Customer contact method and device, computer equipment and readable storage medium

Abstract

The invention provides a customer contact method and device, computer equipment and a readable storage medium, and the method comprises the steps: obtaining customer information of a to-be-contacted target customer, the customer information at least comprising historical insurance information, historical contact information and customer personal information; matching with a plurality of preset customer group types according to the customer information to determine a target group type corresponding to the target customer; confirming a target contact mode with the target customer according to the target group type and the historical contact information, wherein the target contact mode at least comprises telephone contact and online contact; determining a current insurance period of the target customer according to the historical insurance information and the customer personal information so as to determine target contact content corresponding to the target customer; determining a target contact frequency with the target customer according to the historical contact information and the current insurance period; and generating a target contact strategy corresponding to the target customer according to the target contact mode, the target contact content and the target contact frequency, so as to complete contact with the target customer according to the target contact strategy.

Description

Technical Field

[0001] The present application relates to the field of machine learning technology, which is applied in the financial field, and in particular to a customer contact method, device, computer equipment and readable storage medium. Background Art

[0002] In the financial field, especially in the insurance field, it is crucial to have contact with customers before and after the transaction of insurance products. At present, customers are contacted by phone to complete the steps of communication, inquiry, quotation, insurance, etc. For customers, frequent calls will appear abrupt and disturbing, and it is very easy to cause customer complaints. As a result, the current method of contacting customers by phone is not only ineffective, but also has the risk of causing customer complaints, leading to the eventual loss of customers.

[0003] Therefore, there is an urgent need for a customer contact method to improve the work efficiency of business personnel who come into contact with customers and the customer satisfaction. Summary of the invention

[0004] The present application provides a customer contact method, device, computer equipment and readable storage medium, which aims to solve the problem that in the insurance field, contact with customers is crucial both before and after the transaction of insurance products. At present, the method of contacting customers by phone is not only ineffective, but also has the risk of causing customer complaints and eventually leading to the loss of customers.

[0005] In a first aspect, the present application provides a method for contacting a customer, comprising:

[0006] Obtain customer information of the target customer to be contacted, which at least includes historical insurance information, historical contact information and customer personal information;

[0007] Matching the customer information with multiple preset customer group types respectively to determine the target group type corresponding to the target customer among the multiple customer group types;

[0008] Determine the target contact method with the target customers based on the target group type and historical contact information; the target contact method includes at least one or more of telephone contact and online contact;

[0009] Determine the current insurance period of the target customer based on historical insurance information and customer personal information, and determine the target contact content corresponding to the target customer based on the current insurance period and target group type;

[0010] Determine the target contact frequency with target customers based on historical contact information and current insurance cycle;

[0011] Generate a target contact strategy corresponding to the target customer based on the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the target contact strategy.

[0012] In some embodiments, historical insurance information includes at least one or more of historical insurance amounts, historical renewal rates, and historical insurance types, historical contact information includes at least one or more of historical contact times, historical contact times, and historical contact methods, and customer personal information includes at least one or more of customer occupation, customer family composition, customer operations, customer income, and customer background; customer information is matched with a plurality of preset customer group types to determine a target group type corresponding to the target customer from among the plurality of customer group types, including: inputting historical insurance information, historical contact information, and customer personal information into a preset behavior analysis model, the behavior analysis model generating behavior preference information of the target customer; obtaining group preference information corresponding to the customer in each customer group type; calculating the preference similarity between the behavior preference information and each group preference information to determine the target group type from among the plurality of customer group types based on the preference similarity.

[0013] In some embodiments, a target contact method with a target customer is confirmed based on the target group type and historical contact information, including: parsing historical contact information, obtaining multiple historical contacts corresponding to the target customer, historical contact content corresponding to each historical contact, historical contact methods, and historical contact results; obtaining a preferred contact method corresponding to the target group type; and generating a target contact method corresponding to the target customer based on the preferred contact method, historical contact content corresponding to each historical contact, historical contact methods, and historical contact results.

[0014] In some embodiments, the current insurance period of a target customer is determined based on historical insurance information and customer personal information, including: parsing customer personal information to obtain territorial information corresponding to the target customer; obtaining renewal policy information corresponding to the territorial information; obtaining the current policy status corresponding to the target customer based on historical insurance information; and determining the current insurance period corresponding to the target customer based on the current policy status and renewal policy information.

[0015] Exemplarily, the current insurance cycle includes at least any one of an operation period, a warm-up period, a temporary storage period, a first period, a tracking period and a guarantee period; the target contact content corresponding to the target group type and the target customer is determined according to the current insurance cycle, including: parsing the current insurance cycle, and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of the customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; and generating the target contact content according to the group communication content, the cycle focus information and the current policy status.

[0016] In some embodiments, a target contact frequency with a target customer is determined based on historical contact information and a current insurance cycle, including: parsing historical contact information to obtain the target customer's communication intention and the contact interval corresponding to the last historical contact; determining the contact urgency corresponding to the target customer based on the current insurance cycle; and generating a target contact frequency based on the communication intention, contact interval, and contact urgency.

[0017] Exemplarily, after generating the target contact frequency according to communication intention, contact interval and contact urgency, it also includes: if parsing historical contact information can obtain historical complaint information of the target customer for any historical contact, generating the customer sensitivity according to the historical complaint information; and adjusting the target contact frequency according to the customer's sensitivity.

[0018] In a second aspect, the present application provides a customer contact device, comprising:

[0019] An information acquisition module is used to acquire customer information of the target customer to be contacted, wherein the customer information at least includes historical insurance information, historical contact information and customer personal information;

[0020] A group determination module, used for matching the customer information with a plurality of preset customer group types respectively, so as to determine a target group type corresponding to the target customer from among the plurality of customer group types;

[0021] A contact determination module, configured to determine a target contact method with the target customer according to the target group type and the historical contact information; the target contact method includes at least one or more of telephone contact and online contact;

[0022] A content determination module, used to determine the current insurance period of the target customer based on the historical insurance information and the customer personal information, so as to determine the target contact content corresponding to the target customer according to the current insurance period and the target group type;

[0023] A frequency determination module, used to determine a target contact frequency with the target customer based on the historical contact information and the current insurance cycle;

[0024] The contact completion module is used to generate a contact strategy corresponding to the target customer according to the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the contact strategy.

[0025] In a third aspect, the present application further provides a computer device, including:

[0026] Memory and processor;

[0027] The memory is used to store computer programs;

[0028] The processor is used to execute the computer program and implement the steps of the customer contact method described in the first aspect when executing the computer program.

[0029] In a fourth aspect, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the steps of the customer contact method as described in the first aspect above.

