Construction method of customer complaint processing system for coping with supervision complaints

Abstract

The invention relates to a construction method of a customer complaint processing system for coping with supervision complaints, which belongs to the field of customer service and supervision compliance management, and comprises the following steps: (1) establishing diversified complaint channels, and integrating the complaint channels to a unified platform; (2) complaint information is comprehensively collected and intelligently analyzed; (3) establishing a quick response and processing mechanism based on an analysis result of the step (2); (4) performing closed-loop control and continuous improvement on complaints; (5) technical support and informatization construction; and (6) personnel training and ability improvement. The method has the advantages that the processing efficiency is improved, manual intervention is reduced through the automatic processing process, and the complaint response time is shortened; (2) the processing quality is enhanced: the complaint is ensured to be professionally and timely processed by a standardized processing flow and an intelligent distribution mechanism; and (3) resource configuration optimization: tasks are intelligently allocated according to the processing capability, and waste of human resources is avoided. (4) customer satisfaction is improved, complaints are quickly responded and effectively solved, and customer credibility and loyalty are enhanced.

Description

Technical Field

[0001] The present invention relates to a method for constructing a customer complaint processing system for responding to regulatory complaints, and belongs to the field of customer service and regulatory compliance management. Background Art

[0002] With the intensification of market competition and the continuous improvement of consumer rights awareness, the number of regulatory complaints faced by enterprises has shown a significant growth trend. In this context, the traditional customer complaint handling model has been unable to meet the needs of the modern market. These models usually have many shortcomings, such as slow response speed, inability to capture and respond to customer complaints in a timely manner; irregular processing procedures, resulting in low processing efficiency and poor customer experience; incomplete information records, making problem tracking and subsequent analysis difficult; insufficient data analysis capabilities, difficulty in extracting valuable information from large amounts of data, and thus unable to provide strong support for decision-making.

[0003] In order to effectively deal with these challenges, it is particularly urgent to develop a new customer complaint handling system. This system needs to be able to efficiently integrate resources inside and outside the enterprise, achieve rapid response, and ensure that customer complaints are responded to in the shortest possible time. At the same time, the system needs to have accurate analysis capabilities to deeply explore the root causes behind the complaints and provide targeted solutions. In addition, the system should also be able to achieve standardized management of the entire process to ensure that every step of the operation is recorded for subsequent review and improvement.

[0004] More importantly, the system should use modern information technology, such as big data analysis and artificial intelligence, to improve the level of intelligent processing. Through intelligent analysis and prediction, the system can identify potential complaint risks in advance, take proactive measures, and reduce the occurrence of complaints. At the same time, the system can also continuously learn and optimize based on historical data and real-time feedback to improve the accuracy and efficiency of processing.

[0005] In short, building an efficient and intelligent customer complaint handling system can not only improve the company's service quality and customer satisfaction, but also strengthen the company's ability to adapt to market changes and enhance its competitiveness. This is crucial to maintaining the company's long-term development and good reputation in the fierce market competition. Summary of the invention

[0006] In order to overcome the defects of the prior art, the present invention provides a method for constructing a customer complaint handling system for responding to regulatory complaints. The technical solution of the present invention is:

[0007] A method for constructing a customer complaint handling system for responding to regulatory complaints includes the following steps: (1) establishing a variety of complaint channels and integrating them into a unified platform; (2) comprehensively collecting and intelligently analyzing complaint information; (3) establishing a rapid response and handling mechanism based on the analysis results of step (2); (4) closed-loop control and continuous improvement of complaints; (5) technical support and information construction; and (6) personnel training and capacity building.

[0008] The step (1) is specifically as follows: establishing a unified complaint platform to collect complaint information from various channels to the complaint platform; the various channels include telephone complaint channels, online complaint channels, email complaint channels and social media complaint channels, ensuring that customers submit complaints in the most convenient way.

[0009] The step (2) is specifically as follows:

[0010] 2.1 Comprehensively collect complaint information, including the time, location, content, and products or services involved;

[0011] 2.2 Use text content mining technology, semantic analysis technology or natural language processing technology to intelligently analyze complaint information to achieve automatic classification, clustering, and identification of hot issues; in step 2.2, when intelligently analyzing complaint information, the specific steps are as follows: a. Data preprocessing:

[0012] Step 1.1 Data cleaning: remove irrelevant characters in the complaint information and perform text normalization;

[0013] Step 1.2 Word segmentation and part-of-speech tagging: Use the word segmentation tool to segment the text and mark the part of speech of each word; Step 1.3 Stop word removal: Remove high-frequency but meaningless stop words in the text to reduce noise;

[0014] b. Feature extraction:

[0015] Step 2.1 TF-IDF feature extraction: Calculate the product of the word frequency and the inverse document frequency of each word in the document, use this as the weight of the word, and construct the feature vector of the text;

[0016] Step 2.2 Word Embedding: Use a pre-trained word embedding model to convert the text into a vector representation in a high-dimensional space;

[0017] c. Automatic classification:

[0018] Method selection: Use supervised learning algorithm for automatic classification;

[0019] Training process: prepare labeled complaint data as a training set; select a classification algorithm and adjust hyperparameters; use the training set data to train the model and evaluate the model performance; iteratively optimize the model until a satisfactory classification effect is achieved.

[0020] d. Cluster analysis:

[0021] Extract features from preprocessed data; select clustering algorithms and set parameters; execute clustering algorithms to divide complaint information into multiple groups; analyze the characteristics of each group and identify potential complaint topics or categories;

[0022] e. Identification of hot issues:

[0023] Statistical frequency method: Count the number of complaints in each category or cluster, and the one with the largest number is the hot issue;

[0024] Sentiment analysis combined: Based on classification or clustering, sentiment analysis technology is combined to identify categories with strong negative emotions as hot issues;

[0025] Trend analysis: Monitor changes in the number of complaints over a preset time period and identify fast-growing categories as emerging hot issues.

