Multi-source data fusion customer extension method and system for AIGC industry

Abstract

The invention relates to the technical field of B-end intelligent customer extension SaaS, and discloses a multi-source data fusion customer extension method and system for the AIGC industry, and the method comprises the following steps: S1, a multi-source data fusion step; s2, a clue quantitative scoring and life cycle management step; s3, a deep marketing analysis step; s4, a front-end display optimization step; s5, an intelligent early warning and automatic enabling step; according to the invention, the data value is greatly improved, the multi-source data fusion technology realizes a complete data chain of "reaching people-product-enterprise", enterprise and clue portraits are more comprehensive, the coverage rate of key information such as decision chain, budget, operation data and the like is improved from 60% to 95%, the pain point of single data dimension in the prior art is solved, a special data support is especially formed for the AIGC industry, and the method is suitable for the AIGC industry. And a sufficient basis is provided for sales decision.

Description

Technical Field

[0001] This invention belongs to the field of B2B intelligent customer acquisition SaaS technology, specifically a multi-source data fusion customer acquisition method and system for the AIGC industry. Background Technology

[0002] Against the backdrop of the rapid development of the AIGC industry, enterprises are placing higher demands on the accuracy, comprehensiveness, and timeliness of B2B customer acquisition tools. Existing related technologies have several shortcomings: 1. Traditional business data SaaS tools (such as Qichacha and Tianyancha) only provide basic business information of enterprises and have the ability to query static data, but lack the function of capturing real-time traffic intent and cannot link influencer promotion behavior with enterprise advertising needs; 2. Content data tools (such as Chanmama) focus on content analysis and influencer profiling on short video platforms, emphasizing content style dimensions, lacking the ability to correlate with enterprise business data, and have not designed specific functions for the accurate identification and business opportunity conversion of AIGC products. 3. Traditional B2B customer acquisition SaaS (such as Tanji and Kuaiqi Smart Cloud) have basic lead mining functions, but some products have problems such as insufficient real-time performance, single data dimensions, or static BI analysis, which cannot achieve direct conversion from influencer content to enterprise leads. 4. International customer acquisition tools (such as ZoomInfo and HubSpot) have the ability to display decision chains or perform marketing attribution analysis, but they are not well adapted to the localization of short video marketing scenarios in China, have weak cold lead mining capabilities, and do not cover the specific customer acquisition needs of the AIGC industry. 5. Basic OA systems only have the function of storing influencer information, lacking the ability for in-depth data analysis, cross-platform collection, and conversion of business leads, and are unable to uncover the business opportunities for companies behind influencers.

[0003] The core shortcomings of existing technologies are manifested in the following ways: insufficient data dimensions and depth, failing to form a data chain linking "influencer content - product - enterprise"; lack of lead value assessment, relying on subjective judgment to screen leads, resulting in low efficiency; superficial marketing analysis, failing to uncover the real pain points of enterprises and competitor dynamics based on the characteristics of AIGC products; poor front-end experience adaptability, not optimized for sales operation scenarios; lack of automation and proactive empowerment, mostly passive query mode, and facing the challenge of anti-scraping of cross-platform influencer data collection; and insufficient adaptation to AIGC scenarios, unable to efficiently filter highly targeted AIGC products in influencer promotional content, leading to low accuracy in business opportunity discovery. These problems severely restrict the efficiency and conversion effect of B-end customer acquisition in the AIGC industry, urgently requiring a targeted technical solution.

[0004] Based on this, a multi-source data fusion customer acquisition method and system for the AIGC industry is designed. Summary of the Invention

