An e-commerce customer service unified management and collaboration method and system supporting multi-channel access

Abstract

The application discloses a kind of support multi-channel access's e-commerce customer service unified management and collaboration method and system, it is related to electronic commerce technical field, including, by integrating enterprise internal information system and external e-commerce platform API, realize multi-channel access, automatically collect the basic information of customer, historical interaction record and current consultation content, and synchronously to unified data center.System carries out multi-modal sentiment analysis to the customer data collected, extracts sentiment features by voice and image recognition technology, accurately judges the emotional state of customer in combination with deep learning model.In addition, the system also has dynamic task allocation, personalized interaction interface generation, intelligent knowledge base update and cross-department collaboration and other functions, significantly improve the efficiency and user experience of e-commerce customer service system.

Description

[0001] This invention relates to the field of e-commerce technology, and in particular to a unified management and collaboration method and system for e-commerce customer service that supports multi-channel access. Background Technology

[0002] With the rapid development of e-commerce, e-commerce customer service technology has undergone a transformation from human customer service to intelligent customer service. Early e-commerce customer service relied primarily on human intervention, resulting in low efficiency and high costs. In recent years, the application of AI technology has enabled customer service systems to achieve automated interaction through voice and text recognition, providing 24 / 7 uninterrupted service. However, with the diversification and complexity of user needs, traditional customer service systems have gradually revealed their shortcomings in areas such as multi-channel access, emotional perception, and personalized service.

[0003] While existing e-commerce customer service systems have achieved a degree of automation and intelligence, they still have significant shortcomings in the unified management and collaboration of multi-channel access. For example, traditional customer service systems often fail to effectively integrate customer data from different channels when handling multi-channel access, leading to severe information silos and impacting customer service efficiency. Furthermore, existing systems are mostly limited to text analysis in terms of emotion perception, lacking a comprehensive judgment of voice and image emotions, and thus unable to accurately grasp customer emotions. These problems prevent existing technologies from meeting the demands of modern e-commerce customers for efficient and personalized services, limiting the improvement of user experience. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a unified management and collaboration method for e-commerce customer service that supports multi-channel access, solving the problems of difficulty in integrating multi-channel access data, inaccurate emotion perception, and how to achieve personalized service in existing e-commerce customer service systems.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a unified management and collaboration method for e-commerce customer service that supports multi-channel access, characterized by comprising the following steps: The system integrates internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect customers' basic information, historical interaction records, and current consultation content, and synchronize them to a unified data center; The system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through voice and image recognition technology, and combines them with deep learning models to accurately determine the customer's emotional state. Based on the customer's emotional state and the type of problem, the system selects customer service personnel with the corresponding skill tags and the best current state from the customer service personnel database, and dynamically allocates tasks by taking into account workload and historical performance. The system automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The system monitors the resolution of customer issues and feedback information in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. When a customer's problem involves multiple departments, the system automatically activates a cross-departmental collaboration mechanism, breaking down the problem into multiple sub-tasks and assigning them to the relevant department heads. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the problem is resolved within the specified time.

[0007] As a preferred embodiment of the unified management and collaboration method for e-commerce customer service supporting multi-channel access described in this invention, the system achieves multi-channel access by integrating internal enterprise information systems and external e-commerce platform APIs, automatically collecting basic customer information, historical interaction records, and current inquiry content, and synchronizing them to a unified data center. The specific steps are as follows: The collected speech data is preprocessed, including noise reduction and normalization, to improve the quality of the speech signal. Extract emotional features from speech signals, including pitch, speech rate, and volume, and use deep learning models for emotion classification. The acquired image or video data is preprocessed, including face detection and expression recognition, and facial expression features are extracted using convolutional neural networks; By fusing the emotional features of voice and images, a deep learning model is used to comprehensively judge the customer's emotional state.

[0008] As a preferred embodiment of the unified management and collaboration method for e-commerce customer service supporting multi-channel access described in this invention, the system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through voice and image recognition technology, and combines a deep learning model to accurately determine the customer's emotional state. The specific steps are as follows: Based on the type of customer's question and their emotional state, select customer service personnel with corresponding skill tags from the customer service personnel database; Taking into account the real-time workload and historical performance of customer service personnel, a priority score is calculated for each customer service personnel. Based on priority scores, tasks are dynamically assigned to the most suitable customer service personnel, and the assignment results are communicated to the customer service personnel in real time. Monitor the progress of customer service staff in handling tasks and dynamically adjust task allocation based on the task progress.

[0009] As a preferred embodiment of the unified management and collaboration method for e-commerce customer service supporting multi-channel access described in this invention, the following steps are taken: Based on the customer's emotional state and question type, the system selects customer service personnel with corresponding skill tags and currently at their best from the customer service personnel database, and dynamically allocates tasks considering workload and historical performance. Analyze customers' browsing history, purchasing behavior, and emotional state to extract customer preference characteristics; Based on customer preferences, design personalized interactive interface layouts and content display methods; Personalized interactive interfaces are pushed in real time through the customer's access channels; We dynamically adjust the content and layout of the user interface based on real-time customer feedback.

