AI intelligent customer acquisition and lead analysis method for foreign trade enterprises

Abstract

The application discloses an AI intelligent customer acquisition and clue analysis method for foreign trade enterprises, aiming to solve the demand misjudgment caused by cultural cognitive bias and the delivery commitment failure problem caused by the disconnection of supply chain information; the method first synchronously collects customer multi-modal interaction information and internal and external supply chain data, analyzes customer cultural psychological characteristics through a pre-training model, and constructs a dynamic supply chain elasticity graph to quantify future performance; then, the customer cultural semantic features and enterprise performance features are matched and risk assessed in real time, and finally personalized communication tactics, flexible delivery schemes and expected management strategies with cultural adaptability and reliable performance are automatically generated, so as to realize accurate evaluation of overseas customer value, improve customer trust, order conversion rate and long-term cooperation stability.

Description

Technical Field

[0001] This invention relates to the field of intelligent analysis technology, and in particular to an AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises. Background Technology

[0002] Currently, foreign trade enterprises generally rely on Customer Relationship Management (CRM) systems and data analytics tools to identify and follow up with potential customers in their global marketing and customer acquisition processes. However, existing technological solutions mainly focus on analyzing customers' explicit behaviors and structured needs, such as tracking website visits, inquiry keywords, and basic company information. These methods lack the ability to finely decode the deep cultural context, decision-making styles, and true intentions behind communication. Because they fail to incorporate communication habits, expression methods, and risk preferences from different cultural backgrounds into their analytical models, companies are prone to misjudging customers' purchasing intentions, urgency of needs, and value focuses, leading to misallocation of sales resources, missed high-value business opportunities, or over-investment in immature leads.

[0003] On the other hand, when fulfilling international orders, companies' supply chain management (SCM) systems typically operate independently, focusing on the execution and monitoring of internal logistics, inventory, and production. While these systems provide historical and current status data, they lack the ability to continuously and quantitatively predict the reliability of logistics routes, cost fluctuations, and external risks (such as port congestion and exchange rate changes) over the next few weeks. A more significant problem is the complete disconnect between the dynamic capabilities of the supply chain and communication and commitment processes with front-end customers. Sales teams cannot obtain real-time information and make commitments based on future fulfillment risks and flexibility during negotiations, often resulting in delivery dates or quotations that are difficult to achieve in subsequent execution. This leads to customer complaints, order cancellations, and serious damage to business reputation and partnerships. Summary of the Invention

[0004] This application provides AI-powered customer acquisition and lead analysis methods for foreign trade enterprises, which can significantly improve target customer trust, order conversion rates, and long-term cooperation stability in a complex and ever-changing global trade environment, thereby building a sustainable competitive advantage.

[0005] This application provides AI-powered intelligent customer acquisition and lead analysis methods for foreign trade enterprises, including: S1: Collect multimodal interaction information related to the target customers of foreign trade enterprises, extract unstructured text for preprocessing, and then feed the preprocessed unstructured text into a pre-trained cultural adaptation semantic understanding model for cultural semantic parsing. S2 acquires internal and external data streams through APIs and IoT devices, calculates the daily fulfillment reliability index and cost fluctuation range for each major logistics route and each major product category in the coming weeks, and constructs a supply chain resilience map. S3 extracts the cultural and psychological characteristics of target customers and the dynamic performance characteristics of enterprises through cultural semantic analysis and supply chain resilience mapping engine. S4 dynamically matches cultural semantic features with performance capability features and conducts risk assessments. Based on the matching results, it automatically generates culturally adapted communication scripts, flexible performance commitment plans, and proactive expectation management mechanisms.

[0006] Preferably, the construction of the supply chain resilience map specifically includes: training a fulfillment reliability index calculation model, using historical route data to learn the relationship between various features and on-time arrival, and predicting the fulfillment reliability index for each future time period and route based on real-time input; training a cost fluctuation range calculation model, analyzing the statistical relationship between historical costs and key variables, and simulating the cost fluctuation range for each future time period based on real-time input; and storing the predicted fulfillment reliability index and cost fluctuation range in association by time, route, and product category to form a dynamic supply chain capability view that supports multi-dimensional queries.

[0007] Preferably, the extraction of target customer cultural and psychological characteristics and enterprise dynamic fulfillment capability characteristics includes: through a first processing flow, performing cultural semantic analysis on the target customer's interactive text, identifying and quantifying their business intentions, urgency of needs, decision-making style, and risk preferences, and generating a cultural and psychological characteristic vector; through a second processing flow, based on the product, destination, and time window involved in the current interaction, retrieving multiple feasible fulfillment paths matching in the supply chain resilience map, and extracting the quantitative fulfillment characteristics of each path to generate a set of fulfillment capability characteristic vectors.

[0008] Preferably, the automatic generation of culturally adapted communication scripts, flexible performance commitment schemes, and proactive management mechanisms based on matching results specifically includes: S41, automatically identifying and labeling sender roles based on cultural semantic features, analyzing communication metadata to infer internal reporting lines and information flow paths, and constructing a dynamic graph of target customer decision-making relationships after forming role relationship assumptions; S42, establishing a time series classification model based on the dynamic graph of target customer decision-making relationships and the target customer's behavioral history, and outputting a probability curve and time series plan for the target customer to be in a high decision-making tendency state on future days; S43, combining the probability curve and time series plan to generate intelligent outreach strategies based on time zone and cultural awareness; S44, generating differentiated collaborative follow-up strategies for different decision-making participants based on the multi-role structure in the dynamic graph of target customer decision-making relationships.

