B2B customer deep insight and accurate reaching method based on multi-modal large model

Abstract

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

Description

Technical Field

[0001] This invention belongs to the field of precision marketing and intelligent recommendation technology, specifically a method for deep insight and precise outreach to B2B customers based on a multimodal large model. Background Technology

[0002] In the current B2B marketing field, enterprises face multiple challenges such as complex and ever-changing customer needs and intensified industry competition. Traditional marketing methods mainly rely on a single data source, such as purchase records and email promotions, which makes it difficult to fully understand the multi-layered characteristics of customers. Especially in cross-industry and cross-cultural scenarios, existing systems generally suffer from problems such as data silos, one-sided customer profiles, insufficient personalization, and delayed response, resulting in inaccurate recommendations, difficulties in cold start, and increased customer churn rate.

[0003] In recent years, the development of multimodal large models, such as GPT-4, CLIP, and Whisper, has brought groundbreaking technological foundations to the industry. These models can simultaneously process multi-source data such as text, images, audio, and behavior, providing new possibilities for customer insights, intent parsing, and recommendation generation in B2B marketing. However, in industry practice, relying solely on the general capabilities of large models is still insufficient to meet the unique needs of B2B marketing. How to leverage deep clustering, autoencoders, and industry knowledge enhancement to extract business needs, cultural characteristics, and risk sensitivities from customer websites, news, social media activity, emails, and purchasing behavior is a key challenge.

[0004] To address this, this invention proposes a method for in-depth B2B customer insight and precise outreach based on a multimodal large model. It constructs a closed-loop system that extends from multimodal data fusion, dynamic customer profiling, and emotional intent analysis to personalized recommendations, recommendation execution, feedback learning, and transfer optimization. The system not only improves marketing accuracy and response speed but also has cross-industry expansion and cold start optimization capabilities. Summary of the Invention

[0005] The purpose of this invention is to provide a method for in-depth B2B customer insight and precise outreach based on a multimodal large model, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for in-depth B2B customer insight and precise outreach based on a multimodal large model, the specific steps of which are as follows:

[0007] S1, Multimodal Data Fusion and Deep Analysis: Collect customer text data, visual data, audio data and behavioral data. The text data includes customer website, news, social media updates and email correspondence; the visual data includes product images and event videos; the audio data includes meeting or telephone recordings; and the behavioral data includes purchase records, sales cycles and competitor comparison records.

[0008] S2, Customer Multi-Dimensional Preference Modeling: Combining deep clustering, autoencoders, and industry knowledge enhancement, it generates customer profiles that include business needs, decision preferences, cultural traits, risk sensitivity, and other dimensions.

[0009] S3, Intent and Sentiment Analysis Engine: Based on natural language processing technology, it analyzes customer communication records, social interactions and feedback content to identify customers' implicit needs, potential intentions and emotional tendencies;

[0010] S4, Personalized Marketing Strategy Generation; Employing a Transformer-based recommendation engine, combining customer profiles, product features, and emotional intent, it generates personalized recommendation content, optimal reach timing, and communication channels based on a self-attention mechanism, and outputs the reasons for the recommendation and suggestions for follow-up actions;

[0011] S5, Recommendation Execution and Script Optimization: Apply the personalized recommendation content to the sales docking process, including generating recommended scripts, recommended contacts and recommended action plans to support the actual docking of sales personnel or the system;

[0012] S6, Feedback Collection and Reward Function-Driven Optimization: Collect positive feedback signals, negative feedback signals, and social interaction signals from customers, and use the feedback as a reward function to dynamically optimize customer profiles and recommendation models;

[0013] S7, Cold Start Solution and Transfer Learning Application: Based on similar industries or customer groups, it uses feature clustering and K-nearest neighbor screening to quickly build initial customer profiles and recommendation strategies for new customers or new markets using transfer learning technology.