[0005] In response to the above situation and to overcome the shortcomings of the existing technology, this invention provides a multi-source data fusion customer acquisition method and system for the AIGC industry, which effectively solves the current problem of difficulty in automatically completing water replenishment and drainage based on water level changes.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-source data fusion customer acquisition method for the AIGC industry, characterized by comprising the following steps: S1. Multi-source data fusion steps: S1.1 Data Access: Access business data in compliance with regulations by connecting to industrial and commercial data platforms, professional social networking platforms, recruitment platforms, and enterprise database platforms via API; utilize Playwright+JS reverse engineering technology to overcome the anti-scraping mechanisms of the four major short video platforms—Douyin, Kuaishou, Xiaohongshu, and Bilibili—to collect AIGC product-related video lists, influencer information, interaction volume, and trending keyword data; and access existing influencer data from the OA system as the initial data source. S1.2 Data Governance: Establish data cleaning and association rules to achieve cross-source fusion of video traffic data, enterprise operation data, decision chain data, and influencer data; complete the core data structures of enterprise_info, decision_matrix, and creator_profile; based on the "AIGC Product Whitelist Library," "Negative Word Library," and NER confidence score, remove generic words and accurately identify highly targeted AIGC product names; construct a "influencer-product-enterprise" association data chain with the enterprise's unified social credit code as the core association key; S1.3 Data storage: A structured storage solution is adopted, which supports outputting lead data in JSON format, including core fields such as lead_id, lead_scoring, marketing_data, and creator_profile; S2. Lead Quantitative Scoring and Lifecycle Management Steps: S2.1 Dual-dimensional scoring: Based on the characteristics of the AIGC industry, the static score is calculated based on company size, financing round, AIGC industry matching degree, and historical cooperation record, with a total score of 100 points; the dynamic score is calculated based on the growth of AIGC hot keywords, frequency of placement, changes in competitor placement, and recruitment status of AIGC-related positions, with a total score of 40 points; the scores are divided into three levels, A / B / C, and automatically sorted. S2.2 Clue Verification: Systematically clean and verify the accessibility of decision-makers' contact information, and supplement the contact information of key positions such as business and marketing managers in the AIGC industry; S2.3 Lifecycle Management: Define the logic for lead status flow according to "New - Follow-up - Solution in progress - Converted" to achieve closed-loop management; S3, In-depth Marketing Analysis Steps: S3.1 Pain Point and Trend Mining: Based on the LLM large model, analyze the enterprise's AIGC product placement behavior, recruitment needs, and financing dynamics to uncover pain points, market trends, and placement budgets and intentions; S3.2 Competitive Product Offense and Defense Analysis: Establish competitor data association rules, analyze the target company's and competitors' AIGC product placement intensity and market positioning, and identify market windows; S3.3 Data Correlation Analysis: Construct a correlation model between video traffic data and enterprise operation data to form an executable sales work order based on "influencer promotion behavior - product characteristics - enterprise needs"; S4. Front-end display optimization steps: S4.1 List Page Upgrade: Design a "Priority Task Dashboard" to visually display lead level, total score, AIGC related key tags, and freshness. It supports secondary filtering and is sorted by total score in descending order by default. S4.2, Detail Page Restructuring: Set up an overall overview area, a decision-maker matrix area, and detail tabs. The decision-maker matrix area provides the function of "one-click generation of AI icebreaker scripts". The detail tabs include modules for campaign analysis, influencer profiles, enterprise panorama, and competitor analysis. S5, Intelligent Early Warning and Automation Empowerment Steps: S5.1 Intelligent Early Warning: Set thresholds for AIGC hot keyword growth and placement frequency, and automatically push high-value lead warnings when the conditions are triggered; S5.2 Contextualized Proactive Triggering: Construct a three-dimensional triggering system of "clue status + user behavior + real-time signals" to push corresponding reminders and suggestions for key enterprise actions; S5.3 Conversion Funnel Analysis: Introducing a multi-dimensional attribution model to achieve full-link conversion tracking, quantifying the contribution of each channel and tag, intelligently diagnosing the reasons for lead loss and generating optimization suggestions.

[0007] Preferably, in step S1.1, the business data platform includes Qichacha and Tianyancha, the professional social networking platform includes Maimai and LinkedIn, the recruitment platform includes Boss Zhipin and Lagou, and the enterprise database platform includes Xiaolanben; when collecting data from the short video platform, the search interface parameters Signature / Token are restored to overcome the slider and encryption restrictions.

[0008] Preferably, in step S1.2, data cleaning includes time cleaning and content cleaning. Time cleaning removes outdated AIGC video data that was published more than one year ago, while content cleaning uses a fast text matching algorithm and AI-assisted review to remove noisy data.

[0009] Preferably, in step S2.1, the static scoring rules are as follows: 20 points for companies with 10-50 employees, 30 points for companies with 50-100 employees, and 40 points for companies with more than 100 employees; 20 points for Series A funding or above, and 10 points for companies without funding; 20 points for high relevance to the AIGC industry, 10 points for medium relevance, and 5 points for low relevance; the dynamic scoring rules are as follows: 15 points for hot keywords with a 24-hour growth of more than 300%, 10 points for a week-on-week increase in ad placement frequency, 10 points for competitor ad placement changes, and 5 points for recruiting AIGC-related positions.