[0009] Preferably, the step of outputting the probability curve and time series planning of the target customer being in a high decision-making tendency state for each day specifically includes: collecting multi-dimensional time series features from the dynamic graph of the target customer's decision-making relationship, its historical behavior patterns, and the external environment, and integrating them into a fused time series feature sequence; inputting the fused time series feature sequence into a pre-trained time series classification model for processing; and outputting the probability curve formed by the probability values ​​of the target customer entering the high decision-making tendency window for each day, as well as the classification label of the current stage of the decision-making process, as the basis for time series planning.

[0010] Preferably, the intelligent outreach strategy includes: automatically planning the timing, content, and form of outreach based on decision probability curves, stage judgments, and the spatiotemporal rules of the target customer's location.

[0011] Preferably, the output of the probability curve and time-series planning of the target customers in a high decision-making tendency state for each day includes: S421, when the business opportunity enters the deep stage, dynamic discovery and cross-organizational graph construction are carried out across the organization decision network, extracting organizational, individual and project entities and identifying the business relationships between them; S422, quantitatively assessing the influence of key nodes in the cross-organizational graph construction to identify high-influence nodes, analyzing the public information and historical behavior of each node to infer its core interests, and finally generating a structured interest profile for each node; S423, intelligently generating two types of collaborative action plans based on the interest profile: indirect empowerment or direct alliance, and decomposing the generated collaborative action plan into internal execution task cards with different roles and time limits.

[0012] Preferably, the cross-organizational graph is: a dynamic graph that takes the target customer as the core, automatically identifies and associates external organizations and individual entities that have influence in a specific business opportunity, and constructs a graph representing their relationship.

[0013] Preferably, the breakdown into role-based and time-limited internal execution task cards includes: automatically breaking down each generated collaborative strategy into a series of specific, atomic internal execution tasks; clearly specifying the execution role, a detailed description of the task content, a suggested or required completion deadline, the input materials required for task execution, and the expected output results for each task card; and automatically pushing these structured task cards to the team's internal collaborative work platform and associating them with the corresponding sales opportunity projects to form a trackable and collaborative action plan.

[0014] Preferably, the step of breaking down the generated collaborative action plan into internal execution task cards with different roles and time limits further includes: establishing a propagation dynamics model and conducting Monte Carlo simulation based on cross-organizational network graphs and high-influence nodes to evaluate and screen the expected effects of propagation strategies for different target customers; generating a phased and node-based dynamic propagation path blueprint based on the simulation results, and automatically generating personalized task execution plans for each contact point in the path; converting the propagation blueprint into specific execution tasks and monitoring actual propagation signals, and dynamically triggering strategy replanning and content adjustment of personalized task execution plans by comparing the deviation between the blueprint and reality.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: By simultaneously analyzing the true intentions of target customers within their cultural context and assessing their dynamic fulfillment capabilities in real time, this system intelligently coordinates and matches these two aspects for decision-making. This effectively addresses two core pain points of traditional methods: misjudgment of demand due to cultural cognitive biases, and failure of delivery commitments due to supply chain information gaps. It enables accurate and dynamic assessment of the true commercial value of overseas target customers and automatically generates personalized interaction and delivery strategies that align with both the cultural and psychological expectations of target customers and are practically reliable. This significantly enhances customer trust, order conversion rates, and long-term cooperation stability in the complex and ever-changing global trade environment, building a sustainable competitive advantage.

[0016] By automatically constructing target customer decision-making relationship maps and predictive decision windows, it can accurately understand the internal decision-making processes and timing of target customers, achieving synchronization between outreach and the target customer's decision-making rhythm. Its generated timezone and culture-aware outreach strategies and multi-role collaborative follow-up plans ensure the accuracy and consistency of communication. This significantly improves the ability to identify and seize high-value business opportunities, optimizes sales resource allocation, and thus greatly improves sales follow-up efficiency, lead conversion rate, and the certainty of order completion.

[0017] It can transcend the perspective of a single target customer, automatically constructing and analyzing cross-organizational relationship networks influencing transaction decisions, and identifying key influencers and their core interests. Based on this, it can intelligently generate precise indirect empowerment or direct alliance strategies, and decompose complex strategies into executable and traceable collaborative tasks. This enables enterprises to systematically influence multi-party decisions in complex projects, significantly improving insight, strategy accuracy, and team collaboration efficiency in major transactions involving multiple stakeholders, thereby effectively increasing the win rate.

[0018] By simulating the dynamics of information propagation in networks, the impact path can be predicted and optimized in advance, thereby generating personalized communication campaign blueprints and content for each stage and node. This enables systematic and adaptive psychological influence and consensus building in complex projects involving multiple stakeholders, significantly improving the accuracy and efficiency of strategic communication, and fundamentally enhancing the core capability to guide decision-making and win competition in major transactions. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises, as described in an embodiment of the present invention. Detailed Implementation

[0020] To facilitate understanding of the present invention, a more complete description of this application will be given below with reference to the accompanying drawings, which illustrate preferred embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to enable a more thorough and complete understanding of the disclosure of the present invention.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terminology used herein includes any and all combinations of one or more of the associated listed items.