[0010] As a preferred embodiment of the unified management and collaboration method for e-commerce customer service supporting multi-channel access described in this invention, the system automatically generates personalized interactive interfaces based on the customer's browsing history, purchasing behavior, and emotional state, and pushes them in real time through the customer's access channels. The specific steps are as follows: Real-time monitoring of customer problem resolution and feedback information, extracting valuable knowledge content; The knowledge base is automatically updated based on the extracted knowledge content to ensure its accuracy and timeliness. Based on the customer service personnel's skill tags and the type of problem they are currently handling, relevant knowledge content is selected from the knowledge base; The selected knowledge content is pushed to customer service staff in real time to help them quickly resolve issues.

[0011] As a preferred embodiment of the unified management and collaboration method for e-commerce customer service supporting multi-channel access described in this invention, the system monitors the resolution status and feedback information of customer issues in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue currently being handled. The specific steps are as follows: When a customer's problem involves multiple departments, the system automatically identifies the key issues and the responsible department. Break the problem down into multiple sub-tasks, and clarify the responsible department and processing procedures for each sub-task; Sub-tasks are assigned to the heads of the relevant departments, and information and progress are shared in real time through the collaboration platform; Monitor the progress of cross-departmental collaboration, dynamically adjust collaboration strategies based on progress, and ensure that problems are resolved within the stipulated time.

[0012] As a preferred embodiment of the e-commerce customer service unified management and collaboration method supporting multi-channel access described in this invention, the system automatically initiates a cross-departmental collaboration mechanism when a customer issue involves multiple departments. The issue is broken down into multiple sub-tasks and assigned to the responsible persons of the corresponding departments. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the issue is resolved within the specified time. The specific steps are as follows: Standardize the data from multiple channels to ensure consistency in data format; Protect customer data privacy and security using data encryption technology; The collected data is cleaned and preprocessed in real time to remove invalid or duplicate data. The processed data is stored in a unified data center for subsequent analysis and processing.

[0013] Secondly, this invention provides a unified management and collaboration system for e-commerce customer service that supports multi-channel access, including: The system integrates with internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect basic customer information, historical interaction records, and current inquiry content, and synchronize them to a unified data center. The sentiment analysis module performs multimodal sentiment analysis on the collected customer data, extracts emotional features through speech and image recognition technology, and combines them with a deep learning model to accurately determine the customer's emotional state. The task allocation module dynamically allocates tasks based on the customer's emotional state and the type of problem, selecting customer service personnel with the corresponding skill tags and currently in the best condition from the customer service personnel database, taking into account workload and historical performance. The interface generation module automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The knowledge base update module allows the system to monitor the resolution status and feedback of customer issues in real time, automatically update the knowledge base, and push relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. The collaboration management module automatically activates a cross-departmental collaboration mechanism when a customer's issue involves multiple departments. The issue is broken down into multiple sub-tasks and assigned to the responsible persons of the corresponding departments. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the issue is resolved within the specified time.

[0014] Thirdly, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of the unified management and collaboration method for e-commerce customer service supporting multi-channel access as described in the first aspect of the present invention.

[0015] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the unified management and collaboration method for e-commerce customer service supporting multi-channel access as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: Through innovative technologies such as multi-channel access, multimodal sentiment analysis, dynamic task allocation, personalized interactive interfaces, intelligent knowledge base updates, and cross-departmental collaboration, the efficiency and user experience of e-commerce customer service systems are significantly improved. First, multi-channel access and data integration break down information silos in traditional customer service systems, achieving comprehensive collection and unified management of customer data. Second, multimodal sentiment analysis technology accurately judges customer emotions through voice and image recognition, providing customer service personnel with more comprehensive customer status information. The dynamic task allocation mechanism optimizes task allocation based on customer service personnel's skill tags and real-time status, improving problem-solving efficiency. The personalized interactive interface is generated based on the customer's browsing history and emotional state, enhancing the user experience. The dynamic updating of the intelligent knowledge base ensures that customer service personnel can access the latest knowledge content, improving the accuracy and timeliness of responses. Finally, the cross-departmental collaboration mechanism ensures that complex problems can be resolved quickly through task decomposition and real-time information sharing. These innovations work together to not only improve customer service efficiency but also significantly enhance customer satisfaction and loyalty, providing e-commerce companies with a more competitive customer service solution. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the unified management and collaboration method for e-commerce customer service that supports multi-channel access, as described in Example 1. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a unified management and collaboration method for e-commerce customer service that supports multi-channel access, characterized by including the following steps: The system integrates internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect customers' basic information, historical interaction records, and current consultation content, and synchronize them to a unified data center; The system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through voice and image recognition technology, and combines them with deep learning models to accurately determine the customer's emotional state. Based on the customer's emotional state and the type of problem, the system selects customer service personnel with the corresponding skill tags and the best current state from the customer service personnel database, and dynamically allocates tasks by taking into account workload and historical performance. The system automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The system monitors the resolution of customer issues and feedback information in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. When a customer's problem involves multiple departments, the system automatically activates a cross-departmental collaboration mechanism, breaking down the problem into multiple sub-tasks and assigning them to the relevant department heads. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the problem is resolved within the specified time.

[0023] It should be noted that the system achieves multi-channel access by integrating internal enterprise information systems and external e-commerce platform APIs. Specifically, the system first connects to the enterprise's internal Customer Relationship Management (CRM) and Order Management (OMS) systems, as well as the API interfaces of external e-commerce platforms such as Taobao and JD.com. When a customer initiates an inquiry through any channel (such as online customer service, telephone, social media, etc.), the system automatically triggers a data collection mechanism to collect the customer's basic information, historical interaction records, and current inquiry content in real time. The collected data undergoes standardized processing to ensure data format consistency and is then synchronized to a unified data center.