[0014] Preferably, the specific steps of multimodal data fusion and deep analysis in S1 are as follows:

[0015] S11, Multimodal Data Full-Domain Collection and Processing: The system collects multi-source customer data, including official website information, industry news, social media activity, email records, product images, event videos, and telephone or meeting recordings; these text, visual, and audio data are cleaned and standardized to ensure that the multidimensional characteristics of customers are expressed in a unified data framework, laying a comprehensive foundation for subsequent insight analysis;

[0016] S12, Cross-modal Understanding and Correlation Analysis: After integrating behavioral data such as customer purchase records, sales cycles, and competitor comparisons, the system uses cross-modal embedding technology to achieve deep correlation analysis between different data; for example, it compares text descriptions with product images and behavioral characteristics to identify potential preferences and needs; this mechanism transcends the limitations of a single modality and significantly improves the accuracy of customer understanding.

[0017] The principle of cross-modal embedding technology can be expressed by the following formula:

[0018]

[0019] In the formula, q = query vector (e.g., text encoding), d = document or image vector, and f q (q)·f d (d) = encoding function (such as BERT, CLIP output), S(q,d) = cross-modal similarity score.

[0020] The classic formula for information retrieval and multimodal alignment is used to determine the similarity of data from different modalities, providing a foundation for subsequent unified customer profiling.

[0021] Preferably, the specific steps of customer multi-dimensional preference modeling in S2 are as follows:

[0022] S21, Multi-dimensional Customer Profile Construction and Feature Aggregation: The system first uses deep clustering analysis to classify multi-dimensional data such as customer business needs, purchasing history, and industry characteristics, grouping similar features into the same cluster; during the clustering process, the system automatically summarizes the common characteristics of each type of customer, such as industry focus, purchasing rhythm, or cooperation mode; this step is not only based on data distribution but also combined with industry knowledge to identify key features with strong business relevance, ensuring that the profile has practical decision-making significance;

[0023] S22, Autoencoder Learning and Industry Knowledge Enhancement: Building upon clustering, the system further utilizes an autoencoder to extract latent features such as cultural communication preferences, technical certification concerns, and risk sensitivity. Through industry knowledge enhancement, industry standards and typical needs are introduced into feature modeling. This process helps the system learn compression and reconstruction patterns from complex and messy data, achieving fine-grained customer understanding. The resulting multi-dimensional customer profile provides rich support for subsequent intent analysis and recommendation strategies.

[0024] Preferably, the specific steps of the intent and sentiment analysis engine in S3 are as follows:

[0025] S31, Multi-round Communication Content Analysis and Needs Mining: The system analyzes customer emails, meeting minutes, and social media interactions through natural language processing to identify implicit needs and core concerns behind surface-level statements; for example, a customer's indirect mention of cost optimization is categorized by the system as a cost reduction request; the analysis process is not limited to single-round communication, but can also capture the evolution of needs in multiple rounds of interaction to build a deep understanding of customer needs;

[0026] S32, Intent Recognition and Structured Tag Generation: Based on sentiment and intent analysis, the system further identifies customer sentiment attitudes, such as positive response, hesitation, or rejection tendency; the system summarizes the identification results into structured tags that can be used for subsequent recommendations, such as "expansion demand", "certification concern", and "supply chain risk"; these tags provide clear input for personalized recommendations and support the generation of more targeted strategies.

[0027] Preferably, the specific steps for generating personalized marketing strategies in step S4 are as follows:

[0028] S41, Personalized Recommendation Generation and Element Matching: The system is based on the Transformer recommendation engine, which integrates business needs, industry characteristics, and emotional intent extracted from customer profiles and matches them with product functions and service advantages. The recommendation engine uses a self-attention mechanism to dynamically capture key influencing factors from numerous customer characteristics, enabling the system to not only focus on explicit data but also to uncover important implicit clues that affect recommendations, generating highly personalized recommendation content.

[0029] The principle of self-attention mechanism can be expressed by the following formula:

[0030]

[0031] In the formula, Q = query matrix, K = key matrix, V = value matrix, d k =Key dimension;

[0032] Based on the Transformer architecture (Vaswanie et al., 2017), it captures the most important related information in customer profiles and product features to generate accurate recommendations.

[0033] S42, Recommended Output Explanation and Action Suggestions: While outputting personalized content, the recommendation system intelligently determines the best time to reach out and the best communication channel by combining the customer's current status, market dynamics, and historical interaction records, such as choosing email push or telephone invitation; the system will also provide explanatory reasons for the recommendation and subsequent action suggestions, such as recommending what kind of communication script or arranging a trial phase, to help sales or operations personnel implement the recommendation plan efficiently.