[0022] Example 1: Figure 1 This is a flowchart illustrating the AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises, as described in an embodiment of the present invention.

[0023] like Figure 1 As shown, the AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises includes the following steps: S1 collects multimodal interaction information related to the target customers of foreign trade enterprises, extracts unstructured text for preprocessing, and then feeds the preprocessed unstructured text into a pre-trained cultural adaptation semantic understanding model for cultural semantic parsing.

[0024] The preprocessing process involves cleaning, segmenting, and performing language recognition on unstructured text, converting audio information to text, and translating all non-English text using a high-quality machine translation engine while retaining a copy of the original text.

[0025] Specifically, under the premise of obtaining legal authorization and complying with data compliance policies, raw interaction data related to the target customer is collected asynchronously or in real time from multiple channels. These channels include, but are not limited to: business communication channels, historical inquiry emails from target customer relationship management tools, chat logs from instant messaging software, and automatically transcribed text from video conferences; public digital footprints, including homepage information, updates, articles, and comments published by the target customer company on professional social networking platforms; discussion content participated in on industry forums or communities; press releases, product introductions, and investor relations materials from the company's official website; and a background knowledge base, which is integrated into or incorporated into the system using the method described in this application, covering theories such as Hofstede's cultural dimensions, in order to obtain a general cultural orientation index of the target customer's country / region.

[0026] High-quality text sequences of preprocessed raw interaction data are fed into a pre-trained culturally adapted semantic understanding model for in-depth analysis. This process includes the following sub-steps: Context Encoding: The model first understands the complete context and conversational flow of the text, grasping the overall discussion topic and background. Cultural Feature Annotation: Based on cross-cultural corpora learned during pre-training, the model performs multi-level semantic annotation on the text: Intent and Urgency Identification: Identifies the core demands expressed by the target customer and calibrates their true urgency. For example, combining context and regional culture, the desire for expedited progress is categorized as high urgency (expected feedback within one week) or medium urgency (approximately two weeks). Sentiment and Attitude Analysis: Determines the emotional tendency expressed in the text and the intensity of attitude towards specific issues. Sociopragmatism and Style Analysis: Analyzes whether the language style is formal or casual, the communication method is direct or implicit, and the expression of decision-making basis emphasizes data logic or interpersonal relationships, thereby inferring their decision-making style label. Risk Preference Hint Extraction: Analyzes the target customer's potential risk tolerance from their attention to uncertainty, constraints, and guarantee clauses, and labels them as risk-averse, risk-neutral, etc.

[0027] The above analysis results are synthesized and quantified to generate a structured, machine-readable cultural semantic feature vector for the current interactive session or target customer entity. This vector is typically a multi-dimensional array, with each dimension corresponding to a parsed feature and its confidence score. The feature vector generated in this analysis is then merged and updated with the target customer's existing historical feature vectors to form a dynamically evolving cultural preference profile.

[0028] S2 acquires internal and external data streams through APIs and IoT devices, calculates the daily fulfillment reliability index and cost fluctuation range for each major logistics route and each major product category in the coming weeks, and constructs a supply chain resilience map.

[0029] The internal and external data streams are continuously acquired from internal enterprise software and external data services. Internal data includes real-time inventory quantities and locations extracted from enterprise resource planning software, production progress and yield from manufacturing execution systems, and warehouse operational efficiency indicators exported from warehouse management systems. External data is obtained through subscribed commercial data interfaces, such as real-time global port vessel queuing data and berthing forecasts provided by international logistics platforms, flight schedules and cabin availability published by airlines, updated country or regional trade risk briefings from credit rating agencies, and real-time exchange rates and fuel price indices provided by financial market data services. Meanwhile, IoT devices installed in containers, cargo ships, and warehouses continuously transmit sensor data such as GPS coordinates, temperature, humidity, vibration, and hatch opening / closing status.

[0030] Specifically, all internal and external data streams are based on a unified time base and geocoding, and are associated and grouped according to logistics routes and product categories.

[0031] The fulfillment reliability index calculation model is trained by collecting detailed historical data from each major route over the past few years, including daily departure and arrival dates, number of delay days, and corresponding features such as weather conditions, port congestion index, major holidays, and known strikes or customs strikes. Using this historical data, a machine learning model is trained to learn the complex relationship between these features and on-time arrival. Once trained, the model is ready for operation. Each day, it takes the latest internal and external data streams as input to predict a fulfillment reliability index for each future day, each route, and each combination of goods. This index is a percentage probability representing the likelihood that goods scheduled to depart via this route today will be delivered within the promised time window.