[0030] The embodiment of the present application provides a customer contact method, device, computer equipment and readable storage medium. The method first collects detailed customer information of the target customer to be contacted, which includes at least historical insurance information, historical contact information and customer personal information. Historical insurance information can help understand the customer's insurance needs and preferences, historical contact information can evaluate the effect of contacting customers in the past, and customer personal information can help locate customer characteristics more accurately. Then, according to the collected customer information, the target customer is matched with multiple preset customer group types to determine the best target group type corresponding to the target customer. This step can be achieved through big data analysis and machine learning algorithms, so that customers are classified into the most suitable groups and contact strategies are tailored for them. Then, according to the target group type and historical contact information, the most suitable contact method with the target customer is selected, including but not limited to telephone contact and online contact. Different customer groups and contact histories may be suitable for different contact methods. This step can ensure that the selected method can both effectively communicate and reduce disturbance to customers. Further combined with historical insurance information and customer personal information, the current insurance cycle of the target customer is determined. This helps to understand the customer's current insurance needs and possible purchase opportunities, thereby providing customers with timely and relevant information and services. Furthermore, according to the current insurance cycle and the type of target group, the specific content of communication with the target customers is formulated. This content should match the current needs and preferences of the customers and provide valuable insurance information and suggestions. Finally, the contact frequency with the target customers is determined based on the historical contact information and the current insurance cycle. A reasonable contact frequency can ensure that the customer does not feel disturbed too frequently while maintaining effective communication and interaction. Based on the information from the above steps, a target contact strategy corresponding to the target customer is generated. The strategy includes target contact method, target contact content and target contact frequency, which are used to guide specific customer contact operations.

[0031] The method provided thereby has at least the following beneficial effects:

[0032] 1. Improve customer satisfaction: By accurately matching customer group types and customizing contact strategies, unnecessary interruptions and the number of interruptions to customers are reduced, thereby improving overall customer satisfaction.

[0033] 2. Reduce customer complaints: Optimized contact methods and frequency prevent customers from getting annoyed by frequent calls or messages, thus reducing the risk of customer complaints.

[0034] 3. Improve transaction rate: By providing information and services that match customers' current needs and preferences, the customer conversion rate and insurance product transaction rate are improved.

[0035] 4. Prevent customer churn: Customized contact strategies can effectively maintain long-term interaction with customers, increase customer loyalty, and reduce the possibility of customer churn.

[0036] 5. Improve communication efficiency: Reasonable target contact frequency and method ensure that each contact can achieve the expected effect, improving the efficiency and success rate of communication.

[0037] 6. Data-driven decision-making: The entire approach relies on the collection and analysis of customer information. Through big data and machine learning technology, data-driven customer contact strategy formulation is achieved, which improves the scientificity and accuracy of decision-making.

[0038] 7.Flexible adaptation to customer needs: Based on the characteristics of different customer groups and historical contact information, the contact method and content can be flexibly adjusted to ensure that each contact can meet the specific needs of the customer.

[0039] 8. Improve customer experience: Through the introduction of online contact methods, customers can more conveniently obtain insurance information and complete the insurance process, which improves the overall customer experience.

[0040] 9. Reduce operating costs: Reduce invalid and inefficient contacts, improve the effectiveness of each contact, and reduce the workload and operating costs of customer service personnel.

[0041] In summary, this customer contact method has significantly improved customer communication methods in the insurance field through scientific and precise strategy formulation, increased customer satisfaction, transaction rate and operational efficiency, and effectively prevented customer churn.

[0042] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0044] Figure 1This is a schematic flow chart of the steps of a customer contact method provided by an embodiment of the present application;

[0045] Figure 2 is a schematic flow chart of the steps of a method for generating a target contact mode provided in an embodiment of the present application;

[0046] Figure 3 is a schematic flow chart of the steps of a method for determining a current insurance period provided by an embodiment of the present application;

[0047] Figure 4 is a structural schematic diagram of a customer contact device provided in one embodiment of the present application;

[0048] Figure 5 It is a schematic block diagram of the structure of a computer device provided in one embodiment of the present application.

[0049] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0052] It should be understood that, in order to facilitate the clear description of the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, the words "first", "second", etc. are used to distinguish the same items or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second", etc. do not limit the quantity and execution order, and the words "first", "second", etc. do not necessarily limit the difference.

[0053] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0054] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0055] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0056] In the financial field, especially in the insurance field, it is crucial to have contact with customers before and after the transaction of insurance products. At present, customers are contacted by phone to complete the steps of communication, inquiry, quotation, insurance, etc. For customers, frequent calls will appear abrupt and disturbing, and it is very easy to cause customer complaints. As a result, the current method of contacting customers by phone is not only ineffective, but also has the risk of causing customer complaints, leading to the eventual loss of customers.

[0057] Therefore, there is an urgent need for a customer contact method to improve the work efficiency of business personnel who come into contact with customers and the customer satisfaction.

[0058] To solve the above problems, please refer to Figure 1 , Figure 1 1 is a schematic flow chart of a customer contact method provided by an embodiment of the present application. The customer contact method can be implemented by a computer device, and the computer device can be deployed on a single server or a server cluster. It can also be deployed on a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0059] It should be noted that the acquisition of any information mentioned in the provided method is in compliance with relevant regulations and is carried out with the user's consent, and will not infringe on the user's privacy or violate relevant laws and regulations.

[0060] At the same time, the method provided by this application can be applied to business scenarios in the financial field that require frequent contact with customers, in addition to the insurance field, and in other fields to avoid the phenomenon of causing customer disgust through telephone contact. It greatly improves customer satisfaction, so the insurance field is only a specific example of this application, and the specific application scope of the method provided by this application is not limited.

[0061] To solve the above problems, please refer to Figure 1 Specifically, Figure 1 As shown, the provided customer contact method includes steps S101 to S106. The details are as follows:

[0062] Step S101. Obtain customer information of the target customer to be contacted, where the customer information at least includes historical insurance information, historical contact information and customer personal information.

[0063] Specifically, first, the computer device needs to collect detailed information of the target customer to be contacted, including but not limited to historical insurance information, historical contact information and customer personal information. The collection method can be through various channels, such as customer management system (CRM), historical call records, customer submitted forms, etc.

[0064] For example, assuming that you need to contact customer Zhang San, you can obtain Zhang San’s historical insurance records (such as the type of insurance purchased in the past, the term of the policy, etc.) from the CRM system, obtain Zhang San’s historical contact records (such as the time of answering the call, call duration, call content, etc.) from the call / chat records, and obtain Zhang San’s personal information (such as age, gender, occupation, income, etc.) from the form submitted by the customer.

[0065] Furthermore, in combination with step S101 and the corresponding example, step S101 provides basic data for subsequent personalized contact strategies through comprehensive customer information collection, thereby ensuring the accuracy and effectiveness of the strategies.

[0066] Step S102: Match the customer information with a plurality of preset customer group types respectively to determine the target group type corresponding to the target customer from the plurality of customer group types.

