand marketing driven by multimodal AI, which specifically includes:

[0101] Data aggregation and unification:

[0102] Systematically collects data from all configured data sources (as described in the Data Acquisition module).

[0103] Perform preprocessing: cleaning, normalization, transformation, and feature engineering.

[0104] Create a unified data repository or knowledge graph that serves as a single source of truth for the AI ​​core engine.

[0105] AI-driven analysis and insight generation:

[0106] The AI ​​core engine processes the unified data.

[0107] Machine Learning Unit: Performs predictive modeling (e.g., churn risk, propensity to purchase), identifies customer segments, and scores potential customers.

[0108] Natural Language Processing Unit: Analyzes text data to obtain sentiment, themes, and intent; generates drafts of content elements.

[0109] Optimization Unit: Identify potential areas for optimization based on comparing current performance with goals.

[0110] Intelligent Strategy Development:

[0111] The intelligent strategy generation module synthesizes insights from the AI ​​core engine.

[0112] Develop or recommend a comprehensive, multi-channel marketing strategy aligned with brand objectives. This includes defining target audience, messaging, content themes, channel mix, budget allocation, and KPIs.

[0113] Marketing campaign configuration and execution:

[0114] Use the campaign management and execution interface to turn AI-generated strategies into actual marketing campaigns.

[0115] Set up marketing campaigns on various advertising platforms, email systems, and more, either automatically or with human oversight.

[0116] Deploy personalized content and offers.

[0117] Real-time performance monitoring:

[0118] The real-time monitoring and feedback module continuously tracks the KPIs of all active marketing activities across all channels.

[0119] Dynamic Optimization and Adaptation:

[0120] Performance data is fed back to the AI ​​core engine in real time.

[0121] The system autonomously:

[0122] Adjust campaign parameters (bids, budgets, targeting criteria, ad placement) based on performance and optimization algorithms.

[0123] Re-evaluate and optimize customer segments.

[0124] Update the predictive model with new data.

[0125] Suggest or automatically implement changes to content or messaging based on engagement and sentiment feedback.

[0126] Launch new A / B tests to explore further optimization avenues.

[0127] Example 3:

[0128] Based on Examples 1 and 2, an embodiment provides an application of the solution in an actual brand marketing scenario.

[0129] Use Case 1: Optimizing New Product Launch Marketing Campaigns:

[0130] Systematically analyze market data from the pre-release stage and historical release data of similar products.

[0131] Identify early adopter segments and predict initial market demand through AI models.

[0132] Optimize initial advertising spend across different channels to maximize brand awareness and early sales.

[0133] After the product is released, use real-time user feedback and sales data to quickly adjust marketing information, target audiences, and channel strategies. For example, if a specific group of people is found to have positive feedback on feature A of the new product, then increase marketing efforts targeting this group using feature A as a selling point.

[0134] Use Case 2: Enhancing Customer Retention for E-commerce Brands:

[0135] The system uses predictive machine learning models (e.g., logistic regression or survival analysis models trained on features such as user purchase frequency, recent purchase date, average order value, browsing behavior, etc.) to identify customers with a high risk of churn.

[0136] The natural language processing unit analyzes the comments, ratings, or interaction records with customer service left by these high-risk customers in the past to uncover the reasons for their dissatisfaction or possible churn (for example, dissatisfaction with logistics, doubts about product quality, finding alternatives, etc.).

[0137] Based on the AI ​​analysis results, the system generates personalized retention offers (e.g., specific discount coupons, upgraded services, priority experience of new products) or communication strategies (e.g., reassuring emails, proactive care calls) for customer groups with different risk levels and reasons.

[0138] The optimization algorithm unit determines the best time to deliver these retention messages (for example, before the customer's next expected purchase cycle, after they have shown churn-prone behavior) and the most effective channels (for example, email, SMS, in-app push).

[0139] Use Case 3: Improving Brand Sentiment for a Service-Oriented Company:

[0140] The NLP unit continuously monitors mainstream social media platforms (such as Weibo and WeChat public account comment areas), online review websites (such as Dianping.com) and industry forums through API interfaces to collect brand mentions and user comments about the service company in real time.

[0141] The sentiment analysis model is used to score the sentiment polarity (positive, negative, neutral) and sentiment intensity of the collected text data, and combined with topic modeling and named entity recognition, the key drivers that lead to negative or positive sentiment are identified (for example, poor service attitude and long waiting time lead to negative sentiment, while efficient problem solving and experience exceeding expectations lead to positive sentiment).

[0142] Based on the analysis results, the system recommends targeted content strategies to the marketing team (for example, issuing clarification statements to address misunderstandings, creating case stories showcasing high-quality services) or proactive communication plans (for example, communicating with users who express dissatisfaction, initiating user co-creation activities to enhance participation).

[0143] After the implementation of these intervention measures, the system continuously tracks indicators such as changes in sentiment scores of brand-related topics, reductions in negative mentions, and the popularity of positive discussions, quantitatively evaluates the effectiveness of the intervention, and provides data support for the next round of optimization.

