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

Abstract

The present application provides a customer contact method, apparatus, computer equipment and readable storage medium, comprising: obtaining customer information of a target customer to be contacted, the customer information including at least historical insurance information, historical contact information and customer personal information; matching the customer information with a plurality of preset customer group types respectively to determine a target group type corresponding to the target customer; confirming a target contact method with the target customer based on the target group type and the historical contact information, including at least telephone contact and online contact; determining the current insurance cycle of the target customer based on the historical insurance information and the customer personal information to determine the target contact content corresponding to the target customer; determining a target contact frequency with the target customer based on the historical contact information and the current insurance cycle; generating 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 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, apparatus, computer equipment and readable storage medium. Background Art

[0002] In the financial sector, especially in insurance, customer contact, both before and after the transaction, is crucial. Currently, customer communication, price inquiries, quotes, and insurance applications are completed over the phone. Frequent phone calls can be intrusive and disruptive to customers, easily leading to complaints. As a result, this current method of contacting customers over the phone is not only ineffective but also carries the risk of customer complaints, ultimately leading to customer churn.

[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] This application provides a customer contact method, apparatus, computer device, and readable storage medium, which are intended to address the critical issue of customer contact both before and after the transaction of an insurance product in the insurance field. Currently, customer contact via telephone is not only ineffective but also carries the risk of customer complaints, leading to customer loss.

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

[0008] Determine the target contact method with 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 target customer's current insurance cycle 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 cycle and target group type;

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

[0011] Generate target contact strategies corresponding to target customers based on target contact methods, target contact content, and target contact frequency, so as to complete contact with target customers according to the target contact strategies.

[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; 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 respectively to determine the target group type corresponding to the target customer 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 generates behavioral 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 behavioral preference information and each group preference information, to determine the target group type among the plurality of customer group types based on the preference similarity.

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

[0014] In some embodiments, the current insurance period of the 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; determining the target contact content corresponding to the target group type and target customers based on the current insurance cycle includes: parsing the current insurance cycle and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; generating the target contact content based on the group communication content, 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 the 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 based on 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 based on the historical complaint information; adjusting the target contact frequency based on the customer 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 includes at least historical insurance information, historical contact information and customer personal information;

[0020] a group determination module, configured 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 among the plurality of customer group types;

[0021] a contact determination module, configured to determine a target contact method with the target customer based on 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, configured to determine the current insurance period of the target customer based on the historical insurance information and the customer's personal information, and to determine the target contact content corresponding to the target customer based on the current insurance period and the target group type;

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

[0024] The contact completion module is configured 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, comprising:

[0026] memory and processor;

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

[0028] The processor is configured to execute the computer program and implement the steps of the customer contact method described in the first aspect above 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] Embodiments of the present application provide a customer contact method, apparatus, computer device, and readable storage medium. The method first collects detailed customer information of the target customer to be contacted, including at least historical insurance information, historical contact information, and personal information. Historical insurance information can help understand the customer's insurance needs and preferences, historical contact information can assess the effectiveness of past customer contact, and personal information can help more accurately identify customer characteristics. Based on the collected customer information, the target customer is then matched with multiple pre-set customer group types to determine the optimal target group type for the target customer. This step can be achieved through big data analysis and machine learning algorithms, thereby categorizing the customer into the most suitable group and tailoring the contact strategy for them. Based on the target group type and historical contact information, the most appropriate contact method is selected for the target customer, including but not limited to telephone and online contact. Different customer groups and contact histories may be suitable for different contact methods. This step ensures that the selected method is both effective and minimizes disruption to the customer. Furthermore, historical insurance information and personal information are combined to determine the target customer's current insurance cycle. This helps understand the customer's current insurance needs and potential purchase timing, thereby providing the customer with timely and relevant information and services. Furthermore, based on the current insurance cycle and target group type, specific communication content for target customers is developed. This content should align with the customer's current needs and preferences, providing valuable insurance information and recommendations. Finally, combining historical contact information and the current insurance cycle, the frequency of contact with the target customer is determined. A reasonable contact frequency ensures that customers do not feel overly intrusive while maintaining effective communication and engagement. Based on the information from the above steps, a targeted contact strategy for the target customer is generated. This strategy includes target contact methods, target contact content, and target contact frequency 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, thereby reducing the risk of customer complaints.

