Artificial intelligence order after-sales service method

Abstract

The invention discloses an artificial intelligence order after-sales service method. According to the invention, the decision tree algorithm is introduced, so that the after-sales service system has self-learning and optimization capabilities. As more service data are collected and analyzed, the decision tree model can be continuously iterated and optimized, and the classification accuracy and generalization ability of the decision tree model are improved. This means that as time goes on, the system can identify and predict customer demands more accurately, thereby providing more personalized and efficient after-sales services. The data-driven optimization mechanism is helpful for an enterprise to timely discover defects in a service process and carry out targeted improvement, so that the customer satisfaction is improved, the operation cost is reduced, and the competitiveness of the enterprise is enhanced. In general, the application of the decision tree algorithm improves the intelligent level of after-sales service, and provides powerful technical support for constructing a more perfect and efficient customer service system.

Description

Technical Field

[0001] The present invention belongs to the technical field of insurance order after-sales service, and specifically relates to an artificial intelligence order after-sales service method. Background Art

[0002] With the intensification of competition in the insurance market, the role of after-sales service of insurance orders in the insurance business is becoming increasingly important. In the future, insurance companies will continue to improve the after-sales service system, improve service quality, and meet the diverse needs of customers. With its humanized and professional advantages, after-sales service of insurance orders has become a key factor in the competition of insurance companies. In future development, after-sales service of insurance orders will continue to promote the transformation and upgrading of the insurance industry and provide customers with a better service experience. In short, after-sales service of insurance orders provides strong protection for insurance consumers and is expected to achieve more innovative applications in the future. After-sales service of insurance orders is an important part of the insurance business, aiming to provide customers with a full range of high-quality service experience. Since the development of the insurance industry, after-sales service has gradually received attention and has become a key link in improving customer satisfaction and loyalty.

[0003] However, in traditional after-sales service processes, customer service requests usually require manual preliminary diagnosis and classification, which is time-consuming and error-prone. At the same time, without decision tree algorithms, service tickets may be incorrectly assigned to relevant departments due to human misjudgment, resulting in processing delays and customer dissatisfaction. Summary of the invention

[0004] The purpose of the present invention is to provide an artificial intelligence order after-sales service method in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: an artificial intelligence order after-sales service method, comprising the following steps:

[0006] S1: The customer initiates a service request. The customer submits an after-sales service application through the mobile APP, official website or other channels, including the insurance order number, problem description, and contact information;

[0007] S2: Perform preliminary AI diagnosis. The AI ​​system makes a preliminary diagnosis of the problem based on the information submitted by the customer and determines the type of problem, including product quality, logistics issues, and usage consultation;

[0008] S3: Assign service work orders. The system automatically assigns service work orders to corresponding departments based on the preliminary diagnosis results.

[0009] S4: Customer service personnel will communicate with the customer through telephone or online chat after receiving the work order to further understand the details of the problem;

[0010] S5: Provide AI-assisted answers. During customer service communication, the AI ​​assistant provides relevant solutions and answers to common questions to assist customer service personnel in solving problems efficiently.

[0011] S6: Determine the solution. The customer service staff will develop a suitable solution based on the customer's problem description and the information provided by the AI ​​assistant, and confirm it with the customer.

[0012] S7: Execute the solution. The customer service staff will feedback the solution to the relevant department. If return, exchange or repair is required, it will be executed by the relevant department;

[0013] S8: Progress tracking and feedback. The artificial intelligence system tracks the progress of after-sales service in real time and feeds back progress information to customers to ensure that customers understand the progress of the service;

[0014] S9: Conduct customer satisfaction evaluation. After the service is completed, invite customers to evaluate their satisfaction with the after-sales service and collect feedback;

[0015] S10: Conduct data analysis and optimization. The artificial intelligence system collects data from the after-sales service process, analyzes it, identifies service pain points, continuously optimizes the after-sales service process, and improves customer satisfaction.

[0016] In a preferred embodiment, in step S1, in order to ensure the accuracy and completeness of the information, the system will provide a filling guide and examples. When the customer selects the "After-sales Service" option in the mini program and fills in the form, the system will automatically prompt the required items and generate a unique work order number after submission, which will become the unique identifier for all subsequent service activities.