[0067] Specifically, by matching the collected customer information with multiple preset customer group types. These group types can be classified based on multiple characteristics of the customer, such as age, gender, occupation, insurance history, contact history, etc. The matching process can be implemented through machine learning algorithms and data classification technology. For example, according to Zhang San's insurance history (purchased health insurance), contact history (short time to answer the phone, but positive response to text messages and emails) and personal information (35 years old, IT industry practitioners), he is matched to the customer group type of "middle-aged and young IT practitioners". Of course, the specific group type can also be the customer's value, the type of product purchased by the customer, the above customer occupation type, etc., or a fusion of multiple types, which is not limited in the embodiment of the present application. By matching customer group types, the characteristics and preferences of target customers can be more accurately understood, so as to formulate more personalized contact strategies.

[0068] Step S103. Confirm the target contact method with the target customer according to the target group type and historical contact information; the target contact method includes at least one or more of telephone contact and online contact.

[0069] Specifically, the most appropriate contact method is selected based on the target customer group type and historical contact information. Contact methods can include telephone contact, online contact (such as email, text message, instant message, etc.), or a mixture of multiple contacts. The selection process can determine the most effective communication method by analyzing the customer's contact history. For example, based on the group type of "young and middle-aged IT practitioners" and Zhang San's historical contact information (positive response to text messages and emails), it is decided to use email and text messages as the main contact methods. By selecting the appropriate target contact method, the customer response rate can be significantly improved, the disturbance to customers can be reduced, and customer satisfaction can be improved.

[0070] Step S104: Determine the current insurance period of the target customer based on the historical insurance information and the customer's personal information, and determine the target contact content corresponding to the target customer based on the current insurance period and the target group type.

[0071] Specifically, the current insurance cycle of the target customer is determined based on the historical insurance information and personal information of the target customer. This includes the customer's insurance demand period, the expiration date of the insurance product, etc. The customer's current insurance status can be determined through data analysis and prediction models. For example, based on Zhang San's historical insurance information, it is found that his current health insurance is about to expire, and his personal information shows that he has recently become a father, so the system predicts that his insurance needs may change from personal health insurance to family health insurance. By accurately determining the customer's current insurance cycle, timely relevant insurance products and services can be provided to customers, improving their willingness to purchase and satisfaction.

[0072] Step S105. Determine the target contact frequency with the target customer based on the historical contact information and the current insurance cycle.

[0073] Specifically, determine the appropriate contact frequency based on the target customer's historical contact information and current insurance cycle. This can be achieved by analyzing the customer's response time and preferences to ensure that frequent contact does not cause customer disgust. For example, based on Zhang San's historical contact information (he responds well to weekly emails, but is annoyed by daily text messages) and his expiring health insurance, it is decided to send emails once a week and text message reminders once a month. A reasonable contact frequency can increase customer acceptance, reduce customer disgust and complaints, and improve communication effectiveness.

[0074] Step S106: Generate a target contact strategy corresponding to the target customer according to the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the target contact strategy.

[0075] Specifically, by integrating the information obtained in the previous steps, a specific customer contact strategy is generated. The strategy includes the selected contact method, specific contact content and contact frequency. The generated strategy can be implemented through automated scripts or AI algorithms to ensure the accuracy of the strategy and the consistency of execution. For example, the contact strategy generated for Zhang San is as follows: send an email every Thursday with the latest discounts and detailed information on family health insurance; send a text message at the beginning of each month to remind him of the expiration date and renewal discount of health insurance. A comprehensive target contact strategy can ensure that each contact can achieve the best results, increase customer participation and purchase intention, and ultimately increase transaction rate and customer satisfaction.

[0076] In summary, steps S101 to S106 reduce the disturbance to customers and improve the customer's acceptance and satisfaction with communication through personalized and precise contact methods. The reasonable contact frequency and method avoid frequent telephone disturbances and reduce the risk of customer complaints. By providing insurance information and services that match the current needs and preferences of customers, the customer's purchase intention and transaction rate are improved. And maintain long-term and effective interaction, increase customer loyalty, and reduce the possibility of customer churn. Through scientific data analysis and AI technology, the effect of each contact is improved and the cost of ineffective communication is reduced. The whole method relies on rich customer data. Through data analysis and machine learning, data-driven contact strategy formulation is realized, which improves the scientificity and accuracy of decision-making. The method flexibly adjusts the contact method and content according to different customer group types and contact history to ensure that each contact can meet the specific needs of customers. It also introduces a variety of online contact methods, so that customers can obtain information and complete insurance more conveniently, which improves customer experience. And reduce invalid and inefficient contacts, reduce the workload of customer service personnel, and reduce operating costs. Through the detailed description of the above steps, it can be seen that this customer contact method has significant advantages in the insurance field. It can effectively reduce the risk of customer complaints and churn while improving customer satisfaction and transaction rates.

[0077] In some embodiments, historical insurance information includes at least one or more of historical insurance amounts, historical renewal rates, and historical insurance types, historical contact information includes at least one or more of historical contact times, historical contact times, and historical contact methods, and customer personal information includes at least one or more of customer occupation, customer family composition, customer operations, customer income, and customer background; customer information is matched with a plurality of preset customer group types to determine a target group type corresponding to the target customer from among the plurality of customer group types, including: inputting historical insurance information, historical contact information, and customer personal information into a preset behavior analysis model, the behavior analysis model generating behavior preference information of the target customer; obtaining group preference information corresponding to the customer in each customer group type; calculating the preference similarity between the behavior preference information and each group preference information to determine the target group type from among the plurality of customer group types based on the preference similarity.

[0078] The historical insurance information includes at least one or more of the historical insurance amount, historical renewal rate and historical insurance types. Such information can help the computer equipment understand the customer's insurance purchasing behavior and preferences, so as to more accurately predict the customer's current and future needs.

[0079] Assume that the historical insurance information of customer Zhang San includes: Historical insurance amount: the cumulative insurance amount in the past three years is 100,000 yuan. Historical renewal rate: the renewal rate of health insurance in the past three years is 90%. Historical insurance types: mainly health insurance and accidental injury insurance. Historical contact information includes at least one or more of the historical contact number, historical contact time, and historical contact method. This information helps the system understand the customer's communication preferences and response behavior, so as to select the most effective contact method and frequency.

[0080] Assume that the historical contact information of customer Zhang San includes: Historical contact times: 10 times in the past year. Historical contact time: The time of each contact is mainly concentrated between 8 and 10 pm on weekdays. Historical contact method: mainly through phone calls and text messages. Zhang San has a low response rate to phone calls and a high response rate to text messages.

[0081] Customer personal information includes at least one or more of the following: customer occupation, customer family composition, customer operation, customer income and customer background. This information can further refine customer characteristics and help the system understand the customer's life and economic situation more comprehensively.