[0032] The training process calculates the cost fluctuation range by aggregating historical transaction data, including all cost items such as base freight, fuel surcharges, exchange rates, peak season surcharges, demurrage fees, and additional storage fees incurred due to delays. It analyzes the statistical relationships between these cost items and observable variables (such as crude oil price indices, exchange rates, route reliability indices, and cargo volume indices for specific months) to establish regression models or more complex ensemble models. After training, the model receives real-time data during operation, such as current oil prices, exchange rates, and future daily reliability indices calculated by the reliability model. The model runs multiple simulations, considering the random fluctuations of key variables, and ultimately outputs the cost fluctuation range for each day over the next few weeks, typically a range consisting of the lowest and highest possible costs.

[0033] The reliability index and cost range for each future day, each route, and each product category generated daily by the two models described above are written into a dedicated graphical or time-series database. This allows for efficient querying of all possible routes from location A to location B, with a reliability higher than 90% and a cost lower than X dollars, shipped between July 10th and July 20th. The constantly updated panoramic view described above constitutes the supply chain resilience map.

[0034] S3 extracts the cultural and psychological characteristics of target customers and the dynamic performance capabilities of enterprises through cultural semantic analysis and supply chain resilience mapping engine.

[0035] Specifically, this step is triggered upon receiving communication text from a target customer and a real-time business request, initiating two parallel processing threads. In the first thread, the pre-processed target customer interaction text is fed into a pre-trained cultural semantic parsing model. The text is first deeply encoded to understand the complete context, followed by multi-layered semantic analysis. This includes identifying core business intentions and calibrating their true strength, determining the urgency level of the need within a specific cultural context, analyzing writing style to infer whether it belongs to a relationship-oriented or efficiency-driven decision-making style, and extracting implicit attitudes towards risk and uncertainty from the wording. All analysis results are quantified and labeled, ultimately generating a structured target customer cultural psychological characteristic vector, which encapsulates dimensions such as intention, urgency, decision-making style, risk preference, and their confidence levels.

[0036] Simultaneously, a second thread is activated. The supply chain resilience graph engine performs real-time retrieval and calculations within the dynamically updated global supply chain graph based on information such as product categories, potential destinations, and expected time windows involved in the current target customer inquiry. It doesn't provide single data points, but rather extracts a set of fulfillment capability feature vectors from the graph. This set of vectors contains multiple feasible fulfillment path options for the inquiry, each path accompanied by quantified feature values, such as the estimated delivery time and confidence interval for shipments from different warehouses, cost fluctuation range, route reliability score based on a predictive model, and a list of available alternative options.

[0037] The outputs of these two parallel processing threads represent the cultural and psychological feature vectors representing the subjective expectations of the target customers and the performance capability feature vectors representing the objective capabilities of the enterprise.

[0038] S4 dynamically matches cultural semantic features with performance capability features and conducts risk assessments. Based on the matching results, it automatically generates culturally adapted communication scripts, flexible performance commitment plans, and proactive expectation management mechanisms.

[0039] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: By simultaneously analyzing the true intentions of target customers within their cultural context and assessing their dynamic fulfillment capabilities in real time, this system intelligently coordinates and matches these two aspects for decision-making. This effectively addresses two core pain points of traditional methods: misjudgment of demand due to cultural cognitive biases, and failure of delivery commitments due to supply chain information gaps. It enables accurate and dynamic assessment of the true commercial value of overseas target customers and automatically generates personalized interaction and delivery strategies that align with both the cultural and psychological expectations of target customers and are practically reliable. This significantly enhances customer trust, order conversion rates, and long-term cooperation stability in the complex and ever-changing global trade environment, building a sustainable competitive advantage.

[0040] Example 2: In Example 1, the target customer interaction strategy package generated in step S4 is a static optimal solution based on a single matching result. However, the decision-making process within a target customer is a dynamic, multi-stage, and sequential process involving multiple roles. A uniform strategy cannot adapt to the significant differences in the decision-making pace of different target customers, nor is it proactively synchronized with the key nodes of the target customer's decision-making process. This leads to missed decision windows in outreach timing, communication content lagging behind the decision-making stage, and inefficient internal collaboration when facing complex decision chains, thus affecting conversion efficiency. To achieve dynamic perception and proactive collaboration of the target customer's decision-making process, and to enable the outreach strategy to adaptively match changes in the target customer's decision-making state, further optimization and improvement are needed.

[0041] In some embodiments, culturally adapted communication scripts, flexible performance commitment schemes, and proactive management mechanisms are automatically generated based on the matching results. Step S4 further includes: S41 automatically identifies and labels the sender's role based on cultural semantic features, analyzes communication metadata to infer internal reporting lines and information flow paths, and constructs a dynamic graph of target customer decision-making relationships after forming role relationship assumptions.

[0042] Among them, automatic identification and labeling of sender roles is based on clustering and categorizing identity information in interaction records; analysis of communication metadata is based on inferring organizational relationships by observing communication patterns; and construction of a dynamic graph of target customer decision-making relationships is based on dynamically storing and updating roles and relationships in a graph structure.

