WO2019041748A1 - 一种保单数据处理方法、装置、电子设备及介质 - Google Patents

一种保单数据处理方法、装置、电子设备及介质 Download PDF

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Publication number
WO2019041748A1
WO2019041748A1 PCT/CN2018/074850 CN2018074850W WO2019041748A1 WO 2019041748 A1 WO2019041748 A1 WO 2019041748A1 CN 2018074850 W CN2018074850 W CN 2018074850W WO 2019041748 A1 WO2019041748 A1 WO 2019041748A1
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customer
renewal
information
policy
list
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PCT/CN2018/074850
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English (en)
French (fr)
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李毅
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平安科技（深圳）有限公司
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Publication of WO2019041748A1 publication Critical patent/WO2019041748A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2465—Query processing support for facilitating data mining operations in structured databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance

Definitions

  • the present application belongs to the field of data processing technologies, and in particular, to a policy data processing method, apparatus, electronic device, and medium.
  • the insurance policy is referred to as a policy. It is a formal written certificate between the insurer and the insured to conclude an insurance contract. It records the rights and obligations of the parties to the policy, customer information, business staff information, and the policy start time and policy of the policy. Information such as time.
  • the sales system will be used to analyze the existing customer's list data and the corresponding policy data, and identify the policy data in which the renewal is failed, and according to the renewal policy. After the failed policy data determines the list of customers who have failed to renew the insurance, the staff member is asked to return to the customer based on the list of customers whose renewal is failed.
  • the existing sales system can only identify the list of customers who have failed to renew the insurance from the customer list and the corresponding policy data.
  • the function is single, and the existing customer list and corresponding policy data can reach 100 million.
  • the order of magnitude and large amount of data the existing sales system often requires a very long processing time, in order to identify the list of customers who have failed to renew the insurance, and the efficiency of policy data processing is extremely low. Therefore, in the prior art, the analysis and processing of the policy data is inefficient and has a single function, and the degree of intelligence is insufficient.
  • the embodiment of the present application provides a policy data processing method and an electronic device to solve the problem of insufficient intelligence in analyzing and processing policy data in the prior art.
  • a first aspect of the embodiments of the present application provides a policy data processing method, including:
  • the big data analysis tool to classify the customer according to the policy expiration time in the customer's policy data, identify the renewed failed customer whose policy expiration time is less than or equal to the current time, and the policy
  • the difference between the expiration time and the current time is less than the preset time of the insured customer, and the policy expiration time refers to the policy start time of the policy validity period is the closest to the current time in all the policy data corresponding to each customer.
  • a second aspect of the embodiments of the present application provides a policy data processing apparatus, including:
  • the type identification module is configured to use the big data analysis tool to perform customer type division on the customer according to the policy expiration time in the customer's policy data, and identify that the policy expiration time is less than or equal to the current time.
  • the customer, and the difference between the policy expiration time and the current time is less than the preset time of the insured expiring customer, and the policy expiration time refers to the policy start of the policy validity period in all the policy data corresponding to each customer.
  • the policy expiration time in a policy data that is closest to the current time.
  • the first list obtaining module is configured to extract personal information data of the renewal failed customer from the policy data corresponding to the renewal insurance client, and generate a renewal failure customer list according to the personal information data.
  • the list output module is configured to output the list of the renewal failure customers corresponding to the renewal failure customer and the policy data corresponding to the renewal failure customer.
  • the information pushing module is configured to read the customer contact information of the customer who is about to expire, and generate renewal information according to the policy data corresponding to the customer that is about to expire, and according to the customer contact information, Renew your information and push it.
  • a third aspect of the embodiments of the present application provides a policy data processing electronic device including a memory, a processor, and a computer readable instruction executable on the processor, where the processor executes The following steps are implemented when the computer readable instructions are described:
  • the big data analysis tool to classify the customer according to the policy expiration time in the customer's policy data, identify the renewed failed customer whose policy expiration time is less than or equal to the current time, and the policy
  • the difference between the expiration time and the current time is less than the preset time of the insured customer, and the policy expiration time refers to the policy start time of the policy validity period is the closest to the current time in all the policy data corresponding to each customer.
  • a fourth aspect of the embodiments of the present application provides a computer readable storage medium storing computer readable instructions, wherein the computer readable instructions are implemented by at least one processor The following steps:
  • the big data analysis tool to classify the customer according to the policy expiration time in the customer's policy data, identify the renewed failed customer whose policy expiration time is less than or equal to the current time, and the policy
  • the difference between the expiration time and the current time is less than the preset time of the insured customer, and the policy expiration time refers to the policy start time of the policy validity period is the closest to the current time in all the policy data corresponding to each customer.
  • FIG. 1 is a schematic flowchart of an implementation process of a policy data processing method according to Embodiment 1 of the present application;
  • Embodiment 2 is a schematic flowchart of an implementation process of a policy data processing method provided by Embodiment 2 of the present application;
  • FIG. 3 is a schematic flowchart of an implementation process of a policy data processing method according to Embodiment 3 of the present application.
  • FIG. 4 is a schematic flowchart of an implementation process of a policy data processing method provided in Embodiment 4 of the present application;
  • FIG. 5 is a schematic flowchart of an implementation process of a policy data processing method provided in Embodiment 5 of the present application;
  • FIG. 6 is a schematic structural diagram of a policy data processing apparatus according to Embodiment 6 of the present application.
  • FIG. 7 is a schematic diagram of a policy data processing electronic device provided in Embodiment 7 of the present application.
  • FIG. 1 is a flowchart showing an implementation of a policy data processing method according to an embodiment of the present application, which is described in detail as follows:
  • the policy expiration time refers to the policy data of the policy that is closest to the current time in the policy data of each customer. The expiration date of the policy.
  • a professional big data analysis tool is used to process the policy data and the list data, and at the same time, the big data analysis tool has the characteristics of rich data filtering processing functions. As a guarantee for the classification and identification of multiple customer types.
  • the Hive tool is selected as a big data analysis tool when the storage system that identifies the policy data is a distributed file system.
  • the centralized file system refers to storing data data in a storage medium of the same network node.
  • the data file is stored in a local computer storage disk
  • the distributed file system refers to separately storing data data in different network nodes, such as storing data data in servers corresponding to different network nodes. Because different storage systems have different read/store modes, different big data analysis tools are selected for different storage systems in the embodiments of the present application.