[0082] Assume that the personal information of customer Zhang San includes: Customer occupation: engineer in IT industry. Customer family composition: married, with a 3-year-old son. Customer operation: often uses mobile applications to query insurance information. Customer income: annual income of 300,000 yuan. Customer background: lives in a first-tier city and pays attention to health and education.

[0083] By inputting historical insurance information, historical contact information and customer personal information into the preset behavior analysis model. The behavior analysis model generates the behavior preference information of the target customers. The behavior analysis model can use machine learning algorithms such as decision trees, random forests, support vector machines (SVM) or deep learning models (such as neural networks). Zhang San’s historical insurance amount, renewal rate, insurance type, number of contacts, contact time, contact method, occupation, family composition, operating habits, income and background information are input into the behavior analysis model. The behavior preference information generated by the model may include: preference to obtain insurance information via SMS and email. Preference to communicate between 8 and 10 pm on weekdays. High interest in health insurance and family health insurance. High interest in education insurance and children's insurance.

[0084] By presetting multiple customer group types, each group type has corresponding group preference information. The group preference information can be used as a standard to compare with the target customer's behavior preference information to determine the most appropriate customer group type. Assume that the system presets customer group types including "young and middle-aged IT practitioners", "family users", "high-income customers", etc., and each group type has the following group preference information:

[0085] Young and middle-aged IT practitioners: prefer to get information via email and text messages, pay attention to health insurance and accident insurance, and communicate between 8:00 p.m. and 10:00 p.m. on weekdays. Family users: prefer to get information via phone and email, pay attention to family health insurance and education insurance, and communicate between 9:00 a.m. and 11:00 a.m. on weekends. High-income customers: prefer to get information via phone, pay attention to high-end medical insurance and financial insurance, and communicate between 10:00 a.m. and 12:00 p.m. on weekdays.

[0086] Use similarity calculation algorithms, such as cosine similarity, Euclidean distance, Jaccard similarity, etc. By calculating the similarity between the behavior preference information and the preference information of each group, the system can determine the group type that best matches the target customer. For example, the similarity between Zhang San's behavior preference information and the preference information of the "middle-aged and young IT practitioners" group is calculated, and the results are as follows: Similarity with the "middle-aged and young IT practitioners" group: 0.85 Similarity with the "family users" group: 0.60 Similarity with the "high-income customers" group: 0.45

[0087] According to the similarity results, the group type with the highest similarity is selected as the target group type of the target customers. For example, according to the similarity calculation results, it is determined that Zhang San’s target group type is “young and middle-aged IT practitioners”.

[0088] The above embodiment can generate a more accurate and personalized customer contact strategy by collecting detailed historical insurance information, historical contact information and customer personal information, combined with a behavioral analysis model and a similarity calculation algorithm. This method has a significant effect in improving customer satisfaction, reducing customer complaints, increasing transaction rates and preventing customer churn, while improving communication efficiency and customer experience and reducing operating costs. Through data-driven decision-making, this method can achieve more scientific and effective customer management in the insurance field.

[0089] In some embodiments, please refer to Figure 2 , confirming the target contact method with the target customer according to the target group type and the historical contact information, including steps S103a to S103c.

[0090] Step S103a: Analyze the historical contact information to obtain multiple historical contacts corresponding to the target customer, historical contact content corresponding to each historical contact, historical contact method and historical contact result.

[0091] Step S103b: Obtain the preferred contact method corresponding to the target group type.

[0092] Step S103c: Generate a target contact method corresponding to the target customer according to the preferred contact method, the historical contact content corresponding to each historical contact, the historical contact method and the historical contact result.

[0093] In steps S103a to S103c, the computer device needs to analyze the historical contact information of the target customer in detail and obtain detailed data of multiple historical contacts, including the content, method and result of each historical contact. The analysis process can be implemented through natural language processing (NLP) technology, data mining and analysis tools. For example, NLP technology can be used to extract key information from call records, and data mining tools can be used to analyze customer response behaviors.

[0094] Assume that the system needs to determine the best way to contact the target customer Zhang San, and analyze Zhang San's historical contact information as follows: Number of historical contacts: A total of 10 contacts in the past year. The historical contact content corresponding to each historical contact: 1st time: Health insurance renewal reminder, the content is communicated by phone. 2nd time: Accidental injury insurance promotion, the content is communicated by email. 3rd time: Family health insurance recommendation, the content is communicated by text message. 4th time: Health insurance new insurance promotion, the content is communicated by phone. 5th time: Financial insurance consultation, the content is communicated by email. 6th time: Renewal confirmation, the content is communicated by phone. 7th time: Customer satisfaction survey, the content is communicated by text message. 8th time: Accidental injury insurance claim process explanation, the content is communicated by phone. 9th time: Family health insurance questionnaire, the content is communicated by email. 10th time: Children's insurance promotion, the content is communicated by text message.

[0095] The historical contact method corresponding to each historical contact: Phone: 5 times Email: 3 times SMS: 2 times

[0096] Results for each historical contact: Phone: Low response rate, some answered but not interested. Email: High response rate, multiple opens and replies. Text messages: High response rate, multiple reads and replies.

[0097] By presetting multiple customer group types and configuring the corresponding preferred contact methods for each group type, these preferred contact methods can be determined through big data analysis and market research and stored in the system's database. Assume that the target customer Zhang San is matched to the customer group type of "middle-aged and young IT practitioners", the group's preferred contact methods are as follows: Preferred contact methods: email, SMS Non-preferred contact methods: phone.

[0098] Based on the historical contact information obtained through analysis and the preferred contact method corresponding to the target group type, the system generates the best contact method corresponding to the target customer. The generation process can be achieved through technologies such as rule matching and machine learning algorithms. The system can comprehensively determine the final contact method based on the response rate of historical contact content, the favorability of historical contact methods, and the positivity of historical contact results. Email: The response rate is high, the content is diverse (including health insurance, accident insurance, family health insurance, financial insurance, and family health insurance questionnaires), and the results are positive (multiple opens and replies). SMS: The response rate is high, the content is concentrated on family health insurance questionnaires and children's insurance promotions, and the results are positive (multiple reads and replies). Phone: The response rate is low, the content is concentrated on the promotion of health insurance and accident insurance, and the results are not positive (some are answered but not interested).

[0099] Based on the above information, the system generates the target contact method for Zhang San as follows: Main contact method: Email Secondary contact method: SMS Avoid using: Phone

[0100] Steps S103a to S103c generate more accurate and personalized customer contact methods by analyzing the historical contact information of target customers in detail and combining the preferred contact methods of the target group types. This method has a significant effect in improving customer satisfaction, reducing customer complaints, increasing transaction rates and preventing customer churn, while improving communication efficiency and customer experience and reducing operating costs. Through data-driven decision-making and dynamic adjustments, insurance companies can better adapt to changes in market and customer needs and achieve more efficient customer management and services.

[0101] In some embodiments, Figure 3As shown, determining the current insurance period of the target customer based on historical insurance information and customer personal information includes steps S104a to S104c.