[0026] The step (3) is specifically as follows:

[0027] 3.1 Establish a dedicated team: Establish a dedicated complaint handling team, with professionals responsible for receiving and handling complaints. Team members should have experience in complaint handling and the ability to solve problems;

[0028] 3.2 Strictly abide by the processing time limit: ensure that customers are provided with responses and solutions within the specified time. The system can automatically track the progress of complaint handling and set reminders to ensure that no complaints are missed;

[0029] 3.3 Maintain communication: Maintain communication with customers during the complaint handling process, provide timely feedback on the progress and results, and increase customer trust.

[0030] The step (4) is specifically as follows:

[0031] 4.1 Closed-loop control: Closed-loop management of complaints from receipt, processing to feedback to ensure that each complaint is properly handled and the entire process of complaint handling is automatically recorded to facilitate subsequent analysis and improvement;

[0032] 4.2 Continuous Improvement Mechanism: Establish a complaint case analysis mechanism to classify, count and analyze complaints, promptly identify problems and take corresponding improvement measures; conduct in-depth investigations and develop solutions for repetitive complaints to prevent similar problems from happening again.

[0033] The step (5) is specifically as follows:

[0034] 5.1 Build a customer complaint management system to realize the rapid entry, query and statistics of complaint information;

[0035] The customer complaint management system comprises:

[0036] The complaint workbench is used to receive, process and track complaints. It is the main interface for users to interact with the platform. It displays a list of complaints to be processed and provides an entry point for handling complaints.

[0037] The complaint process progress monitoring module is used to monitor the progress of complaint handling and provide real-time feedback. Users can view the current processing status of each complaint, including processed, pending and in progress. The complaint work order management module is used to manage specific complaint work orders, including creation, modification, deletion and query of work orders.

[0038] The complaint statistics module is used to generate complaint statistics; the early warning management module is used to issue warnings in advance to prevent potential problems from occurring. By setting thresholds and rules, when the number of complaints or indicators exceeds the preset value, the system automatically triggers an early warning;

[0039] The monitoring management module is used to monitor the system operation status and detect abnormal situations in time; the system security module is used to protect the system from potential threats through security policies, identity authentication and access control;

[0040] The knowledge base module is used to store all the knowledge and information of complaints; the operation management module is used for process configuration, time limit management and responsibility management; the indicator analysis module is used to analyze the complaint data; the customer service module is used to provide customer service to customers; the data exchange module is used to exchange data with other systems or departments; the knowledge sharing and evaluation module is used to share knowledge and experience and regularly evaluate the effectiveness of knowledge; the performance evaluation module is used to evaluate the performance of employees;

[0041] 5.2 Use big data and artificial intelligence to analyze customer complaint data, explore potential problems and trends, and provide reference for decision-making; Use artificial intelligence to analyze complaint data. The specific steps are as follows: 5.2.1 Data collection and preprocessing Comprehensive data collection: Collect customer complaint data; Data cleaning: Format the data; Data integration: Integrate data from different sources to form a unified data set; 5.2.2 Artificial intelligence processing:

[0042] Text analysis: Use NLP technology to convert the text content of customer complaints into structured data and extract keywords, phrases and themes;

[0043] Sentiment analysis: Identify sentiment in customer feedback and quantify customer satisfaction or dissatisfaction; Semantic understanding: Understand the specific content and intent of customer complaints and identify potential needs and problems; Classification and clustering: Use machine learning algorithms to automatically classify and cluster complaints, grouping similar issues into one category for quick response and processing;

[0044] Predictive analysis: Based on historical data, predict the type, quantity or trend of complaints that will occur within a preset time, providing a basis for preventive measures;

[0045] Anomaly detection: Identify complaints that are significantly different from normal patterns; Complex pattern recognition: Use deep learning models to process data and discover complex patterns and associations hidden in the data; Multi-language support: Automatically translate and analyze multi-language complaint data;

[0046] 5.2.3 Exploring potential problems and trends:

[0047] Root cause analysis: Combine NLP and machine learning technologies to deeply explore the root causes behind customer complaints;

[0048] Trend forecasting: Based on historical data and current trends, forecast hot issues and changing trends of customer complaints in the future, and provide direction for product improvement and service optimization;

[0049] Relevance analysis: Analyze the relevance and impact between different complaints and identify the problem points that trigger chain reactions;

[0050] 5.2.4 Provide reference for decision-making:

[0051] Optimize service process: Optimize service process based on complaint data analysis results to reduce complaint rate and processing time;

[0052] Product improvement: Propose improvement suggestions in response to complaints about product design or functions to enhance product quality and user experience;

[0053] Marketing strategy adjustment: Analyze customer feedback on needs and dissatisfaction, adjust marketing strategies, and improve market competitiveness;

[0054] Resource allocation: Rationally allocate corporate resources based on the urgency and importance of complaint data to ensure that problems are resolved in a timely manner.

[0055] The step (6) is specifically as follows:

[0056] 6.1 Provide professional training and skills improvement opportunities for complaint handling personnel so that they have rich complaint handling experience and problem-solving capabilities. The training content may include communication skills, laws and regulations, and product knowledge;

[0057] 6.2 Establish a good teamwork atmosphere, promote communication and collaboration among personnel, improve the overall efficiency of complaint handling, regularly organize knowledge sharing and experience exchange meetings, and promote the continuous optimization and improvement of complaint handling work.