[0010] A multi-source data fusion customer acquisition system for the AIGC industry includes: Multi-source data fusion module: including data access layer, data governance layer, and data storage layer. The data access layer realizes compliant access to data from multiple platforms and data collection from short video platforms. The data governance layer completes data cleaning, correlation fusion, and accurate identification of AIGC products. The data storage layer realizes structured clue data storage. The lead quantification scoring and lifecycle management module includes a dual-dimensional scoring unit, a lead verification unit, and a lifecycle management unit. The dual-dimensional scoring unit performs static and dynamic score calculations and classifies the lead into levels. The lead verification unit verifies and supplements the decision-maker's contact information. The lifecycle management unit realizes the closed-loop flow of lead status. In-depth marketing analysis module: includes pain point and trend mining unit, competitor attack and defense analysis unit, and data correlation analysis unit, which respectively realize AIGC industry demand mining, competitor analysis and sales work order generation; Front-end display optimization module: includes list page display unit and detail page display unit. The list page display unit implements the priority task dashboard function, and the detail page display unit provides an overview, decision-maker matrix and multi-dimensional detail display. The intelligent early warning and automation empowerment module includes an intelligent early warning unit, a scenario-based proactive triggering unit, and a conversion funnel analysis unit. The intelligent early warning unit pushes high-value lead warnings, the scenario-based proactive triggering unit pushes targeted reminders and suggestions, and the conversion funnel analysis unit realizes conversion tracking and churn diagnosis.

[0011] Preferably, the data access layer of the multi-source data fusion module is configured with a dynamic IP pool and an account rotation mechanism, and a crawling rate threshold is set, so that a single Douyin account can crawl no more than 500 items per day.

[0012] Preferably, the front-end display optimization module is developed using the Vue3+Element Plus framework, the details page display unit uses ECharts to draw AIGC product placement trend charts and competitor comparison radar charts, and the AI ​​icebreaker function calls the GPT-4 API to generate customized scripts.

[0013] Preferably, the conversion funnel analysis unit of the intelligent early warning and automation empowerment module integrates HubSpot's multi-dimensional attribution algorithm, supporting attribution analysis such as first contact and last contact.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention significantly enhances the value of data. The multi-source data fusion technology realizes a complete data chain of "influencer-product-enterprise", providing a more comprehensive profile of enterprises and leads. The coverage of key information such as decision chain, budget, and operational data has increased from 60% to 95%, solving the pain point of the single data dimension of existing technologies. It provides specialized data support for the AIGC industry, providing sufficient basis for sales decisions. 2. This invention improves both the efficiency and quality of lead screening. The dual-dimensional quantitative scoring system enables automatic sorting of high-value leads in the AIGC industry. Combined with contact information verification and lifecycle management, it reduces invalid follow-ups, improves effective reach and lead conversion rates, and is expected to increase lead conversion ROI by 200%. The data cleaning accuracy rate is over 85%. 3. This invention enhances marketing insight depth. Competitive analysis and AIGC industry pain point mining technologies enable sales to accurately grasp market opportunities. Data correlation capabilities break down information silos, upgrading "marketing reports" to "executable work orders," increasing the probability of closing deals and discovering AIGC industry business opportunities 15-30 days in advance. 4. This invention improves sales efficiency. The front-end "battle board" design and AI-powered sales script tools are adapted to the needs of AIGC industry lead display and matching, reducing sales decision-making costs and operational barriers. Sales decision-making time is shortened from an average of 15 minutes / lead to 3 minutes / lead. Newcomers can quickly get started with high-value leads, and team collaboration efficiency is improved. 5. This invention leads in the timeliness of business opportunity capture. Its intelligent early warning and proactive push technology responds in real time to key signals such as the growth of AIGC hot words and the frequency of ad placement. The response time for lead follow-up is improved from within 24 hours to within 2 hours. Compared with traditional tools, it has a significant time advantage and helps sales seize the market window. 6. This invention has significant competitive advantages, forming a unique driving model of "content big data + business big data + AIGC special functions". It not only makes up for the shortcomings of real-time intent in traditional business SaaS, but also solves the problem of insufficient business certainty of content tools. At the same time, it overcomes the anti-crawling difficulties of four major platforms, has localized adaptation advantages and full-link automation capabilities, and is far more competitive than existing similar products in the AIGC industry customer acquisition scenario. 7. This invention has stable cross-platform data collection capabilities. Through Playwright+JS reverse engineering technology and risk control strategies (reducing crawling rate, changing IP / account), it achieves stable data collection from four major short video platforms. The core collection capability recovery time is controlled within 24 hours, ensuring the continuity of the data pipeline. Attached Figure Description

[0015] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0016] In the attached diagram: Figure 1 This is a flowchart of the method steps of the present invention; Figure 2 This is a system module block diagram of the present invention; Figure 3 This is a calculation logic diagram of the two-dimensional scoring model of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Depend on Figures 1-3 The present invention takes "data fusion + quantitative scoring + intelligent early warning" as its core, and combines the characteristics of the AIGC industry with the cross-platform data collection needs to build a complete technical solution from five major modules: data acquisition, lead evaluation, marketing analysis, front-end display and automation empowerment.

