Social media intelligent customer acquisition management system

Abstract

The invention discloses a social media intelligent guest obtaining management system, and belongs to the technical field of data processing systems. The system comprises a hotspot statistics module, an information release module, a customer source statistics module, a data verification module and a data feedback module. According to the system, a unique hash customer source mark generated by published content, an account and a timestamp is embedded into the content through an information publishing module, and a customer source statistics module captures the mark, so that an accurate tracking link from content to conversion is established. And the data feedback module further performs backtracking analysis on the customer obtaining process, identifies key nodes and time consumption, and iteratively extracts an efficient content normal form and hotspot features. And an analysis result is automatically fed back to an upstream hotspot statistics and information issuing module, adaptive optimization of content generation and hotspot screening strategies is driven, a closed-loop customer obtaining management system from accurate tracking to intelligent decision making is formed, and the customer obtaining efficiency is remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of data processing systems, and in particular to a social media intelligent customer acquisition management system. Background Technology

[0002] With the widespread adoption of social media, more and more businesses are using platforms like WeChat, Weibo, and Douyin for brand promotion and customer acquisition. However, current practices still present numerous challenges in social media customer acquisition management. Existing operational models typically rely on manual operations and a combination of multiple, fragmented tools. For example, operators need to manually track trending topics, write content, and publish information, then analyze customer acquisition data through backend or third-party statistical tools. This approach is not only fragmented and inefficient, but also has significant shortcomings in data tracking and analysis. First, in tracking customer acquisition sources, commonly used methods such as channel link parameters often only pinpoint the source to a large channel or a specific event, making it difficult to pinpoint exactly which piece of content or which account's posting directly led to customer conversion. This results in ambiguity in attribution analysis, making it impossible for businesses to accurately assess the true ROI of different content strategies. Secondly, the data generated by existing systems are mostly isolated, endpoint statistics, such as total customer acquisitions. They lack in-depth insights into the complete lifecycle from content publication to customer conversion and cannot retrospectively analyze process indicators such as key nodes and time consumption in the customer acquisition process. Therefore, it is difficult to identify and optimize bottlenecks in the conversion chain.

[0003] More importantly, the entire operational process lacks an intelligent closed-loop feedback mechanism. Data analysis results struggle to automatically and effectively feed back into front-end content creation and trend selection strategies. Operational decisions heavily rely on human experience and judgment, failing to leverage massive amounts of data for self-learning and strategy iteration, resulting in slow and uncertain improvements in customer acquisition efficiency. Furthermore, the lack of effective data verification methods during data flow and statistics fails to guarantee the authenticity and completeness of customer acquisition records, creating potential problems for subsequent analysis and decision-making. Therefore, the market urgently needs an integrated intelligent customer acquisition management system capable of achieving everything from trend tracking, intelligent content generation, accurate attribution, in-depth process analysis, to adaptive strategy optimization. Summary of the Invention

[0004] To address the aforementioned problems in the existing technology, the present invention aims to provide a social media intelligent customer acquisition management system, comprising: The hot topic statistics module is used to collect and analyze trending information published on social media platforms, and convert the term data of trending information into tag information for the management system.

[0005] The information publishing module is used to obtain the tagging information from the hot topic statistics module, generate publishing content based on the tagging information and preset content, and publish social media information after compliance checks.

[0006] The customer acquisition statistics module is used to statistically manage customer acquisition data, which includes customer acquisition source, acquisition time, and customer acquisition quantity statistics.

[0007] The data verification module is used to verify the customer acquisition data obtained from the customer source statistics module.

[0008] The data feedback module is used to perform data analysis by backtracking the data and return the analysis results to the associated modules.

[0009] Preferably, the customer acquisition sources include the tagging information of the hotspot statistics module, the published content, the publishing account, the customer conversion timestamp, and the hash value generated by concatenating the published content, the publishing account, and the customer conversion timestamp as customer source tags.

[0010] Preferably, the customer source tag serves as a process identifier, and the data feedback module includes a process backtracking analysis unit. This unit is used to define multiple key event nodes in the customer acquisition process, and record the event types and trigger times associated with each process identifier at each node, forming a process event log. Based on a set of process event logs associated with the same process identifier, it calculates and stores the chain-like time consumption from upstream to downstream nodes. According to user instructions, based on the process identifier, it backtracks and presents the complete sequence of event nodes corresponding to the specified customer acquisition process and the chain-like time consumption, for tracking a single customer acquisition journey.