[0034] Preferably, the specific steps for execution and script optimization in S5 are as follows:

[0035] S51, Matching Sales Scripts with Contact Persons: After generating personalized recommendations, the system automatically matches the most suitable sales scripts and contact persons. For example, for technology-driven clients, it recommends professional scripts that emphasize product performance and certifications; for decision-oriented clients, it recommends business scripts that highlight business benefits and cooperation prospects. At the same time, the system recommends the optimal contact person, such as a technical consultant or business manager, to ensure efficient and accurate communication.

[0036] S52, Recommended Action Plan and Implementation: Recommendations go beyond content delivery; they also include the design of follow-up action plans, such as arranging product trials, sending case studies, or providing customized quotes. The system combines customer history, current status, and industry practices to provide clear execution guidelines for sales or operations teams, avoiding vague follow-ups. By translating recommended content into concrete actions, the system helps teams improve conversion efficiency and customer experience.

[0037] Preferably, the specific steps of feedback collection and reward function-driven optimization in S6 are as follows:

[0038] S61, Multidimensional Feedback Signal Acquisition and Attribution Analysis: The system collects multidimensional customer feedback in real time, including positive signals such as clicks, trials, and orders; negative signals such as unsubscribing and explicit rejection; and interactive signals such as likes, comments, and reposts on social media platforms. By analyzing this feedback, the system can not only understand the acceptance level of recommended content, but also perform attribution analysis on changes in customer preferences, providing traceable data support based on real interactions for subsequent recommendation strategies.

[0039] S62, Reward-Driven Dynamic Optimization: Combining attribution analysis results, the system uses a reinforcement learning reward mechanism to dynamically adjust customer profiles and recommendation models; positive feedback enhances the recommendation direction, negative feedback prompts strategy correction, and social interaction feedback provides external verification references; this mechanism enables the model to be continuously optimized in practice, not only improving recommendation accuracy but also adapting to real-time changes in customer behavior and preferences, ensuring maximum long-term marketing effectiveness;

[0040] The formula for the reward mechanism in reinforcement learning is as follows:

[0041]

[0042] In the formula, Q(s,a) = the value of choosing action a in state s, r = reward, α = learning rate, and γ = discount factor.

[0043] It employs classic reinforcement learning algorithms and dynamically adjusts recommendation strategies based on customer feedback signals for continuous optimization.

[0044] Preferably, the specific steps of the cold start solution and transfer learning application in S7 are as follows:

[0045] S71, Similar Customer Group Feature Clustering Analysis: When facing new customers or new markets, the system first performs industry feature clustering analysis based on known customer groups in historical data, extracting key tags such as business scale, purchasing characteristics, and industry needs. Through this clustering, the system can quickly identify feature combinations that are highly similar to new customers, providing a reliable reference for subsequent knowledge transfer and ensuring the accuracy and industry matching of the initial profile.

[0046] S72, Similarity Filtering and Transfer Learning Application: Based on the clustering results, the system adopts the nearest neighbor filtering method, which accurately selects the reference objects that are closest to the new customers based on the multi-dimensional similarity between customers in terms of behavior, preferences, feedback, etc. Then, using transfer learning, the knowledge and recommendation strategies accumulated by these similar customers are transferred to the new customer scenario, so that the system can provide effective and customized recommendation solutions even in the cold start stage, shortening the adaptation time.

[0047] The formula for expressing the principle of similarity filtering is:

[0048]

[0049] In the formula, sim(u,v) = the similarity between users u and v, I uv =A collection of items where u and v interact together. = Average rating of user u.

[0050] Quickly identify similar customers and transfer knowledge to new customer cold start scenarios.

[0051] The beneficial effects of this invention are as follows:

[0052] 1. This invention integrates multimodal data such as customer websites, news information, social media updates, email correspondence, product images, and meeting recordings to achieve a panoramic insight into customers from the business level to behavioral details. Combined with a pre-trained large model for deep analysis of text, images, and audio, the system breaks through the limitations of traditional data silos, not only to obtain explicit needs, but also to understand customers' industry characteristics, cultural traits, and potential concerns.