[0058] The step 2.2 specifically includes:

[0059] 2.2.1 Performance indicator setting: Setting key performance indicators, including complaint volume / rate, complaint handling efficiency and effectiveness, to evaluate the overall effectiveness of the customer complaint handling system;

[0060] 2.2.2 Complaint life cycle management: including complaint reception, analysis, prevention, early warning, handling and effect evaluation. Through process standardization, ensure that each complaint can be handled in a timely and professional manner;

[0061] 2.2.3 Through in-depth analysis of complaint data, identify potential risk points and take preventive measures to reduce the occurrence of future complaints. At the same time, implement cycle management strategies to continuously optimize and improve the complaint handling process;

[0062] Specific analysis steps for the complaint data:

[0063] a. Data collection and integration:

[0064] Collect data from different modules of the complaint management platform to form a unified data set;

[0065] b. Data preprocessing: cleaning data; formatting data;

[0066] c. Data analysis:

[0067] Classification and clustering: Use machine learning algorithms to classify and cluster complaints and identify common complaint types and patterns;

[0068] Sentiment analysis: Use natural language processing technology to analyze the emotional tendencies in customer feedback and quantify customer satisfaction or dissatisfaction;

[0069] Trend forecasting: Based on historical data, predict the trends and hot spots of complaints within a preset time period.

[0070] Relevance analysis: Analyze the relevance and impact between different complaints and identify risk points that may trigger chain reactions;

[0071] Statistical analysis: Generate complaint statistics to identify potential problems.

[0072] d. Risk point identification:

[0073] Based on the analysis results, identify potential risk points;

[0074] 2.2.4 Based on the in-depth mining and analysis of complaint data, make targeted investment decisions to optimize products and services and reduce sources of complaints;

[0075] 2.2.5 Establish a data analysis platform to conduct in-depth mining and comprehensive utilization of complaint information. The advantages of this invention are:

[0076] (1) Improve processing efficiency: Automated processing procedures reduce manual intervention and shorten complaint response time.

[0077] (2) Enhanced processing quality: Standardized processing procedures and intelligent allocation mechanisms ensure that complaints are handled professionally and promptly.

[0078] (3) Optimize resource allocation: Intelligently allocate tasks based on processing capacity to avoid waste of human resources.

[0079] (4) Improve customer satisfaction: Respond quickly and effectively to complaints to enhance customer trust and loyalty.

[0080] (5) Assist decision making: Reports based on data analysis provide strong support to corporate management. DETAILED DESCRIPTION

[0081] The present invention will be further described below in conjunction with specific embodiments, and the advantages and features of the present invention will become clearer as the description proceeds. However, these embodiments are exemplary only and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention may be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the scope of protection of the present invention.

[0082] The present invention relates to a method for constructing a customer complaint handling system for responding to regulatory complaints, which is characterized by comprising the following steps:

[0083] (1) Establish diversified complaint channels and integrate them into a unified platform; (2) Comprehensively collect and intelligently analyze complaint information; (3) Establish a rapid response and processing mechanism based on the analysis results of step (2); (4) Closed-loop control and continuous improvement of complaints; (5) Technical support and information construction; (6) Personnel training and capacity building.

[0084] The step (1) is specifically as follows: establishing a unified complaint platform to collect complaint information from various channels to the complaint platform; the various channels include telephone complaint channels, online complaint channels, email complaint channels and social media complaint channels, ensuring that customers submit complaints in the most convenient way.

[0085] The step (2) is specifically as follows:

[0086] 2.1 Comprehensively collect complaint information, including the time, location, content, and products or services involved;

[0087] 2.2 Use text content mining technology, semantic analysis technology or natural language processing technology to conduct intelligent analysis of complaint information to achieve automatic classification, clustering and hot issue identification functions.

[0088] a. Data preprocessing:

[0089] Step 1.1 Data cleaning: Remove irrelevant characters (such as HTML tags, special symbols, duplicate data, etc.) from the complaint information, and perform text normalization, such as unifying case, standardizing punctuation, etc.;

[0090] Step 1.2 Word segmentation and part-of-speech tagging: Use a suitable word segmentation tool (such as jieba for Chinese and NLTK for English) to segment the text and tag the part of speech of each word; this step is the basis for subsequent processing.

[0091] Step 1.3 Stop word removal: Remove high-frequency but meaningless stop words in the text, such as "de", "le", etc., to reduce noise;

[0092] b. Feature extraction:

[0093] Step 2.1 TF-IDF feature extraction: Calculate the product of the term frequency (TF) and inverse document frequency (IDF) of each word in the document as the weight of the word, and construct the feature vector of the text; TF-IDF can effectively evaluate the importance of a word for a document set or a single document in a corpus.

[0094] Step 2.2 Word Embedding: Use a pre-trained word embedding model (such as Word2Vec, GloVe, or the Embeddings of BERT) to convert the text into a vector representation in a high-dimensional space; these vectors can capture the semantic relationships between words.

[0095] c. Automatic classification:

[0096] Method selection: Adopt supervised learning algorithms, such as logistic regression, support vector machine (SVM), random forest, or deep learning models (such as convolutional neural network CNN, long short-term memory network LSTM combined with Attention mechanism) for automatic classification;

[0097] Training process: Prepare the labeled complaint data as the training set; select a suitable classification algorithm and adjust the hyperparameters; use the training set data to train the model and evaluate the model performance (such as accuracy, recall rate, F1 score, etc.); iterate and optimize the model until a satisfactory classification effect is achieved.

[0098] d. Cluster analysis:

[0099] Method selection: Unsupervised learning methods, such as K-means, hierarchical clustering, DBSCAN, or density-based clustering methods.

[0100] Implementation steps:

[0101] Extract features from preprocessed data (feature extraction steps in classification can be reused); select clustering algorithm and set parameters (such as K value in K-means); execute clustering algorithm to divide complaint information into multiple groups; analyze the characteristics of each group and identify potential complaint topics or categories;

[0102] e. Identification of hot issues:

[0103] Statistical frequency method: Count the number of complaints in each category or cluster, and the one with the largest number is the hot issue;

[0104] Sentiment analysis combination: Based on classification or clustering, sentiment analysis techniques (such as dictionary-based sentiment analysis or machine learning sentiment classifiers) are combined to identify categories with strong negative emotions as hot issues;

[0105] Trend analysis: Monitor changes in the number of complaints over a preset time period and identify fast-growing categories as emerging hot issues.