  • Hive tool Hadoop a data warehouse tool, can map structured data files into a database table, and provide a simple structured query language (SQL) query function, is a big data in the distributed file system analyzing tool.
  • the Hive tool is selected, in order to improve the processing speed of the policy data.
  • the analysis and processing of policy data is performed.
  • the insurance company will store the basic information in the customer's policy, such as the customer's personal information, insurance type, policy validity period, insurance beneficiary and business personnel, as the customer's corresponding information. Policy data.
  • the personal information of all customers who have purchased insurance such as name and mobile phone number, will be organized into a total customer list data for storage, so as to facilitate the daily work of the staff.
  • the policy validity period includes the policy start time and the policy expiration time. Since the customer may renew after the expiration of a policy expiration date, each customer may have multiple corresponding policies with different validity periods.
  • the policy expiration time used for customer type identification in the example refers to the policy expiration time in the policy of the last policy of the current period from the current policy.
  • the customer type includes the renewed failed customer and the insured expiring customer. Since the policy expiration time of the two customers' policy validity period is respectively before and after the current time, the policy can be based on the policy validity period. Expiration time to distinguish between customer types.
  • the policy expiration time of the validity period in the policy when the policy expiration time of the validity period in the policy is less than or equal to the current time, it means that the current policy has passed the validity period.
  • the policy expiration time in the embodiment of the present application refers to the policy expiration time in a policy whose validity period is the closest to the current time. Therefore, when the policy expires, the policy expires.
  • the period of time is less than or equal to the current time, it means that the customer has not renewed the current time, that is, the customer who has failed to renew the insurance.
  • the difference between the policy expiration time and the current time is compared with the preset time.
  • the preset time may be set by the technician according to the actual situation, but in S101, the policy is to identify the customer who is about to expire due to the insurance policy, and therefore, the preset time is not suitable. Too long, optional, such as setting the preset time to 20 days.
  • the staff can set an insurance policy start time of the policy data and input, such as setting the analysis policy start time. For January 1st last year. After receiving the analysis policy start time input by the staff member, the policy data of all valid period of the policy expiration time is filtered out before the analysis policy start time, that is, only the policy expiration time of the valid period is analyzed. The policy data after the policy start time is analyzed and processed.
  • a corresponding list of customers to be expired is generated for the staff to Use when needed.
  • the customer type classification is performed on the customer corresponding to the policy data by the policy expiration time, and the customer list corresponding to each customer type is classified, and only the customer list of the renewal failure can be obtained compared with the prior art. In other words, it enriches the function of policy data processing.
  • the obtained list of renewed failed customers and their corresponding policy data are output to the current working staff, so that the staff can be renewed according to the maintenance.
  • S104 reading the customer contact information of the customer who is about to expire, generating the renewal information according to the policy data corresponding to the customer who is about to expire, and pushing the renewal information according to the customer contact information.
  • the customer contact information is included in the customer personal information in the policy data, and the customer contact information includes, but is not limited to, a customer's telephone number and an email address.
  • the renewal information includes the policy expiration time of the customer's current policy and some insurance information related to the customer's insurance type. Insured expiring customers' policy validity period has not expired, but it is also very close to the policy expiration time. Therefore, in order to improve the customer's renewal success rate, in the embodiment of the present application, some related items will be pushed to the customer.
  • the renewal information information reminds the customer that the policy will expire immediately, and also provides some insurance information for the customer to refer to in order to help the customer to renew in time.
  • the specific manner of pushing the information information renewal information is not limited, and may be set by a technician according to actual conditions.
  • the selection should be made in combination with a specific customer contact method.
  • the customer contact information includes an email address
  • the email may be pushed by means of an email
  • the telephone number is included, the email may be pushed by means of a short message.
  • the list of upcoming customers and the corresponding policy data may be output to the staff to help the staff to track and control the customers who are about to expire in time. Insured status, timely return visits to customers who are about to expire, to improve the success rate of subsequent customer renewal.
  • the identification of the customer who is about to expire after the insurance is about to expire is added and passed. Renewal information information push and other means to remind and help insured customers to renew their insurance, enrich the function of analyzing and processing policy data, and improve the success rate of renewal of customers.
  • the method further includes:
  • S201 Receive a customer return visit result marking instruction input by the user, and perform a return visit token on the renewal failed customer in the renewal failed customer list according to the customer return visit result marking instruction, and obtain the customer return visit data.
  • the return visit mark refers to the classification mark of the renewed failing customer according to the result of the return visit after the staff member returns to the customer who has failed to renew the insurance.
  • the types of the return visit mark and the specific meanings are as follows:
  • the loss of the linked mark refers to the failure of the renewed failed customer's contact information has expired, unable to obtain contact renewal failure
  • Abandoning the renewal of the insurance mark refers to the renewal of the failed customer who clearly indicated that he does not want to renew the insurance in the return visit;
  • the renewal success mark refers to the renewed failed customer who successfully renewed the insurance during the return visit
  • the intention to renew the insurance mark refers to the failure to renew the insurance, but indicates that there is a willingness to renew the failed customer.
  • the staff After completing the return visit for each renewal failure customer, the staff will mark the renewal failure customer according to the actual return visit result, and input the corresponding customer return visit result marking instruction, which can complete the renewal failure in the renewal failure customer list. Customer's return visit mark.
  • S203 Filter the stored customer list data according to the return visit failure list, and obtain an active insurance list for storage.
  • Both the lost link and the waiver of the renewal mark represent the renewed failure of the return visit and the customer will not renew the insurance, that is, the staff member fails to return to the customer for these renewal failures.
  • the renewed failed customers who have failed in the return visits will sort out a list of returning failures, and then remove the renewed failed customers from the stored total customer list data to obtain a copy containing the normal insured.
  • Active insured list of customers and customers who wish to renew their insurance Since the active insurance list is full of insurance and the customers who are more active in insurance, the insurance company's insurance business is relatively high. Therefore, after the active insurance list is obtained, it can be output to the staff. To help staff with follow-up customer tracking/promotion work.
  • the method further includes: receiving an activity push instruction input by the user and activity data, and pushing the activity data to the client in the active insurance list.
  • the staff can directly use the active insurance list to carry out the activity data push to improve the efficiency of the event promotion.