[0011] Preferably, the data feedback module further includes a strategy iteration optimization unit, which is used to: acquire and aggregate event logs and chained time consumption data of multiple different processes from the process backtracking analysis unit; extract content feature parameters of content generation nodes from the process event logs and associate them with user conversion results recorded by subsequent nodes; identify content paradigms that can improve conversion efficiency and shorten the conversion cycle by establishing an efficiency correlation model between the content feature parameters, the user conversion results, and the chained time consumption; and use the content paradigms as optimization context to feed back and output to the upstream data providing unit of the system to drive the adaptive adjustment of the content generation strategy.

[0012] Preferably, when publishing the content, the information publishing module also embeds the unique customer source tag associated with the content into the publishing link and content carrier in the form of hidden metadata, so that it can be directly captured by the customer source statistics module when the customer source is converted, so as to establish a direct tracking link between the publishing behavior and the conversion behavior; the hidden metadata includes at least one of link parameter embedding and content carrier embedding.

[0013] Preferably, the link parameter embedding involves appending the unique customer source tag as a URL query parameter to the target redirect link contained in the published content, and optionally converting the complete link carrying the parameter into a short link through a short link service before publishing; the content carrier embedding involves directly writing the unique customer source tag into the digital file metadata of the published content itself, the digital file metadata including interchangeable image file format information, extensible metadata platform information, and being encoded as a digital watermark into the content data stream.

[0014] Preferably, the data feedback module provides data feedback to the customer acquisition statistics module. The data feedback includes quantifying the content paradigm generated by the strategy iteration optimization unit into a set of content evaluation weight parameters; the data feedback module updates the content evaluation weight parameters to the customer acquisition statistics module; when the customer acquisition statistics module performs statistics on new customer acquisition data, it uses the content evaluation weight parameters to initially score the quality of the customer acquisition data and records the score result together with the original customer acquisition data.

[0015] Preferably, the data feedback module returns the analysis results to the information publishing module to provide the efficient content paradigm as a reinforcement prompt for the generative model and a high-priority template for the content template library, which is used for automatically generated publishing content; and it provides the hotspot statistics module with hotspot tagging features associated with high customer acquisition efficiency, which are used to assign higher recommendation weights to new hotspots with similar features in subsequent hotspot screening.

[0016] Preferably, it includes an adaptive policy optimization method, comprising the following steps: The strategy iteration optimization unit gathers and analyzes process event logs and chained time consumption data associated with multiple different process identifiers, and extracts the high-efficiency content paradigm that can characterize high conversion efficiency and the hot spot marking features associated with high customer acquisition efficiency based on the analysis results. The extracted content paradigm is converted into a set of specific execution instructions for the information publishing module. The execution instructions include enhanced prompts for generative models or high-priority identifiers for the content template library, and are then sent to the information publishing module. The extracted hotspot marking features are converted into a set of weight adjustment parameters for the hotspot statistics module, and the hotspot screening model is updated based on these weight adjustment parameters so as to prioritize the recommendation of new hotspots with similar features in subsequent hotspot screening.

[0017] Preferably, the method includes a customer acquisition data verification method, comprising the following steps: Among them, the data verification module selects a customer acquisition data record to be verified from the customer source statistics module, which contains the original published content, the publishing account, the customer source conversion timestamp, and the stored customer source tag. Specifically, the original published content, publishing account, and customer conversion timestamp are extracted from the customer acquisition data record to be verified, and the same hash algorithm used when generating the stored customer tag is used to recalculate the concatenated data of these three to generate a verification hash value. Specifically, the newly generated verification hash value is precisely compared with the customer source tag already stored in the customer acquisition data record to be verified; if the comparison result is completely consistent, the customer acquisition data record is determined to be valid and the data is complete; if the comparison result is inconsistent, the customer acquisition data record is marked as abnormal data, and a preset alarm or data isolation process is triggered.

[0018] Compared to existing technologies, the advantages of this invention are as follows: Through its unique hash-based customer acquisition tagging and process backtracking analysis, this invention significantly enhances the granularity and depth of social media customer acquisition management. It upgrades the traditionally vague channel attribution to precise performance management of every posting action leading to customer conversion, making the contribution of each piece of content clearly measurable. More importantly, the system can fully present and quantify the conversion path and time spent for each potential customer, transforming the invisible customer acquisition process into a transparent, diagnosable, and optimizable management process, achieving refined process control over the entire customer acquisition lifecycle.