[0043] Specifically, the system automatically scans all historical and real-time interaction records related to target customers, extracting sender and receiver identity information from each communication message, including email addresses, names, and job titles in email signatures. This identity information is then compared with publicly available information on professional social networking platforms to supplement and verify job information. Through cluster analysis, based on email domains, department name keywords, job titles, and other information, participants are automatically categorized and labeled with preset role tags such as purchasing specialist, technical consultant, and department director. Simultaneously, the system deeply analyzes communication metadata, including but not limited to: statistically analyzing the composition and changing patterns of personnel in the CC list of email threads; observing the order of email replies on different topics, whether they are individual or group replies; and analyzing the frequency and context of different individuals being mentioned by @ in instant messaging. Based on these patterns, the system preliminarily infers the reporting relationships, collaborative relationships, and main information flow paths between different roles, forming a dynamic hypothetical network of role relationships. The identified roles are used as nodes, the inferred relationships are used as edges, and the cultural semantic feature vectors of the corresponding senders generated in step S1 are used as node attributes. The time-series signals such as the decision urgency index extracted from the current communication text through semantic analysis are used as edge attributes. These are combined to construct an updatable dynamic graph of target customer decision relationships that is stored in a graph structure.

[0044] S42, based on the dynamic graph of target customer decision-making relationships and the history of target customer behavior, establish a time series classification model, and output the probability curve and time series planning of the target customer being in a high decision-making tendency state on each day in the future.

[0045] Among them, establishing a time series classification model utilizes multi-source time series features such as graph status, behavioral history, and external environment to predict the probability of target customer decision-making activity within a specific future time window.

[0046] Specifically, dynamic features reflecting the current decision-making status are extracted from the target customer decision-making relationship dynamic graph constructed by S41. These features include the current decision urgency index in the graph edge attributes, the time interval after the intervention of the last person marked as a key decision-maker, and the concentration of core issues in the current communication network. Secondly, historical behavioral characteristics of the target customer's dynamic evolution are obtained, including the average decision-making cycle of their historical projects and the typical duration of each stage. Simultaneously, external environmental time-series data is incorporated, such as future public holidays in the target customer's location obtained from a public calendar interface, release season windows obtained from their company's financial report forecasts, and exhibition or product launch schedule dates queried from industry databases.

[0047] The aforementioned multi-dimensional features from geographic data, behavioral history, and environment are integrated into a unified time-series feature sequence along a unified timeline. This sequence is then input into a pre-trained time-series classification model, such as one based on a Long Short-Term Memory (LSTM) network. This model uses the target customer behavior sequences and corresponding features prior to historical transactions or key milestone events as training data. The model outputs a probability curve representing the probability that a target customer will enter a high-decision-prone window each day within a future period, such as the next 14 days. Simultaneously, the model outputs a classification label for the current stage of the decision-making process, such as the demand clarification period, technology evaluation period, business negotiation period, or final approval period, serving as the basis for time-series planning.

[0048] S43 combines probability curves with time-series planning to generate intelligent outreach strategies based on time zone and cultural awareness.

[0049] Among them, the intelligent outreach strategy automatically plans the timing, content, and form of outreach based on the decision probability curve, stage judgment, and the spatiotemporal rules of the target customer's location.

[0050] Specifically, the system reads the high-probability decision dates and corresponding decision stage labels output by S42, and automatically aligns and validates these dates against the target customer's country or region's standard time zone, work calendar, and common cultural habits, such as avoiding major holidays and religious festivals. It calculates the suggested specific time window for reaching out to the target customer during their local working hours for each high-probability date, for example, 10:00 AM to 11:00 AM on a weekday. Then, it matches the outreach content with the appropriate channels. This process accesses a pre-defined decision stage-content-channel rule base for matching.

[0051] For example, when the probability curve shows a low decision probability and the stage is the technology evaluation period, it automatically recommends sending relevant technical white papers, third-party evaluation reports, and other nurturing content to the technical roles identified in the graph via email. When the probability increases significantly and the stage enters the business negotiation period, it automatically prompts to send meeting invitations to the purchasing and decision-making roles, and uses an interface to call the flexible performance commitment plan and ROI analysis materials generated in the S4 steps that match the target customer through an interface. When the probability is high and the stage is the final approval period, it strongly recommends conducting a phone or video call, and automatically prepares documents such as contract summaries and final commitment lists. Finally, for each suggested reach point, a structured intelligent reach task draft is generated, clearly recording the target date, specific time, target role, recommended communication channels, core communication points, and links to the materials to be attached.

[0052] S44 generates differentiated collaborative follow-up strategies for different decision-making participants based on the multi-role structure in the dynamic graph of target customer decision-making relationships.

[0053] Among them, generating differentiated collaborative follow-up strategies is based on the functions, influence, and decision-making stages of different roles in the graph, and plans the content, sequence, and collaborative rhythm for contact with multiple roles.

[0054] Specifically, when two or more key decision-making roles are identified in the dynamic graph of target customer decision-making relationships constructed by S41, a multi-role collaborative follow-up matrix is ​​generated based on the role types, historical interaction patterns, and centrality in the communication network identified in the graph. This matrix defines the differentiated communication strategies that should be adopted for different roles at the current decision-making stage predicted by S42.