  • the third embodiment of the present application includes:
  • the embodiment of the present application can directly obtain the staff information corresponding to each policy according to the policy data. Then, through the staff information in the policy, the corresponding staff contact information is determined, and the corresponding contact information of each customer in the renewal customer list can be determined. Since the staff information stored in the actual policy is sometimes relatively simple, such as only the name and the job number of the staff member, the embodiment of the present application can query the detailed information of the staff member according to the name and job number of the staff member. To get the required staff contact.
  • S1032 Generate, according to the personal information and policy data corresponding to each renewed failed customer in the renewed failed customer list, generate customer return visit information corresponding to each renewed failed customer, and renew the failed customer list through the staff contact method. And the customer return visit information is output, so that the staff member can receive the customer return visit information corresponding to each renewal failing customer in the list of renewed failed customers.
  • the success rate of the return visit renewal is relatively large. Therefore, after obtaining the list of the failed renewal customer, the embodiment of the present application will continue.
  • Each customer in the list of failed customers generates a corresponding customer return visit information, and sends the customer return visit information to the staff recorded in the policy corresponding to the customer to enhance the customer success rate.
  • the contact information of the staff corresponding to the customer in the policy data of the renewal failed customer list is read, and the customer return information generated by the customer personal information and the policy data corresponding to the customer is contacted by the staff member.
  • the method is sent to the staff corresponding to the customer, further enriching the function of analyzing and processing the policy data, and utilizing the relatively large success rate of the original staff returning to the customer to renew the insurance, and improving the success rate of the customer renewal.
  • the fourth embodiment of the present application includes:
  • the policy with the closest policy start time and the current time is searched, and the policy found is used as the policy data corresponding to each customer who is about to expire. . That is, in S1041, the policy data corresponding to each customer who is about to expire is only included in the policy whose validity period is the closest to the current time.
  • S1042 Obtain N pieces of insurance information related to the policy data corresponding to the insured customer, and which is closest to the current time, and generate renewal reminding information, where N is a positive integer.
  • the N pieces of insurance information related to the policy data corresponding to the insured customer to be expired and closest to the current time means that the customer who is about to expire after each insurance is determined to read the corresponding policy data after S1041
  • the insurance type of the policy and from the preset insurance information database, find the N insurance information that is the same as the insurance type of the policy and whose update time is closest to the current time.
  • the specific value of N needs to be set by the technician according to actual needs.
  • Insurance information includes but is not limited to information such as insurance clauses and insurance policies. If N is 3 and the policy type is sickness insurance, the latest 3 insurance information will be selected from the insurance information related to sickness insurance in the preset insurance information database.
  • the renewal reminder information is used to remind the customer that the validity period of the policy is about to expire. It needs to be renewed in time, including but not limited to any one or more of text, voice or video.
  • the renewal information information includes the policy expiration time, the renewal insurance information and the N insurance information.
  • the policy expiration time, the renewal information and the N insurance information are used as the renewal information to be finally pushed. Push one by one according to the contact information of each customer who is about to expire.
  • Each customer who is about to expire will be able to obtain the renewal information information corresponding to their own, get the policy expiration time of their policy and the renewal reminder, and also obtain the insurance information related to the insurance they have already invested.
  • the insurance information is obtained by insuring the customer who is about to expire, and the corresponding renewal reminding information is generated, and the policy expiration time, the renewal reminding information, and the N insurance information are used as the renewal information.
  • the information is pushed to remind and help the insurance customers who are about to expire in the insured to renew their insurance, which enriches the function of analyzing and processing the policy data and improves the success rate of renewal of the customer.
  • the method includes:
  • the insured customer is classified by the customer type, and the renewed failed customer whose renewed insurance fails and the insurance is about to expire are proposed. Insuring the concept of a customer that is about to expire. However, in the actual situation, in addition to the above two types of customers, there are still a special type of customers whose policy expiration time is greater than the current time. The insurance amount is large, the renewal is carried out and the renewal is timely.
  • a quality customer which is also known as a quality customer. Because high-quality customers have higher recognition of insurance policies and a stronger sense of renewal of insurance relative to ordinary customers, timely tracking and returning visits to quality customers can greatly enhance the success rate of quality customers.
  • the customers who use the policy from the expiration date of the policy whose expiration time is greater than the current time will be used for screening the quality customers, and The relevant information of high-quality customers is output, so that the staff can track the quality customers in time.
  • the stored policy is first filtered through the insurance amount in the policy data to find a policy with a larger insurance amount and a corresponding customer.
  • the specific value of the preset amount needs to be determined and set by the technical staff according to the actual situation.
  • S106 Calculating the renewal and timely rate of the policy data with the insurance amount greater than the preset amount, identifying all the quality customers whose renewal time ratio is greater than the preset threshold, and obtaining a list of quality customers corresponding to the quality customer.
  • the specific method for calculating the renewal guarantee time rate is: querying the renewal policy for each customer corresponding to the S105, and reading the policy expiration time of the policy validity period before the renewal time.
  • renewal time rate 1 - (renewal time - policy expiration time) / planned renewal period, to calculate the renewal rate of each customer.
  • the plan renewal period is a buffer time for the customer to renew the insurance after the insurance policy expires.
  • the duty cycle is set to 30 days.
  • the policy expiration time is June 1, 2017, the renewal time is June 10, 2017, and the planned renewal period is 30 days.
  • the high-quality customer prompt information is also produced and output at the same time.
  • the high-quality customer is identified and the corresponding high-quality customer related information and prompt information are outputted by the staff, so that the staff can timely and effectively track and return the high-quality customer, thereby improving the success rate of the quality customer. It enriches the analysis and processing functions of policy data.
  • the big data analysis tool such as Hive is used to process the policy data and the list data with a large amount of data, which greatly improves the efficiency of processing the policy data.
  • the customer is classified into three different customer types, and the customer information output or the renewal of the information information is separately processed according to the characteristics of each customer type, so as to help the staff to track the customer in time.
  • Return visits reminding customers to renew their insurance, greatly enriching the function of policy data analysis and analysis, and improving the success rate of customers' renewal.
  • FIG. 6 is a block diagram showing the structure of the policy data processing apparatus provided by the embodiment of the present application, and only parts related to the embodiment of the present application are shown for convenience of description.
  • the policy data processing apparatus illustrated in FIG. 6 may be the execution body of the policy data processing method provided in the foregoing first embodiment.
  • the policy data processing apparatus includes:
  • the type identification module 61 is configured to use the big data analysis tool to perform customer type division according to the policy expiration time in the customer's policy data, and identify that the policy expiration time is less than or equal to the current time renewal.