[0019] This invention constructs an intelligent closed-loop customer acquisition management mechanism, achieving a fundamental shift from "passive statistics" to "proactive optimization." The system can proactively learn from massive amounts of data, automatically extract efficient customer acquisition strategies, and seamlessly apply them to front-end content generation and hot topic filtering. This data-driven adaptive capability frees customer acquisition management from reliance on human experience, forming an intelligent management system capable of self-iteration and continuous improvement in conversion efficiency, while built-in data verification ensures the reliability of the entire management decision-making cycle. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram illustrating the module structure of the management system of the present invention.

[0021] Figure 2This is an exemplary flowchart of the adaptive strategy optimization method of the present invention.

[0022] Figure 3 This is an exemplary flowchart of the customer acquisition data verification method of the present invention. Detailed Implementation

[0023] The present invention will be further described below with reference to specific embodiments.

[0024] like Figure 1 The diagram shown is a schematic block diagram of the module structure of a social media intelligent customer acquisition management system provided in this embodiment, including: The hot topic statistics module is used to collect and statistically analyze trending information published on social media platforms, converting the keyword data of these trending information into tagging information for the management system. In one embodiment, the hot topic statistics module serves as the system's content inspiration source, typically consisting of one or more web crawlers and data analysis services deployed on a cloud server. This module periodically or in real-time monitors mainstream social media platforms such as Weibo, Douyin, and Xiaohongshu, obtaining trending information such as hot search lists, popular topics, and high-traffic videos by calling the platform's public APIs or parsing web page content. For each acquired hot topic, the module extracts its core keyword data and converts it into unified tagging information within the system. For example, for a trending keyword "where to go this weekend," the converted tagging information might be a structured JSON object: {"hotspot_id": "hotspot_001", "keywords": ["weekend", "travel", "guides"], "heat_score": 95, "platform": "weibo"}.

[0025] The information publishing module is used to obtain the tagging information from the hot topic statistics module, generate publishing content based on the tagging information and preset content, and publish social media information after compliance checks.

[0026] In one embodiment, the information publishing module serves as the system's content creation and distribution center. It acquires tagging information generated by the trending topics statistics module and, based on this tagging information and a pre-defined content template library or generative AI model, automatically generates trending-related content for publication. For example, based on the aforementioned tagging information, the system might generate text such as: "Still struggling to decide where to go this weekend? Here's a travel guide for you!" After content generation, the system performs compliance checks, such as using sensitive word filtering and copyright image detection services, to ensure the content complies with platform regulations. Once the checks are passed, the system automatically publishes the content to the corresponding platforms via pre-configured social media account APIs.

[0027] The customer acquisition statistics module is used to statistically manage customer acquisition data, which includes customer acquisition source, acquisition time, and customer acquisition quantity statistics. In this embodiment, it receives and manages all customer acquisition data generated by content published through this system through a data receiving interface. The customer acquisition data includes customer acquisition source, i.e., which content and account converted the user; customer acquisition time, i.e., the precise timestamp of the user completing the conversion; and customer acquisition quantity statistics, i.e., the total number of customers acquired from each source.

[0028] The data verification module is used to verify the customer acquisition data obtained from the customer source statistics module. In this embodiment, to accurately track the source of each customer acquisition, the system generates a unique customer source tag for each publishing activity. This customer source tag is a hash value, generated by concatenating the core text of the published content, the ID of the publishing account, and the precise timestamp of the publication, and then applying a hash algorithm such as SHA-256. For example, concatenating "Weekend Travel Guide..." + "company_account_01" + "2023-10-27T10:00:00Z" generates a hash value 'a1b2c3d4...'. This hash value, as the customer source tag, will be uniquely associated with this publishing activity and embedded in the published content.

[0029] The data feedback module is used to perform data analysis by backtracking the data and return the analysis results to the associated modules.

[0030] The customer acquisition sources include the tagging information of the hotspot statistics module, the published content, the publishing account, the customer conversion timestamp, and the hash value generated by concatenating the published content, the publishing account, and the customer conversion timestamp as customer source tags.