[0106] Among them, the algorithm and formula examples are:

[0107] TF-IDF formula: \text{TF-IDF}(t,d)=\text{TF}(t,d)\times\text{IDF}(t)\]Where, -\(\text{TF}(t,d)\) is the frequency of term \(t\) in document \(d\). -\(\text{IDF}(t)=\log\left(\frac{N}{|\{d\in D:t\in d\}|}\right)\), \(N\) is the total number of documents, \(|\{d\in D:t\ind\}|\) is the number of documents containing term \(t\). **Word2Vec model**: Although Word2Vec itself is not directly presented through a formula, it is a word vector model trained based on a neural network that can capture the semantic similarity between words.

[0108] Conclusion: Through the above technical solutions, an efficient complaint information intelligent analysis system can be built to realize the automatic classification, clustering and identification of hot issues of complaint information, providing strong data support for decision makers. In practical applications, it is also necessary to adjust the algorithm parameters according to the specific scenario and optimize the model performance.

[0109] The step (3) is specifically as follows:

[0110] 3.1 Establish a dedicated team: Establish a dedicated complaint handling team, with professionals responsible for receiving and handling complaints. Team members should have experience in complaint handling and the ability to solve problems;

[0111] 3.2 Strictly abide by the processing time limit: ensure that customers are provided with responses and solutions within the specified time. The system can automatically track the progress of complaint handling and set reminders to ensure that no complaints are missed;

[0112] 3.3 Maintain communication: Maintain communication with customers during the complaint handling process, provide timely feedback on the progress and results, and increase customer trust.

[0113] When handling complaints, specifically:

[0114] 1. Point acceptance: As the entrance to the entire system, it is responsible for receiving complaint information from different channels, including but not limited to call centers, departmental channels, business outlets, the Internet, etc. This link reflects the system's extensive coverage and efficient collection capabilities of complaint information.

[0115] 2. Centralized pre-processing and dispatching: After receiving the complaint information, the system first conducts centralized pre-processing, including information screening, classification, deduplication, etc., to ensure the accuracy and efficiency of subsequent processing. Then, according to the nature and type of the complaint, the processing task is intelligently assigned to the corresponding functional department or professional department.

[0116] 3. Comprehensive response: After receiving the processing task, each functional department or professional department will immediately start the comprehensive response mechanism, conduct in-depth investigation and analysis of the complaint, and formulate a solution. This link emphasizes the rapid response and positive handling attitude to the complaint.

[0117] 4. Centralized response: After the solution is determined, the system will respond to the complainant through a unified channel to ensure the accuracy and consistency of the information. At the same time, the system is also responsible for collecting feedback from the complainant in order to evaluate and improve the treatment effect.

[0118] 5. Other related departments and institutions: The diagram also shows other departments and institutions closely related to this system, such as customer service centers (repeated listing may indicate multiple customer service centers or emphasize their importance), consumer associations, branches, channels, industry associations, etc. These departments and institutions may play supporting, coordinating or supervisory roles in the complaint handling process, and jointly promote the effective resolution of complaints.

[0119] 6. Responsible departments and social media: Although not fully displayed at the bottom of the diagram, "respective departments" and "social media" are mentioned, suggesting that the system may also be supervised by higher-level competent departments and needs to pay attention to public opinion trends on social media in order to adjust the handling strategy in a timely manner and maintain the company's reputation.

[0120] When conducting complaint process management, it is divided into main complaint process management and auxiliary process management. The main complaint process management is specifically as follows:

[0121] Receiving / processing / transferring: This step is the entrance to the system and is responsible for receiving customer complaints through various channels (such as telephone, email, online platforms, etc.). The system first performs pre-processing, including recording, classifying and preliminary analysis of complaint information to distinguish simple complaints from complex complaints. For simple complaints, the system will directly process them quickly; for complex complaints or complaints that cannot be processed immediately, they will enter the transfer stage and transfer the complaint task to the corresponding professional department or team for in-depth investigation and processing.

[0122] Processing: This step is for quick processing of classified simple complaints. The system will automatically generate solutions or suggestions based on the preset processing procedures and rules, and reply to the complainant through the corresponding channels. At the same time, the system will also record the processing process and results to provide a basis for subsequent analysis and improvement.

[0123] Response: After the complaint is handled, the system sends a response message to the complainant through a unified channel to inform the handling result. This step emphasizes timeliness and accuracy to ensure that the complainant can quickly understand the complaint handling status.

[0124] Responsibility Assignment: For complaints that require responsibility assignment, the system will determine and assign internal responsible departments based on the handling and investigation results. This step helps to clarify the attribution of responsibilities and provides a basis for subsequent accountability and improvement work.

[0125] Verification: The system verifies the processing results to ensure that they comply with relevant regulations and corporate standards. Through verification, the system can promptly identify and correct possible problems and improve the accuracy and reliability of complaint handling.

[0126] Feedback: The system collects feedback from complainants and evaluates their satisfaction with the handling results. This step not only helps to understand customers' feelings about the complaint handling process, but also provides important reference for subsequent optimization and improvement of the system.

[0127] Archiving: All processed complaint information will be archived. The system provides a complete archive management function, supports query and statistics by time, type and other methods, and facilitates the subsequent complaint analysis and management of enterprises.

[0128] The specific steps of auxiliary process management are:

[0129] Receiving complaints: The system receives customer complaints through various channels, including telephone, email, online platforms, etc.

[0130] Pre-processing: Preliminary screening, classification and deduplication of complaint information to distinguish simple complaints from complex ones.

[0131] Task allocation: Intelligently allocate the processing tasks to the corresponding functional departments or professional teams based on the type and nature of the complaint.

[0132] Processing and response: The relevant department or team conducts in-depth investigation and analysis of the complaint and formulates a solution. After the processing is completed, a response message is sent to the complainant through a unified channel.

[0133] Archiving and evaluation: All processed complaint information will be archived and evaluated for effectiveness, providing a basis for subsequent improvement and optimization.