[0055] For example, during the final approval phase, the matrix might stipulate: First, on day D, send a final technical specification confirmation to the technical lead, focusing on the technical details and quality commitments; one business day later, on day D+1, send the final commercial contract and quotation based on the technical confirmation to the purchasing manager, shifting the focus to terms and costs; another business day later, on day D+2, send a highly concise decision summary email to the senior management role inferred from the matrix, highlighting the project's core value, risk control measures, and minutes of previously reached consensus. The matrix will also suggest appropriate communication channels, such as prioritizing phone calls or one-on-one emails for decision-makers. Internally, a unified collaborative follow-up dashboard is generated for the sales team, centrally displaying the follow-up status of different roles in the same target client project, the content of the last contact, the next planned actions, and the suggested execution time, ensuring information synchronization within the sales team and forming a consistent external communication rhythm.

[0056] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: By automatically constructing target customer decision-making relationship maps and predictive decision windows, it can accurately understand the internal decision-making processes and timing of target customers, achieving synchronization between outreach and the target customer's decision-making rhythm. Its generated timezone and culture-aware outreach strategies and multi-role collaborative follow-up plans ensure the accuracy and consistency of communication. This significantly improves the ability to identify and seize high-value business opportunities, optimizes sales resource allocation, and thus greatly improves sales follow-up efficiency, lead conversion rate, and the certainty of order completion.

[0057] Example 3: In Example 2, a time-zone and culture-aware intelligent outreach strategy was generated by constructing a dynamic graph of the target customer's internal decision-making relationships and predicting decision windows. However, this method focuses on the target customer's internal roles and processes, failing to incorporate the external organizational network influencing decision-making into the collaborative analysis. In complex global trade and project-based sales, key decisions are often influenced by multiple parties, including the target customer's external partners, end users, regulatory agencies, or financial institutions. Outreach only to the target customer may not effectively address resistance from external stakeholders or leverage external impetus. To develop a more systematic influence strategy in complex transactions involving multiple stakeholders, it is necessary to transcend the boundaries of a single target customer and automatically identify, analyze, and strategically coordinate key nodes throughout the entire cross-organizational decision-making network.

[0058] In some embodiments, step S42, which outputs the probability curve and time series planning of the target customer being in a high decision-making propensity state for each future day, further includes: S421, when business opportunities enter the deep stage, performs dynamic discovery of cross-organizational decision-making networks and construction of cross-organizational graphs, extracting organizational, individual and project entities and identifying the business relationships between them.

[0059] Among them, the cross-organizational graph is a dynamic graph that automatically identifies and associates external organizations and individuals that have influence in specific business opportunities with the target customer at its core, and constructs a graph representing their relationships.

[0060] Specifically, this step is triggered when the interaction with the target customer enters a deeper stage (such as submitting a preliminary proposal). First, data is collected from business databases, public news, and communication records between the two parties (such as meeting minutes and tender documents), automatically extracting entities such as companies, individuals, and projects mentioned. Second, relationship extraction technology is used to identify relationship phrases between entities, such as supply, strategic cooperation, and regulation. Next, a cross-organizational decision-making network graph is constructed, with nodes representing entities and edges representing relationships. Based on the co-occurrence frequency of entities, the clarity of relationship descriptions, and the timeliness of information, each relationship is assigned a quantified strength value and its nature is labeled, such as supply chain dependence, strategic alliance, or regulatory constraint.

[0061] S422 quantifies the influence assessment of key nodes in cross-organizational graph construction to identify high-influence nodes, analyzes the public information and historical behavior of each node to infer its core interests, and finally generates a structured interest profile for each node.

[0062] Among them, quantitative influence assessment of key nodes is to calculate the potential influence of nodes on current business decisions from multiple dimensions, while core interest concern inference is to infer their position by analyzing the node's own attributes and behaviors.

[0063] Specifically, in the cross-organizational decision-making network map constructed by S421, key nodes other than foreign trade enterprises and direct target customers using this method are quantitatively assessed for influence from three dimensions: decision-making power, professional authority, and relationship hub, in order to identify high-influence external nodes. The decision-making power dimension assesses whether they possess legal approval or standard-setting authority; the professional authority dimension assesses their industry reputation and technological standing; and the relationship hub dimension assesses their intermediary centrality through graph calculation. After identifying high-influence nodes through comprehensive scoring, their core interests (such as end-target customers' focus on product launch speed and financing parties' focus on risk control) are inferred by analyzing their public reports, strategic priorities, and historical cooperation patterns, forming a structured profile of their interests.

[0064] S423, based on interest profiles, intelligently generates two types of collaborative action plans: indirect empowerment and direct alliance. The generated collaborative action plans are then broken down into internal execution task cards with different roles and time limits.

[0065] Among them, generating a collaborative action plan involves planning specific tactics based on the node analysis results, while breaking it down into task cards transforms the strategy into specific work items that can be assigned and tracked.

[0066] Specifically, based on the node interest profile generated in step S422, the node influence score, the relationship strength calculated in S421, the preset compliance rule base, and the existing relationship records between the enterprise using this method and the node, the system automatically decides and generates two types of strategy content. Type A, Indirect Empowerment Strategy, is suitable for nodes with high influence but which the enterprise using this method cannot or should not directly contact. An empowerment material package is generated for this type of node, customized based on the node's interest profile. This includes core persuasive points to assist the enterprise using this method in lobbying its direct target customers, preset responses to questions the node might raise, and a summary of third-party evidence supporting the advantages of the enterprise's solution using this method. Type B, Proactive Alliance Strategy, is suitable for nodes with significant shared interests and where contact is compliant. Specific alliance plans are designed for this type of node, including refining a joint value proposition that integrates the strengths of both parties, suggesting the departments or contacts to initially contact, drafting the core agenda and key points of communication, and planning simple collaborative actions, such as jointly preparing a technical white paper or arranging a joint solution demonstration.