[0031] The customer acquisition tag serves as a process identifier. The data feedback module includes a process backtracking analysis unit, which is used to define multiple key event nodes in the customer acquisition process and record the event types and trigger times associated with each process identifier at each node, forming a process event log. Based on a set of process event logs associated with the same process identifier, the unit calculates and stores the chained time consumption from upstream to downstream nodes. According to user instructions, based on the process identifier, the unit backtracks and presents the complete sequence of event nodes corresponding to the specified customer acquisition process and the chained time consumption, for tracking a single customer acquisition process.

[0032] The data feedback module further includes a strategy iteration and optimization unit. This unit is used to: acquire and aggregate event logs and chained time consumption data from multiple different processes from the process backtracking analysis unit; extract content feature parameters of content generation nodes from the process event logs and associate them with user conversion results recorded in subsequent nodes; identify content paradigms that can improve conversion efficiency and shorten the conversion cycle by establishing a performance correlation model between the content feature parameters, user conversion results, and chained time consumption; and use these content paradigms as optimization context, feeding them back to the upstream data providing unit of the system to drive adaptive adjustments to the content generation strategy.

[0033] In one embodiment, the data feedback module aggregates a large amount of event logs and time-consuming data from different processes. It extracts content feature parameters from the event logs for content generation nodes, such as trending keywords, text length, image style, and video duration, and correlates these parameters with subsequent user conversion results. A machine learning model, such as a regression or classification model, is built to analyze the relationship between content feature parameters and conversion rates and conversion times. Through model analysis, the system can identify efficient content paradigms, such as "travel guides that include video and have text lengths under 100 words have a 30% higher conversion rate and a 50% shorter conversion cycle than pure text and images." This identified efficient content paradigm serves as optimization context, fed back to the information publishing module, so that this paradigm will be prioritized when generating content in the future.

[0034] When publishing the content, the information publishing module also embeds the unique customer source tag associated with the published content into the publishing link and content carrier in the form of hidden metadata. This is so that it can be directly captured by the customer source statistics module when the customer source is converted, thereby establishing a direct tracking link between the publishing behavior and the conversion behavior. The hidden metadata includes at least one of link parameter embedding and content carrier embedding.

[0035] The link parameter embedding involves appending the unique customer source identifier as a URL query parameter to the target redirect link contained in the published content, and optionally converting the complete link carrying the parameter into a short link through a short link service before publishing; the content carrier embedding involves directly writing the unique customer source identifier into the digital file metadata of the published content itself, the digital file metadata including the file's exchangeable image file format information, scalable metadata platform information, and being encoded as a digital watermark into the content data stream.

[0036] For example, the connection parameter embedding involves appending the unique customer source tag as a URL query parameter to the target redirect link included in the published content. For instance, a link might become 'http: / / example.com / product?source=a1b2c3d4...'. Optionally, the complete link carrying this parameter can be shortened using a link shortening service before publishing. When a user clicks this link and completes the conversion, the backend customer source statistics module can directly capture this customer source tag from the URL. The content carrier embedding involves directly writing the unique customer source tag into the digital file metadata of the published content itself, such as EXIF ​​information for images, XMP metadata for videos, or encoding it as a digital watermark into the content data stream. When a user converts by downloading images or scanning QR codes, the customer source statistics module can capture the customer source tag by parsing this metadata.

[0037] The data feedback module provides data feedback to the customer acquisition statistics module. The data feedback includes quantifying the content paradigm generated by the strategy iteration optimization unit into a set of content evaluation weight parameters; updating the content evaluation weight parameters to the customer acquisition statistics module; and when the customer acquisition statistics module performs statistics on new customer acquisition data, using the content evaluation weight parameters to initially score the quality of the customer acquisition data and recording the score results together with the original customer acquisition data.

[0038] The data feedback module returns the analysis results to the information publishing module, providing the efficient content paradigm as a reinforcement prompt for the generative model and a high-priority template for the content template library, used for automatically generated publishing content; it also provides the hotspot statistics module with hotspot tagging features associated with high customer acquisition efficiency, which are used to assign higher recommendation weights to new hotspots with similar features in subsequent hotspot screening.

[0039] like Figure 2 The diagram shown is an example flowchart of the adaptive strategy optimization method implemented in this embodiment, which includes the following steps: Step S101: The strategy iteration optimization unit gathers and analyzes process event logs and chained time consumption data associated with multiple different process identifiers, and extracts the high-efficiency content paradigm that can characterize high conversion efficiency and the hot spot marking features associated with high customer acquisition efficiency based on the analysis results.