[0134] The functional features of this auxiliary process management:

[0135] Process visualization: Through intuitive flowchart display, the complaint handling process is clear at a glance, making it easier for employees to understand and operate.

[0136] Intelligent task allocation: The system can intelligently assign processing tasks to corresponding functional departments or professional teams based on the nature and type of complaints, thereby improving processing efficiency.

[0137] Standardized processing process: Each step follows the preset processing procedures and rules to ensure the standardization and consistency of the complaint handling process.

[0138] Automation of information flow: Through the automated flow mechanism within the system, complaint information can be seamlessly transmitted between various links, reducing human errors and delays.

[0139] Feedback and supervision: The system supports timely feedback and evaluation of processing results, and provides supervision functions to ensure that the complaint handling process complies with relevant regulations and corporate standards.

[0140] The step (4) is specifically as follows:

[0141] 4.1 Closed-loop control: Closed-loop management of complaints from receipt, processing to feedback to ensure that each complaint is properly handled and the entire process of complaint handling is automatically recorded to facilitate subsequent analysis and improvement;

[0142] 4.2 Continuous Improvement Mechanism: Establish a complaint case analysis mechanism to classify, count and analyze complaints, promptly identify problems and take corresponding improvement measures; conduct in-depth investigations and develop solutions for repetitive complaints to prevent similar problems from happening again.

[0143] The step (5) is specifically as follows:

[0144] 5.1 Build a customer complaint management system to realize the rapid entry, query and statistics of complaint information and improve work efficiency. The system should support mobile complaint management to facilitate customers to submit complaints and obtain processing results anytime and anywhere;

[0145] The customer complaint management system comprises:

[0146] The complaint workbench is used to receive, process and track complaints. It is the main interface for users to interact with the platform. It displays a list of complaints to be processed and provides an entry point for handling complaints.

[0147] The complaint process progress monitoring module is used to monitor the progress of complaint handling and provide real-time feedback. Users can view the current processing status of each complaint, including processed, pending and in progress. The complaint work order management module is used to manage specific complaint work orders, including creation, modification, deletion and query of work orders, etc. This is the core part of complaint handling, which records the specific content, handling process and results of the complaint in detail.

[0148] The complaint statistics module is used to generate complaint statistics, such as the number of complaints, complaint categories, etc. It helps managers understand the overall situation of complaints so as to formulate targeted improvement measures.

[0149] The early warning management module includes early warning threshold management, early warning rule management and early warning method management. Function: Used to issue early warnings to prevent potential problems from occurring. By setting thresholds and rules, when the number of complaints or indicators exceeds the preset value, the system automatically triggers an early warning;

[0150] The monitoring management module includes monitoring interface management, monitoring threshold management, and monitoring parameter settings. It is used to monitor the system operation status and detect abnormal situations in a timely manner. It ensures stable operation of the system and responds quickly when abnormalities are found.

[0151] System security module, used for functions: ensuring the security of the system and preventing unauthorized access. Protecting the system from potential threats through security policies, identity authentication and access control; Knowledge base module, used for knowledge collection, speech management, knowledge base retrieval. Function: used to store all complaint knowledge and information for employees to refer to when handling complaints. Supporting knowledge updating, sharing and retrieval to improve processing efficiency and quality.

[0152] Operation management module, used for functions: responsible for the entire operation process, including the formulation and implementation of complaint management strategies. Including process configuration, time limit management and responsibility management, to ensure the efficiency and compliance of the complaint handling process.

[0153] The indicator analysis module is used to analyze complaint data to optimize the complaint handling process. Through data mining and analysis technology, bottlenecks and problems in the process are discovered to provide a basis for improvement. The customer service module is used to provide customers with high-quality customer service. Although not directly listed in the figure, according to the nature of the "complaint management platform", customer service is an indispensable part, including complaint consultation, complaint tracking, etc.

[0154] The data exchange module is used to exchange data with other systems or departments. It ensures the integration and data sharing of the complaint management platform with other systems (such as CRM, ERP, etc.) to improve information utilization.

[0155] The knowledge sharing and evaluation module is used for knowledge sharing and knowledge evaluation; functions: sharing knowledge and experience to improve overall efficiency; regularly evaluating the effectiveness of knowledge to ensure the accuracy and practicality of knowledge.

[0156] Performance evaluation wear block, including evaluation item management, evaluation template management, scoring management, etc. Function: used to evaluate employee performance; to motivate employees to improve work efficiency and service quality;

[0157] 5.2 Use big data and artificial intelligence technologies to analyze customer complaint data, explore potential problems and trends, and provide reference for decision-making.

[0158] 5.2.1 Data collection and preprocessing

[0159] Comprehensive data collection: Collect customer complaint data through multiple channels (such as telephone, email, social media, online platforms, etc.); ensure the integrity and diversity of the data.

[0160] Data cleaning: remove duplicate, erroneous, and incomplete data, and format the data, such as unifying the time format and removing irrelevant characters.

[0161] Data integration: Integrate data from different sources to form a unified data set for subsequent analysis.

[0162] 5.2.2 Artificial Intelligence Processing:

[0163] Text analysis: Use NLP technology to convert the text content of customer complaints into structured data and extract keywords, phrases and themes;

[0164] Sentiment analysis: Identify the emotional tendencies in customer feedback (such as positive, negative, neutral) and quantify the degree of customer satisfaction or dissatisfaction;

[0165] Semantic understanding: understand the specific content and intent of customer complaints and identify potential needs and problems;

[0166] Classification and clustering: Use machine learning algorithms to automatically classify and cluster complaints, grouping similar issues together for quick response and processing;

[0167] Predictive analysis: Based on historical data, predict the type, quantity or trend of complaints that will occur within a preset time, providing a basis for preventive measures;

[0168] Anomaly detection: Identifying complaints that differ significantly from normal patterns and could be potentially significant issues or fraud.