[0017] In a preferred embodiment, in step S2, a decision tree machine learning algorithm is used to classify data. Decision tree is a commonly used classification and regression algorithm that can learn a series of rules from data to classify new data, specifically including:

[0018] S2-1. Data collection and preprocessing: Collect historical after-sales service records, including problem descriptions, diagnostic results, and solutions. Clean the data, remove invalid and erroneous data, and perform feature extraction and encoding;

[0019] S2-2. Feature selection: Determine the features used to train the decision tree, including product categories, keywords in problem descriptions, and sentiment in customer feedback;

[0020] S2-3. Model training: Use the preprocessed data to train the decision tree model;

[0021] S2-4. Model validation: Use cross-validation method to verify the accuracy and generalization ability of decision tree model;

[0022] S2-5. Model deployment: Deploy the trained model to the after-sales system for preliminary diagnosis.

[0023] The calculation formula of the decision tree algorithm is information gain, which is as follows:

[0024]

[0025] in:

[0026] D represents the original data set.

[0027] A refers to the features used to calculate information gain.

[0028] H(D) refers to the entropy of the data set D, which indicates the uncertainty of the data set.

[0029] p(x i ) is represented as the probability of the i-th class sample in the data set DD.

[0030] Values(A) represents all possible values ​​of feature A.

[0031] D v It is represented as a data subset whose feature A takes the value v.

[0032] IG(D,A) is expressed as the information gain of feature A, which is used to evaluate the impact of feature AA on the classification results.

[0033] In a preferred embodiment, in step S3, based on the preliminary diagnosis results, the system will use a complex decision tree algorithm or rule-based logic to automatically assign the service ticket to the most appropriate department. This process may involve multi-dimensional evaluation, including the urgency, complexity and workload of the department. If the diagnosis result points to a "technical failure", the system will intelligently assign the ticket to the most appropriate team or individual based on the current ticket processing status of the technical support department and the engineer's professional field.

[0034] In a preferred embodiment, in step S4, the customer service staff will use professional communication skills to patiently listen to the customer's questions and record all relevant information in detail, including the specific situation of the problem and the customer's emotional state. This information will be entered into the CRM system for subsequent query and analysis.

[0035] In a preferred embodiment, in step S5, the AI ​​assistant quickly locates relevant solutions and FAQs by searching a huge knowledge base and using an efficient search algorithm. When a customer service staff encounters a common problem during a chat, the AI ​​assistant immediately pushes a pre-set answer or suggestion, thereby speeding up the problem resolution process.

[0036] In a preferred embodiment, in step S6, logical reasoning, empirical judgment and case matching methods are used, which involves comparison and selection of multiple solutions. Customer service personnel will fully communicate with customers to ensure that the solution can meet customer needs and is recognized by customers.

[0037] In step S7, cross-departmental collaboration is involved, and a workflow management system is required to ensure that tasks are executed according to predetermined processes and standards.

[0038] In a preferred embodiment, in step S8, the artificial intelligence system uses event-driven and state monitoring technology to achieve real-time tracking of after-sales service progress. The system can not only capture every key node in the service process, but also automatically send service progress updates to customers according to preset trigger conditions. These update information is conveyed to customers through multiple channels, including but not limited to SMS, email, and push notifications of mobile phone APPs, to ensure the accessibility and timeliness of information.

[0039] In a preferred embodiment, step S9 is implemented by an online questionnaire, including using the NetPromoter Score (NPS) rating system, or a five-star rating system to collect customer feedback. Such feedback is crucial for measuring service quality, customer satisfaction and improving service processes.

[0040] In a preferred embodiment, in step S10, the artificial intelligence system collects all data in the after-sales service process, including response time, resolution rate, and customer satisfaction, and uses data analysis techniques, such as regression analysis and cluster analysis, to identify bottlenecks and improvement points in the service process. Through these analyses, enterprises continuously optimize service processes, improve service efficiency and quality, and thus improve customer satisfaction. If the analysis finds that the resolution time of a certain type of problem is too long, the enterprise may adjust the staffing or optimize the solution library.

[0041] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0042] 1. In the present invention, the decision tree algorithm can efficiently perform preliminary diagnosis and classification of problems submitted by customers by learning patterns in historical after-sales service records. This automated preliminary diagnosis greatly improves the efficiency of problem handling, reduces the direct involvement of customer service personnel in simple problems, and enables them to devote more time and energy to more complex service requests that require more manual intervention. In addition, the classification results of the decision tree are highly accurate, which can ensure that service work orders are accurately assigned to the corresponding departments, thereby speeding up the entire service process and improving customer satisfaction with the timeliness of the service.