[0019] I. Multi-source data fusion module This module is responsible for the access, governance, and storage of data from multiple channels, providing data support for subsequent clue mining and analysis.

[0020] Data Access Layer: This layer connects via API to Qichacha / Tianyancha (deep business registration data), Maimai / LinkedIn (professional social networking data), Boss Zhipin / Lagou (recruitment data), and Xiaolanben (enterprise database data) to compliantly obtain information such as company size, financing dynamics, decision-maker contact information, and recruitment needs. Using Playwright+JS reverse engineering technology, it overcomes the anti-scraping mechanisms of four major platforms: Douyin, Kuaishou, Xiaohongshu, and Bilibili, restoring search interface parameters (Signature / Token), breaking through slider and encryption restrictions, and collecting data such as video lists, associated influencer information, interaction volume, and hot keywords related to AIGC products. It also integrates existing influencer data from the OA system as the initial data source for lead generation. Simultaneously, a dynamic IP pool and account rotation mechanism are configured, and a crawling rate threshold is set (no more than 500 crawls per Douyin account per day) to avoid triggering risk control measures.

[0021] Data Governance Layer: Establish data cleaning and association rules to achieve cross-source fusion of video traffic data, enterprise operation data, decision chain data, and influencer data, and supplement core data structures such as enterprise_info, decision_matrix, and creator_profile; design a special filtering mechanism for AIGC products, establish an "AIGC Product Whitelist" and a "Negative Keyword Library," and combine NER confidence scores to remove generic words (such as "artificial intelligence" and "cutting-edge technology") to accurately identify highly targeted AIGC product names; and construct an association data chain of "influencer-product-enterprise" using the enterprise's unified social credit code as the core association key. Data cleaning includes time cleaning (removing outdated AIGC video data published more than one year ago) and content cleaning (using a fast text matching algorithm + AI-assisted review, comparing Video Title and AIGC Keywords to remove noisy data that is "riding the wave" but not actually promoting).

[0022] Data storage layer: Design a structured data storage solution that supports the output of optimized lead data in JSON format, including core fields such as lead_id, lead_scoring, marketing_data, and creator_profile, to ensure efficient data querying and retrieval.

[0023] II. Lead Quantitative Scoring and Lifecycle Management Module This module enables accurate assessment of lead value and full lifecycle tracking, helping sales teams focus on high-value leads.

[0024] Dual-dimensional scoring model (adapted to AIGC industry characteristics): Static score is based on fixed attributes such as company size, funding round, AIGC industry matching degree, and historical cooperation record. The scoring rules are as follows: 10-50 employees: 20 points; 50-100 employees: 30 points; 100+ employees: 40 points; Series A funding or above: 20 points; no funding: 10 points; high AIGC industry matching: 20 points; medium matching: 10 points; low matching: 5 points; total score: 100 points. Dynamic score is based on real-time signals such as AIGC hot keyword growth, ad placement frequency, and recruitment urgency. The scoring rules are as follows: hot keyword growth exceeding 300% in 24 hours: 15 points; ad placement frequency increasing by 200% week-on-week: 10 points; competitor ad placement changes: 10 points; recruiting for AIGC-related positions: 5 points; total score: 40 points. The total score is divided into three levels: A, B, and C, and high-value leads are automatically sorted.

[0025] Lead verification mechanism: Systematically clean and verify the reachability of decision-makers' contact information (mobile phone number, email, etc.) to improve the effective reach rate, and supplement the contact information of key positions such as business and marketing managers for key AIGC industries.

[0026] Lifecycle Management: Define the logic for lead status transition from "new creation - follow-up - solution in progress - converted" to achieve closed-loop management and focus on tracking the lead conversion progress of companies that have launched AIGC products.

[0027] III. In-depth Marketing Analysis Module This module performs in-depth analysis based on fused data, providing the sales team with actionable marketing decision support.

[0028] Pain Point and Trend Discovery: Based on the LLM big data model, we comprehensively analyze enterprises' AIGC product placement behavior, recruitment needs and financing dynamics to uncover real pain points and market trends, and accurately determine enterprises' investment budget and intentions in the AIGC field. Competitive analysis: Establish rules for associating competitor data, automatically analyze the AIGC product placement intensity and market positioning (leading / following) of target companies and competitors, and identify market windows; Data correlation analysis: Construct a correlation model between video traffic data and enterprise operation data to form an executable sales work order of "influencer promotion behavior - product characteristics - enterprise needs", highlighting the promotion highlights of AIGC products and the value of enterprise investment.