[0040] For example, the strategy iteration optimization unit executes a batch or stream processing task, aggregating event logs and time-consuming data from all customer acquisition processes over the past 24 hours. By performing statistical analysis and machine learning modeling on this aggregated data, the system extracts efficient content paradigms, such as "content published on Friday nights containing the keyword 'where to go this weekend' and accompanied by a short video has an average conversion cycle of 2 hours," and high-efficiency hotspot tagging features, such as "hotspot tags related to 'local life' have the lowest average customer acquisition cost."

[0041] Step S102: The extracted content paradigm is converted into a set of specific execution instructions for the information publishing module. The execution instructions include enhanced prompts for generative models or high-priority identifiers for the content template library, and are sent to the information publishing module.

[0042] In one embodiment, the system converts the efficient content paradigm extracted in S101 into a set of specific execution instructions. For example, the paradigm of "with short video" is converted into a reinforcement prompt for generative AI models: "--stylevideo_included", or the priority flag of content templates conforming to this paradigm in the template library is raised from "normal" to "high". These instructions are sent to the information publishing module via internal API, so that it automatically adopts these optimization strategies the next time it generates content.

[0043] Step S103: The extracted hotspot marking features are converted into a set of weight adjustment parameters for the hotspot statistics module, and the hotspot screening model is updated based on the weight adjustment parameters so as to prioritize the recommendation of new hotspots with similar features in subsequent hotspot screening.

[0044] like Figure 3 As shown, this embodiment includes a customer acquisition data verification method, comprising the following steps: Step S201: The data verification module selects a customer acquisition data record to be verified from the customer source statistics module, which contains the original published content, the publishing account, the customer source conversion timestamp, and the stored customer source tag.

[0045] Step S202: Extract the original published content, publishing account, and customer conversion timestamp from the customer acquisition data record to be verified. Using the same hash algorithm as when generating the stored customer tag, recalculate the concatenated data to generate a verification hash value. For example, the data verification module will extract the fields 'Weekend Travel Guide...', 'company_account_01', and '2023-10-27T11:35:00Z' from the record and concatenate them. Then, using the exact same hash algorithm as when initially generating the customer tag, such as SHA-256, recalculate the concatenated string to generate a new verification hash value, such as 'a1b2c3d4...'.

[0046] Step S203: The newly generated verification hash value is precisely compared with the customer source tag already stored in the customer acquisition data record to be verified. If the comparison result is completely consistent, the customer acquisition data record is determined to be valid and complete. If the comparison result is inconsistent, the customer acquisition data record is marked as abnormal data, and a preset alarm or data isolation process is triggered. For example, the data verification module will perform a precise string comparison between the newly generated verification hash value 'a1b2c3d4...' in S202 and the customer source tag 'a1b2c3d4...' already stored in the record. If the two are completely consistent, the record is determined to be valid. If they are inconsistent, for example, if the stored customer source tag is 'e5f6g7h8...', the record is marked as abnormal, and an alarm notification may be triggered to the system administrator, or the data may be moved to an isolation table for manual verification.

[0047] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A social media intelligent customer acquisition management system, characterized in that: include, The hot topic statistics module is used to collect and analyze hot topic information published on social media platforms, and convert the term data of hot topic information into tag information for the management system. The information publishing module is used to obtain the tagging information from the hot topic statistics module, generate publishing content based on the tagging information and preset content, and publish social media information after compliance checks. The customer acquisition statistics module is used to statistically manage customer acquisition data, which includes customer acquisition source, acquisition time, and customer acquisition quantity statistics. The data verification module is used to obtain customer acquisition data from the customer source statistics module and verify the data. The data feedback module is used to perform data analysis by backtracking the data and return the analysis results to the associated modules.

2. The social media intelligent customer acquisition management system according to claim 1, characterized in that: The customer acquisition sources include the tagging information of the hotspot statistics module, the published content, the publishing account, the customer conversion timestamp, and the hash value generated by concatenating the published content, the publishing account, and the customer conversion timestamp as customer source tags.

3. The social media intelligent customer acquisition management system according to claim 2, characterized in that: The customer source tag serves as a process identifier. The data feedback module includes a process backtracking analysis unit, which is used to define multiple key event nodes in the customer acquisition process and record the event types and trigger times associated with each process identifier at each node, forming a process event log. Based on a set of process event logs associated with the same process identifier, calculate and store the chained time consumption from upstream node to downstream node; Based on user instructions and the process identifier, the system traces back and presents the complete sequence of event nodes and the chain-like time duration corresponding to the specified customer acquisition process, for tracking a single customer acquisition journey.