  • the policy expiration time in a policy data that is closest to the current time.
  • the first list obtaining module 62 is configured to extract personal information data of the renewal failed customer from the policy data corresponding to the renewal insurance client, and generate a renewal failure customer list according to the personal information data.
  • the list output module 63 is configured to output the renewal failure customer list corresponding to the renewal failure client and the policy data corresponding to the renewal failure client.
  • the information pushing module 64 is configured to read the customer contact information of the insured customer that is about to expire, generate renewal information according to the policy data corresponding to the insured customer, and according to the customer contact information Renewal information information is pushed.
  • the policy data processing device further includes:
  • the return visit tag module receives the customer return visit result tag instruction input by the user, and performs a return visit mark on the renewal failing customer in the renewal failing customer list according to the customer return visit result tag instruction, and obtains the customer return visit data.
  • the list generating module extracts, from the customer return visit data, a return visit failure list of the renewed failed customer that is marked as a lost link flag and a renewed renewal mark.
  • the list screening module selects the stored customer list data according to the return visit failure list, and obtains an active insurance list for storage.
  • the list output module 63 includes:
  • the contact determining sub-module is configured to read the corresponding contact information of each of the renewed failed customers in the renewed failed customer list.
  • a policy output sub-module configured to generate, according to the personal information corresponding to each of the renewed failed customers in the renewed failed customer list and the policy data, a customer return visit information corresponding to each of the renewed failed customers, And outputting, by the staff contact information, the renewal failure customer list and the customer return visit information, so that the staff member can receive the renewal failure customer list and each of the renewal failure customers Corresponding customer return visit information.
  • the information pushing module 64 includes:
  • the policy identification sub-module is configured to read the policy data corresponding to the insured customer that is about to expire.
  • the information generating sub-module is configured to acquire N pieces of insurance information related to the policy data corresponding to the insured customer that is about to expire, and which is closest to the current time, and generate renewal reminding information, where the N is a positive integer .
  • the information pushing sub-module is configured to read the customer contact mode corresponding to the insured customer, and perform the pushing of the renewal information information according to the customer contact information, where the renewal information information includes the policy expiration time The renewal reminding information and the N pieces of insurance information.
  • the policy data processing device further includes:
  • the insured judgment module is configured to determine whether the insurance amount in the policy data is greater than a preset amount when it is identified that the policy expiration time is greater than the current time.
  • a second list obtaining module configured to calculate a renewal time rate of the policy data whose insurance amount is greater than the preset amount, and identify all the quality customers whose renewal time ratio is greater than a preset threshold, and A list of quality customers corresponding to the quality customers is obtained.
  • the information output module is configured to generate high-quality customer prompt information, and output the high-quality customer list, the policy data corresponding to the high-quality customer, and the high-quality customer prompt information.
  • first, second, etc. are used in the text to describe various elements in the embodiments of the present application, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • the first contact can be named a second contact, and similarly, the second contact can be named the first contact without departing from the scope of the various described embodiments. Both the first contact and the second contact are contacts, but they are not the same contact.
  • FIG. 7 is a schematic diagram of a policy data processing electronic device according to an embodiment of the present application.
  • the policy data processing electronic device 7 of this embodiment includes a processor 70, a memory 71, and computer readable instructions 72 stored in the memory 71 and executable on the processor 70, such as Computer readable instructions.
  • the processor 70 when executing the computer readable instructions 72, implements the steps in the various embodiments of the policy data processing methods described above, such as steps 101 through 104 shown in FIG.
  • the processor 70 when executing the computer readable instructions 72, implements the functions of the various modules/units in the various apparatus embodiments described above, such as the functions of the modules 61-64 shown in FIG.
  • the policy data processing electronic device 7 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the policy data processing electronic device can include, but is not limited to, processor 70, memory 71. It will be understood by those skilled in the art that FIG. 7 is merely an example of the policy data processing electronic device 7, and does not constitute a limitation to the policy data processing electronic device 7, and may include more or less components than those illustrated, or a combination of certain Components, or different components, such as the policy data processing electronics, may also include input and output devices, network access devices, buses, and the like.
  • the so-called processor 70 can be a central processing unit (Central Processing Unit, CPU), can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (Application Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 71 may be an internal storage unit of the policy data processing electronic device 7, such as a hard disk or memory of the policy data processing electronic device 7.
  • the memory 71 may also be an external storage device of the policy data processing electronic device 7, such as a plug-in hard disk equipped with the policy data processing electronic device 7, a smart memory card (SMC), and a secure digital device. (Secure Digital, SD) card, flash card, etc.
  • SMC smart memory card
  • secure digital device Secure Digital, SD
  • the memory 71 may also include both an internal storage unit of the policy data processing electronic device 7 and an external storage device.
  • the memory 71 is configured to store the computer readable instructions and other programs and data required by the policy data processing electronic device.
  • the memory 71 can also be used to temporarily store data that has been output or is about to be output.
  • the disclosed apparatus/electronic device and method may be implemented in other manners.
  • the device/electronic device embodiment described above is merely illustrative.
  • the division of the module or unit is only a logical function division.
  • there may be another division manner for example, multiple units.
  • components may be combined or integrated into another system, or some features may be omitted or not performed.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium.
  • the present application implements all or part of the processes in the foregoing embodiments, and may also be implemented by computer readable instructions, which may be stored in a computer readable storage medium.
  • the computer readable instructions when executed by a processor, may implement the steps of the various method embodiments described above.
  • the computer readable instructions comprise computer readable instruction code, which may be in the form of source code, an object code form, an executable file or some intermediate form or the like.
  • the computer readable medium can include any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only) Memory), random access memory (RAM, Random) Access Memory), electrical carrier signals, telecommunications signals, and software distribution media.
  • ROM Read Only memory
  • RAM Random Access Memory
  • electrical carrier signals telecommunications signals
  • telecommunications signals and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media Does not include electrical carrier signals and telecommunication signals.