[0102] Step S104a: Analyze the customer's personal information to obtain the location information corresponding to the target customer.

[0103] Step S104b. Obtain the renewal policy information corresponding to the territorial information; obtain the current policy status corresponding to the target customer based on the historical insurance information.

[0104] Step S104c. Determine the current insurance period corresponding to the target customer based on the current policy status and renewal policy information.

[0105] The method requires detailed analysis of the target customer's personal information, especially the customer's location information (such as residence, work place, etc.). The analysis process can be achieved through the combination of natural language processing (NLP) technology, data extraction tools and geographic information systems. For example, location information can be extracted from the customer's address, work unit address or mobile phone number location.

[0106] Assume that the personal information of target customer Zhang San includes: Address: XX District, XX City Workplace address: XX District, XX City Mobile phone number: XXXXXXYYYY (place of origin is XX City) By parsing this information, the system determines that Zhang San's place of origin is XX City. It is necessary to obtain the renewal policy information corresponding to the place of origin based on the place of origin information of the target customer. This information may include renewal conditions, preferential measures, local regulations, etc. of insurance products. Renewal policy information is usually stored in the database of the insurance company, and the method can obtain this information through API calls or database queries. According to the place of origin information (XX City), query the database to obtain the renewal policy information of XX City as follows: Health insurance renewal policy: The renewal period is once a year, and a renewal discount of 5% can be enjoyed. Accidental injury insurance renewal policy: The renewal period is once every two years, and there is no renewal discount. Family health insurance renewal policy: The renewal period is once a year, and a renewal discount of 10% can be enjoyed.

[0107] It is necessary to obtain the status of the current insurance policy based on the historical insurance information of the target customer. This includes information such as the validity period, expiration date, and whether the policy has been renewed. This information is usually stored in the insurance company's customer management system (CRM) or policy management system and can be obtained through API calls or database queries.

[0108] For example, according to Zhang San's historical insurance information, query the CRM system to obtain Zhang San's current policy status as follows: Health insurance: The current policy is valid until December 31, 2024, and has not been renewed. Accidental injury insurance: The current policy is valid until June 30, 2025, and has not been renewed. Family health insurance: The current policy is valid until November 30, 2024, and has not been renewed.

[0109] Based on the current policy status and renewal policy information, the target customer's current insurance cycle is determined. This includes the expiration date of the insurance product, renewal conditions and preferential measures. Through logical judgment and rule matching, the system can determine the current insurance cycle of each insurance product and generate corresponding renewal reminders or promotional content.

[0110] For example, for health insurance: Based on the current policy status (valid until December 31, 2024) and renewal policy information (annual renewal with a 5% discount), the system determines that Zhang San's current health insurance period is from January 1, 2024 to December 31, 2024, and generates a renewal reminder:

[0111] "Dear Zhang San, your health insurance will expire on December 31, 2024. We have prepared a renewal offer for you, and you can enjoy a 5% premium discount for renewal. Please complete the renewal procedures as soon as possible to ensure your insurance rights."

[0112] Accidental injury insurance: Based on the current policy status (valid until June 30, 2025) and renewal policy information (renewal every two years, no discount), the system determines that Zhang San's current accidental injury insurance period is from July 1, 2023 to June 30, 2025, and generates a renewal reminder:

[0113] "Dear Zhang San, your accidental injury insurance will expire on June 30, 2025. Please pay attention to renewal matters in advance to ensure your insurance rights and interests."

[0114] Family health insurance: Based on the current policy status (valid until November 30, 2024) and renewal policy information (annual renewal, enjoy 10% discount), the system determines that Zhang San’s current family health insurance period is from December 1, 2023 to November 30, 2024, and generates a renewal reminder:

[0115] "Dear Zhang San, your family health insurance will expire on November 30, 2024. We have prepared a renewal offer for you, and you can enjoy a 10% premium discount for renewal. Please complete the renewal procedures as soon as possible to ensure the insurance rights of you and your family."

[0116] In summary, the above embodiment obtains territorial information by analyzing the customer's personal information in detail, and generates a more accurate and personalized current insurance cycle by combining the renewal policy information corresponding to the territorial information and the customer's current policy status. This method has a significant effect in improving customer satisfaction, reducing customer complaints, increasing renewal rates and preventing customer churn, while improving communication efficiency and customer experience and reducing operating costs. Through data-driven and local policy adaptation, insurance companies can better meet the actual needs of customers and achieve more efficient customer management and services.

[0117] Exemplarily, the current insurance cycle includes at least any one of an operation period, a warm-up period, a temporary storage period, a first period, a tracking period and a guarantee period; the target contact content corresponding to the target group type and the target customer is determined according to the current insurance cycle, including: parsing the current insurance cycle, and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of the customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; and generating the target contact content according to the group communication content, the cycle focus information and the current policy status.

[0118] The method needs to analyze the target customer's current insurance cycle, determine the time period the customer is currently in, and obtain the main tasks and key issues in each time period. These cycles include the operation period, warm-up period, temporary storage period, first cycle, tracking period, and backup period. Through preset rules and algorithms, the customer's current insurance cycle stage is determined based on the current date and the validity period of the policy. Each stage has specific focus information, such as:

[0119] Operational period: Emphasize regular insurance services and support to ensure that customers understand the basic functions and rights of insurance.

[0120] Warm-up period: Remind customers of the upcoming renewal period in advance and provide necessary information and guidance.

[0121] Suspension Period: Provide guidance and support on reinstating a customer’s policy while it is suspended or lapsed.

[0122] First cycle: The first contact after the new policy takes effect, explaining how to use the policy and common problems.

[0123] Follow-up period: Regularly track the customer's policy usage and provide additional support and guidance.

[0124] Guarantee period: The deadline before the policy expires, when customers are frequently reminded to renew their insurance to avoid policy lapse.

[0125] Assume that the current insurance period of target customer Zhang San is from January 1, 2024 to December 31, 2024, and the current date is November 24, 2024. The system determines that Zhang San is in the guarantee period, and analyzes the key information of the guarantee period as follows: Key information of the guarantee period: frequently remind customers that their insurance is about to expire, provide renewal procedures and preferential measures, and ensure that customers understand the importance and urgency of renewal.

[0126] According to the target customer group type (such as high net worth customers, ordinary family customers, corporate customers, etc.), the system needs to obtain the group communication content that the group should pay attention to in the current insurance cycle from the preset insurance management platform. These contents can include renewal tips, claims guidelines, new policy interpretations, etc. The insurance management platform usually stores communication content templates for various types of groups in different insurance cycles, and the system obtains these templates through API calls or database queries. These templates can be updated according to real-time market demand and policy changes.