[0053] 2. This invention constructs dynamically updated customer profiles through deep clustering, autoencoders, and industry knowledge enhancement. These profiles not only include business needs and purchasing preferences but also risk sensitivity, decision-making chains, and industry sentiment. The system automatically strengthens authentication information during recommendations and uses a self-attention mechanism to filter key features, generating personalized content, recommended scripts, and contact persons for customers. Recommendations go beyond content and are refined to execution strategies, greatly improving customer experience and sales conversion rates.

[0054] 3. By collecting positive and negative customer feedback and incorporating it into a reward mechanism, this invention enables the system to continuously optimize and self-evolve the recommendation model. When facing new customers or new markets, the system uses feature clustering and nearest neighbor filtering based on similar industries, and applies transfer learning technology to quickly construct initial profiles and recommendation strategies, effectively solving the cold start problem. Prior industry knowledge can be directly transferred to similar customer groups, achieving a dual improvement in recommendation accuracy and adaptation efficiency during the cold start phase, providing enterprises with stronger market competitiveness. Attached Figure Description

[0055] Figure 1 This is a flowchart of the B2B customer deep insight and precise outreach method based on a multimodal large model, as described in this invention. Detailed Implementation

[0056] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for in-depth B2B customer insight and precise outreach based on a multimodal large model. The specific steps of this method are as follows:

[0058] S1, Multimodal Data Fusion and Deep Analysis: Collects customer text data, visual data, audio data and behavioral data. Text data includes customer website, news, social media updates and email correspondence. Visual data includes product images and event videos. Audio data includes meeting or telephone recordings. Behavioral data includes purchase records, sales cycles and competitor comparison records.

[0059] Example of multimodal data fusion and deep analysis: A new energy equipment manufacturer inputs customer website introductions, industry news, social media posts, email correspondence, product images and exhibition videos into the system; the system uses a multimodal large model to encode and standardize information from different sources such as text, images, and audio, enabling the system to identify the customer's long-term focus on hydrogen electrolyzers and energy storage technologies, providing a comprehensive data foundation for subsequent analysis.

[0060] S2, Customer Multi-Dimensional Preference Modeling: Combining deep clustering, autoencoders, and industry knowledge enhancement, it generates customer profiles that include business needs, decision preferences, cultural traits, risk sensitivity, and other dimensions.

[0061] Example of multi-dimensional customer preference modeling: In cluster analysis, the system found that a multinational manufacturing company’s procurement priorities not only include capacity improvement, but also focus on improving market access through certification; combined with industry sentiment, technical standards and historical interactions, the system automatically generates a multi-dimensional profile that includes business needs, technical preferences, cultural communication habits (such as a greater emphasis on formal honorifics) and risk sensitivity, and marks it as a high-potential customer.

[0062] S3, Intent and Sentiment Analysis Engine: Based on natural language processing technology, it analyzes customer communication records, social interactions and feedback content to identify customers' implicit needs, potential intentions and emotional tendencies;

[0063] Example of an intent and sentiment analysis engine: The system analyzes the communication records between the sales team and an auto parts supplier, identifying the underlying cost-cutting pressures and concerns about delivery risks beneath the surface politeness; the system combines relevant comments on social media and historical email content to automatically generate structured tags such as "cost reduction needs" and "supply chain stability concerns", providing accurate input for the recommendation module.

[0064] S4, Personalized Marketing Strategy Generation; Employing a Transformer-based recommendation engine, combining customer profiles, product features, and emotional intent, it generates personalized recommendation content, optimal reach timing, and communication channels based on a self-attention mechanism, and outputs the reasons for the recommendation and suggestions for follow-up actions;

[0065] Personalized marketing strategy generation example: Based on the customer profile and intent tags, the system generates recommended content for the customer, such as a product mix that emphasizes the selling points of "low energy consumption and low cost", recommends the best time to communicate as a weekday morning, and suggests reaching out via a combination of email and phone; the recommendation module also generates explanatory information to help sales personnel understand the customer needs priorities behind the recommendations.