[0024] By integrating data from multiple channels, the information silos of traditional customer service systems have been broken down, enabling comprehensive collection and unified management of customer data. This not only improves customer service efficiency but also provides a solid data foundation for subsequent multimodal sentiment analysis and personalized services.

[0025] The system performs multimodal sentiment analysis on the collected customer data. Specific steps include: preprocessing the collected speech data, such as noise reduction and normalization, to improve the quality of the speech signal; extracting emotional features from the speech signal, including pitch, speech rate, and volume, and using a deep learning model for sentiment classification; preprocessing the collected image or video data, including face detection and expression recognition, and using a convolutional neural network to extract facial expression features; finally, fusing the emotional features of the speech and images, and using a deep learning model to comprehensively determine the customer's emotional state.

[0026] Multimodal sentiment analysis technology uses voice and image recognition to accurately determine customer emotions, providing customer service personnel with more comprehensive customer status information. This helps customer service personnel better understand customer needs and emotions, thereby providing more targeted services and improving customer satisfaction.

[0027] Based on the customer's emotional state and the type of issue, the system selects customer service personnel with the corresponding skill tags and currently at their best from the customer service personnel database. Specifically, the system first analyzes the type of customer issue, such as inquiries, complaints, and after-sales service, and then, combined with the customer's emotional state, selects customer service personnel with the corresponding skill tags from the database. The system comprehensively considers the real-time workload and historical performance of each customer service personnel to calculate a priority score. Based on the priority score, tasks are dynamically assigned to the most suitable customer service personnel, and the assignment results are communicated to the personnel in real time. Simultaneously, the system monitors the task processing progress of customer service personnel and dynamically adjusts task allocation based on the task processing status.

[0028] The dynamic task allocation mechanism optimizes task distribution based on customer service personnel's skill tags and real-time status, improving problem-solving efficiency. This not only ensures that customer issues are resolved promptly and effectively but also enhances the utilization efficiency of customer service resources.

[0029] The system automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state. Specific steps include: analyzing customers' browsing history and purchasing behavior to extract their preference characteristics; designing personalized interactive interface layouts and content display methods based on these preferences; pushing personalized interactive interfaces in real-time through customers' access channels; and dynamically adjusting the content and layout of the interactive interface based on real-time customer feedback.

[0030] Personalized user interfaces are generated based on customers' browsing history and emotional state, enhancing the user experience. This helps improve customer satisfaction with the customer service system and strengthens interaction and trust between customers and the company.

[0031] The system monitors the resolution status and feedback of customer issues in real time and automatically updates the knowledge base. Specific steps include: real-time monitoring of customer issue resolution and feedback to extract valuable knowledge content; automatically updating the knowledge base based on the extracted knowledge content to ensure its accuracy and timeliness; filtering relevant knowledge content from the knowledge base based on customer service personnel's skill tags and the type of issue being handled; and pushing the filtered knowledge content to customer service personnel in real time to assist them in quickly resolving problems.

[0032] The dynamic updates to the intelligent knowledge base ensure that customer service personnel have access to the latest knowledge, improving the accuracy and timeliness of their responses. This not only enhances the efficiency of customer service personnel but also further improves customer satisfaction.

[0033] When a customer issue involves multiple departments, the system automatically initiates a cross-departmental collaboration mechanism. Specific steps include: the system automatically identifies key points and responsible departments for the issue; the issue is broken down into multiple sub-tasks, clarifying the responsible department and processing flow for each sub-task; sub-tasks are assigned to the relevant department heads, and information and progress are shared in real-time through the collaboration platform; the progress of cross-departmental collaboration is monitored, and collaboration strategies are dynamically adjusted based on the progress to ensure the issue is resolved within the stipulated time.

[0034] Cross-departmental collaboration mechanisms, through task breakdown and real-time information sharing, ensure that complex problems can be resolved quickly. This not only improves operational efficiency but also enhances overall customer satisfaction.

[0035] Specifically, the system integrates internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collects basic customer information, historical interaction records, and current inquiry content, and synchronizes them to a unified data center. The specific steps are as follows: The collected speech data is preprocessed, including noise reduction and normalization, to improve the quality of the speech signal. Extract emotional features from speech signals, including pitch, speech rate, and volume, and use deep learning models for emotion classification. The acquired image or video data is preprocessed, including face detection and expression recognition, and facial expression features are extracted using convolutional neural networks; By fusing the emotional features of voice and images, a deep learning model is used to comprehensively judge the customer's emotional state.

[0036] It should be noted that in e-commerce customer service systems with multi-channel access, preprocessing of voice data is a crucial step in ensuring the accuracy of subsequent sentiment analysis. The system first performs noise reduction on the received voice signal to remove background noise and interference signals, thereby improving the clarity of the voice signal. Subsequently, the processed voice signal undergoes normalization, adjusting the amplitude and frequency range of the voice signal to meet the requirements of subsequent processing modules. This process utilizes advanced digital signal processing technology to ensure that the voice data reaches optimal quality before entering the sentiment analysis module.

[0037] By preprocessing voice data, the system can more accurately extract emotional features from speech, thereby improving the accuracy of sentiment analysis. This not only helps customer service personnel better understand customers' emotional states but also provides more reliable data support for subsequent personalized services.