[0127] Assuming that Zhang San belongs to the high net worth customer group, the system obtains the group communication content of high net worth customers during the guarantee period from the insurance management platform as follows: Group communication content: emphasize personalized service, provide renewal discounts for high-end insurance products and contact information of exclusive account managers, answer customers' questions about high-end insurance products, and introduce exclusive service content.

[0128] The method integrates group communication content, period-focused information, and the customer's current policy status to generate personalized target contact content. This content can include emails, text messages, phone communications, physical letters, etc., aiming to provide targeted services and reminders. Through template generation technology and content recommendation algorithms, the system can generate customized communication content based on the specific needs of customers. For example, natural language generation (NLG) technology can be used to generate email or text message content, and the rule engine can be used to ensure that the content meets the customer's needs and insurance policy.

[0129] For example, based on Zhang San’s group communication content (emphasizing personalized services, renewal discounts for high-end insurance products), period-focused information (frequent reminders that insurance is about to expire), and current policy status (not renewed), the following target contact content is generated: Email content:

[0130] Subject: "Mr. Zhang, your exclusive insurance service reminder"

[0131] Text: "Dear Mr. Zhang San, your health insurance will expire on December 31, 2024. In order to ensure your insurance rights, we remind you to go through the renewal procedures in time. As a high-net-worth customer, you will enjoy exclusive renewal discounts for high-end insurance products, and you will be arranged to have a one-to-one service from your exclusive account manager Guo Moumou. If you have any questions or need claims guidance, please feel free to contact us. I wish you and your family all the best, and we will serve you wholeheartedly."

[0132] Content of SMS:

[0133] "Dear Mr. Zhang San, your health insurance will expire on December 31, 2024. As a high net worth customer, you will enjoy exclusive renewal discounts. For details, please contact the exclusive account manager X, phone XXX-XXXX-XXX.

[0134] I wish you all the best. ”

[0135] By analyzing the current insurance cycle and target group type, combined with the customer's current policy status, accurate and personalized target contact content is generated. This method has a significant effect in improving communication efficiency, enhancing customer experience, increasing customer engagement, increasing renewal rates and reducing customer complaints. Through data-driven decision-making and adapting to needs at different stages, insurance companies can better meet the actual needs of customers, achieve more efficient customer management and services, and enhance brand image and market competitiveness.

[0136] In some embodiments, a target contact frequency with a target customer is determined based on historical contact information and a current insurance cycle, including: parsing historical contact information to obtain the target customer's communication intention and the contact interval corresponding to the last historical contact; determining the contact urgency corresponding to the target customer based on the current insurance cycle; and generating a target contact frequency based on the communication intention, contact interval, and contact urgency.

[0137] The method requires detailed analysis of the target customer's historical contact information to obtain the customer's communication intention (i.e., the customer's preference and response to different communication methods) and the last contact interval (i.e., the interval between the last communication and the current time). The analysis process can be achieved through natural language processing (NLP) technology, data mining and analysis tools. The system can extract customer feedback and behavior data from historical contact records to determine the customer's communication intention and the time of the last contact.

[0138] Assume that the historical contact information of target customer Zhang San is as follows: Communication intention: In the past year, Zhang San has a higher response rate via email and text messages, and a lower response rate via phone. Last historical contact: The last contact with Zhang San via email was on November 15, 2024, the last contact via text message was on October 30, 2024, and the last contact by phone was on August 10, 2024.

[0139] The urgency of contact within the target customer's current insurance cycle is determined. The urgency of contact varies at different stages of the insurance cycle. For example, customers may need to be contacted more frequently during the renewal reminder period. The urgency of contact can be determined by preset rules and algorithms. The system can determine the urgency based on the specific stage of the current insurance cycle (such as the warm-up period, the first cycle, the tracking period, the bottom-up period, etc.) and the customer's policy status.

[0140] Assuming that Zhang San's current insurance period is from January 1, 2024 to December 31, 2024, and the current date is November 24, 2024, the system determines that Zhang San is in the guarantee period (one month before the insurance expires). The contact urgency during the guarantee period is high, and customers need to be reminded frequently to renew their insurance to avoid policy expiration.

[0141] Generate a personalized target contact frequency based on the customer's communication intention, the interval between the last historical contact, and the contact urgency of the current insurance cycle. This includes determining the frequency, method, and time of contact. Generating the target contact frequency can be achieved through rule matching, machine learning algorithms, and multi-factor comprehensive analysis. The system can dynamically adjust the contact frequency and method based on the customer's historical behavior data and urgency.

[0142] For example, communication intention: Zhang San prefers email and SMS, and has a lower response rate by phone. Contact interval: The last contact by email was 9 days ago, the last contact by SMS was 25 days ago, and the last contact by phone was 106 days ago. Contact urgency: Currently in the cover period, the urgency is high. Based on the above information, the target contact frequency for Zhang San is generated as follows: Email: Send a renewal reminder every 3 days. SMS: Send a renewal reminder every 5 days. Phone: Make a phone call every 10 days.

[0143] By considering the customer's communication intention and choosing the communication method that the customer is more willing to accept, the customer's resistance and anxiety can be reduced and customer satisfaction can be improved. Regular contact and reminders make customers feel the care and professionalism of the insurance company and enhance their sense of trust. Reasonable contact frequency avoids the problem of excessive or insufficient contact and reduces customer complaints caused by improper communication. By setting the time interval for telephone contact, frequent calls can be avoided from interfering with the customer's life and work. By increasing the frequency of contact during periods of high urgency (such as the guarantee period), it can ensure that customers receive renewal reminders in a timely manner and improve the renewal rate. Targeting the customer's preferred and more effective communication methods increases the customer's attention and response rate to renewal information.

[0144] Exemplarily, after generating the target contact frequency according to communication intention, contact interval and contact urgency, it also includes: if parsing historical contact information can obtain historical complaint information of the target customer for any historical contact, generating the customer sensitivity according to the historical complaint information; and adjusting the target contact frequency according to the customer's sensitivity.

[0145] The method requires detailed analysis of the target customer's historical contact information to obtain the customer's communication intention (i.e., the customer's preference and response to different communication methods), the interval between the last historical contact, and the customer's complaint information in any historical contact. The analysis process can be achieved through natural language processing (NLP) technology, data mining and analysis tools. The system can extract customer feedback and behavior data from historical contact records to determine the customer's communication intention and historical complaint information.

[0146] Assume that the historical contact information of target customer Zhang San is as follows:

[0147] Communication intention: Zhang San prefers email and text messages, and has a lower response rate to phone calls.

[0148] Contact interval: The last contact by email was 9 days ago, the last contact by text message was 25 days ago, and the last contact by phone was 106 days ago.

[0149] Historical complaint information: Zhang San complained by phone on October 10, 2024, expressing dissatisfaction with frequent telephone calls.

[0150] The method generates customer sensitivity to different communication methods based on historical complaint information. Sensitivity can be divided into different levels, such as low sensitivity, medium sensitivity and high sensitivity. Customer sensitivity can be determined by preset rules and algorithms. The system can automatically calculate customer sensitivity based on the number of historical complaints, the severity of the complaint content and the frequency of customer feedback.