[0066] S5, Recommendation Execution and Script Optimization: Apply personalized recommendations to the sales process, including generating recommended scripts, recommended contacts, and recommended action plans to support actual interactions between sales personnel or the system;

[0067] Recommended implementation and script optimization example: The system generates a tiered script plan for the sales team: When technical consultants connect, they emphasize product performance and certification details; when business personnel connect, they highlight cost-effectiveness and industry position, and provide operational guidance, such as arranging small-batch trials, sending industry case materials, and setting follow-up reminders after two weeks, to help the sales team promote customer conversion in a more targeted manner.

[0068] S6, Feedback Collection and Reward Function-Driven Optimization: Collect positive feedback signals, negative feedback signals, and social interaction signals from customers, and use feedback as a reward function to dynamically optimize customer profiles and recommendation models;

[0069] Feedback collection and reward function-driven optimization example: After the recommended content is delivered, the system detects that customers clicked on the product introduction link in the email and participated in the online promotion meeting, but did not reply to the price discussion; the system uses these positive, negative and neutral feedback as optimization signals, adjusts the "price sensitivity" tag in the customer profile, and optimizes the next round of recommendation strategy, such as increasing the recommendation direction of discounts or flexible pricing.

[0070] S7, Cold Start Solution and Transfer Learning Application: Based on similar industries or customer groups, it uses feature clustering and K-nearest neighbor screening to quickly build initial customer profiles and recommendation strategies for new customers or new markets using transfer learning technology.

[0071] Cold start solution and transfer learning application example: When the system first contacts an overseas energy company, it identifies a domestic customer group similar to the company through industry feature clustering and nearest neighbor filtering (both are medium-sized, focus on hydrogen energy, and pay attention to certification); the system transfers the strategies and recommendation logic accumulated by these customer groups to quickly generate an initial marketing plan for new customers that includes key selling points, recommendation scripts, and expected pain points, so that marketing can be launched efficiently even under information scarcity.

[0072] In S1, multimodal data fusion and deep analysis refers to the system collecting multi-source customer data, including official website information, industry news, social media activity, email records, product images, event videos, and telephone or meeting recordings. This text, visual, and audio data undergoes cleaning and standardization to ensure that multidimensional customer characteristics are expressed within a unified data framework, laying a comprehensive foundation for subsequent insightful analysis. After integrating customer purchase records, sales cycles, and competitor comparisons, the system utilizes cross-modal embedding technology to achieve deep correlation analysis between different data. For example, it compares text descriptions with product images and behavioral characteristics to identify potential preferences and needs. This mechanism transcends the limitations of a single modality, significantly improving the accuracy of customer understanding.

[0073] The principle of cross-modal embedding technology can be expressed by the following formula:

[0074]

[0075] In the formula, q = query vector (e.g., text encoding), d = document or image vector, and f q (q)·f d (d) = encoding function (such as BERT, CLIP output), S(q,d) = cross-modal similarity score.

[0076] In S2, customer multi-dimensional preference modeling refers to the system first using deep clustering analysis to categorize multi-dimensional data such as customer business needs, purchasing history, and industry characteristics, grouping similar features into the same cluster. During clustering, the system automatically summarizes the common characteristics of each customer category, such as industry focus, purchasing rhythm, or cooperation mode. This step is not only based on data distribution but also incorporates industry knowledge to identify key features with strong business relevance, ensuring that the profile has practical decision-making significance. Based on clustering, the system further uses an autoencoder to extract latent features, such as cultural communication preferences, technical certification focus, and risk sensitivity. Through industry knowledge enhancement, industry standards and typical needs are introduced into feature modeling. This process helps the system learn compression and reconstruction rules from complex and messy data, achieving fine-grained customer understanding. The resulting multi-dimensional customer profile provides rich support for subsequent intent analysis and recommendation strategies.