[0038] In the preprocessed speech signal, the system extracts key emotional features, such as pitch, speech rate, and volume. Changes in pitch can reflect the customer's emotional fluctuations, the speed of speech may indicate the customer's urgency, and the volume may be related to the intensity of the customer's emotions. These features are analyzed and classified using a deep learning model. The model, trained on a large amount of labeled data, can accurately identify different emotional states, such as anger, satisfaction, and anxiety.

[0039] By classifying the emotional features of speech using deep learning models, the system can accurately determine a customer's emotional state in real time. This enables customer service personnel to provide more targeted services based on the customer's emotions, thereby improving customer satisfaction and loyalty.

[0040] The system preprocesses the acquired image or video data, first performing face detection to determine the location of faces in the image or video. Then, it performs expression recognition on the detected faces, extracting facial expression features such as furrowed eyebrows and upturned corners of the mouth. These features are analyzed using a convolutional neural network, which can automatically learn and extract key features from images, thereby accurately recognizing the customer's expressions and emotions.

[0041] By analyzing the sentiment of images and videos, the system can gain a more comprehensive understanding of customers' emotional states, especially when customers are making video calls or uploading pictures. This not only enriches the dimensions of sentiment analysis but also provides customer service personnel with more intuitive emotional feedback, helping to offer more humane and personalized services.

[0042] The system fuses extracted voice and image emotion features and uses a deep learning model to comprehensively analyze this multimodal data. By learning the correlation between voice and image features, the model can more accurately determine the customer's emotional state. For example, when the voice signal indicates that the customer is calm, but the image shows that the customer is tense, the model can integrate this information to make a more accurate emotion judgment.

[0043] Through multimodal sentiment analysis, the system can more accurately grasp customers' emotional states, providing customer service personnel with more comprehensive customer information. This not only improves customer service efficiency but also enhances customer experience, enabling customer service personnel to resolve customer issues more effectively and increase customer satisfaction.

[0044] Specifically, the system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through speech and image recognition technology, and combines this with a deep learning model to accurately determine the customer's emotional state. The specific steps are as follows: Based on the type of customer's question and their emotional state, select customer service personnel with corresponding skill tags from the customer service personnel database; Taking into account the real-time workload and historical performance of customer service personnel, a priority score is calculated for each customer service personnel. Based on priority scores, tasks are dynamically assigned to the most suitable customer service personnel, and the assignment results are communicated to the customer service personnel in real time. Monitor the progress of customer service staff in handling tasks and dynamically adjust task allocation based on the task progress.

[0045] It should be noted that the system first categorizes received customer inquiries, identifying their type, such as consultation, complaint, or after-sales service. Simultaneously, combining the emotional state output from the multimodal sentiment analysis module, the system filters customer service personnel with corresponding skill tags from its database. These skill tags include, but are not limited to, professional knowledge, communication skills, and emotional management abilities. By matching question type and emotional state, the system ensures that the selected customer service personnel can better understand and handle customer issues.

[0046] By accurately selecting customer service personnel with the relevant skills, the system can ensure that customer issues are handled more professionally and effectively, thereby improving problem-solving efficiency and customer satisfaction.

[0047] The system monitors customer service staff's workload in real time, including the number and complexity of tasks currently being processed. Simultaneously, the system calculates a priority score for each customer service representative based on their historical performance data, such as problem-solving speed and customer satisfaction ratings. This priority score comprehensively reflects the representative's current work ability and status, and the system dynamically allocates tasks based on this score.

[0048] By taking into account workload and historical performance, the system can allocate tasks more rationally, avoid customer service staff being overloaded or idle, optimize the utilization efficiency of customer service resources, and improve the overall quality of customer service.

[0049] The system dynamically assigns tasks to the most suitable customer service personnel based on calculated priority scores. The assignment process considers the urgency and complexity of the tasks to ensure timely processing. The assignment results are communicated to customer service personnel in real-time through the customer service system interface, allowing them to begin processing the assigned tasks immediately.

[0050] The dynamic task allocation mechanism ensures that tasks are assigned to the most suitable customer service personnel in a timely and efficient manner, improving the speed and efficiency of problem resolution and enhancing the job satisfaction of customer service personnel.

[0051] The system monitors customer service staff's task processing progress in real time, including task processing time and customer feedback. Based on the task processing status, the system dynamically adjusts task allocation. For example, if a customer service staff member takes too long to process a task, the system may reassign some tasks to other customer service staff to ensure that tasks are completed on time.

[0052] By monitoring and dynamically adjusting task allocation in real time, the system can ensure efficient and timely task processing, further improving the overall performance of the customer service system and customer satisfaction.

[0053] Specifically, based on the customer's emotional state and the type of problem, the system filters customer service personnel from the database who possess the corresponding skill tags and are currently in the best condition. Taking into account workload and historical performance, tasks are dynamically allocated. The specific steps are as follows: Analyze customers' browsing history, purchasing behavior, and emotional state to extract customer preference characteristics; Based on customer preferences, design personalized interactive interface layouts and content display methods; Personalized interactive interfaces are pushed in real time through the customer's access channels; We dynamically adjust the content and layout of the user interface based on real-time customer feedback.