4. The social media intelligent customer acquisition management system according to claim 1, characterized in that: The data feedback module also includes a strategy iteration and optimization unit, which is used to obtain and aggregate event logs and chained time consumption data of multiple different processes from the process backtracking analysis unit; extract content feature parameters of content generation nodes from the process event logs and associate them with user conversion results recorded in subsequent nodes; and identify content paradigms that can improve conversion efficiency and shorten the conversion cycle by establishing an efficiency correlation model between the content feature parameters, the user conversion results and the chained time consumption. The content paradigm is used as an optimization context and fed back to the upstream data providing unit of the system to drive the adaptive adjustment of the content generation strategy.

5. The social media intelligent customer acquisition management system according to claim 1, characterized in that: When publishing the content, the information publishing module also embeds the unique customer source tag associated with the published content into the publishing link and content carrier in the form of hidden metadata. This is so that it can be directly captured by the customer source statistics module when the customer source is converted, thereby establishing a direct tracking link between the publishing behavior and the conversion behavior. The hidden metadata includes at least one of link parameter embedding and content carrier embedding.

6. The social media intelligent customer acquisition management system according to claim 5, characterized in that: The link parameter embedding involves appending the unique customer source identifier as a URL query parameter to the target redirect link contained in the published content, and optionally converting the complete link carrying the parameter into a short link through a short link service before publishing; the content carrier embedding involves directly writing the unique customer source identifier into the digital file metadata of the published content itself, the digital file metadata including the file's exchangeable image file format information, scalable metadata platform information, and being encoded as a digital watermark into the content data stream.

7. The social media intelligent customer acquisition management system according to claim 1, characterized in that: The data feedback module provides data feedback to the customer statistics module. The data feedback includes the content paradigm generated by the strategy iteration optimization unit being quantified into a set of content evaluation weight parameters. The data feedback module updates the content evaluation weight parameters to the customer acquisition statistics module; when the customer acquisition statistics module performs statistics on new customer acquisition data, it uses the content evaluation weight parameters to give a preliminary score to the quality of the customer acquisition data, and records the score results together with the original customer acquisition data.

8. The social media intelligent customer acquisition management system according to claim 1, characterized in that: The data feedback module returns the analysis results to the information publishing module, providing the efficient content paradigm as a reinforcement prompt for the generative model and a high-priority template for the content template library, used for automatically generated publishing content; it also provides the hotspot statistics module with hotspot tagging features associated with high customer acquisition efficiency, which are used to assign higher recommendation weights to new hotspots with similar features in subsequent hotspot screening.

9. The social media intelligent customer acquisition management system according to claim 1, characterized in that: This includes adaptive policy optimization methods, comprising the following steps: Step S101: The strategy iteration optimization unit gathers and analyzes process event logs and chained time consumption data associated with multiple different process identifiers, and extracts the high-efficiency content paradigm that can characterize high conversion efficiency and the hot spot marking features associated with high customer acquisition efficiency based on the analysis results. Step S102: The extracted content paradigm is converted into a set of specific execution instructions for the information publishing module. The execution instructions include enhanced prompts for generative models or high-priority identifiers for the content template library, and are sent to the information publishing module. Step S103: The extracted hotspot marking features are converted into a set of weight adjustment parameters for the hotspot statistics module, and the hotspot screening model is updated based on the weight adjustment parameters so as to prioritize the recommendation of new hotspots with similar features in subsequent hotspot screening.

10. A social media intelligent customer acquisition management system according to claim 1, characterized in that: It also includes customer acquisition data verification methods, including the following steps: Step S201: The data verification module selects a customer acquisition data record to be verified from the customer source statistics module, which contains the original published content, the publishing account, the customer source conversion timestamp, and the stored customer source tag. Step S202: Extract the original published content, publishing account, and customer conversion timestamp from the customer acquisition data record to be verified, and recalculate the concatenated data using the same hash algorithm as when generating the stored customer tag to generate a verification hash value. Step S203: The newly generated verification hash value is precisely compared with the customer source tag already stored in the customer acquisition data record to be verified; if the comparison result is completely consistent, the customer acquisition data record is determined to be valid and the data is complete; if the comparison result is inconsistent, the customer acquisition data record is marked as abnormal data and a preset alarm or data isolation process is triggered.

Images