[0077] The S3 intent and sentiment analysis engine refers to the system's use of natural language processing to analyze customer emails, meeting minutes, and social media interactions to identify implicit needs and core concerns behind surface-level statements. For example, a customer's indirect mention of cost optimization is categorized as a cost reduction request. The analysis process is not limited to single-round communication but can capture the evolution of needs across multiple interactions, building a deep understanding of customer needs. Building on sentiment and intent analysis, the system further identifies customer emotional attitudes, such as positive responses, hesitation, or rejection tendencies. The system summarizes the identification results into structured tags that can be used for subsequent recommendations, such as "expansion needs," "certification concerns," and "supply chain risks." These tags provide clear input for personalized recommendations, supporting the generation of more targeted strategies.

[0078] In S4, personalized marketing strategy generation refers to the system matching business needs, industry characteristics, and emotional intent extracted from customer profiles with product functions and service advantages based on the Transformer recommendation engine. The recommendation engine dynamically captures key influencing factors from numerous customer characteristics through a self-attention mechanism, enabling the system to not only focus on explicit data but also to uncover important implicit clues that affect recommendations, generating highly personalized recommendation content.

[0079] The principle of self-attention mechanism can be expressed by the following formula:

[0080]

[0081] In the formula, Q = query matrix, K = key matrix, V = value matrix, d k=Key Dimension; While outputting personalized content, the recommendation system intelligently determines the best time and communication channel to reach out, such as choosing email or telephone invitations, by combining the customer's current status, market dynamics, and historical interaction records. The system also provides explainable reasons for the recommendation and suggestions for follow-up actions, such as recommending what kind of communication script or arranging a trial, to help sales or operations personnel efficiently implement the recommendation plan.

[0082] In S5, the recommendation execution and script optimization refer to the system automatically matching the most suitable sales script and contact person after generating personalized recommendation content. For example, for technology-driven customers, it recommends professional scripts emphasizing product performance and certifications; for decision-oriented customers, it recommends business scripts highlighting business benefits and cooperation prospects. Simultaneously, the system recommends the optimal contact person, such as a technical consultant or business manager, to ensure efficient and accurate communication. Recommendations are not limited to content delivery but also include the design of follow-up action plans, such as arranging product trials, sending case materials, or customized quotes. The system combines customers' historical behavior, current status, and industry practices to provide clear execution guidelines for sales or operations teams, avoiding vague follow-ups. By translating recommended content into concrete actions, the system helps teams improve conversion efficiency and customer experience.

[0083] In S6, feedback collection and reward function-driven optimization refer to the system's real-time collection of multi-dimensional customer feedback, including positive signals such as clicks, trials, and orders; negative signals such as unsubscribing and explicit rejection; and interactive signals such as likes, comments, and reposts on social media platforms. By analyzing this feedback, the system not only understands the acceptance level of recommended content but also attributes changes in customer preferences, providing traceable data support based on real interactions for subsequent recommendation strategies. Combining the attribution analysis results, the system uses a reinforcement learning reward mechanism to dynamically adjust customer profiles and recommendation models. Positive feedback enhances the recommendation direction, negative feedback prompts strategy correction, and social interaction feedback provides external validation. This mechanism enables the model to be continuously optimized in practice, not only improving recommendation accuracy but also adapting to real-time changes in customer behavior and preferences, ensuring maximum long-term marketing effectiveness.

[0084] The formula for the reward mechanism in reinforcement learning is as follows:

[0085]

[0086] In the formula, Q(s,a) = the value of choosing action a in state s, r = reward, α = learning rate, and γ = discount factor.

[0087] The S7 cold start solution and transfer learning application refer to the system's approach when facing new customers or new markets. First, based on historical data and known customer groups, the system performs industry characteristic clustering analysis to extract key tags such as business scale, purchasing characteristics, and industry needs. Through this clustering, the system can quickly identify feature combinations highly similar to new customers, providing a reliable reference for subsequent knowledge transfer and ensuring the accuracy and industry matching of the initial profile. Based on the clustering results, the system uses a nearest neighbor filtering method, based on multi-dimensional similarity between customers in terms of behavior, preferences, and feedback, to accurately select the reference objects closest to the new customers. Then, using transfer learning, the knowledge and recommendation strategies accumulated from these similar customers are transferred to the new customer scenario, enabling the system to provide effective and customized recommendation solutions even during the cold start phase, shortening the adaptation time.