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Abstract

本申请提供了保单数据处理方法、装置、电子设备及介质，适用于数据处理技术领域，该方法包括：利用大数据分析工具，根据客户的保单数据中的保单到期时间，对客户进行客户类型划分；根据保单数据中的个人信息数据得出续保失败客户对应的续保失败客户名单；将续保失败客户对应的续保失败客户名单，以及续保失败客户对应的保单数据进行输出；读取投保即将到期客户的客户联系方式，根据投保即将到期客户对应的保单数据生成续保资讯信息，并根据客户联系方式对续保资讯信息进行推送。通过提高对保单数据处理的效率以及丰富分析处理保单数据的功能，极大的提高了对保单数据分析处理的智能化程度。

Description

一种保单数据处理方法、装置、电子设备及介质
本申请要求于2017年08月30日提交中国专利局、申请号为201710764496.3、发明名称为“一种保单数据处理方法及终端设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请属于数据处理技术领域，尤其涉及一种保单数据处理方法、装置、电子设备及介质。
 背景技术
保险单简称保单，是保险人与被保险人订立保险合同的正式书面证明，其中记载着保单双方当事人的权利义务及责任、客户信息、业务工作人员信息以及保单有效期的保单起始时间和保单到期时间等信息。实际情况中，为了提高客户续保成功率，会使用电销系统对已有的客户的名单数据以及对应的保单数据进行续保情况分析，识别出其中续保失败的保单数据，并根据续保失败的保单数据确定出续保失败的客户名单后，让工作人员根据该续保失败的客户名单来对客户进行回访。
然而现有的电销系统仅能做到从客户名单以及对应的保单数据中，识别出续保失败的客户名单，功能单一，同时由于已有的客户名单以及对应的保单数据可达亿级的数量级，数据量庞大，现有的电销系统往往需要非常长的处理时间，才能从中识别出续保失败的客户名单，对保单数据处理的效率极其低下。因此，现有技术中，对保单数据的分析处理的效率低下且功能单一，智能化程度不足。
 技术问题
有鉴于此，本申请实施例提供了一种保单数据处理方法及电子设备，以解决现有技术中对保单数据的分析处理智能化不足的问题。
 技术解决方案
本申请实施例的第一方面提供了一种保单数据处理方法，包括：
利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
本申请实施例的第二方面提供了一种保单数据处理装置，包括：
类型识别模块，用于利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间。
第一名单获取模块，用于从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单。
名单输出模块，用于将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出。
资讯推送模块，用于读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
本申请实施例的第三方面提供了一种保单数据处理电子设备，包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现如下步骤：
利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
本申请实施例的第四方面提供了一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，其特征在于，所述计算机可读指令被至少一个处理器执行时实现如下步骤：
利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
 有益效果
通过专用的大数据分析工具来对数据量庞大的保单数据以及名单数据进行处理，极大的提高了对保单数据处理的效率。同时，通过保单数据来对客户进行客户类型分类，并对不同客户类型的客户的保单数据分别进行处理，对保单到期时间小于或等于当前时间的续保失败的续保失败客户，将对应的续保失败客户名单以及保单数据进行输出，以供工作人员进行查询和回访，对保单到期时间与当前时间的差值小于预设时间的保单即将到期的投保即将到期客户，进行续保资讯信息的推送，帮助客户了解适合的自己的保险资讯，以提醒和帮助客户进行续保，丰富了对保单数据的分析处理的功能。通过提高对保单数据处理的效率以及丰富分析处理保单数据的功能，极大的提高了对保单数据分析处理的智能化程度，同时还有助于提高续保的成功率。
 附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例一提供的保单数据处理方法的实现流程示意图；
图2是本申请实施例二提供的保单数据处理方法的实现流程示意图；
图3是本申请实施例三提供的保单数据处理方法的实现流程示意图；
图4是本申请实施例四提供的保单数据处理方法的实现流程示意图；
图5是本申请实施例五提供的保单数据处理方法的实现流程示意图；
图6是本申请实施例六提供的保单数据处理装置的结构示意图；
图7是本申请实施例七提供的保单数据处理电子设备的示意图。
 本发明的实施方式
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、技术之类的具体细节，以便透彻理解本申请实施例。然而，本领域的技术人员应当清楚，在没有这些具体细节的其它实施例中也可以实现本申请。在其它情况中，省略对众所周知的系统、装置、电路以及方法的详细说明，以免不必要的细节妨碍本申请的描述。
为了说明本申请所述的技术方案，下面通过具体实施例来进行说明。
图1示出了本申请实施例一保单数据处理方法的实现流程图，详述如下：
S101，利用大数据分析工具，根据客户的保单数据中的保单到期时间，对客户进行客户类型划分，识别出保单到期时间小于或等于当前时间的续保失败客户，以及保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间。
由于现有的保单数据以及客户的名单数据的数据量过于庞大，而传统的电销系统处理效率低下，有时一整晚的时间都筛选不出续保失败的客户名单，使得传统的电销系统已经难以跟上实际的需求。本申请实施例中，为了提升对数据的处理速度，采用专业的大数据分析工具，来进行保单数据和名单数据的处理，同时，利用大数据分析工具具有丰富的数据筛选处理功能的特性，来作为后续多种客户类型分类识别的保障。
进一步地，在S101之前还包括：当识别出保单数据的存储系统为分布式文件系统时，选用Hive工具作为大数据分析工具。