[0054] It should be noted that the system first collects customer browsing history and purchasing behavior data, including browsing paths, dwell time, purchase frequency, and purchase categories on the e-commerce platform. Simultaneously, combined with the emotional state output from the multimodal sentiment analysis module, the system conducts in-depth analysis of customer behavioral data. Through data mining and machine learning algorithms, the system can identify customer preference characteristics, such as preferences for specific brands, price ranges, and product features. This process not only considers customers' explicit behavior but also incorporates sentiment analysis results to gain a more comprehensive understanding of customer needs.

[0055] By analyzing customers' browsing history, purchasing behavior, and emotional state, the system can accurately extract customers' preference characteristics, providing data support for subsequent personalized services. This not only improves customer satisfaction but also enhances interaction and trust between customers and the platform.

[0056] The system designs personalized user interfaces based on extracted customer preference characteristics. Specifically, the system adjusts the interface layout and content display according to customer preferences for different product categories. For example, for customers who prefer electronic products and are price-sensitive, the interface will highlight recommendations and promotional information for high-value products. Simultaneously, the system adjusts the interface's color scheme and language style based on the customer's emotional state to provide a more comfortable and user-friendly experience.

[0057] By designing personalized user interfaces, the system can better meet customers' individual needs and enhance user experience. This personalized service not only increases customer satisfaction but also strengthens customer loyalty.

[0058] The system pushes personalized interactive interfaces in real time based on the customer's access channel (such as web, mobile, social media, etc.). Utilizing real-time data transmission technology, the system ensures a consistent and personalized experience for customers across all access channels. For example, when a customer accesses the system via a mobile device, it will push a mobile-optimized interactive interface, optimizing display effects and workflow.

[0059] By pushing personalized interactive interfaces in real time, the system ensures that customers receive the best experience across any access channel. This consistent experience across multiple channels not only improves customer satisfaction but also enhances customer trust in the platform.

[0060] The system monitors customer feedback in real time, including click behavior, dwell time, and scrolling behavior. Based on this real-time feedback, the system dynamically adjusts the content and layout of the user interface. For example, if a customer spends a long time on a product page, the system automatically adjusts the page layout to highlight detailed information and recommendations for the relevant product. Simultaneously, the system adjusts the language style and color scheme based on customer feedback to better suit the customer's emotional state.

[0061] By dynamically adjusting the content and layout of the user interface, the system can respond to customer needs and feedback in real time, providing more considerate and personalized services. This dynamic adjustment not only improves customer satisfaction but also enhances customer engagement and loyalty.

[0062] Specifically, the system automatically generates a personalized interactive interface based on the customer's browsing history, purchasing behavior, and emotional state, and pushes it in real time through the customer's access channels. The specific steps are as follows: Real-time monitoring of customer problem resolution and feedback information, extracting valuable knowledge content; The knowledge base is automatically updated based on the extracted knowledge content to ensure its accuracy and timeliness. Based on the customer service personnel's skill tags and the type of problem they are currently handling, relevant knowledge content is selected from the knowledge base; The selected knowledge content is pushed to customer service staff in real time to help them quickly resolve issues.

[0063] It should be noted that the system integrates various data monitoring tools to track the processing progress and feedback information of customer issues in real time. These tools include, but are not limited to, customer feedback forms, real-time chat logs, and voice call records. The system utilizes Natural Language Processing (NLP) technology and machine learning algorithms to analyze the collected feedback information and extract key information and knowledge content. For example, the system can identify common problems in customer feedback, the effectiveness of solutions, and customer satisfaction. This knowledge content will be stored in a knowledge base for later use.

[0064] By monitoring and analyzing customer feedback in real time, the system can promptly identify problems and shortcomings in its services, providing a basis for updating the knowledge base. This not only improves the response speed of the customer service system but also enhances the targetedness and effectiveness of problem-solving, thereby increasing customer satisfaction.

[0065] The system utilizes extracted knowledge content and employs automated scripts and data processing tools to regularly update the knowledge base. The update process includes verifying, correcting, and supplementing existing knowledge entries, as well as creating new ones. Through machine learning algorithms, the system automatically identifies outdated information in the knowledge base and updates it based on the latest customer feedback and solutions. For example, when new product features are launched or policies and regulations change, the system can automatically update the relevant content in the knowledge base, ensuring that it always reflects the latest business information.

[0066] Automatically updated knowledge bases ensure customer service staff have access to the latest and most accurate information, improving the accuracy and timeliness of responses. This not only enhances customer service efficiency but also strengthens customer trust in the system, further increasing customer satisfaction.

[0067] The system uses intelligent search algorithms to filter relevant knowledge content from the knowledge base based on the customer service representative's skill tags and the type of problem being handled. Skill tags include the customer service representative's area of ​​expertise and experience in handling problems. The system analyzes the keywords and context of the current problem, matches relevant entries in the knowledge base, and pushes this knowledge content to the customer service representative. For example, when a customer service representative handles a question about a product feature, the system will automatically push knowledge entries related to that product feature, including solutions to common problems and product features.

[0068] By precisely filtering knowledge content, the system can provide customer service personnel with more targeted support, helping them quickly resolve issues. This not only improves customer service efficiency but also reduces customer wait times, further enhancing the customer experience.

[0069] The system uses real-time data transmission technology to instantly push selected knowledge content to customer service personnel. This content includes problem solutions, relevant product information, and frequently asked questions (FAQs). Customer service personnel can directly access this knowledge content through the customer service system interface and apply it to handling current customer issues. For example, when a customer service representative is communicating with a customer, the system will display relevant knowledge items in real time, allowing the representative to refer to this content and quickly provide an accurate response.