[0088] The formula for expressing the principle of similarity filtering is:

[0089]

[0090] In the formula, sim(u,v) = the similarity between users u and v, I uv =A collection of items where u and v interact together. = Average rating of user u.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0092] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for in-depth B2B customer insight and precise outreach based on a multimodal large model, characterized by: The specific steps of this B2B customer deep insight and precise outreach method based on a multimodal large model are as follows: S1, Multimodal Data Fusion and Deep Analysis: Collect customer text data, visual data, audio data and behavioral data. The text data includes customer website, news, social media updates and email correspondence; the visual data includes product images and event videos; the audio data includes meeting or telephone recordings; and the behavioral data includes purchase records, sales cycles and competitor comparison records. S2, Customer Multi-Dimensional Preference Modeling: Combining deep clustering, autoencoders, and industry knowledge enhancement, it generates customer profiles that include business needs, decision preferences, cultural traits, and risk sensitivity. S3, Intent and Sentiment Analysis Engine: Based on natural language processing technology, it analyzes customer communication records, social interactions and feedback content to identify customers' implicit needs, potential intentions and emotional tendencies; S4, Personalized Marketing Strategy Generation; Employing a Transformer-based recommendation engine, combining customer profiles, product features, and emotional intent, it generates personalized recommendation content, optimal reach timing, and communication channels based on a self-attention mechanism, and outputs the reasons for the recommendation and suggestions for follow-up actions; S5, Recommendation Execution and Script Optimization: Apply the personalized recommendation content to the sales docking process, including generating recommended scripts, recommended contacts and recommended action plans to support the actual docking of sales personnel or the system; S6, Feedback Collection and Reward Function-Driven Optimization: Collect positive feedback signals, negative feedback signals, and social interaction signals from customers, and use the feedback as a reward function to dynamically optimize customer profiles and recommendation models; S7, Cold Start Solution and Transfer Learning Application: Based on similar industries or customer groups, it uses feature clustering and K-nearest neighbor screening to quickly build initial customer profiles and recommendation strategies for new customers or new markets using transfer learning technology.

2. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps of multimodal data fusion and deep analysis in S1 are as follows: S11, Multimodal Data Full-Domain Collection and Processing: The system collects multi-source customer data, including official website information, industry news, social media activity, email records, product images, event videos, and telephone or meeting recordings; these text, visual, and audio data are cleaned and standardized to ensure that the multidimensional characteristics of customers are expressed in a unified data framework, laying a comprehensive foundation for subsequent insight analysis; S12, Cross-modal understanding and correlation analysis: After integrating customer purchase records, sales cycles, and competitor comparisons, the system uses cross-modal embedding technology to achieve deep correlation analysis between different data. The principle of cross-modal embedding technology can be expressed by the following formula: In the formula, q = query vector (e.g., text encoding), d = document or image vector, and f q (q)·f d (d) = encoding function (such as BERT, CLIP output), S(q,d) = cross-modal similarity score.

3. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps for modeling customer multi-dimensional preferences in S2 are as follows: S21, Multi-dimensional customer profile construction and feature aggregation: The system first classifies multi-dimensional data such as customer business needs, procurement history, and industry characteristics through deep clustering analysis, and groups similar features into the same cluster; During the clustering process, the system automatically summarizes the common characteristics of each customer category; S22, Autoencoder Learning and Industry Knowledge Enhancement: Based on clustering, the system further uses an autoencoder to extract latent features such as cultural communication preferences, technical certification concerns, and risk sensitivity. Through industry knowledge enhancement, industry standards and typical requirements are introduced into feature modeling. This process helps the system learn compression and reconstruction patterns from complex and messy data, enabling fine-grained customer understanding.

4. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps of the intent and sentiment analysis engine in S3 are as follows: S31, Multi-round Communication Content Analysis and Needs Mining: The system analyzes customer emails, meeting minutes, and social media interactions through natural language processing to identify implicit needs and core concerns behind surface-level statements; the analysis process is not limited to single-round communication, but can also capture the evolution of needs in multiple rounds of interaction to build a deep understanding of customer needs; S32, Intent Recognition and Structured Tag Generation: Based on sentiment and intent analysis, the system further identifies the customer's emotional attitude. The system summarizes the recognition results into structured tags that can be used for subsequent recommendations. These tags provide clear input for personalized recommendations and support the generation of more targeted strategies.

5. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps for generating personalized marketing strategies in S4 are as follows: S41, Personalized Recommendation Generation and Element Matching: The system is based on the Transformer recommendation engine, which integrates business needs, industry characteristics, and emotional intent extracted from customer profiles and matches them with product functions and service advantages. The recommendation engine uses a self-attention mechanism to dynamically capture key influencing factors from numerous customer characteristics, enabling the system to not only focus on explicit data but also to uncover important implicit clues that affect recommendations, generating highly personalized recommendation content. The principle of self-attention mechanism can be expressed by the following formula: In the formula, Q = query matrix, K = key matrix, V = value matrix, d k =Key dimension; S42, Recommended Output Explanation and Action Suggestions: While outputting personalized content, the recommendation system intelligently determines the best time to reach out and the best communication channel by combining the customer's current status, market dynamics and historical interaction records. The system will also provide explanatory reasons for the recommendation and subsequent action suggestions.

6. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps for execution and script optimization recommended in S5 are as follows: S51, Recommended Sales Script and Contact Person Matching: After generating personalized recommendations, the system automatically matches the most suitable sales script and contact person; for decision-oriented clients, it recommends business scripts that highlight business benefits and cooperation prospects; at the same time, the system recommends the optimal contact person to ensure efficient and accurate communication; S52, Recommended Action Plans and Implementation: Recommendations are not limited to content delivery, but also include the design of follow-up action plans; the system combines customers' historical behavior, current status and industry practices to provide clear execution guidelines for sales or operations teams, avoiding general follow-up; and implements recommended content into specific actions.

7. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps of feedback collection and reward function-driven optimization in S6 are as follows: S61, Multidimensional Feedback Signal Acquisition and Attribution Analysis: The system collects multidimensional customer feedback in real time, including positive signals such as clicks, trials, and orders, negative signals such as unsubscribing and explicit rejection, and interactive signals such as likes, comments, and reposts on social media platforms. By analyzing this feedback, the system can not only understand the acceptance level of recommended content, but also perform attribution analysis on changes in customer preferences, providing traceable data support based on real interactions for subsequent recommendation strategies. S62, Reward-driven dynamic optimization: Combining attribution analysis results, the system dynamically adjusts customer profiles and recommendation models using a reinforcement learning reward mechanism; Positive feedback enhances the recommendation direction, negative feedback prompts strategy correction, and social interaction feedback provides external validation references; This mechanism enables the model to be continuously optimized in practice, not only improving recommendation accuracy but also adapting to real-time changes in customer behavior and preferences, ensuring maximum long-term marketing effectiveness. The formula for the reward mechanism in reinforcement learning is as follows: In the formula, Q(s,a) = the value of choosing action a in state s, r = reward, α = learning rate, and γ = discount factor.

8. The method for deep B2B customer insight and precise outreach based on a multimodal large model as described in claim 1, characterized in that: The specific steps for the cold start solution and transfer learning application in S7 are as follows: S71, Similar Customer Group Feature Clustering Analysis: When facing new customers or new markets, the system first performs industry feature clustering analysis based on known customer groups in historical data, extracting key tags such as business scale, purchasing characteristics, and industry needs. Through this clustering, the system can quickly identify feature combinations that are highly similar to new customers, providing a reliable reference for subsequent knowledge transfer and ensuring the accuracy and industry matching of the initial profile. S72, Similarity Filtering and Transfer Learning Application: Based on the clustering results, the system adopts the nearest neighbor filtering method, which accurately selects the reference object that is closest to the new customer based on the multi-dimensional similarity of customers' behavior, preferences and feedback. Subsequently, by using transfer learning, the knowledge and recommendation strategies accumulated from these similar customers are transferred to the new customer scenario, so that the system can provide effective and customized recommendation solutions even in the cold start phase, shortening the adaptation time. The formula for expressing the principle of similarity filtering is: In the formula, sim(u,v) = the similarity between users u and v, I uv =A collection of items where u and v interact together.

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