[0043] 2. In the present invention, the introduction of the decision tree algorithm enables the after-sales service system to have the ability of self-learning and optimization. As more service data is collected and analyzed, the decision tree model can be continuously iterated and optimized to improve its classification accuracy and generalization ability. This means that as time goes by, the system can more accurately identify and predict customer needs, and thus provide more personalized and efficient after-sales service. This data-driven optimization mechanism helps companies to promptly discover defects in the service process and make targeted improvements, thereby improving customer satisfaction while reducing operating costs and enhancing the competitiveness of the company. In general, the application of the decision tree algorithm improves the level of intelligence of after-sales service and provides strong technical support for building a more complete and efficient customer service system. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] Example:

[0046] An artificial intelligence order after-sales service method, comprising the following steps:

[0047] S1: The customer initiates a service request. The customer submits an after-sales service application through the mobile APP, official website or other channels, including the insurance order number, problem description, contact information and other information;

[0048] S2: Perform preliminary AI diagnosis. The AI ​​system makes a preliminary diagnosis of the problem based on the information submitted by the customer and determines the type of problem, including product quality, logistics issues, and usage consultation;

[0049] S3: Assign service work orders. The system automatically assigns service work orders to corresponding departments based on the preliminary diagnosis results.

[0050] S4: Customer service personnel will communicate with the customer through telephone or online chat after receiving the work order to learn more about the problem;

[0051] S5: Provide AI-assisted answers. During customer service communication, the AI ​​assistant can provide relevant solutions and answers to common questions to assist customer service personnel in solving problems efficiently.

[0052] S6: Determine the solution. The customer service staff will develop a suitable solution based on the customer's problem description and the information provided by the AI ​​assistant, and confirm it with the customer.

[0053] S7: Execute the solution. The customer service staff will feedback the solution to the relevant department. If return, exchange or repair is required, it will be executed by the relevant department;

[0054] S8: Progress tracking and feedback. The artificial intelligence system tracks the progress of after-sales service in real time and feeds back progress information to customers to ensure that customers understand the progress of the service;

[0055] S9: Conduct customer satisfaction evaluation. After the service is completed, invite customers to evaluate their satisfaction with the after-sales service and collect feedback;

[0056] S10: Conduct data analysis and optimization. The artificial intelligence system collects data from the after-sales service process, analyzes it, identifies service pain points, continuously optimizes the after-sales service process, and improves customer satisfaction.

[0057] In step S1, in order to ensure the accuracy and completeness of the information, the system usually provides filling guides and examples. For example, when a customer selects the "After-sales Service" option in the mini program and fills in the form, the system will automatically prompt the required items and generate a unique work order number after submission. This number will become the unique identifier for all subsequent service activities.

[0058] In step S2, a decision tree machine learning algorithm is used to classify data. Decision tree is a commonly used classification and regression algorithm that can learn a series of rules from data to classify new data, including:

[0059] S2-1. Data collection and preprocessing: Collect historical after-sales service records, including problem descriptions, diagnosis results, solutions, etc. Clean the data, remove invalid and erroneous data, and perform feature extraction and encoding;

[0060] S2-2. Feature selection: Determine the features used to train the decision tree, such as product categories, keywords in problem descriptions, and sentiment in customer feedback;

[0061] S2-3. Model training: Use the preprocessed data to train the decision tree model;

[0062] S2-4. Model validation: Use methods such as cross-validation to verify the accuracy and generalization ability of the decision tree model;

[0063] S2-5. Model deployment: Deploy the trained model to the after-sales system for preliminary diagnosis.

[0064] The commonly used calculation formula for decision tree algorithm is information gain, which is as follows:

[0065]

[0066] in:

[0067] D represents the original data set.

[0068] A refers to the features used to calculate information gain.

[0069] H(D) refers to the entropy of the data set D, which indicates the uncertainty of the data set.

[0070] p(xi) represents the probability of the i-th class sample in the data set DD.

[0071] Values(A) represents all possible values ​​of feature A.

[0072] Dv represents the data subset whose feature A takes the value v.

[0073] IG(D,A) is expressed as the information gain of feature A, which is used to evaluate the impact of feature AA on the classification results.

[0074] In step S3, based on the preliminary diagnosis results, the system will use complex decision tree algorithms or rule-based logic to automatically assign the service ticket to the most appropriate department. This process may involve multi-dimensional evaluation, including the urgency, complexity and workload of the problem. For example, if the diagnosis result points to a "technical failure", the system will intelligently assign the ticket to the most appropriate team or individual based on the current ticket processing status of the technical support department and the engineer's professional field.

[0075] In step S4, the customer service staff will use professional communication skills, listen patiently to the customer's questions, and record all relevant information in detail, including the specific situation of the problem, the customer's emotional state, etc. This information will be entered into the CRM system for subsequent query and analysis.