[0034] 3. Improve transaction rates: 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, thereby 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 scientific nature and accuracy of decision-making.

[0038] 7. Flexible adaptation to customer needs: Based on the characteristics and historical contact information of different customer groups, 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 staff.

[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 rates 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 following is a brief introduction to the drawings required for use in the description of the embodiments. 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 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 This is a schematic flow chart of the steps of a method for generating a target contact mode provided in one embodiment of the present application;

[0046] Figure 3 This 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 This is a schematic structural diagram of a customer contact device provided in one embodiment of the present application;

[0048] Figure 5 This 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 this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0051] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0052] It should be understood that, in order to clearly describe the technical solutions of the embodiments of the present invention, in the embodiments of the present invention, terms such as "first" and "second" are used to distinguish between identical or similar items having substantially the same functions and effects. Those skilled in the art will understand that terms such as "first" and "second" do not limit the quantity or order of execution, and that terms such as "first" and "second" do not necessarily define differences.

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

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

[0055] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features therein may be combined with each other.

[0056] In the financial sector, especially in insurance, customer contact, both before and after the transaction, is crucial. Currently, customer communication, price inquiries, quotes, and insurance applications are completed over the phone. Frequent phone calls can be intrusive and disruptive to customers, easily leading to complaints. As a result, this current method of contacting customers over the phone is not only ineffective but also carries the risk of customer complaints, ultimately leading to customer churn.

[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 resolve the above issues, please refer to Figure 1 , Figure 1 This is a schematic flow chart of a customer engagement method provided in one embodiment of the present application. This customer engagement method can be implemented by a computer device, which can be deployed on a single server or a server cluster. Alternatively, the computer device can be deployed on a handheld terminal, a laptop computer, a wearable device, or a robot.

[0059] It should be noted that the acquisition of any information mentioned in the provided method complies 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, in addition to the insurance field, that require frequent contact with customers, as well as in other fields, to avoid the phenomenon of customers being disgusted by telephone contact. This can significantly improve customer satisfaction. Therefore, 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, the computer equipment first needs to collect detailed information about the target customer to be contacted, including but not limited to historical insurance information, historical contact information, and personal information. This information can be collected through various channels, such as customer relationship management (CRM) systems, historical call records, and forms submitted by customers.

[0064] For example, suppose 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 when the call was answered, the duration of the call, the content of the call, 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 such as the customer's age, gender, occupation, insurance history, contact history, etc. The matching process can be achieved through machine learning algorithms and data classification technology. For example, based on 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 practitioner), 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-mentioned customer occupation type, etc., or a fusion of multiple types, and the embodiment of this application does not limit this. By matching customer group types, the characteristics and preferences of target customers can be more accurately understood, thereby formulating more personalized contact strategies.

[0068] Step S103: Determine the target contact method with the target customer 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.

[0069] Specifically, the most appropriate contact method is selected based on the target customer's group type and historical contact information. Contact methods can include telephone contact, online contact (such as email, text message, instant messaging, etc.), or a combination of multiple methods. 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 "middle-aged IT practitioners" and Zhang San's historical contact information (positive response to text messages and emails), email and text messages are decided as the primary contact methods. By selecting the appropriate target contact method, customer response rates can be significantly improved, interruptions to customers can be reduced, and customer satisfaction can be increased.

[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 system determines the target customer's current insurance cycle based on their historical insurance information and personal information. This includes information such as the period of time the customer needs insurance and when their insurance product expires. Data analysis and predictive models can be used to determine the customer's current insurance status. For example, based on Zhang San's historical insurance information, it is discovered that his current health insurance policy is about to expire. However, his personal information indicates that he has recently become a father. Therefore, the system predicts that his insurance needs may shift from individual health insurance to family health insurance. By accurately determining a customer's current insurance cycle, relevant insurance products and services can be provided to the customer in a timely manner, increasing their purchase intention and satisfaction.

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

[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 customer response times and preferences to ensure that frequent contact does not cause customer annoyance. 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 upcoming health insurance expiration, decide to send weekly emails and monthly text message reminders. A reasonable contact frequency can increase customer acceptance, reduce customer annoyance 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 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 engagement strategy is generated. This strategy includes the selected engagement methods, specific engagement content, and engagement frequency. Strategy generation can be implemented through automated scripts or AI algorithms to ensure strategy accuracy and consistent execution. For example, a strategy generated for Zhang San might be as follows: an email is sent every Thursday with the latest offers and details on family health insurance; and a text message is sent at the beginning of each month to remind him of health insurance expiration dates and renewal discounts. A comprehensive, targeted engagement strategy ensures that each engagement achieves optimal results, increasing customer engagement and purchase intent, ultimately boosting closing rates and customer satisfaction.