[0067] Each collaborative strategy (Type A or Type B) generated above is automatically broken down into a series of specific, atomic internal execution tasks. Each task card clearly specifies the executor, a detailed description of the task content, a suggested or required completion deadline, the associated input materials, and the expected output. These structured task cards are automatically pushed to the team's internal collaborative work platform and associated with the corresponding sales opportunity project, forming a trackable and collaborative action plan.

[0068] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: It can transcend the perspective of a single target customer, automatically constructing and analyzing cross-organizational relationship networks influencing transaction decisions, and identifying key influencers and their core interests. Based on this, it can intelligently generate precise indirect empowerment or direct alliance strategies, and decompose complex strategies into executable and traceable collaborative tasks. This enables enterprises to systematically influence multi-party decisions in complex projects, significantly improving insight, strategy accuracy, and team collaboration efficiency in major transactions involving multiple stakeholders, thereby effectively increasing the win rate.

[0069] Example 4: In Example 3, targeted collaborative strategies and tasks were generated by constructing a cross-organizational network graph and identifying high-influence nodes. However, this method, when planning information propagation paths, mainly relies on static network structures and relationship strengths, lacking quantitative simulation of the dynamic propagation process and attenuation patterns of information in complex social networks. This results in potentially suboptimal contact sequences, an inability to predict the transmission efficiency and acceptance probability of different narrative content between specific nodes, and difficulty in dynamically adjusting propagation paths based on actual feedback during strategy execution. To achieve more scientific, predictable, and adaptive impact in complex multi-node decision-making networks, it is necessary to introduce a propagation dynamics model to simulate narrative diffusion and deeply optimize content and paths based on the mental models of nodes, thereby achieving further optimization and improvement.

[0070] In some embodiments, step S423, which breaks down the generated collaborative action plan into internal execution task cards with assigned roles and time limits, further includes: 3a. Based on cross-organizational network graphs and high-influence nodes, a propagation dynamics model is established and Monte Carlo simulation is performed to evaluate and screen the expected effects of propagation strategies for different target customers.

[0071] 3b. Based on the simulation results, a phased and node-based dynamic propagation path blueprint is generated, and personalized task execution plans are automatically generated for each contact point in the path.

[0072] Among them, generating a propagation path blueprint is based on integrating the optimal impact sequence and decision branches from simulation data, while generating a personalized task execution plan is to transform each action in the blueprint into a work instruction that can be assigned to a specific role and includes customized materials.

[0073] Specifically, the first step is to generate a dynamic propagation blueprint. Hundreds of effective propagation paths from the Monte Carlo simulation are integrated and analyzed to extract key node activation sequences that frequently occur and ultimately achieve high overall network acceptance. Based on this, a dynamic propagation path blueprint presented in the form of a decision tree is generated. This blueprint clearly delineates multiple stages from initiation, penetration, traversal to consensus locking, defining the core objectives, recommended initial contact nodes, and alternative contact nodes for each stage. For key decision points, such as when the initial contact with a node fails to achieve the expected results, the blueprint pre-plans branch paths, such as switching to another node with a strong connection to that node to influence it, or changing the recommended narrative prototype.

[0074] For each planned contact point in the blueprint, a deep, personalized analysis is conducted. The narrative mental model and narrative receptivity prediction vector for that point are retrieved, and combined with its role and influence within the overall cross-organizational decision-making network, the core communication objectives for that point are determined. These objectives may include gaining initial approval, obtaining public endorsement, or addressing specific concerns. Based on this analysis, a personalized task execution plan is automatically generated for that contact point.

[0075] 3c transforms the communication blueprint into specific execution tasks and monitors actual communication signals. By comparing the deviation between the blueprint and reality, it dynamically triggers strategic replanning and content adjustment of personalized task execution plans.

[0076] The technical solutions described in the embodiments of this application have at least the following technical effects or advantages: By simulating the dynamics of information propagation in networks, the impact path can be predicted and optimized in advance, thereby generating personalized communication campaign blueprints and content for each stage and node. This enables systematic and adaptive psychological influence and consensus building in complex projects involving multiple stakeholders, significantly improving the accuracy and efficiency of strategic communication, and fundamentally enhancing the core capability to guide decision-making and win competition in major transactions.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises, characterized in that: include: S1: Collect multimodal interaction information related to the target customers of foreign trade enterprises, extract unstructured text for preprocessing, and then feed the preprocessed unstructured text into a pre-trained cultural adaptation semantic understanding model for cultural semantic parsing. S2 acquires internal and external data streams through APIs and IoT devices, calculates the daily fulfillment reliability index and cost fluctuation range for each major logistics route and each major product category in the coming weeks, and constructs a supply chain resilience map. S3 extracts the cultural and psychological characteristics of target customers and the dynamic performance characteristics of enterprises through cultural semantic analysis and supply chain resilience mapping engine. S4 dynamically matches cultural semantic features with performance capability features and conducts risk assessments. Based on the matching results, it automatically generates culturally adapted communication scripts, flexible performance commitment plans, and proactive expectation management mechanisms.

2. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 1, characterized in that, The construction of the supply chain resilience map specifically includes: training a fulfillment reliability index calculation model, using historical route data to learn the relationship between various characteristics and on-time arrival, and predicting the fulfillment reliability index for each future time period and route based on real-time input; training a cost fluctuation range calculation model, analyzing the statistical relationship between historical costs and key variables, and simulating and calculating the cost fluctuation range for each future time period based on real-time input; and storing the predicted fulfillment reliability index and cost fluctuation range in association by time, route, and product category to form a dynamic supply chain capability view that supports multi-dimensional queries.

3. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 1, characterized in that, The extraction of target customer cultural and psychological characteristics and enterprise dynamic performance capability characteristics includes: through a first processing flow, performing cultural semantic analysis on the target customer's interactive text, identifying and quantifying their business intentions, urgency of needs, decision-making style, and risk preferences, and generating a cultural and psychological characteristic vector; through a second processing flow, based on the product, destination, and time window involved in the current interaction, retrieving multiple feasible performance paths matching in the supply chain resilience map, and extracting the quantitative performance characteristics of each path to generate a set of performance capability characteristic vectors.

4. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 1, characterized in that, The automatic generation of culturally adapted communication scripts, flexible performance commitment schemes, and proactive management mechanisms based on matching results specifically includes: S41, automatically identifying and labeling sender roles based on cultural semantic features, analyzing communication metadata to infer internal reporting lines and information flow paths, and constructing a dynamic graph of target customer decision-making relationships after forming role relationship assumptions; S42, establishing a time series classification model based on the dynamic graph of target customer decision-making relationships and the target customer's behavioral history, and outputting the probability curve and time series plan of the target customer being in a high decision-making tendency state on future days; S43, combining the probability curve and time series plan to generate intelligent outreach strategies based on time zone and cultural awareness; S44, generating differentiated collaborative follow-up strategies for different decision-making participants based on the multi-role structure in the dynamic graph of target customer decision-making relationships.

5. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 4, characterized in that, The output of the probability curve and time series planning of the target customers in a high decision-making tendency state for each day in the future specifically includes: collecting multi-dimensional time series features from the dynamic graph of the target customer's decision-making relationship, its historical behavior patterns and external environment, and integrating them into a fused time series feature sequence; inputting the fused time series feature sequence into a pre-trained time series classification model for processing; outputting the probability curve formed by the probability values ​​of the target customers entering the high decision-making tendency window for each day in the future, as well as the classification label of the current stage of the decision-making process, as the basis for time series planning.

6. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 4, characterized in that, The intelligent outreach strategy includes: automatically planning the timing, content, and form of outreach based on decision probability curves, stage judgments, and the spatiotemporal rules of the target customer's location.

7. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 4, characterized in that, The output of the probability curve and time-series planning of the target customers in a high decision-making state for the next day includes: S421, when the business opportunity enters the deep stage, dynamic discovery and cross-organizational graph construction of cross-organizational decision-making network are carried out, extracting organizational, individual and project entities and identifying the business relationships between them; S422, quantitative impact assessment of key nodes in the cross-organizational graph construction to identify high-influence nodes, analyzing the public information and historical behavior of each node to infer its core interests, and finally generating a structured interest profile for each node; S423, intelligently generating two types of collaborative action plans based on the interest profile: indirect empowerment or direct alliance, and breaking down the generated collaborative action plan into internal execution task cards with different roles and time limits.

8. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 7, characterized in that, The cross-organizational graph is specifically defined as follows: It is a dynamic graph that automatically identifies and associates external organizations and individuals that have influence in specific business opportunities with the target customer as the core, and constructs a graph representing their relationships.

9. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 7, characterized in that, The breakdown into role-based and time-limited internal execution task cards includes: automatically breaking down each generated collaborative strategy into a series of specific, atomic internal execution tasks; clearly specifying the execution role, a detailed description of the task content, a suggested or required completion deadline, the input materials required for task execution, and the expected output for each task card; and automatically pushing these structured task cards to the team's internal collaborative work platform and associating them with the corresponding sales opportunity projects to form a trackable and collaborative action plan.

10. The AI-powered intelligent customer acquisition and lead analysis method for foreign trade enterprises according to claim 7, characterized in that, The process of breaking down the generated collaborative action plan into internal execution task cards with different roles and time limits also includes: establishing a propagation dynamics model and conducting Monte Carlo simulations based on cross-organizational network graphs and high-influence nodes to evaluate and screen the expected effects of propagation strategies for different target customers; generating a phased and node-based dynamic propagation path blueprint based on the simulation results, and automatically generating personalized task execution plans for each contact point in the path; transforming the propagation blueprint into specific execution tasks and monitoring actual propagation signals, and dynamically triggering strategy replanning and content adjustment of personalized task execution plans by comparing the deviation between the blueprint and reality.

Images