[0070] Real-time knowledge updates provide customer service staff with immediate support, helping them handle customer issues more efficiently. This not only improves the speed and quality of problem resolution but also enhances the professionalism of customer service staff, further increasing customer satisfaction.

[0071] Specifically, the system monitors the resolution status and feedback of customer issues in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. The specific steps are as follows: When a customer's problem involves multiple departments, the system automatically identifies the key issues and the responsible department. Break the problem down into multiple sub-tasks, and clarify the responsible department and processing procedures for each sub-task; Sub-tasks are assigned to the heads of the relevant departments, and information and progress are shared in real time through the collaboration platform; Monitor the progress of cross-departmental collaboration, dynamically adjust collaboration strategies based on progress, and ensure that problems are resolved within the stipulated time.

[0072] It should be noted that the system, through an integrated multi-department collaboration module, analyzes the content and context of customer issues in real time, and uses Natural Language Processing (NLP) technology to identify key nodes in the problem. Based on preset business processes and departmental responsibility rules, the system automatically determines the departments involved and breaks down the problem into multiple sub-tasks. For example, when a customer reports a problem involving product defects and after-sales service, the system can identify the key nodes, such as product defect identification and after-sales service handling, and assign them to the quality control department and the after-sales service department, respectively.

[0073] By automatically identifying key issues and responsible departments, the system can quickly initiate cross-departmental collaboration mechanisms, reducing waiting time for problem handling and improving the efficiency and accuracy of problem resolution.

[0074] The system breaks down a problem into multiple sub-tasks based on its key elements, clearly defining the responsible department and processing flow for each sub-task. For example, for a problem involving product defects and after-sales service, the system breaks it down into two sub-tasks: "product defect identification" and "after-sales service handling." The quality control department is responsible for product defect identification, and the after-sales service department is responsible for handling customer returns and exchanges. The system uses a task management module to assign a unique task number to each sub-task and records the task's progress and the responsible person.

[0075] By breaking down problems into multiple sub-tasks, the system can clearly define the responsibilities and processing procedures of each department, avoiding unclear responsibilities and chaotic processes, and ensuring that problems can be solved efficiently and in an orderly manner.

[0076] The system uses a collaboration platform to assign sub-tasks to the relevant department heads and shares task information and processing progress in real time. The collaboration platform supports task creation, assignment, tracking, and feedback, ensuring that department heads are promptly informed of the latest task status. For example, the head of the quality control department can receive a "product defect identification" task through the collaboration platform and provide timely feedback after completion; the head of the after-sales service department can process customer return and exchange requests based on the identification results and update the processing progress on the collaboration platform.

[0077] By sharing information and progress in real time through a collaborative platform, the system can ensure timely information transmission and collaborative work between departments, improving the efficiency and transparency of cross-departmental collaboration.

[0078] The system monitors the processing progress of each subtask in real time through a task management module and evaluates the progress using data analysis tools. If the processing time of a subtask exceeds expectations, the system will automatically issue an alert and dynamically adjust the collaboration strategy according to preset rules. For example, if the "product defect identification" task of the quality control department takes too long to process, the system can automatically raise the task priority and notify relevant departments to speed up the processing; if necessary, the system can also reassign the task to other departments with available resources. In this way, the system ensures that problems can be resolved within the specified time and avoids the delay of one department affecting the overall problem-solving progress.

[0079] By monitoring the progress of cross-departmental collaboration and dynamically adjusting collaboration strategies, the system can ensure that problems are resolved within a specified time, thereby improving customer satisfaction and loyalty.

[0080] Specifically, when a customer issue involves multiple departments, the system automatically initiates a cross-departmental collaboration mechanism, breaking down the issue into multiple sub-tasks and assigning them to the relevant department heads. Personnel from each department share information and progress in real time through the collaboration platform, ensuring the issue is resolved within the stipulated time. The specific steps are as follows: Standardize the data from multiple channels to ensure consistency in data format; Protect customer data privacy and security using data encryption technology; The collected data is cleaned and preprocessed in real time to remove invalid or duplicate data. The processed data is stored in a unified data center for subsequent analysis and processing.

[0081] It should be noted that the system standardizes the multi-channel data it receives through a data processing module. This process includes unifying data formats, adjusting encoding standards, and optimizing data structures. Specifically, the system first identifies format differences between data from different channels, such as the encoding format of text data and the resolution of image data. Then, using data conversion tools, the system converts this data into a unified format to ensure consistency and compatibility in subsequent processing. For example, for order data from different e-commerce platforms, the system converts it into a unified XML or JSON format for later analysis and processing.

[0082] Through standardized processing, the system can effectively integrate data from different channels, breaking down information silos and providing a solid foundation for subsequent data analysis and processing. This not only improves data processing efficiency but also ensures data consistency and accuracy, laying the groundwork for unified management of multi-channel data.

[0083] The system employs advanced data encryption technology to encrypt collected customer data. During data transmission, the system uses encryption protocols such as SSL / TLS to ensure data security and confidentiality during transmission. In the data storage phase, sensitive data, such as customer personal information and order details, is stored in encrypted form. Furthermore, the system conducts regular security audits to check the integrity and effectiveness of data encryption. For example, the system regularly tests the encryption strength of customer data stored in the data center to ensure that the data cannot be illegally accessed or leaked under any circumstances.