[0076] In summary, steps S101 to S106 reduce customer disruptions and improve customer acceptance and satisfaction through personalized and targeted contact methods. Appropriate contact frequency and methods avoid frequent phone calls and reduce the risk of customer complaints. Providing insurance information and services tailored to customers' current needs and preferences increases customer purchase intent and closing rates. Maintaining long-term, effective interactions enhances customer loyalty and reduces the likelihood of churn. Scientific data analysis and AI technology improve the effectiveness of each contact and reduce the cost of ineffective communication. The entire method relies on abundant customer data. Through data analysis and machine learning, data-driven contact strategy development is implemented, enhancing the scientific nature and accuracy of decision-making. The method flexibly adjusts contact methods and content based on different customer segments and contact history, ensuring that each contact meets the customer's specific needs. Multiple online contact methods are also introduced, enabling customers to more conveniently obtain information and complete insurance applications, improving the customer experience. Furthermore, ineffective and inefficient contact is reduced, alleviating the workload of customer service staff and lowering 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; 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 respectively to determine the target group type corresponding to the target customer 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 generates behavioral 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 behavioral preference information and each group preference information, to determine the target group type among the plurality of customer group types based on the preference similarity.

[0078] Historical insurance information includes at least one or more of historical insured amounts, historical renewal rates, and historical insurance types. This information can help computers understand customers' insurance purchasing behavior and preferences, thereby more accurately predicting their current and future needs.

[0079] Suppose customer Zhang San's insurance history includes: Historical insured amount: The cumulative insured amount over the past three years is 100,000 yuan. Historical renewal rate: The renewal rate for health insurance over the past three years is 90%. Historical insurance types: Health insurance and accident insurance are the primary purchases. Historical contact information includes at least one or more of the following: number of contacts, duration of contacts, and method of contact. This information helps the system understand the customer's communication preferences and response behavior, thereby selecting the most effective contact method and frequency.

[0080] Suppose customer Zhang's contact history includes the following: Number of contacts: 10 contacts in the past year. Time of contact: Each contact occurred primarily between 8:00 PM and 10:00 PM on weekdays. Method of contact: Primarily via phone and text messages. Zhang 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, family composition, customer operations, customer income, and customer background. This information can further refine customer characteristics and help the system gain a more comprehensive understanding of the customer's life and financial situation.

[0082] Suppose customer Zhang's personal information includes: Occupation: IT engineer. Family: Married with a 3-year-old son. User activity: Frequently uses mobile apps to check insurance information. Income: Annual income: 300,000 yuan. Background: Lives in a first-tier city and focuses on health and education.

[0083] By inputting historical insurance information, historical contact information and customer personal information into a preset behavioral analysis model. The behavioral analysis model generates behavioral preference information of target customers. The behavioral 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 behavioral analysis model. The behavioral preference information generated by the model may include: preference for obtaining insurance information via text messages and emails. Preference for communication between 8 and 10 pm on weekdays. High interest in health insurance and family health insurance. Also high interest in education insurance and children's insurance.

[0084] By pre-setting multiple customer groups, each with corresponding group preference information, you can use this group preference information as a benchmark and compare it with the target customer's behavioral preference information to determine the most appropriate customer group type. Assume the system pre-set customer groups include "Young and Middle-Aged IT Professionals," "Home Users," "High-Income Customers," etc. Each group type has the following group preference information:

[0085] Young and middle-aged IT professionals prefer to get information via email and text messages, are interested in health insurance and accident insurance, and communicate between 8:00 PM and 10:00 PM on weekdays. Family users prefer to get information via phone and email, are interested in family health insurance and education insurance, and communicate between 9:00 AM and 11:00 AM on weekends. High-income customers prefer to get information via phone, are interested in high-end medical insurance and wealth management insurance, and communicate between 10:00 AM and 12:00 AM on weekdays.

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

[0087] Based on the similarity results, the group type with the highest similarity is selected as the target group type for the target customers. For example, based on the similarity calculation results, the target group type for Zhang San is determined to be "middle-aged and young IT practitioners."