[0076] In step S5, the AI ​​assistant quickly locates relevant solutions and FAQs by searching a huge knowledge base and using efficient search algorithms such as BM25. For example, when a customer service representative encounters a common problem in a chat, the AI ​​assistant can immediately push a pre-set answer or suggestion, thereby speeding up the problem resolution process.

[0077] In step S6, logical reasoning, empirical judgment and case matching are used, which involves the comparison and selection of multiple solutions. Customer service staff will fully communicate with customers to ensure that the solution can meet customer needs and is recognized by customers.

[0078] In step S7, cross-departmental collaboration is involved, and a workflow management system is needed to ensure that tasks are executed according to predetermined processes and standards.

[0079] In step S8, the AI ​​system uses event-driven and state monitoring technology to achieve real-time tracking of after-sales service progress. The system can not only capture every key node in the service process, but also automatically send service progress updates to customers based on preset trigger conditions. These update information is conveyed to customers through multiple channels, including but not limited to SMS, email, and push notifications of mobile phone apps to ensure the accessibility and timeliness of information.

[0080] In step S9, customer feedback is usually collected through online questionnaires, such as using the Net Promoter Score (NPS) rating system, or through a five-star rating system. Such feedback is crucial for measuring service quality, customer satisfaction, and improving service processes.

[0081] In step S10, the artificial intelligence system will collect all data in the after-sales service process, including response time, resolution rate, customer satisfaction, etc., and use data analysis techniques, such as regression analysis and cluster analysis, to identify bottlenecks and improvement points in the service process. Through these analyses, enterprises can continuously optimize service processes, improve service efficiency and quality, and thus improve customer satisfaction. For example, if the analysis finds that the resolution time of a certain type of problem is too long, the enterprise may adjust the staffing or optimize the solution library.

[0082] In the present invention, the decision tree algorithm can efficiently perform preliminary diagnosis and classification of problems submitted by customers by learning patterns in historical after-sales service records. This automated preliminary diagnosis greatly improves the efficiency of problem handling, reduces the direct involvement of customer service personnel in simple problems, and enables them to devote more time and energy to more complex service requests that require more manual intervention. In addition, the classification results of the decision tree are highly accurate, which can ensure that service work orders are accurately assigned to the corresponding departments, thereby speeding up the entire service process and improving customer satisfaction with the timeliness of the service.

[0083] In the present invention, the introduction of the decision tree algorithm enables the after-sales service system to have the ability of self-learning and optimization. As more service data is collected and analyzed, the decision tree model can be continuously iterated and optimized to improve the accuracy and generalization ability of its classification. This means that as time goes by, the system can more accurately identify and predict customer needs, and then provide more personalized and efficient after-sales service. This data-driven optimization mechanism helps companies to promptly discover defects in the service process and make targeted improvements, thereby improving customer satisfaction while also reducing operating costs and enhancing the competitiveness of the company. In general, the application of the decision tree algorithm improves the intelligence level of after-sales service and provides strong technical support for building a more complete and efficient customer service system.

[0084] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0085] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence order after-sales service method, characterized by: The steps include: S1: The customer initiates a service request. The customer submits an after-sales service application through the mobile APP, official website or other channels, including the insurance order number, problem description, and contact information; S2: Perform preliminary AI diagnosis. The AI ​​system performs preliminary diagnosis of the problem based on the information submitted by the customer and determines the type of problem, including product quality, logistics issues, and usage consultation; S3: Assign service work orders. The system automatically assigns service work orders to corresponding departments based on the preliminary diagnosis results. S4: Customer service intervention and communication. After receiving the work order, the customer service staff will contact the customer through telephone or online chat to further understand the details of the problem; S5: Provide AI-assisted answers. During customer service communication, the AI ​​assistant provides relevant solutions and answers to common questions to assist customer service personnel in solving problems efficiently. S6: Determine the solution. The customer service staff will develop a suitable solution based on the customer's problem description and the information provided by the AI ​​assistant, and confirm it with the customer. S7: Execute the solution. The customer service staff will feedback the solution to the relevant department. If return, exchange or repair is required, it will be executed by the relevant department; S8: Progress tracking and feedback. The artificial intelligence system tracks the progress of after-sales service in real time and feeds back progress information to customers to ensure that customers understand the progress of the service; S9: Conduct customer satisfaction evaluation. After the service is completed, invite customers to evaluate their satisfaction with the after-sales service and collect feedback; S10: Conduct data analysis and optimization. The artificial intelligence system collects data from the after-sales service process, analyzes it, identifies service pain points, continuously optimizes the after-sales service process, and improves customer satisfaction.