[0151] For example, based on Zhang San’s historical complaint information, his customer sensitivity level is generated as follows: Customer sensitivity level: highly sensitive (dissatisfied with frequent phone calls).

[0152] Adjust the contact frequency of target customers based on the generated customer sensitivity. If the customer sensitivity is high, the frequency of certain communication methods can be appropriately reduced to avoid further distress to the customer. Adjusting the target contact frequency can be achieved through rule matching and machine learning algorithms. The system can automatically adjust the contact frequency and method based on the customer sensitivity, ensuring that the communication strategy is more humane and effective.

[0153] Raw target contact frequency:

[0154] Email: Renewal reminders are sent every 3 days.

[0155] SMS: Renewal reminders are sent every 5 days.

[0156] Phone calls: One call every 10 days.

[0157] Adjusted target contact frequency:

[0158] Email: Renewal reminders sent every 3 days (remains the same).

[0159] SMS: Renewal reminders will be sent every 5 days (remains unchanged).

[0160] Telephone: Make a call every 20 days (adjusted from every 10 days to every 20 days to reduce the frequency of telephone contact).

[0161] By analyzing historical contact information, obtaining the customer's communication intention, contact interval and historical complaint information, generating customer sensitivity, and adjusting the target contact frequency according to the customer's sensitivity. This method has a significant effect in improving customer satisfaction, reducing customer complaints, increasing customer loyalty, optimizing communication strategies, improving communication efficiency, enhancing customer relationship management and improving brand image. Through data-driven decision-making, insurance companies can better meet the actual needs of customers and achieve more efficient and humane customer management and services.

[0162] The method provided in this application has at least the following beneficial effects:

[0163] 1. Improve customer satisfaction: By accurately matching customer group types and customizing contact strategies, unnecessary interruptions and the number of interruptions to customers are reduced, thereby improving overall customer satisfaction.

[0164] 2. Reduce customer complaints: Optimized contact methods and frequency prevent customers from getting annoyed by frequent calls or messages, thus reducing the risk of customer complaints.

[0165] 3. Improve transaction rate: By providing information and services that match customers' current needs and preferences, the customer conversion rate and insurance product transaction rate are improved.

[0166] 4. Prevent customer churn: Customized contact strategies can effectively maintain long-term interaction with customers, increase customer loyalty, and reduce the possibility of customer churn.

[0167] 5. Improve communication efficiency: Reasonable target contact frequency and method ensure that each contact can achieve the expected effect, improving the efficiency and success rate of communication.

[0168] 6. Data-driven decision-making: The entire approach relies on the collection and analysis of customer information. Through big data and machine learning technology, data-driven customer contact strategy formulation is achieved, which improves the scientificity and accuracy of decision-making.

[0169] 7.Flexible adaptation to customer needs: Based on the characteristics of different customer groups and historical contact information, the contact method and content can be flexibly adjusted to ensure that each contact can meet the specific needs of the customer.

[0170] 8. Improve customer experience: Through the introduction of online contact methods, customers can more conveniently obtain insurance information and complete the insurance process, which improves the overall customer experience.

[0171] 9. Reduce operating costs: Reduce invalid and inefficient contacts, improve the effectiveness of each contact, and reduce the workload and operating costs of customer service personnel.

[0172] In summary, this customer contact method has significantly improved customer communication methods in the insurance field through scientific and precise strategy formulation, increased customer satisfaction, transaction rate and operational efficiency, and effectively prevented customer churn.

[0173] See also Figure 4 As shown, Figure 4 1 is a schematic diagram of the structure of a customer contact device 200 provided in an embodiment of the present application. The customer contact device 200 is used to execute the steps of the customer contact method shown in the above embodiments. The customer contact device 200 can be a single server or a server cluster, or the customer contact device 200 can be a terminal, which can be a handheld terminal, a laptop computer, a wearable device or a robot, etc.

[0174] like Figure 4 As shown, the customer contact device 200 includes:

[0175] Information acquisition module 201, used to acquire customer information of the target customer to be contacted, the customer information at least includes historical insurance information, historical contact information and customer personal information;

[0176] A group determination module 202 is used to match the customer information with a plurality of preset customer group types respectively, so as to determine a target group type corresponding to the target customer from the plurality of customer group types;

[0177] A contact determination module 203 is used to determine a target contact method with the target customer according to the target group type and the historical contact information; the target contact method includes at least one or more of telephone contact and online contact;

[0178] The content determination module 204 is used to determine the current insurance period of the target customer based on the historical insurance information and the customer personal information, so as to determine the target contact content corresponding to the target customer according to the current insurance period and the target group type;

[0179] A frequency determination module 205, configured to determine a target contact frequency with the target customer based on the historical contact information and the current insurance period;

[0180] The contact completion module 206 is used to generate a contact strategy corresponding to the target customer according to the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the contact strategy.

[0181] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working processes of the customer contact device and each module described above can refer to the corresponding processes in the customer contact method embodiments described in the above embodiments, and will not be repeated here.

[0182] The above-mentioned customer contact method can be implemented in the form of a computer program. Figure 4 Run on the device shown.

[0183] See also Figure 5 , Figure 5 1 is a schematic block diagram of the structure of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory and a network interface connected via a device bus, wherein the memory may include a storage medium and an internal memory.

[0184] The storage medium can store an operating device and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any one of the customer contact methods.

[0185] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0186] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any customer contact method.

[0187] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the terminal to which the scheme of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0188] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0189] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:

[0190] Obtain customer information of the target customer to be contacted, which at least includes historical insurance information, historical contact information and customer personal information;

[0191] Matching the customer information with multiple preset customer group types respectively to determine the target group type corresponding to the target customer among the multiple customer group types;

[0192] Determine the target contact method with the target customers based on the target group type and historical contact information; the target contact method includes at least one or more of telephone contact and online contact;

[0193] Determine the current insurance period of the target customer based on historical insurance information and customer personal information, and determine the target contact content corresponding to the target customer based on the current insurance period and target group type;

[0194] Determine the target contact frequency with target customers based on historical contact information and current insurance cycle;

[0195] Generate a target contact strategy corresponding to the target customer based on the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the target contact strategy.

[0196] In some embodiments, historical insurance information includes at least one or more of historical insurance amounts, historical renewal rates, and historical insurance types, historical contact information includes at least one or more of historical contact times, historical contact times, and historical contact methods, and customer personal information includes at least one or more of customer occupation, customer family composition, customer operations, customer income, and customer background; customer information is matched with a plurality of preset customer group types to determine a target group type corresponding to the target customer from among the plurality of customer group types, including: inputting historical insurance information, historical contact information, and customer personal information into a preset behavior analysis model, the behavior analysis model generating behavior preference information of the target customer; obtaining group preference information corresponding to the customer in each customer group type; calculating the preference similarity between the behavior preference information and each group preference information to determine the target group type from among the plurality of customer group types based on the preference similarity.