[0088] The above-described embodiment, by collecting detailed historical insurance information, historical contact information, and personal customer information, combined with behavioral analysis models and similarity calculation algorithms, can generate more precise and personalized customer engagement strategies. This approach has significantly improved customer satisfaction, reduced customer complaints, increased closing rates, and prevented customer churn. It also enhances communication efficiency and customer experience while reducing operating costs. Through data-driven decision-making, this method enables more scientific and effective customer management in the insurance sector.

[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 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 results.

[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 based on the preferred contact method, the historical contact content corresponding to each historical contact, the historical contact method, and the historical contact results.

[0093] In steps S103a to S103c, the computer device needs to analyze the target customer's historical contact information in detail, obtaining detailed data on multiple historical contacts, including the content, method, and results of each contact. This analysis process can be achieved through natural language processing (NLP) technology, data mining, and analysis tools. For example, NLP technology can be used to extract key information from call logs, and data mining tools can be used to analyze customer response behavior.

[0094] Suppose the system needs to determine the best way to contact target customer Zhang San. Zhang San's historical contact information is analyzed as follows: Number of historical contacts: 10 contacts in the past year. The corresponding historical contact content for each historical contact is as follows: 1st contact: Health insurance renewal reminder, delivered by phone. 2nd contact: Accident insurance promotion, delivered by email. 3rd contact: Family health insurance recommendation, delivered by text message. 4th contact: New health insurance product introduction, delivered by phone. 5th contact: Financial management insurance consultation, delivered by email. 6th contact: Renewal confirmation, delivered by phone. 7th contact: Customer satisfaction survey, delivered by text message. 8th contact: Accident insurance claim process explanation, delivered by phone. 9th contact: Family health insurance questionnaire, delivered by email. 10th contact: Children's insurance promotion, delivered by text message.

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

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

[0097] Multiple customer segments are pre-defined, and each segment is assigned a corresponding preferred contact method. These preferred contact methods are determined through big data analysis and market research and stored in the system's database. For example, suppose target customer Zhang San is matched to the "Middle-aged and Young IT Professionals" segment. The preferred contact methods for this segment are as follows: Preferred contact methods: Email, SMS; Dispreferred 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 optimal 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 calls are answered but not interested).

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

[0100] Steps S103a to S103c generate more precise and personalized customer engagement strategies by analyzing the target customer's historical engagement information in detail and combining it with the preferred engagement methods of the target group. This approach has significantly improved customer satisfaction, reduced customer complaints, increased closing rates, and prevented customer churn. It also enhances communication efficiency and customer experience while reducing operating costs. Through data-driven decision-making and dynamic adjustments, insurance companies can better adapt to changes in market and customer needs, achieving more efficient customer management and service.

[0101] In some embodiments, as 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] This method requires detailed analysis of target customers' personal information, particularly their geographic location (such as place of residence and work). This analysis can be accomplished through a combination of natural language processing (NLP) techniques, data extraction tools, and geographic information systems. For example, geographic information can be extracted from a customer's residential address, workplace address, or mobile phone number.

[0106] Suppose the target customer Zhang San's personal information includes: Address: XX District, XX City; Work Address: XX District, XX City; Mobile Phone Number: XXXXXXYYYY (Location: XX City). By parsing this information, the system determines that Zhang San's location is XX City. Based on the target customer's location information, it is necessary to obtain the corresponding renewal policy information for that location. This information may include renewal conditions, preferential measures, local regulations, and so on for insurance products. Renewal policy information is typically stored in the insurance company's database and can be obtained through API calls or database queries. By querying the database based on the location information (XX City), the following renewal policy information for XX City is obtained: Health Insurance Renewal Policy: The renewal period is once a year, and a 5% renewal discount is available. Accident 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 10% renewal discount is available.

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

[0108] For example, based on Zhang San's historical insurance information, the CRM system is queried to obtain Zhang San's current policy status as follows: Health Insurance: The current policy expires on December 31, 2024, and has not been renewed. Accident Insurance: The current policy expires on June 30, 2025, and has not been renewed. Family Health Insurance: The current policy expires on November 30, 2024, and has not been renewed.

[0109] By integrating current policy status and renewal policy information, the system determines the target customer's current insurance cycle. This includes information about the product's expiration date, renewal conditions, and preferential policies. Through logical analysis and rule matching, the system determines the current insurance cycle for each product and generates corresponding renewal reminders or promotional content.