[0029] IV. Front-end Display Optimization Module This module optimizes the front-end interactive experience for sales operation scenarios, improving decision-making efficiency and ease of operation.

[0030] List page upgrade: Developed using Vue3+Element Plus framework, featuring a "Priority Task Kanban" that visually displays lead levels (A / B / C, marked with red, yellow, and blue respectively), total score, key AIGC-related tags (such as #AIGC ad spending surge# high purchasing power after funding), and freshness. It supports secondary filtering by industry, freshness, and alert status, and defaults to sorting by total score in descending order. Details page redesign: The overall overview area adopts a card-style design, highlighting the comprehensive score, lead level, AIGC-related core tags, lead freshness, and alert status, making key information readily apparent; the decision-maker matrix area displays decision-maker information (name, position, contact information) for multiple core positions in a horizontal business card format, providing a "one-click generation of AI icebreaker scripts" button, calling the GPT-4 API to develop customized scripts based on AIGC industry pain points and job requirements; the details tab (in-depth report area) includes campaign analysis (displaying a list of AIGC product videos on various platforms, interaction data, and campaign trend charts drawn using ECharts), influencer profiles (displaying influencer style, sales performance, and commercial value score based on LLM analysis), company overview (integrating in-depth business registration data, recent recruitment activities, and financing information), and competitor analysis (displaying a radar chart comparing the campaign intensity and market positioning of competitor AIGC products drawn using ECharts).

[0031] V. Intelligent Early Warning and Automation Empowerment Module This module enables proactive lead push and full-link automated conversion support, improving the timeliness of lead follow-up and conversion efficiency.

[0032] Intelligent early warning mechanism: Set thresholds for AIGC hot keyword growth and ad frequency. When the conditions are triggered (such as 24-hour AIGC hot keyword growth exceeding 300% or ad frequency increasing by 200% week-on-week), automatically push high-value lead warnings and push them to sales for priority follow-up. Contextualized proactive triggering: Construct a three-dimensional triggering system of "lead status + user behavior + real-time signals". When enterprises take key actions such as AIGC-related recruitment or financing receipt, "must-follow-up business opportunity reminder", "follow-up warning" and "strategy suggestions" will be automatically pushed; when sales do not follow up on newly created AIGC leads within 24 hours, "follow-up warning" will be pushed. Conversion Funnel Analysis: Integrating HubSpot's multi-dimensional attribution model (first contact, last contact, etc.) to achieve end-to-end conversion tracking from lead acquisition to outreach to solution to sale, quantifying the contribution of various short video platforms and AIGC hot keywords to sales, and analyzing the reasons for AIGC lead loss based on LLM (such as outreach failure, budget mismatch, competitor interception) to generate actionable optimization suggestions (such as supplementing backup contact methods, adjusting AIGC product pricing plans).

[0033] Example 1: Implementation of Multi-Source Data Fusion and Clue Scoring (AIGC Scenario) Data Access Configuration: Apply for API access through the Qichacha Open Platform to obtain data such as the number of employees paying social security, financing rounds, and software copyrights (with a focus on AIGC-related copyrights); connect to the Maimai API to obtain the work emails and mobile phone numbers of key contacts such as the CMO, Marketing Director, and Business Manager; use the Playwright+JS reverse engineering plugin to collect AIGC product-related video data (number of videos, interaction volume, hot keywords, influencer information) from Douyin, Xiaohongshu, Kuaishou, and Bilibili, restoring the search interface parameters (Signature / Token) of each platform and overcoming slider and encryption restrictions; access existing influencer data from the OA system as the initial data source. Configure a dynamic IP pool containing more than 100 dynamic IPs, with an account rotation cycle of 2 hours / time, and set a daily crawling limit of 500 entries per Douyin account.

[0034] Data fusion process: Using the enterprise's unified social credit code as the core association key, business registration data, AIGC-related recruitment data (AIGC operation and advertising job information crawled from Boss Zhipin), and financing data are integrated into the enterprise_info field; according to job level and decision weight, a decision_matrix array is constructed, supplemented with the contact information and decision weight tags of each decision-maker, and the relevant person in charge of AIGC business is highlighted; based on the influencer's past AIGC product commercial order data and sales performance, commercial indicators such as commercial_value_score and conversion_rate are calculated to improve the creator_profile; entities in the video are extracted using the NER algorithm, and combined with the "AIGC Product Whitelist Library" (containing 500+ mainstream AIGC product names) and the "Negative Word Library" (containing 200+ general and irrelevant words) and the NER confidence score (threshold set to 0.8), strongly targeted AIGC product names are filtered. Time cleaning removes video data that was published more than one year ago. Content cleaning uses a fast text matching algorithm to compare the match between the Video Title and AIGC Keywords. Data with a match score lower than 0.6 is considered noise and is removed.