[0197] In some embodiments, a target contact method with a target customer is confirmed based on the target group type and historical contact information, including: parsing historical contact information, obtaining multiple historical contacts corresponding to the target customer, historical contact content corresponding to each historical contact, historical contact methods, and historical contact results; obtaining a preferred contact method corresponding to the target group type; and generating a target contact method corresponding to the target customer based on the preferred contact method, historical contact content corresponding to each historical contact, historical contact methods, and historical contact results.

[0198] In some embodiments, the current insurance period of a target customer is determined based on historical insurance information and customer personal information, including: parsing customer personal information to obtain territorial information corresponding to the target customer; obtaining renewal policy information corresponding to the territorial information; obtaining the current policy status corresponding to the target customer based on historical insurance information; and determining the current insurance period corresponding to the target customer based on the current policy status and renewal policy information.

[0199] Exemplarily, the current insurance cycle includes at least any one of an operation period, a warm-up period, a temporary storage period, a first period, a tracking period and a guarantee period; the target contact content corresponding to the target group type and the target customer is determined according to the current insurance cycle, including: parsing the current insurance cycle, and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of the customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; and generating the target contact content according to the group communication content, the cycle focus information and the current policy status.

[0200] In some embodiments, a target contact frequency with a target customer is determined based on historical contact information and a current insurance cycle, including: parsing historical contact information to obtain the target customer's communication intention and the contact interval corresponding to the last historical contact; determining the contact urgency corresponding to the target customer based on the current insurance cycle; and generating a target contact frequency based on the communication intention, contact interval, and contact urgency.

[0201] Exemplarily, after generating the target contact frequency according to communication intention, contact interval and contact urgency, it also includes: if parsing historical contact information can obtain historical complaint information of the target customer for any historical contact, generating the customer sensitivity according to the historical complaint information; and adjusting the target contact frequency according to the customer's sensitivity.

[0202] The present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of the customer contact method described in the first aspect above.

[0203] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0204] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A customer contact method, characterized in that: include: Acquire customer information of the target customer to be contacted, wherein the customer information at least includes historical insurance information, historical contact information and customer personal information; Matching the customer information with a plurality of preset customer group types respectively, so as to determine a target group type corresponding to the target customer from among the plurality of customer group types; Determining a target contact method with the target customer according to the target group type and the historical contact information; the target contact method includes at least one or more of telephone contact and online contact; Determine the current insurance period of the target customer according to the historical insurance information and the customer personal information, and determine the target contact content corresponding to the target customer according to the current insurance period and the target group type; determining a target contact frequency with the target customer based on the historical contact information and the current insurance cycle; A target contact strategy corresponding to the target customer is generated according to the target contact method, target contact content and target contact frequency, so as to complete contact with the target customer according to the target contact strategy.

2. The method according to claim 1, characterized in that The historical insurance information includes at least one or more of the historical insurance amount, the historical renewal rate and the historical insurance types; the historical contact information includes at least one or more of the historical contact times, the historical contact time and the historical contact method; the customer personal information includes at least one or more of the customer occupation, the customer family composition, the customer operation, the customer income and the customer background; the matching of the customer information with the preset multiple customer group types respectively to determine the target group type corresponding to the target customer from the multiple customer group types includes: According to the historical insurance information, historical contact information and customer personal information input into a preset behavior analysis model, the behavior analysis model generates the behavior preference information of the target customer; Obtaining group preference information corresponding to customers in each of the customer group types; The preference similarity between the behavior preference information and each of the group preference information is calculated to determine the target group type from among the plurality of customer group types according to the preference similarity.

3. The method according to claim 1, characterized in that The step of confirming a target contact method with the target customer according to the target group type and the historical contact information includes: Parsing the historical contact information, obtaining multiple historical contacts corresponding to the target customer, historical contact content corresponding to each historical contact, historical contact methods and historical contact results; Obtaining the preferred contact method corresponding to the target group type; The target contact method corresponding to the target customer is generated according to the preferred contact method, the historical contact content corresponding to each historical contact, the historical contact method and the historical contact result.

4. The method according to claim 1, characterized in that: The determining the current insurance period of the target customer according to the historical insurance information and the customer personal information includes: Parsing the customer personal information to obtain the location information corresponding to the target customer; Obtaining the renewal policy information corresponding to the territorial information; Acquire the current policy status corresponding to the target customer according to the historical insurance information; The current insurance period corresponding to the target customer is determined according to the current policy status and the renewal policy information.

5. The method according to claim 4, characterized in that The current insurance cycle includes at least one of an operation period, a warm-up period, a temporary storage period, a first period, a tracking period, and a guarantee period; the target contact content corresponding to the target customer of the target group type determined according to the current insurance cycle includes: Parsing the current insurance cycle to obtain cycle emphasis information corresponding to the current insurance cycle; Acquire group communication content of customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; The target contact content is generated according to the group communication content, period focus information and the current policy status.

6. The method according to claim 1, characterized in that The determining the target contact frequency with the target customer according to the historical contact information and the current insurance cycle includes: Parsing the historical contact information to obtain the communication intention of the target customer and the contact interval corresponding to the last historical contact; Determining the contact urgency level corresponding to the target customer according to the current insurance cycle; The target contact frequency is generated according to the communication intention, the contact interval and the contact urgency.

7. The method according to claim 6, characterized in that After generating the target contact frequency according to the communication intention, the contact interval and the contact urgency, the method further includes: If analyzing the historical contact information can obtain the historical complaint information of the target customer regarding any of the historical contacts, generating the customer sensitivity level according to the historical complaint information; The target contact frequency is adjusted according to the level of the customer.

8. A customer contact device, characterized in that: include: An information acquisition module is used to acquire customer information of the target customer to be contacted, wherein the customer information at least includes historical insurance information, historical contact information and customer personal information; A group determination module, used for matching the customer information with a plurality of preset customer group types respectively, so as to determine a target group type corresponding to the target customer from among the plurality of customer group types; A contact determination module, configured to determine a target contact method with the target customer according to the target group type and the historical contact information; the target contact method includes at least one or more of telephone contact and online contact; A content determination module, used to determine the current insurance period of the target customer based on the historical insurance information and the customer personal information, so as to determine the target contact content corresponding to the target customer according to the current insurance period and the target group type; A frequency determination module, used to determine a target contact frequency with the target customer based on the historical contact information and the current insurance cycle; The contact completion module is used to generate a contact strategy corresponding to the target customer according to the target contact method, target contact content and target contact frequency, so as to complete the contact with the target customer according to the contact strategy.

9. A computer device, characterized in that: The computer device includes a memory and a processor; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the method according to any one of claims 1 to 7.

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