[0084] Through data encryption technology, the system effectively protects the privacy and security of customer data, preventing data leaks and misuse. This not only enhances customer trust in the system but also complies with data protection regulations, providing a guarantee for the company's compliant operation.

[0085] The system uses a data cleaning module to clean and preprocess the collected data in real time. This process includes removing duplicate data, correcting erroneous data, and filling in missing data. Specifically, the system first uses a data deduplication algorithm to identify and delete duplicate data records. Then, it uses data validation tools to check the completeness and accuracy of the data and correct erroneous data. For missing data, the system will fill it in appropriately based on contextual information. For example, for missing address information in a customer order, the system will fill it in based on other historical order information of the customer. In addition, the system will also perform data normalization processing to ensure data comparability and consistency.

[0086] Through real-time cleaning and preprocessing, the system ensures high-quality and usable data, improving the efficiency and accuracy of data processing. This not only reduces the impact of invalid data on system performance but also provides reliable data support for subsequent data analysis and decision-making.

[0087] The system stores the cleaned and pre-processed data in a unified data center. The data center employs a distributed storage architecture to ensure high availability and scalability. During data storage, the system categorizes and indexes the data according to its type and purpose. For example, basic customer information is stored in a relational database for quick querying and updates, while customer behavioral data is stored in a non-relational database for large-scale data analysis. Furthermore, the system regularly backs up the data center to ensure data security and integrity.

[0088] By storing processed data in a unified data center, the system enables centralized data management and efficient utilization. This not only improves data processing efficiency but also provides a unified data foundation for subsequent data analysis and decision-making, further enhancing the overall system performance and user experience.

[0089] This embodiment also provides a unified management and collaboration system for e-commerce customer service that supports multi-channel access, including: The system integrates with internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect basic customer information, historical interaction records, and current inquiry content, and synchronize them to a unified data center. The sentiment analysis module performs multimodal sentiment analysis on the collected customer data, extracts emotional features through speech and image recognition technology, and combines them with a deep learning model to accurately determine the customer's emotional state. The task allocation module dynamically allocates tasks based on the customer's emotional state and the type of problem, selecting customer service personnel with the corresponding skill tags and currently in the best condition from the customer service personnel database, taking into account workload and historical performance. The interface generation module automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The knowledge base update module allows the system to monitor the resolution status and feedback of customer issues in real time, automatically update the knowledge base, and push relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. The collaboration management module automatically activates a cross-departmental collaboration mechanism when a customer's issue involves multiple departments. The issue is broken down into multiple sub-tasks and assigned to the responsible persons of the corresponding departments. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the issue is resolved within the specified time.

[0090] This embodiment also provides a computer device applicable to a unified management and collaboration method for e-commerce customer service that supports multi-channel access, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the unified management and collaboration method for e-commerce customer service that supports multi-channel access as proposed in the above embodiment.

[0091] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0092] This embodiment also provides a storage medium storing a computer program. When executed by a processor, the program implements the unified management and collaboration method for e-commerce customer service supporting multi-channel access as proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0093] In summary, this invention significantly improves the efficiency and user experience of e-commerce customer service systems through innovative technologies such as multi-channel access, multimodal sentiment analysis, dynamic task allocation, personalized interactive interfaces, intelligent knowledge base updates, and cross-departmental collaboration. First, multi-channel access and data integration break down information silos in traditional customer service systems, enabling comprehensive collection and unified management of customer data. Second, multimodal sentiment analysis technology accurately judges customer emotions through voice and image recognition, providing customer service personnel with more comprehensive customer status information. The dynamic task allocation mechanism optimizes task allocation based on customer service personnel's skill tags and real-time status, improving problem-solving efficiency. The personalized interactive interface is generated based on the customer's browsing history and emotional state, enhancing the user experience. The dynamic updating of the intelligent knowledge base ensures that customer service personnel can access the latest knowledge content, improving the accuracy and timeliness of responses. Finally, the cross-departmental collaboration mechanism ensures that complex problems can be resolved quickly through task decomposition and real-time information sharing. These innovations work together to not only improve customer service efficiency but also significantly enhance customer satisfaction and loyalty, providing e-commerce companies with a more competitive customer service solution.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A unified management and collaboration method for e-commerce customer service that supports multi-channel access, characterized in that, Includes the following steps: The system integrates internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect customers' basic information, historical interaction records, and current consultation content, and synchronize them to a unified data center; The system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through voice and image recognition technology, and combines them with deep learning models to accurately determine the customer's emotional state. Based on the customer's emotional state and the type of problem, the system selects customer service personnel with the corresponding skill tags and the best current state from the customer service personnel database, and dynamically allocates tasks by taking into account workload and historical performance. The system automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The system monitors the resolution of customer issues and feedback information in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. When a customer's problem involves multiple departments, the system automatically activates a cross-departmental collaboration mechanism, breaking down the problem into multiple sub-tasks and assigning them to the relevant department heads. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the problem is resolved within the specified time.