[0035] Scoring model deployment: Static scoring rules are implemented based on company size, financing round, and AIGC industry relevance, with a total score of 100 points; dynamic scoring rules are implemented based on AIGC hot keyword growth, ad frequency, competitor ad changes, and AIGC-related job postings, with a total score of 40 points; the scoring algorithm is deployed through Python scripts to calculate the total lead score in real time and classify leads into three levels: A, B, and C.

[0036] Implementation results: The completeness of lead data increased from 60% to 95%, the scoring accuracy reached 85%, the efficiency of identifying high-value AIGC industry leads increased by 3 times, and the first batch of 100+ high-intent advertising company leads were successfully selected.

[0037] Example 2: Front-end display and intelligent early warning implementation (adapted to AIGC clues) Front-end development: The list page and detail page are developed using the Vue3 + Element Plus framework. The list page uses color-coded lead levels (red for A, yellow for B, and blue for C), displaying the total score, key AIGC-related tags (#AIGC ad spending surge, #high purchasing power after funding), and freshness. It defaults to descending order of total score and supports filtering by industry (covering 10+ sub-sectors including AIGC generative AI and AIGC software tools), alert status (already alerted, not alerted), and AIGC product type (text generation, image generation, etc.). The detail page's overall overview area uses a card-style design, with cards measuring 300px x 200px, highlighting the overall score, AIGC-related tags, and alert status. The decision-maker matrix area displays contact information in horizontal business cards (800px x 100px / card), integrating an AI-generated script button that calls GPT-4 upon clicking. The API generates three customized icebreakers based on AIGC industry pain points (such as "high customer acquisition costs" and "low content creation efficiency") and job titles (such as marketing director and business manager); the details tab uses ECharts to draw AIGC product placement trend charts (line charts) and competitor comparison radar charts (including three dimensions: placement intensity, market coverage, and user feedback).

[0038] Intelligent early warning configuration: Threshold rules are set in the backend using Java. When the AIGC hot keyword of a certain lead increases by more than 300% in 24 hours or the frequency of ad placement increases by 200% week-on-week, an early warning signal is triggered. The early warning signal is pushed to the corresponding sales account through the internal message channel of the system, and an SMS reminder is sent at the same time (for A-level leads).

[0039] Implementation results: Sales decision time was reduced from an average of 15 minutes per lead to 3 minutes per lead; lead follow-up response time was improved from within 24 hours to within 2 hours; conversion rate of high-value AIGC leads increased by 200%; and sales coordination efficiency was significantly improved.

[0040] Example 3: Conversion Funnel and Active Trigger Implementation (AIGC Industry Adaptation) Attribution Model Deployment: Integrates HubSpot's multi-dimensional attribution algorithm and connects to HubSpot's attribution analysis module via API interface to achieve full-link conversion tracking from "lead acquisition to outreach to solution to transaction," quantifying the contribution of various short video platforms (Douyin, Kuaishou, Xiaohongshu, Bilibili) and AIGC hot keywords and tags (such as #AI painting and #intelligent writing) to transactions.

[0041] Proactive trigger rule configuration: Dynamic lead trigger: When a company receives funding (monitored by Xiaolan Book data) or publishes AIGC-related business or job postings (monitored by Boss Zhipin and Lagou data), an "AIGC lead reminder with high purchasing power" will be automatically pushed; Sales behavior trigger: The sales follow-up status will be monitored through a backend scheduled task (executed once every 24 hours). If the sales staff does not follow up on a newly created AIGC lead within 24 hours, a "follow-up warning" will be pushed. The churn diagnosis module analyzes the reasons for AIGC lead churn based on LLM (using the GPT-3.5 Turbo model) (reach failure, budget mismatch, competitor interception, etc.). For reach failure, it generates suggestions to "supplement backup contact methods" and for budget mismatch, it generates suggestions to "adjust AIGC product pricing plan (such as launching a basic package)". Implementation Results: Sales team management efficiency improved by 30%, dropout rate at each stage of the funnel decreased by 15%, AIGC lead conversion cycle was shortened by 20%, further enhancing lead conversion value.