在实际应用中，针对数据量较大的数据资料，一般存在集中式文件系统和分布式文件系统两种存储系统，其中集中式文件系统是指将数据资料存储在同一个网络节点的存储介质中，如将数据资料都存储在本地计算机存储盘中，而分布式文件系统，是指将数据资料分开存储在不同的网络节点中，如将数据资料存储在不同网络节点对应的服务器中。由于不同的存储系统，其读取/存储方式存在差异，因此，本申请实施例中针对不同的存储系统会选取不同的大数据分析工具。其中，Hive工具Hadoop的一个数据仓库工具，可以将结构化的数据文件映射为一张数据库表，并提供简单的结构化查询语言（SQL）查询功能，是分布式文件系统中的一种大数据分析工具。考虑到其具有学习成本地且分析处理速度较快等优点，为了提升对保单数据的处理速度，本申请实施例中，优选地，当保单数据的存储系统为分布式文件系统时，选用Hive工具作为大数据分析工具，来进行保单数据的分析处理。
实际操作中，在客户购买保险并签订保单后，保险公司会将客户保单中的基本信息，如客户的个人信息、保险类型、保单有效期、保险受益人以及业务办理人等基本信息存储为客户对应的保单数据。同时，还会将所有购买过保险的客户的个人信息，如姓名、手机号等，整理为一份总的客户名单数据进行存储，以便于工作人员日常工作的调用。其中，保单有效期包括保单起始时间以及保单到期时间，由于客户可能会在一份保单有效期到期后进行续保，因此每个客户都可能会有着对应的多份不同有效期的保单。在本申请实施例中，由于需要识别出相对当前时间，客户的保单有效期到期情况，并以此来进行客户类型的分类，故需要以客户最新的保单作为参考对象，因此，在本申请实施例中用来进行客户类型识别的保单到期时间，是指每个客户对应的所有保单中，有效期的保单起始时间离当前时间最近的一份保单中的保单到期时间。
在本申请实施例中，客户类型包括续保失败客户以及投保即将到期客户，由于两种客户的保单有效期的保单到期时间分别处于当前时间之前当前时间以及之后，因此可以根据保单有效期的保单到期时间来进行客户类型的区分识别。
其中，当保单中有效期的保单到期时间小于或等于当前时间时，即说明当前保单已经过了有效期。同时，由上述说明可知，本申请实施例中的保单到期时间，是指有效期的保单起始时间离当前时间最近的一份保单中的保单到期时间，因此，当保单中有效期的保单到期时间小于或等于当前时间时，即说明客户在当前时间并未进行续保，即属于所述续保失败客户。
保单到期时间与当前时间的差值，如保单到期时间为2017年10月30日，当前时间为2017年10月1日，则得到的差值为2017年10月30日-2017年10月1日=29天，再将该差值29天与预设时间进行比较。本申请实施例中，预设时间可由技术人员根据实际情况进行设定，但是由于S101中，是通过保单数据识别出投保即将到期的投保即将到期客户，因此，优选的，预设时间不宜过长，可选的，如将预设时间设置为20天。
作为本申请的一种具体实现方式，由于存储的保单数据时间跨度较大，但实际操作中，有时只是想知道某一特定时间点到当前时间内的客户的续保情况，如去年1月1日到当前时间内的所有客户的续保情况。此时，为了减小对保单数据以及名单数据的处理工作量，本申请实施例中，可以由工作人员设定一个保单数据的分析保单起始时间并进行输入，如设定分析保单起始时间为去年1月1日。在接收到工作人员输入的分析保单起始时间后，对所有有效期的保单到期时间在分析保单起始时间之前的保单数据进行滤除，即此时只需对有效期的保单到期时间在分析保单起始时间之后的保单数据进行分析处理。
S102，从续保失败客户对应的保单数据中提取出续保失败客户的个人信息数据，并根据个人信息数据生成续保失败客户名单。
在本申请实施例中，除了可以生成续保失败客户名单以外，还可以根据S101中识别出来的投保即将到期客户的保单数据，来生成相应的投保即将到期客户名单，以供工作人员在需要的时候使用。
本申请实施例中，通过保单到期时间对保单数据对应的客户进行客户类型分类，并分类得出每种客户类型对应的客户名单，相对现有技术仅能得出续保失败的客户名单而言，丰富了对保单数据处理的功能。
S103，将续保失败客户对应的续保失败客户名单，以及续保失败客户对应的保单数据进行输出。
在本申请实施例中，在得出续保失败客户名单之后，会将得出的续保失败客户名单以及其对应的保单数据输出给当前的正在进行操作的工作人员，以便工作人员根据续保失败客户名单以及其对应的保单数据来进行续保失败客户的回访，提高客户续保的成功率。
S104，读取投保即将到期客户的客户联系方式，根据投保即将到期客户对应的保单数据生成续保资讯信息，并根据客户联系方式对续保资讯信息进行推送。
本申请实施例中，客户联系方式包含在保单数据中的客户个人信息中，客户联系方式包括但不限于如客户的电话号码以及邮箱地址等。续保资讯信息包括客户当前保单的保单到期时间，以及与客户保险种类相关的一些保险资讯。投保即将到期客户的保单有效期虽然还没有到期，但也距离保单到期时间也非常接近了，因此，为了提高客户的续保成功率，在本申请实施例中，会向客户推送一些相关的续保资讯信息，在提醒客户其保单马上到期的同时，还给客户提供了一些保险资讯进行参考，以帮助客户进行及时续保。
在本申请实施例中，并未对续保资讯信息推送的具体方式进行限定，可由技术人员根据实际情况进行设定。优选地，应当结合具体的客户联系方式进行选取，如当客户联系方式包含邮箱地址时，可通过邮件的方式进行推送，当包含电话号码时，可通过短信的方式进行推送。
作为本申请的一个优选实施例，在进行续保资讯信息推送的同时，还可以将即将到期客户名单以及相应的保单数据进行输出至工作人员，以帮助工作人员及时跟踪掌握投保即将到期客户的投保状态，及时回访投保即将到期客户，以提高后续的客户续保成功率。
在本申请实施例中，为了进一步地提高客户续保成功率，除了对续保失败的客户进行保单数据处理识别以外，还增添了对投保即将到期的投保即将到期客户的识别，并通过续保资讯信息推送等方式来提醒和帮助投保即将到期客户进行续保，丰富了对保单数据分析处理的功能，提高了对客户的续保成功率。
作为本申请实施例二，如图2所示，在S103之后，还包括：
S201，接收用户输入的客户回访结果标记指令，并根据客户回访结果标记指令对续保失败客户名单中的续保失败客户进行回访标记，得到客户回访数据。
在将续保失败客户名单以及保单数据输出给工作人员之后，工作人员会根据这些信息对续保失败客户进行一一回访，以提高续保失败客户的续保率。其中，回访标记是指工作人员对续保失败客户进行回访后，根据回访的结果对续保失败客户进行分类标记，其中包含的回访标记种类以及具体意义如下：
失联标记，是指续保失败客户的联系方式已经失效，无法取得联系的续保失败客户；
放弃续保标记，是指在回访中明确表示不想再进行续保的续保失败客户；
续保成功标记，是指回访过程中，成功进行了续保的续保失败客户；