[0110] For example, for health insurance: Based on the current policy status (valid until December 31, 2024) and the 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, offering a 5% premium discount. 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."

[0114] Family Health Insurance: Based on the current policy status (valid until November 30, 2024) and the renewal policy information (annual renewal with a 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, offering a 10% premium discount. Please complete the renewal procedures as soon as possible to ensure the insurance benefits for you and your family."

[0116] In summary, the above embodiment obtains local information by carefully analyzing customer personal information. This information is then combined with the renewal policy information corresponding to the local information and the customer's current policy status to generate a more accurate and personalized picture of the current insurance cycle. This approach has significantly improved customer satisfaction, reduced customer complaints, increased renewal rates, and prevented customer churn. It also enhances communication efficiency and customer experience, while reducing operating costs. By leveraging data-driven approaches and adapting to local policies, insurance companies can better meet the actual needs of their customers and achieve more efficient customer management and service.

[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; determining the target contact content corresponding to the target group type and target customers based on the current insurance cycle includes: parsing the current insurance cycle and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; generating the target contact content based on the group communication content, cycle focus information, and the current policy status.

[0118] This method analyzes the target customer's current insurance cycle, identifies the time period they are currently in, and captures the key tasks and priorities for each period. These periods include the operational period, warm-up period, temporary period, initial period, tracking period, and fallback period. Using pre-set rules and algorithms, the customer's current insurance cycle phase is determined based on the current date and policy expiration date. Each phase has specific focus information, such as:

[0119] Operational period: Emphasize routine 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 target customer Zhang'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 is in the guaranteed coverage period and analyzes the key information during the guaranteed coverage period as follows: Key information during the guaranteed coverage period: Frequent reminders of the impending expiration of insurance, provision of renewal procedures and preferential treatment, ensuring that customers understand the importance and urgency of renewal.

[0126] Based on the target customer group type (e.g., high-net-worth individuals, ordinary households, corporate customers, etc.), the system needs to retrieve the communication content that this group should focus on during the current insurance cycle from the pre-set insurance management platform. This content may include renewal tips, claims guidance, and interpretation of new policies. The insurance management platform typically stores communication content templates for each group type across different insurance cycles. The system retrieves these templates through API calls or database queries. These templates can be updated based on 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, periodic focus information, and the customer's current policy status to generate personalized targeted contact content. This content can include emails, text messages, phone calls, physical mail, and more, designed to provide targeted services and reminders. Using template generation technology and content recommendation algorithms, the system can generate customized communication content based on the customer's specific needs. For example, natural language generation (NLG) technology can be used to generate email or text message content, and a rules engine can ensure that the content aligns with the customer's needs and insurance policy.

[0129] For example, based on Zhang San's group communication content (emphasizing personalized services and renewal discounts for high-end insurance products), period-focused information (frequent reminders of impending insurance expiration), 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] The text reads: "Dear Mr. Zhang San, your health insurance will expire on December 31, 2024. To ensure your insurance rights, we remind you to complete the renewal procedures promptly. As a high-net-worth client, you will enjoy exclusive renewal discounts on high-end insurance products and will be assigned a dedicated account manager, Guo Moumou, to provide one-on-one service. If you have any questions or need claims guidance, please feel free to contact us. We wish you and your family all the best and will be happy to serve you."

[0132] Content of SMS:

[0133] Dear Mr. Zhang San, your health insurance will expire on December 31, 2024. As a high-net-worth client, you will enjoy exclusive renewal discounts. For more information, please contact your dedicated account manager, Mr. X, at 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, precise and personalized targeted engagement content is generated. This approach has significantly improved communication efficiency, enhanced customer experience, increased customer engagement, increased renewal rates, and reduced customer complaints. Through data-driven decision-making and adapting to the needs of different stages, insurance companies can better meet the actual needs of customers, achieve more efficient customer management and service, and enhance their 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 the 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] This method requires detailed analysis of target customers' historical contact information to determine their communication intentions (i.e., their preferences and responses to different communication methods) and the time since last contact (i.e., the time between the last communication and the current time). This analysis can be accomplished using natural language processing (NLP) techniques, data mining, and analytical tools. The system can extract customer feedback and behavioral data from historical contact records to determine the customer's communication intentions and the time since last contact.