[0042] Example 4: Cross-platform anti-scraping and data cleaning implementation Anti-scraping strategy deployment: Playwright+JS reverse engineering technology is used to restore the search interface parameters (Signature / Token generation logic) of the four major platforms, and the interface call simulation is implemented through Node.js; a dynamic IP pool (containing more than 200 highly anonymous dynamic IPs) and an account rotation mechanism (more than 50 accounts, rotating once per hour) are configured, and crawling rate thresholds are set: no more than 500 crawls per day for a single Douyin account, and no more than 800 crawls per day for a single Kuaishou, Xiaohongshu, and Bilibili account, to avoid triggering risk control; Data cleaning process: Time cleaning: Using Python scripts to read the video release time field, outdated AIGC video data older than one year is automatically removed to ensure the timeliness of ad placement needs; Content cleaning: Introducing a fast text matching algorithm (using Levenshtein distance algorithm) + AI-assisted review (calling Baidu AI content review API), comparing VideoTitle and AIGC Keywords to remove noisy data that is "riding the wave" but not actually promoting; Product screening: Using the "AIGC product whitelist library" and "negative keyword library" combined with NER confidence score (threshold 0.8), generic words are removed to accurately identify highly targeted AIGC products; Implementation results: The data collection success rate of the four major platforms has remained stable at over 85%. When encountering risk control measures, the core collection capabilities can be restored within 24 hours through IP / account rotation and rate adjustment. The data cleaning accuracy rate is over 85%, effectively ensuring the quality of leads.

Claims

1. A multi-source data fusion customer acquisition method for the AIGC industry, characterized by: Includes the following steps: S1. Multi-source data fusion steps: S1.1 Data Access: Access business data in compliance with regulations by connecting to industrial and commercial data platforms, professional social networking platforms, recruitment platforms, and enterprise database platforms via API; utilize Playwright+JS reverse engineering technology to overcome the anti-scraping mechanisms of the four major short video platforms—Douyin, Kuaishou, Xiaohongshu, and Bilibili—to collect AIGC product-related video lists, influencer information, interaction volume, and trending keyword data; and access existing influencer data from the OA system as the initial data source. S1.2 Data Governance: Establish data cleaning and association rules to achieve cross-source fusion of video traffic data, enterprise operation data, decision chain data, and influencer data; complete the core data structures of enterprise_info, decision_matrix, and creator_profile; based on the "AIGC Product Whitelist Library," "Negative Word Library," and NER confidence score, remove generic words and accurately identify highly targeted AIGC product names; construct a "influencer-product-enterprise" association data chain with the enterprise's unified social credit code as the core association key; S1.3 Data storage: A structured storage solution is adopted, which supports outputting lead data in JSON format, including core fields such as lead_id, lead_scoring, marketing_data, and creator_profile; S2. Lead Quantitative Scoring and Lifecycle Management Steps: S2.1 Dual-dimensional scoring: Based on the characteristics of the AIGC industry, the static score is calculated based on company size, financing round, AIGC industry matching degree, and historical cooperation record, with a total score of 100 points; the dynamic score is calculated based on the growth of AIGC hot keywords, frequency of placement, changes in competitor placement, and recruitment status of AIGC-related positions, with a total score of 40 points; the scores are divided into three levels, A / B / C, and automatically sorted. S2.2 Clue Verification: Systematically clean and verify the accessibility of decision-makers' contact information, and supplement the contact information of key positions such as business and marketing managers in the AIGC industry; S2.3 Lifecycle Management: Define the lead status flow logic according to "New - Follow-up - Solution in progress - Converted" to achieve closed-loop management; S3, In-depth Marketing Analysis Steps: S3.1 Pain Point and Trend Mining: Based on the LLM large model, analyze the enterprise's AIGC product placement behavior, recruitment needs, and financing dynamics to uncover pain points, market trends, and placement budgets and intentions; S3.2 Competitive Product Offense and Defense Analysis: Establish competitor data association rules, analyze the target company's and competitors' AIGC product placement intensity and market positioning, and identify market windows; S3.3 Data Correlation Analysis: Construct a correlation model between video traffic data and enterprise operation data to form an executable sales work order based on "influencer promotion behavior - product characteristics - enterprise needs"; S4. Front-end display optimization steps: S4.1 List Page Upgrade: Design a "Priority Task Dashboard" to visually display lead level, total score, AIGC related key tags, and freshness. It supports secondary filtering and is sorted by total score in descending order by default. S4.2, Detail Page Restructuring: Set up an overall overview area, a decision-maker matrix area, and detail tabs. The decision-maker matrix area provides the function of "one-click generation of AI icebreaker scripts". The detail tabs include modules for campaign analysis, influencer profiles, enterprise panorama, and competitor analysis. S5, Intelligent Early Warning and Automation Empowerment Steps: S5.1 Intelligent Early Warning: Set thresholds for AIGC hot keyword growth and placement frequency, and automatically push high-value lead warnings when the conditions are triggered; S5.2 Contextualized Proactive Triggering: Construct a three-dimensional triggering system of "clue status + user behavior + real-time signals" to push corresponding reminders and suggestions for key enterprise actions; S5.3 Conversion Funnel Analysis: Introducing a multi-dimensional attribution model to achieve full-link conversion tracking, quantifying the contribution of each channel and tag, intelligently diagnosing the reasons for lead loss and generating optimization suggestions.