有意续保标记，是指回访过程中虽然没有进行续保，但表示有意愿进行续保的续保失败客户。
工作人员在对每个续保失败客户回访完成后，根据实际回访结果对续保失败客户进行标记，并输入相应的客户回访结果标记指令，即可完成对续保失败客户名单中的续保失败客户的回访标记。
S202，从客户回访数据中提取出回访标记为失联标记以及放弃续保标记的续保失败客户的回访失败名单。
S203，根据回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
失联标记和放弃续保标记都代表着此次回访的续保失败客户不会再进行续保，即工作人员对这些续保失败客户回访失败。将这些回访失败的续保失败客户整理出一份回访失败名单，再从存储好的总的客户名单数据中，将这些续保失败客户进行剔除，即可得出一份包含着正常在保的客户以及有意愿续保的客户的活跃投保名单。由于活跃投保名单中都是在保以及有意愿续保等投保较为活跃的客户，对保险公司保险业务的认可的相对较高，因此在得出活跃投保名单后，可将其输出给工作人员，以帮助工作人员进行后续的客户跟踪/推广工作。
作为本申请的一个实施例，在S203之后，还包括：接收用户输入的活动推送指令以及活动数据，并对活跃投保名单中的客户推送活动数据。在本申请实施例中，工作人员可以直接利用活跃投保名单来进行活动数据推送，以提升活动推广的效率。
作为S103的一种具体实现方式，作为本申请实施例三，如图3所示，包括：
S1031，读取出续保失败客户名单中每个续保失败客户分别对应的工作人员联系方式。
由于每个保单中都记录了该保单对应的客户信息以及工作人员（即办理保单的业务工作人员）信息，因此，本申请实施例根据保单数据能够直接获取到每个保单对应的工作人员信息，再通过保单中的工作人员信息确定出对应的工作人员联系方式，即可确定出续保失败客户名单中每个客户分别对应的工作人员联系方式。由于实际保单中存储的工作人员信息有时较为简单，如只包含工作人员的姓名和工号，此时，本申请实施例可以根据工作人员的姓名和工号，来对工作人员的详细信息进行查询，从而得出所需的工作人员联系方式。
S1032，根据续保失败客户名单中的每个续保失败客户对应的个人信息以及保单数据，生成每个续保失败客户对应的客户回访信息，并通过工作人员联系方式，将续保失败客户名单以及客户回访信息进行输出，以使得工作人员能接收到续保失败客户名单中与每个续保失败客户对应的客户回访信息。
本申请实施例中，考虑到和客户签署保单的工作人员与相互客户比较熟悉，进行回访续保的成功率相对较大，因此，在得出续保失败客户名单后，本申请实施例会对续保失败客户名单中的每个客户都生成一个对应的客户回访信息，并将该客户回访信息发送给与该客户对应的保单中记录的工作人员，以提升客户续保成功率。本申请实施例中，通过读取续保失败客户名单的出保单数据中与客户对应的工作人员的联系方式，并将由客户个人信息以及客户对应的保单数据生成的客户回访信息，通过工作人员联系方式一一发送给与客户对应的工作人员，进一步地丰富了对保单数据分析处理的功能，利用原工作人员回访客户续保的成功率相对较大特点，同时提升了客户续保成功率。
作为S104的一种具体实现方式，作为本申请实施例四，如图4所示，包括：
S1041，读取出投保即将到期客户对应的保单数据。
对每个投保即将到期客户对应保单数据，分别进行有效期的保单起始时间与当前时间最接近的保单的查找，并以查找出的保单作为每个投保即将到期客户所分别对应的保单数据。即在S1041中，每个投保即将到期客户对应的保单数据，仅包含一份有效期的保单起始时间与当前时间最接近的保单。
S1042，获取与投保即将到期客户对应的保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，N为正整数。
其中，与投保即将到期客户对应的保单数据相关的且与当前时间最为接近的N条保险资讯，是指对每个投保即将到期客户，在S1041确定出其对应的保单数据之后，读取该保单的保险种类，并从预设的保险资讯库中，找出与该保单的保险种类相同的，且更新时间与当前时间最接近的N条保险资讯。其中，N的具体数值需由技术人员根据实际需求进行设定，保险资讯包括但不限于如保险条款以及保险政策等信息。如当N为3，保单种类为疾病保险时，会从预设的保险资讯库中的疾病保险相关的保险资讯中，选取出最新的3条保险资讯。
续保提示信息，用于提醒客户其保单的有效期即将到期，需及时进行续保，包括但不限于文字、语音或视频等提示方式中的任意一种或多种。
S1043，读取投保即将到期客户对应的客户联系方式，根据客户联系方式，进行续保资讯信息的推送，续保资讯信息包括保单到期时间、续保提示信息以及N条保险资讯。
在得出每个投保即将到期客户对应的N条保险资讯以及续保提示信息之后，将保单到期时间、续保提示信息以及N条保险资讯作为最终所需推送的续保资讯信息，并根据每个投保即将到期客户的联系方式，进行一一推送。使得每个投保即将到期客户都能获取到与自己对应的续保资讯信息，获知自己保单的保单到期时间以及续保提醒，同时还能获取到与自己已投的保险相关的保险资讯。
在本申请实施例中，通过对投保即将到期客户进行相应的保险资讯获取，并生成相应的续保提示信息，再将保单到期时间、续保提示信息以及N条保险资讯作为续保资讯信息进行推送，以提醒和帮助投保即将到期的投保即将到期客户进行续保，丰富了对保单数据分析处理的功能提高了对客户的续保成功率。
作为本申请的一个优选实施例五，如图5所示，包括：
S105，当识别出保单到期时间大于当前时间时，判断保单数据中的保险金额是否大于预设金额。
在本申请实施例一中，按照保单数据中保单到期时间与当前时间的大小关系将投保过的客户进行了客户类型区分，并提出了续保失败的续保失败客户以及投保即将到期的投保即将到期客户的概念。但实际情况中，除了上述两种客户类型以外，在保单的有效期保单到期时间大于当前时间的客户中，还存在着一类较为特殊的，保险金额较大、进行续保且续保较为及时的优质客户，该优质客户又被称为优质客户。由于优质客户对保单的认可度较高，同时相对一般的客户，具有更强的续保意识，因此，对优质客户的及时跟踪回访，可以极大的提升优质客户的续保成功率。在本申请实施例中，为了保证对优质客户的及时跟踪，以提升优质客户的续保成功率，会利用从保单的有效期保单到期时间大于当前时间的客户中进行优质客户的筛选，并将优质客户的相关信息进行输出，以供工作人员对优质客户的及时跟踪。
在本申请实施例中，首先会通过保单数据中的保险金额，对存储的保单进行筛选，以找出保险金额较大的保单以及相应的客户。其中，预设金额的具体值，需由技术人员根据实际情况进行确定并设置。
S106，对保险金额大于预设金额的保单数据进行续保及时率的计算，识别出所有续保及时率大于预设阈值的优质客户，并得出优质客户对应的优质客户名单。