[0138] Assume that the target customer Zhang San's contact history information is as follows: Communication Intention: Zhang San has a high response rate via email and text message over the past year, and a low response rate via phone. Last Contact: Zhang San was last contacted by email on November 15, 2024, by text message on October 30, 2024, and by phone on August 10, 2024.

[0139] The system determines the urgency of contact within the target customer's current insurance cycle. The urgency level varies across different stages of the insurance cycle; for example, more frequent contact may be required during the renewal reminder period. The urgency level of contact can be determined using pre-set rules and algorithms. The system can determine the urgency level based on the specific stage of the current insurance cycle (e.g., warm-up period, initial cycle, tracking period, and back-up period) and the customer's policy status.

[0140] Assume 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 policy expires). The guarantee period has a higher level of urgency, and customers need to be reminded frequently to renew their insurance to avoid policy lapse.

[0141] A personalized target contact frequency is generated by combining the customer's communication preferences, the time since their last contact, and the urgency of contact within the current insurance cycle. This involves determining the frequency, method, and timing of contact. This target contact frequency is achieved through rule matching, machine learning algorithms, and multi-factor analysis. The system can dynamically adjust the frequency and method of contact based on the customer's historical behavior data and urgency.

[0142] For example, Zhang San prefers email and text messages, with a lower phone response rate. Contact interval: The last email contact was 9 days ago, the last text contact was 25 days ago, and the last phone contact was 106 days ago. Contact urgency: Currently in the coverage period, the urgency is high. Based on this information, Zhang San's target contact frequency is as follows: Email: Send renewal reminders every 3 days. Text: Send renewal reminders every 5 days. Phone: Make a phone call every 10 days.

[0143] By considering the customer's communication intentions 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. A reasonable contact frequency avoids the problem of excessive or insufficient contact and reduces customer complaints caused by improper communication. By setting time intervals 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 higher urgency (such as the guarantee period), it can ensure that customers receive renewal reminders in a timely manner, thereby improving the renewal rate. Targeting the customer's preferred and more effective communication methods increases customer attention and response rate to renewal information.

[0144] Exemplarily, after generating the target contact frequency based on 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 based on the historical complaint information; adjusting the target contact frequency based on the customer sensitivity.

[0145] This method requires detailed analysis of target customers' historical contact information to determine their communication preferences (i.e., their preferences and responses to different communication methods), the time since the last contact, and any complaints from each previous contact. This analysis can be accomplished using natural language processing (NLP) techniques, data mining, and analytical tools. The system can extract customer feedback and behavioral data from historical contact records to determine their communication preferences and historical complaints.

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

[0147] Communication preferences: 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 the frequent phone calls.

[0150] This method generates customer sensitivity levels to different communication methods based on historical complaint information. Sensitivity can be categorized into different levels, such as low, medium, and high. Customer sensitivity levels can be determined using pre-set rules and algorithms. The system automatically calculates customer sensitivity based on the number of historical complaints, the severity of the complaints, 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 frequency of contact with target customers based on the generated customer sensitivity level. If a customer's sensitivity is high, the frequency of certain communication methods can be appropriately reduced to avoid further distress. Adjusting the target contact frequency is achieved through rule matching and machine learning algorithms. The system can automatically adjust the frequency and method of contact based on customer sensitivity, ensuring a more personalized and effective communication strategy.

[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 call: One call every 10 days.

[0157] Adjusted target contact frequency:

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

[0159] SMS: Renewal reminders will be sent every 5 days (remain 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, we can determine customers' communication preferences, contact intervals, and past complaints, generate customer sensitivity levels, and adjust target contact frequency accordingly. This approach has proven effective in improving customer satisfaction, reducing customer complaints, enhancing customer loyalty, optimizing communication strategies, improving communication efficiency, strengthening customer relationship management, and enhancing brand image. Through data-driven decision-making, insurance companies can better meet customers' real needs and achieve more efficient and personalized customer management and service.

[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, thereby reducing the risk of customer complaints.

[0165] 3. Improve transaction rates: 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, thereby 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 scientific nature and accuracy of decision-making.

[0169] 7. Flexible adaptation to customer needs: Based on the characteristics and historical contact information of different customer groups, 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 staff.

[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 rates and operational efficiency, and effectively prevented customer churn.