2. An artificial intelligence order after-sales service method as claimed in claim 1, characterized in that: In step S1, in order to ensure the accuracy and completeness of the information, the system will provide a filling guide and examples; when the customer selects the "After-sales Service" option in the mini program and fills in the form, the system will automatically prompt the required items and generate a unique work order number after submission. This number will become the unique identifier for all subsequent service activities.

3. An artificial intelligence order after-sales service method as claimed in claim 1, characterized in that: In step S2, a decision tree machine learning algorithm is used to classify data; Decision tree is a commonly used classification and regression algorithm that can learn a series of rules from data to classify new data, including: S2-1. Data collection and preprocessing: Collect historical after-sales service records, including problem descriptions, diagnosis results, and solutions; clean the data, remove invalid and erroneous data, and perform feature extraction and coding; S2-2. Feature selection: Determine the features used to train the decision tree, including product categories, keywords in problem descriptions, and sentiment in customer feedback; S2-3. Model training: Use the preprocessed data to train the decision tree model; S2-4. Model validation: Use cross-validation method to verify the accuracy and generalization ability of decision tree model; S2-5. Model deployment: Deploy the trained model to the after-sales system for preliminary diagnosis; The calculation formula of the decision tree algorithm is information gain, which is as follows: in: D represents the original data set; A refers to the feature used to calculate information gain; H(D) refers to the entropy of the data set D, which indicates the uncertainty of the data set; p(x i ) is represented as the probability of the i-th class sample in the data set DD; Values(A) represents all possible values ​​of feature A; D v It is represented as a data subset with feature A value v; IG(D,A) is represented by the information gain of feature A, which is used to evaluate the impact of feature AA on the classification results.

4. The artificial intelligence order after-sales service method according to claim 1, characterized in that: In step S3, based on the preliminary diagnosis results, the system will use complex decision tree algorithms or rule-based logic to automatically assign the service ticket to the most appropriate department; this process may involve multi-dimensional evaluation, including the urgency and complexity of the problem and the workload of the department; if the diagnosis result points to a "technical failure", the system will intelligently assign the ticket to the most appropriate team or individual based on the current ticket processing status of the technical support department and the engineer's professional field.

5. The artificial intelligence order after-sales service method according to claim 1, characterized in that: In step S4, the customer service staff will use professional communication skills, listen patiently to the customer's questions, and record all relevant information in detail, including the specific circumstances of the problem and the customer's emotional state; this information will be entered into the CRM system for subsequent query and analysis.

6. The artificial intelligence order after-sales service method according to claim 1, characterized in that: In step S5, the artificial intelligence assistant quickly locates relevant solutions and answers to common questions by searching a huge knowledge base and using an efficient search algorithm; when the customer service staff encounters a common problem during the chat, the artificial intelligence assistant immediately pushes pre-set answers or suggestions, thereby speeding up problem solving.

7. The artificial intelligence order after-sales service method according to claim 1, characterized in that: In step S6, logical reasoning, empirical judgment and case matching methods are used, which involves comparison and selection of multiple solutions; customer service personnel will fully communicate with customers to ensure that the solution can meet customer needs and is recognized by customers; In step S7, cross-departmental collaboration is involved, and a workflow management system is required to ensure that tasks are executed according to predetermined processes and standards.

8. An artificial intelligence order after-sales service method as claimed in claim 1, characterized in that: In step S8, the artificial intelligence system uses event-driven and state monitoring technology to achieve real-time tracking of after-sales service progress; the system can not only capture every key node in the service process, but also automatically send service progress updates to customers according to preset trigger conditions; These updated information are communicated to customers through multiple channels, including but not limited to SMS, email, and push notifications of mobile apps to ensure the accessibility and timeliness of the information.

9. An artificial intelligence order after-sales service method as claimed in claim 1, characterized in that: In step S9, the step is implemented by an online questionnaire, including using a Net Promoter Score rating system or a five-star rating system to collect customer feedback; This feedback is crucial for measuring service quality, customer satisfaction and improving service processes.

10. An artificial intelligence order after-sales service method as claimed in claim 1, characterized in that: In step S10, the artificial intelligence system collects all data in the after-sales service process, including response time, resolution rate, and customer satisfaction, and uses data analysis techniques, such as regression analysis and cluster analysis, to identify bottlenecks and improvement points in the service process; Through these analyses, companies continuously optimize service processes, improve service efficiency and quality, and thus increase customer satisfaction; If analysis reveals that a certain type of problem takes too long to resolve, the company may adjust staffing or optimize the solution library.

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