2. The unified management and collaboration method for e-commerce customer service supporting multi-channel access as described in claim 1, characterized in that: The system integrates internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collects basic customer information, historical interaction records, and current inquiry content, and synchronizes them to a unified data center. The specific steps are as follows: The collected speech data is preprocessed, including noise reduction and normalization, to improve the quality of the speech signal. Extract emotional features from speech signals, including pitch, speech rate, and volume, and use deep learning models for emotion classification. The acquired image or video data is preprocessed, including face detection and expression recognition, and facial expression features are extracted using convolutional neural networks; By fusing the emotional features of voice and images, a deep learning model is used to comprehensively judge the customer's emotional state.

3. The e-commerce customer service unified management and collaboration method supporting multi-channel access as described in claim 2, characterized in that: The system performs multimodal sentiment analysis on the collected customer data, extracts emotional features through speech and image recognition technology, and combines this with a deep learning model to accurately determine the customer's emotional state. The specific steps are as follows: Based on the type of customer's question and their emotional state, select customer service personnel with corresponding skill tags from the customer service personnel database; Taking into account the real-time workload and historical performance of customer service personnel, a priority score is calculated for each customer service personnel. Based on priority scores, tasks are dynamically assigned to the most suitable customer service personnel, and the assignment results are communicated to the customer service personnel in real time. Monitor the progress of customer service staff in handling tasks and dynamically adjust task allocation based on the task progress.

4. The unified management and collaboration method for e-commerce customer service supporting multi-channel access as described in claim 3, characterized in that: Based on the customer's emotional state and the type of problem, the system filters customer service personnel from the database who possess the corresponding skill tags and are currently in the best condition. Taking into account workload and historical performance, tasks are dynamically allocated. The specific steps are as follows: Analyze customers' browsing history, purchasing behavior, and emotional state to extract customer preference characteristics; Based on customer preferences, design personalized interactive interface layouts and content display methods; Personalized interactive interfaces are pushed in real time through the customer's access channels; We dynamically adjust the content and layout of the user interface based on real-time customer feedback.

5. The e-commerce customer service unified management and collaboration method supporting multi-channel access as described in claim 4, characterized in that: The system automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes these interfaces in real time through the customer's access channels. The specific steps are as follows: Real-time monitoring of customer problem resolution and feedback information, extracting valuable knowledge content; The knowledge base is automatically updated based on the extracted knowledge content to ensure its accuracy and timeliness. Based on the customer service personnel's skill tags and the type of problem they are currently handling, relevant knowledge content is selected from the knowledge base; The selected knowledge content is pushed to customer service staff in real time to help them quickly resolve issues.

6. The e-commerce customer service unified management and collaboration method supporting multi-channel access as described in claim 5, characterized in that: The system monitors the resolution status and feedback of customer issues in real time, automatically updates the knowledge base, and pushes relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. The specific steps are as follows: When a customer's problem involves multiple departments, the system automatically identifies the key issues and the responsible department. Break the problem down into multiple sub-tasks, and clarify the responsible department and processing procedures for each sub-task; Sub-tasks are assigned to the heads of the relevant departments, and information and progress are shared in real time through the collaboration platform; Monitor the progress of cross-departmental collaboration, dynamically adjust collaboration strategies based on progress, and ensure that problems are resolved within the stipulated time.

7. The e-commerce customer service unified management and collaboration method supporting multi-channel access as described in claim 6, characterized in that: When a customer issue involves multiple departments, the system automatically initiates a cross-departmental collaboration mechanism, breaking down the issue into multiple sub-tasks and assigning them to the relevant department heads. Personnel from each department share information and progress in real time through the collaboration platform, ensuring the issue is resolved within the stipulated timeframe. The specific steps are as follows: Standardize the data from multiple channels to ensure consistency in data format; Protect customer data privacy and security using data encryption technology; The collected data is cleaned and preprocessed in real time to remove invalid or duplicate data. The processed data is stored in a unified data center for subsequent analysis and processing.

8. A unified management and collaboration system for e-commerce customer service supporting multi-channel access, based on the unified management and collaboration method for e-commerce customer service supporting multi-channel access as described in any one of claims 1 to 7, characterized in that: include, The system integrates with internal enterprise information systems and external e-commerce platform APIs to achieve multi-channel access, automatically collect basic customer information, historical interaction records, and current inquiry content, and synchronize them to a unified data center. The sentiment analysis module performs multimodal sentiment analysis on the collected customer data, extracts emotional features through speech and image recognition technology, and combines them with a deep learning model to accurately determine the customer's emotional state. The task allocation module dynamically allocates tasks based on the customer's emotional state and the type of problem, selecting customer service personnel with the corresponding skill tags and currently in the best condition from the customer service personnel database, taking into account workload and historical performance. The interface generation module automatically generates personalized interactive interfaces based on customers' browsing history, purchasing behavior, and emotional state, and pushes them in real time through customers' access channels. The knowledge base update module allows the system to monitor the resolution status and feedback of customer issues in real time, automatically update the knowledge base, and push relevant knowledge base content in real time based on the customer service personnel's skill tags and the type of issue being handled. The collaboration management module automatically activates a cross-departmental collaboration mechanism when a customer's issue involves multiple departments. The issue is broken down into multiple sub-tasks and assigned to the responsible persons of the corresponding departments. Personnel from each department share information and progress in real time through the collaboration platform, ensuring that the issue is resolved within the specified time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the unified management and collaboration method for e-commerce customer service that supports multi-channel access as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the unified management and collaboration method for e-commerce customer service that supports multi-channel access as described in any one of claims 1 to 7.

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