[0169] Complex pattern recognition: Using deep learning models (such as neural networks) to process large amounts of data to discover complex patterns and associations hidden in the data;

[0170] Multi-language support: For multinational companies, deep learning technology can realize automatic translation and analysis of multi-language complaint data, improving processing efficiency.

[0171] 5.2.3 Exploring potential problems and trends:

[0172] Root cause analysis: Combine NLP and machine learning technologies to deeply explore the root causes behind customer complaints, such as product design defects, poor service processes, etc.

[0173] Trend forecasting: Based on historical data and current trends, forecast hot issues and changing trends of customer complaints in the future, and provide direction for product improvement and service optimization;

[0174] Relevance analysis: Analyze the relevance and impact between different complaints and identify the problem points that trigger chain reactions;

[0175] 5.2.4 Provide reference for decision-making:

[0176] Optimize service process: Optimize service process based on complaint data analysis results to reduce complaint rate and processing time;

[0177] Product improvement: Propose improvement suggestions in response to complaints about product design or functions to enhance product quality and user experience;

[0178] Marketing strategy adjustment: Analyze customer feedback on needs and dissatisfaction, adjust marketing strategies, and improve market competitiveness;

[0179] Resource allocation: Rationally allocate corporate resources based on the urgency and importance of complaint data to ensure that problems are resolved in a timely manner.

[0180] Specific analysis steps for the complaint data:

[0181] a. Data collection and integration:

[0182] Collect data from different modules of the complaint management platform (such as complaint workstation, complaint statistics, monitoring alarm, etc.) to form a unified data set;

[0183] b. Data preprocessing: clean the data and remove duplicate, erroneous, and incomplete records; format the data, such as unifying the time format and standardizing text descriptions.

[0184] c. Data analysis: classification and clustering: Use machine learning algorithms to classify and cluster complaints and identify common complaint types and patterns;

[0185] Sentiment analysis: Use natural language processing technology to analyze the emotional tendencies in customer feedback and quantify customer satisfaction or dissatisfaction;

[0186] Trend forecasting: Based on historical data, predict the trends and hot spots of complaints within a preset time period.

[0187] Relevance analysis: Analyze the relevance and impact between different complaints and identify risk points that may trigger chain reactions;

[0188] Statistical analysis: Generate complaint statistics, such as complaint volume, complaint category, processing time, etc. to identify potential problems.

[0189] d. Risk point identification:

[0190] Based on the analysis results, potential risk points are identified; these risk points may include:

[0191] 1. Product quality issues: Frequent complaints about product performance, quality or safety.

[0192] 2. Poor service process: complaint handling takes a long time, the process is complicated or there are bottlenecks.

[0193] 3. Employee service attitude: Customers are dissatisfied with the attitude, professionalism or response speed of service staff.

[0194] 4. System stability issues: Frequent alarms in the monitoring system indicate potential technical problems in the system.

[0195] 5. Insufficient knowledge base: The questions asked by customers cannot be answered in the knowledge base, or the answers are inaccurate.

[0196] 6. Poor communication: There are problems in communication between departments or with customers, which leads to escalation or improper handling of complaints.

[0197] Precautions

[0198] Targeted improvements:

[0199] 1. Develop specific improvement measures for identified risk points, such as strengthening product quality control, optimizing service processes, and improving employee training.

[0200] Improve the complaint management mechanism:

[0201] 1. Establish a sound complaint management mechanism, including complaint reception, processing, tracking and feedback.

[0202] 2. Establish a dedicated complaint handling team to quickly respond to and handle customer complaints.

[0203] Strengthen internal collaboration and communication: Establish a cross-departmental collaboration mechanism to ensure that different departments can communicate and collaborate effectively to jointly resolve customer complaints.

[0204] Regular evaluation and feedback:

[0205] 1. Regularly evaluate the complaint handling process, collect customer feedback and opinions, and understand customer satisfaction and expectations on complaint handling.

[0206] 2. Continuously improve and optimize the complaint handling process based on evaluation results and customer feedback.

[0207] Technology upgrade and innovation:

[0208] 1. Introduce new technologies and management methods, such as artificial intelligence and big data analysis, to improve the efficiency and effectiveness of complaint handling.

[0209] The step (6) is specifically as follows:

[0210] 6.1 Provide professional training and skills improvement opportunities for complaint handling personnel so that they have rich complaint handling experience and problem-solving capabilities. The training content may include communication skills, laws and regulations, and product knowledge;

[0211] 6.2 Establish a good teamwork atmosphere, promote communication and collaboration among personnel, improve the overall efficiency of complaint handling, regularly organize knowledge sharing and experience exchange meetings, and promote the continuous optimization and improvement of complaint handling work.

[0212] 8. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 3, characterized in that the step 2.2 specifically comprises:

[0213] 2.2.1 Performance indicator setting: Setting key performance indicators, including complaint volume / rate, complaint handling efficiency and effectiveness, to evaluate the overall effectiveness of the customer complaint handling system;

[0214] 2.2.2 Complaint life cycle management: including complaint reception, analysis, prevention, early warning, handling and effect evaluation. Through process standardization, ensure that each complaint can be handled in a timely and professional manner;

[0215] 2.2.3 Through in-depth analysis of complaint data, identify potential risk points and take preventive measures to reduce the occurrence of future complaints. At the same time, implement cycle management strategies to continuously optimize and improve the complaint handling process;

[0216] Specific analysis steps for the complaint data:

[0217] a. Data collection and integration:

[0218] Collect data from different modules of the complaint management platform to form a unified data set;

[0219] b. Data preprocessing: cleaning data; formatting data;

[0220] c. Data analysis:

[0221] Classification and clustering: Use machine learning algorithms to classify and cluster complaints and identify common complaint types and patterns;

[0222] Sentiment analysis: Use natural language processing technology to analyze the emotional tendencies in customer feedback and quantify customer satisfaction or dissatisfaction;

[0223] Trend forecasting: Based on historical data, predict the trends and hot spots of complaints within a preset time period.