[0173] See also Figure 4 As shown, Figure 4 2 is a schematic diagram of the structure of a customer contact device 200 provided in an embodiment of the present application. This customer contact device 200 is used to perform the steps of the customer contact method described in each of the above embodiments. This customer contact device 200 can be a single server or a server cluster, or it can be a terminal, such as a handheld terminal, a laptop computer, a wearable device, or a robot.

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

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

[0176] A group determination module 202 is configured 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 among the plurality of customer group types;

[0177] A contact determination module 203 is configured to determine a target contact method with the target customer based on 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 configured to 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 according to the current insurance period and the target group type;

[0179] A frequency determination module 205 is 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 configured 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 those skilled in the art can clearly understand that, for the convenience and brevity 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. The computer program can be used in 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, which, when executed, can cause a processor to perform 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 perform 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 solution of the present application, and does not constitute a limitation on the terminal to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0188] It should be understood that the processor may be a central processing unit (CPU), or 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. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0189] In one embodiment, the processor is configured to execute 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 customer information with multiple preset customer group types to determine the target group type corresponding to the target customer among the multiple customer group types;

[0192] Determine the target contact method with 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 target customer's current insurance cycle 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 cycle and target group type;

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

[0195] Generate target contact strategies corresponding to target customers based on target contact methods, target contact content, and target contact frequency, so as to complete contact with target customers according to the target contact strategies.

[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; 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 respectively to determine the target group type corresponding to the target customer 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 generates behavioral 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 behavioral preference information and each group preference information, to determine the target group type among the plurality of customer group types based on the preference similarity.

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

[0198] In some embodiments, the current insurance period of the 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; determining the target contact content corresponding to the target group type and target customers based on the current insurance cycle includes: parsing the current insurance cycle and obtaining the cycle focus information corresponding to the current insurance cycle; obtaining the group communication content of customers corresponding to the target group type in the current insurance cycle in a preset insurance management platform; generating the target contact content based on the group communication content, 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 the 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 based on 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 based on the historical complaint information; adjusting the target contact frequency based on the customer 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 aforementioned 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 SmartMedia Card (SMC), a Secure Digital (SD) card, a flash memory card, etc., equipped on the computer device.

[0204] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A customer contact method, characterized in that: include: Obtaining customer information of the target customer to be contacted, wherein the customer information includes at least historical insurance information, historical contact information, and customer personal information; Matching 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; Determining a target contact method with the target customer based on 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; Determining the current insurance period of the target customer based on the historical insurance information and the customer's personal information, and determining the target contact content corresponding to the target customer based on 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 period; 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 following: historical insurance amounts, historical renewal rates, and historical insurance types; the historical contact information includes at least one or more of the following: historical contact times, historical contact times, and historical contact methods; and the customer personal information includes at least one or more of the following: customer occupation, customer family composition, customer operations, customer income, and customer background; and matching the customer information with a plurality of preset customer group types to determine a target group type corresponding to the target customer from the plurality of 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 group preference information is calculated, so as 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 determining of the target contact method with the target customer based on the target group type and the historical contact information includes: Parsing the historical contact information to obtain 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, wherein Determining the current insurance period of the target customer based on the historical insurance information and the customer's 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; Obtaining the current policy status corresponding to the target customer based on the historical insurance information; The current insurance period corresponding to the target customer is determined based on 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, an initial period, a tracking period, and a safety net period; and determining the target contact content corresponding to the target customer based on the current insurance cycle and the target group type includes: Parsing the current insurance period to obtain period-focused information corresponding to the current insurance period; Obtaining, in a preset insurance management platform, group communication content of customers corresponding to the target group type during the current insurance cycle; 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 of the target contact frequency with the target customer based on the historical contact information and the current insurance period includes: Parsing the 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 level corresponding to the target customer based on 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 historical complaint information of the target customer regarding any of the historical contacts, generating a customer sensitivity level based on the historical complaint information; The target contact frequency is adjusted according to the customer sensitivity.

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 includes at least historical insurance information, historical contact information and customer personal information; a group determination module, configured 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 among the plurality of customer group types; a contact determination module, configured to determine a target contact method with the target customer based on 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, configured to determine the current insurance period of the target customer based on the historical insurance information and the customer's personal information, and to determine the target contact content corresponding to the target customer based on the current insurance period and the target group type; a frequency determination module, configured to determine a target contact frequency with the target customer based on the historical contact information and the current insurance period; The contact completion module is configured 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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