[0144] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0145] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A multimodal AI-driven full-link intelligent optimization system for brand marketing, characterized by: include: A data acquisition and preprocessing module configured to collect and process raw data related to brands, markets, customers, and marketing activities from multiple heterogeneous data sources to generate a unified analytical data set; an artificial intelligence core engine, connected to the data acquisition and preprocessing module, configured to utilize the unified analytical data set, through a built-in machine learning unit, natural language processing unit, and optimization algorithm unit, to perform at least one or more of the following operations: predicting customer behavior, segmenting target audiences, analyzing text sentiment and intent, generating personalized marketing content, and determining optimization recommendations for marketing campaign parameters; an intelligent strategy generation module, connected to the AI ​​core engine and configured to automatically generate or assist in generating a multi-channel brand marketing integration strategy based on the insights and optimization suggestions output by the AI ​​core engine and in combination with preset brand goals and business rules; A marketing campaign management and execution interface, connected to the intelligent strategy generation module, configured to configure, initiate, and manage marketing campaigns on various marketing platforms based on the generated brand marketing integration strategy; The real-time monitoring and feedback module is connected to the marketing campaign management and execution interface and the AI ​​core engine, and is configured to continuously monitor the real-time performance data of executed marketing campaigns and feed back the performance data and analysis results to the AI ​​core engine to drive the AI ​​core engine to adaptively adjust and optimize its internal models and algorithms, thereby guiding the intelligent strategy generation module to dynamically update the brand marketing integration strategy.

2. The multimodal AI-driven full-link intelligent optimization system for brand marketing according to claim 1 is characterized in that: The machine learning unit is configured to adopt at least one machine learning model selected from a supervised learning algorithm group, an unsupervised learning algorithm group, a reinforcement learning algorithm group or a deep learning algorithm group, wherein the supervised learning algorithm group includes a classification algorithm and a regression algorithm, and the unsupervised learning algorithm group includes a clustering algorithm and an association rule mining algorithm.

3. The multimodal AI-driven full-link intelligent optimization system for brand marketing according to claim 1 is characterized in that: The NLP unit is configured to perform at least one process selected from sentiment analysis, topic modeling, named entity recognition, natural language understanding, or natural language generation.

4. The multimodal AI-driven full-link intelligent optimization system for brand marketing according to claim 1 is characterized in that: The optimization algorithm unit is configured to adopt at least one optimization technology selected from linear programming, nonlinear programming, heuristic algorithm, multi-objective optimization algorithm or real-time bidding algorithm to optimize marketing budget allocation, bidding strategy, marketing activity parameter combination or personalized offer recommendation.

5. The multimodal AI-driven full-link intelligent optimization system for brand marketing according to claim 1 is characterized in that: The intelligent strategy generation module is further configured to adopt explainable AI technology to provide human-understandable decision-making basis for the brand marketing integration strategy it generates.

6. The multimodal AI-driven full-link intelligent optimization system for brand marketing according to claim 1 is characterized in that: The real-time monitoring and feedback module is further configured to automatically trigger the suspension of poorly performing marketing activities, the expansion of high-performing marketing activities, or the initiation of new A / B tests based on preset thresholds or rules.

7. A multimodal AI-driven full-link intelligent optimization method for brand marketing, characterized by: The following steps are involved: The data acquisition and preprocessing module collects and processes raw data related to brands, markets, customers, and marketing activities from multiple heterogeneous data sources to generate a unified analytical data set; The artificial intelligence core engine utilizes the unified analytical data set to perform in-depth analysis through its built-in machine learning unit, natural language processing unit, and optimization algorithm unit to generate customer insights, content recommendations, and marketing parameter optimization solutions; The intelligent strategy generation module develops a multi-channel brand marketing integration strategy based on the insights, suggestions and optimization plans generated by the AI ​​core engine and combined with brand goals and business rules; Deploy and execute marketing activities on the marketing platform according to the formulated brand marketing integration strategy through the marketing activity management and execution interface; The real-time monitoring and feedback module tracks the real-time performance indicators of the executed marketing activities, and feeds back the tracked real-time performance indicators and analysis results to the AI ​​core engine. The AI ​​core engine adaptively learns and adjusts its internal models and algorithms, and based on the adjusted results, iteratively optimizes and dynamically updates the brand marketing integration strategy through the intelligent strategy generation module, forming a closed-loop intelligent optimization process.

8. The multimodal AI-driven full-link intelligent optimization method for brand marketing according to claim 7 is characterized in that: The in-depth analysis described above is used to generate customer insights, content recommendations, and marketing parameter optimization solutions, including: using machine learning models to predict customers' future behavior or the probability of responding to marketing activities; and using NLP technology to analyze sentiment trends and key topics in text data such as social media and customer reviews.

9. The multimodal AI-driven full-link intelligent optimization method for brand marketing according to claim 7 is characterized in that: The aforementioned multi-channel brand marketing integration strategy includes: determining target audience segmentation, personalized messaging themes, content creative direction, optimal channel combination, budget allocation plan among channels, and key performance indicators.

10. The multimodal AI-driven full-link intelligent optimization method for brand marketing according to claim 7, characterized in that: The intelligent strategy generation module is used to iteratively optimize and dynamically update the brand marketing integration strategy, including automatically adjusting bids, budget allocations, target audience positioning, or advertising creative elements of ongoing marketing activities based on real-time performance feedback.

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