2. The multi-source data fusion customer acquisition method for the AIGC industry according to claim 1, characterized in that: In step S1.1, the business data platform includes Qichacha and Tianyancha, the professional social networking platform includes Maimai and LinkedIn, the recruitment platform includes Boss Zhipin and Lagou, and the enterprise database platform includes Xiaolanben; when collecting data from the short video platform, the search interface parameters Signature / Token are restored to overcome the slider and encryption restrictions.

3. The multi-source data fusion customer acquisition method for the AIGC industry according to claim 1, characterized in that: In step S1.2, data cleaning includes time cleaning and content cleaning. Time cleaning removes outdated AIGC video data that was published more than one year ago, while content cleaning uses a fast text matching algorithm and AI-assisted review to remove noisy data.

4. The multi-source data fusion customer acquisition method for the AIGC industry according to claim 1, characterized in that: In step S2.1, the static scoring rules are as follows: 20 points for companies with 10-50 employees, 30 points for companies with 50-100 employees, and 40 points for companies with more than 100 employees; 20 points for Series A funding or above, and 10 points for companies without funding; 20 points for high relevance to the AIGC industry, 10 points for medium relevance, and 5 points for low relevance; the dynamic scoring rules are as follows: 15 points for hot keywords with a 24-hour growth of over 300%, 10 points for a 200% week-on-week increase in ad placement frequency, 10 points for competitor ad placement changes, and 5 points for recruiting AIGC-related positions.

5. A multi-source data fusion customer acquisition system for the AIGC industry, characterized in that, include: Multi-source data fusion module: including data access layer, data governance layer, and data storage layer. The data access layer realizes compliant access to data from multiple platforms and data collection from short video platforms. The data governance layer completes data cleaning, correlation fusion, and accurate identification of AIGC products. The data storage layer realizes structured clue data storage. The lead quantification scoring and lifecycle management module includes a dual-dimensional scoring unit, a lead verification unit, and a lifecycle management unit. The dual-dimensional scoring unit performs static and dynamic score calculations and classifies the lead into levels. The lead verification unit verifies and supplements the decision-maker's contact information. The lifecycle management unit realizes the closed-loop flow of lead status. In-depth marketing analysis module: includes pain point and trend mining unit, competitor attack and defense analysis unit, and data correlation analysis unit, which respectively realize AIGC industry demand mining, competitor analysis and sales work order generation; Front-end display optimization module: includes list page display unit and detail page display unit. The list page display unit implements the priority task dashboard function, and the detail page display unit provides an overview, decision-maker matrix and multi-dimensional detail display. The intelligent early warning and automation empowerment module includes an intelligent early warning unit, a scenario-based proactive triggering unit, and a conversion funnel analysis unit. The intelligent early warning unit pushes high-value lead warnings, the scenario-based proactive triggering unit pushes targeted reminders and suggestions, and the conversion funnel analysis unit realizes conversion tracking and churn diagnosis.

6. A multi-source data fusion customer acquisition system for the AIGC industry according to claim 5, characterized in that: The data access layer of the multi-source data fusion module is configured with a dynamic IP pool and account rotation mechanism, and a crawling rate threshold is set, with a single Douyin account crawling no more than 500 items per day.

7. A multi-source data fusion customer acquisition system for the AIGC industry according to claim 5, characterized in that: The front-end display optimization module is developed using the Vue3+Element Plus framework. The details page display unit uses ECharts to draw AIGC product placement trend charts and competitor comparison radar charts. The AI ​​icebreaker function calls the GPT-4 API to generate customized scripts.

8. A multi-source data fusion customer acquisition system for the AIGC industry according to claim 5, characterized in that: The intelligent early warning and automation empowerment module's conversion funnel analysis unit integrates HubSpot's multi-dimensional attribution algorithm, supporting attribution analysis such as first contact and last contact.

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