[0224] Relevance analysis: Analyze the relevance and impact between different complaints and identify risk points that may trigger chain reactions;

[0225] Statistical analysis: Generate complaint statistics to identify potential problems.

[0226] d. Risk point identification:

[0227] Based on the analysis results, identify potential risk points;

[0228] 2.2.4 Based on the in-depth mining and analysis of complaint data, formulate targeted investment decisions to optimize products and services and reduce the sources of complaints; at the same time, conduct real-time tracking and evaluation of the execution effect of investment decisions to ensure that resources are used effectively.

[0229] Make targeted investment decisions:

[0230] Problem prioritization: Based on the analysis results, prioritize the identified problems and determine which problems need to be solved first.

[0231] Investment plan formulation:

[0232] Product optimization: Develop product optimization plans for product design defects or insufficient functions, including improving product design, adding new functions, or optimizing user experience.

[0233] Service improvement: Develop service improvement plans to address service quality issues, including strengthening employee training, optimizing service processes, and improving response speed.

[0234] Technology upgrade: Develop technology upgrade plans to address system stability issues or technical bottlenecks, introduce new technologies or upgrade existing systems to improve stability and performance.

[0235] Market strategy adjustment: Adjust market strategy according to customer needs and market trends, such as launching products that better meet market demand and optimizing marketing strategies.

[0236] Return on investment assessment:

[0237] Conduct a detailed ROI assessment for each investment plan, including cost budget, expected benefits, implementation cycle, etc.

[0238] Evaluate the feasibility and risks of each plan to ensure the rationality and robustness of investment decisions.

[0239] Decision making and implementation

[0240] Based on the evaluation results, make specific investment decisions, and clarify the implementation plan and responsible persons. Track the implementation of investment projects to ensure that the projects proceed as planned and achieve the expected results.

[0241] Continuous optimization and improvement

[0242] During the implementation of investment decisions, we will continue to pay attention to changes in complaint data and make adjustments and optimizations based on new issues.

[0243] Regularly evaluate and provide feedback on the effectiveness of investment decisions, summarize experiences and lessons, and provide reference for future investment decisions.

[0244] 2.2.5 Establish a data analysis platform to conduct in-depth mining and comprehensive utilization of complaint information. By analyzing the causes, trends, and customer satisfaction of complaints, data support can be provided for product improvement, service optimization, and strategic decision-making. In addition, the team is encouraged to continuously propose improvement suggestions to promote continuous iteration and upgrading of the system.

[0245] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for constructing a customer complaint handling system for responding to regulatory complaints, characterized in that: The following steps are involved: (1) Establish diversified complaint channels and integrate them into a unified platform; (2) Comprehensively collect and intelligently analyze complaint information; (3) Establish a rapid response and processing mechanism based on the analysis results of step (2); (4) Closed-loop control and continuous improvement of complaints; (5) Technical support and information construction; (6) Personnel training and capacity building.

2. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1 is characterized in that: The step (1) is specifically as follows: establishing a unified complaint platform to collect complaint information from various channels to the complaint platform; the various channels include telephone complaint channels, online complaint channels, email complaint channels and social media complaint channels, ensuring that customers submit complaints in the most convenient way.

3. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1 is characterized in that: The step (2) is specifically as follows: 2.1 Comprehensively collect complaint information, including the time, location, content, and products or services involved; 2.2 Use text content mining technology, semantic analysis technology or natural language processing technology to conduct intelligent analysis of complaint information to achieve automatic classification, clustering and identification of hot issues; In step 2.2, when performing intelligent analysis on complaint information, the specific steps are as follows: a. Data preprocessing: Step 1.1 Data cleaning: remove irrelevant characters in the complaint information and perform text normalization; Step 1.2 Word segmentation and part-of-speech tagging: Use the word segmentation tool to segment the text and mark the part of speech of each word; Step 1.3 Stop word removal: Remove high-frequency but meaningless stop words in the text to reduce noise; b. Feature extraction: Step 2.1 TF-IDF feature extraction: Calculate the product of the word frequency and the inverse document frequency of each word in the document, use this as the weight of the word, and construct the feature vector of the text; Step 2.2 Word Embedding: Use a pre-trained word embedding model to convert the text into a vector representation in a high-dimensional space; c. Automatic classification: Method selection: Use supervised learning algorithm for automatic classification; Training process: prepare labeled complaint data as a training set; select a classification algorithm and adjust hyperparameters; use the training set data to train the model and evaluate the model performance; iteratively optimize the model until a satisfactory classification effect is achieved. d. Cluster analysis: Extract features from preprocessed data; select clustering algorithms and set parameters; execute clustering algorithms to divide complaint information into multiple groups; analyze the characteristics of each group and identify potential complaint topics or categories; e. Identification of hot issues: Statistical frequency method: Count the number of complaints in each category or cluster, and the one with the largest number is the hot issue; Sentiment analysis combined: Based on classification or clustering, sentiment analysis technology is combined to identify categories with strong negative emotions as hot issues; Trend analysis: Monitor changes in the number of complaints over a preset time period and identify fast-growing categories as emerging hot issues.

4. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1 is characterized in that: The step (3) is specifically as follows: 3.1 Establish a dedicated team: Establish a dedicated complaint handling team, with professionals responsible for receiving and handling complaints. Team members should have experience in complaint handling and the ability to solve problems; 3.2 Strictly abide by the processing time limit: ensure that customers are provided with responses and solutions within the specified time. The system can automatically track the progress of complaint handling and set reminders to ensure that no complaints are missed; 3.3 Maintain communication: Maintain communication with customers during the complaint handling process, provide timely feedback on the progress and results, and increase customer trust.

5. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1 is characterized in that: The step (4) is specifically as follows: 4.1 Closed-loop control: Closed-loop management of complaints from receipt, processing to feedback to ensure that each complaint is properly handled and the entire process of complaint handling is automatically recorded to facilitate subsequent analysis and improvement; 4.2 Continuous Improvement Mechanism: Establish a complaint case analysis mechanism to classify, count and analyze complaints, promptly identify problems and take corresponding improvement measures; conduct in-depth investigations and develop solutions for repetitive complaints to prevent similar problems from happening again.

6. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1, characterized in that: The step (5) is specifically as follows: 5.1 Build a customer complaint management system to realize the rapid entry, query and statistics of complaint information; The customer complaint management system comprises: The complaint workbench is used to receive, process and track complaints. It is the main interface for users to interact with the platform. It displays a list of complaints to be processed and provides an entry point for handling complaints. The complaint process progress monitoring module is used to monitor the progress of complaint handling and provide real-time feedback. Users can view the current processing status of each complaint, including processed, pending, and in progress. The complaint ticket management module is used to manage specific complaint tickets, including creation, modification, deletion, and query of tickets; The complaint statistics module is used to generate complaint statistics; the early warning management module is used to issue warnings in advance to prevent potential problems from occurring. By setting thresholds and rules, when the number of complaints or indicators exceeds the preset value, the system automatically triggers an early warning; The monitoring management module is used to monitor the system operation status and detect abnormal situations in time; the system security module is used to protect the system from potential threats through security policies, identity authentication and access control; The knowledge base module is used to store all the knowledge and information of complaints; the operation management module is used for process configuration, time limit management and responsibility management; the indicator analysis module is used to analyze the complaint data; The customer service module is used to provide customer service to customers; the data exchange module is used to exchange data with other systems or departments; the knowledge sharing and evaluation module is used to share knowledge and experience and regularly evaluate the effectiveness of knowledge; the performance evaluation module is used to evaluate the performance of employees; 5.2 Use big data and artificial intelligence to analyze customer complaint data, explore potential problems and trends, and provide reference for decision-making; Use artificial intelligence to analyze complaint data. The specific steps are as follows: 5.2.1 Data collection and preprocessing Comprehensive data collection: collect customer complaint data; data cleaning: format the data; data integration: integrate data from different sources to form a unified data set; 5.2.2 Artificial Intelligence Processing: Text analysis: Use NLP technology to convert the text content of customer complaints into structured data and extract keywords, phrases and themes; Sentiment analysis: Identify emotional tendencies in customer feedback and quantify customer satisfaction or dissatisfaction; Semantic understanding: understand the specific content and intent of customer complaints and identify potential needs and problems; Classification and clustering: Use machine learning algorithms to automatically classify and cluster complaints, grouping similar issues together for quick response and processing; Predictive analysis: Based on historical data, predict the type, quantity or trend of complaints that will occur within a preset time, providing a basis for preventive measures; Anomaly detection: Identify complaints that are significantly different from normal patterns; Complex pattern recognition: Use deep learning models to process data and discover complex patterns and associations hidden in the data; Multi-language support: automatic translation and analysis of multi-language complaint data; 5.2.3 Exploring potential problems and trends: Root cause analysis: Combine NLP and machine learning technologies to deeply explore the root causes behind customer complaints; Trend forecasting: Based on historical data and current trends, forecast hot issues and changing trends of customer complaints in the future, and provide direction for product improvement and service optimization; Relevance analysis: Analyze the relevance and impact between different complaints and identify the problem points that trigger chain reactions; 5.2.4 Provide reference for decision-making: Optimize service process: Optimize service process based on complaint data analysis results to reduce complaint rate and processing time; Product improvement: Propose improvement suggestions in response to complaints about product design or functions to enhance product quality and user experience; Marketing strategy adjustment: Analyze customer feedback on needs and dissatisfaction, adjust marketing strategies, and improve market competitiveness; Resource allocation: Rationally allocate corporate resources based on the urgency and importance of complaint data to ensure that problems are resolved in a timely manner.

7. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 1, characterized in that: The step (6) is specifically as follows: 6.1 Provide professional training and skills improvement opportunities for complaint handling personnel so that they have rich complaint handling experience and problem-solving capabilities. The training content may include communication skills, laws and regulations, and product knowledge; 6.2 Establish a good teamwork atmosphere, promote communication and collaboration among personnel, improve the overall efficiency of complaint handling, regularly organize knowledge sharing and experience exchange meetings, and promote the continuous optimization and improvement of complaint handling work.

8. The method for constructing a customer complaint handling system for responding to regulatory complaints according to claim 3 is characterized in that: The step 2.2 specifically includes: 2.2.1 Performance indicator setting: Setting key performance indicators, including complaint volume / rate, complaint handling efficiency and effectiveness, to evaluate the overall effectiveness of the customer complaint handling system; 2.2.2 Complaint life cycle management: including complaint reception, analysis, prevention, early warning, handling and effect evaluation. Through process standardization, ensure that each complaint can be handled in a timely and professional manner; 2.2.3 Through in-depth analysis of complaint data, identify potential risk points and take preventive measures to reduce the occurrence of future complaints. At the same time, implement cycle management strategies to continuously optimize and improve the complaint handling process; Specific analysis steps for the complaint data: a. Data collection and integration: Collect data from different modules of the complaint management platform to form a unified data set; b. Data preprocessing: cleaning data; formatting data; c. Data analysis: Classification and clustering: Use machine learning algorithms to classify and cluster complaints and identify common complaint types and patterns; Sentiment analysis: Use natural language processing technology to analyze the emotional tendencies in customer feedback and quantify customer satisfaction or dissatisfaction; Trend prediction: Based on historical data, predict the trend and hot spots of complaints within a preset time period. Relevance analysis: Analyze the relevance and impact between different complaints, and identify risk points that may trigger chain reactions; Statistical analysis: Generate complaint statistics to identify potential problems. d. Risk point identification: Based on the analysis results, identify potential risk points; 2.2.4 Based on the in-depth mining and analysis of complaint data, make targeted investment decisions to optimize products and services and reduce sources of complaints; 2.2.5 Establish a data analysis platform to conduct in-depth mining and comprehensive utilization of complaint information.