其中，续保及时率的计算的具体方法为：对S105筛选出的每个客户对应的保单，进行续保时间的查询，并读取出续保时间前的保单有效期的保单到期时间。根据公式：续保及时率=1-（续保时间-保单到期时间）/计划续保周期，来进行筛选出的每个客户的续保及时率的分别计算。其中，计划续保周期是预先设置的客户在投保到期后再次续保的缓冲时间，具体的需由技术人员根据实际需求进行设定，在本申请实施例中，优选地，将该计划续保周期设置为30天。以保单到期时间为2017年6月1日、续保时间为2017年6月10日以及计划续保周期为30天为例进行说明，此时续保及时率=1-（2017年6月10日-2017年6月1日）/30天，即续保及时率=1-9/30=70%。
应当理解地，虽然在保单的有效期保单到期时间大于当前时间的客户中，存在着一些保险金额大于预设金额的首次投保的新客户，但由于仅仅只有续保过的客户的保单中，存在续保时间的数据，因此对于新客户而言，其对应的保单中，不存在续保时间这一数据。因此，在本申请实施例中，无需担心会将这些新客户误识别为续保过的优质客户。在得出客户的续保及时率之后，将续保及时率与预设阈值进行比较，并将续保及时率大于预设阈值的客户认定为优质客户，生成优质客户对应的优质客户名单。
S107，生成优质客户提示信息，并将优质客户名单、优质客户对应的保单数据以及优质客户提示信息进行输出。
在得出优质客户对应的优质客户名单后，需要将该优质客户名单以及每个优质客户对应的保单数据等优质客户的相关信息，都输出至工作人员。同时为了使工作人员知道这些信息是优质客户的相关信息，需要及时跟踪，本申请实施例中，还会同时生产并输出优质客户提示信息。
作为S107的一种具体实现方式，可参考本申请实施例三，来将优质客户的相关信息进行对应工作人员的一一输出，以提升优质客户的续保成功率。
在本申请实施例中，通过对优质客户识别并对工作人员输出相应的优质客户相关信息以及提示信息，使得工作人员能对优质客户进行及时有效的跟踪回访，从而提升优质客户的续保成功率，丰富了对保单数据的分析处理功能。
在本申请实施例中，通过Hive等大数据分析工具，来对数据量庞大的保单数据以及名单数据进行处理，极大的提高了对保单数据处理的效率。同时，通过保单数据来对客户进行三种不同客户类型的分类，并针对每种客户类型的特点来进行客户信息输出或续保资讯信息的推送的分别处理，以帮助工作人员对客户的及时跟踪回访，提醒帮助客户进行续保，极大的丰富了对保单数据处理分析的功能，提升了客户的续保成功率。通过提升对保单数据的处理数据，以及丰富对保单数据的分析处理功能，使得对保单数据分析处理的智能化程度得到了极大地提高。
对应于上文实施例的方法，图6示出了本申请实施例提供的保单数据处理装置结构框图，为了便于说明仅示出了与本申请实施例相关的部分。图6示例的保单数据处理装置可以是前述实施例一提供的保单数据处理方法的执行主体。
参照图6，该保单数据处理装置包括：
类型识别模块61，用于利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间。
第一名单获取模块62，用于从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单。
名单输出模块63，用于将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出。
资讯推送模块64，用于读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
进一步地，该保单数据处理装置，还包括：
回访标记模块，由于接收用户输入的客户回访结果标记指令，并根据所述客户回访结果标记指令对所述续保失败客户名单中的所述续保失败客户进行回访标记，得到客户回访数据。
名单生成模块，由于从所述客户回访数据中提取出回访标记为失联标记以及放弃续保标记的所述续保失败客户的回访失败名单。
名单筛选模块，由于根据所述回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
进一步地，名单输出模块63，包括：
联系确定子模块，用于读取出所述续保失败客户名单中每个所述续保失败客户分别对应的工作人员联系方式。
保单输出子模块，用于根据所述续保失败客户名单中的每个所述续保失败客户对应的个人信息以及所述保单数据，生成每个所述续保失败客户对应的客户回访信息，并通过所述工作人员联系方式，将所述续保失败客户名单以及所述客户回访信息进行输出，以使得工作人员能接收到所述续保失败客户名单中与每个所述续保失败客户对应的所述客户回访信息。
进一步地，资讯推送模块64，包括：
保单识别子模块，用于读取出所述投保即将到期客户对应的所述保单数据。
信息生成子模块，用于获取与所述投保即将到期客户对应的所述保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，所述N为正整数。
资讯推送子模块，用于读取所述投保即将到期客户对应的客户联系方式，根据所述客户联系方式，进行续保资讯信息的推送，所述续保资讯信息包括所述保单到期时间、所述续保提示信息以及所述N条保险资讯。
进一步地，该保单数据处理装置，还包括：
保额判断模块，用于当识别出所述保单到期时间大于当前时间时，判断所述保单数据中的保险金额是否大于预设金额。
第二名单获取模块，用于对所述保险金额大于所述预设金额的所述保单数据进行续保及时率的计算，识别出所有所述续保及时率大于预设阈值的优质客户，并得出所述优质客户对应的优质客户名单。
信息输出模块，用于生成优质客户提示信息，并将所述优质客户名单、所述优质客户对应的所述保单数据以及所述优质客户提示信息进行输出。
本实施例提供的保单数据处理装置中各模块实现各自功能的过程，具体可参考前述图1所示实施例一的描述，此处不再赘述。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
还将理解的是，虽然术语“第一”、“第二”等在文本中在一些本申请实施例中用来描述各种元素，但是这些元素不应该受到这些术语的限制。这些术语只是用来将一个元素与另一元素区分开。例如，第一接触可以被命名为第二接触，并且类似地，第二接触可以被命名为第一接触，而不背离各种所描述的实施例的范围。第一接触和第二接触都是接触，但是它们不是同一接触。
图7是本申请一实施例提供的保单数据处理电子设备的示意图。如图7所示，该实施例的保单数据处理电子设备7包括：处理器70、存储器71以及存储在所述存储器71中并可在所述处理器70上运行的计算机可读指令72，例如计算机可读指令。所述处理器70执行所述计算机可读指令72时实现上述各个保单数据处理方法实施例中的步骤，例如图1所示的步骤101至104。或者，所述处理器70执行所述计算机可读指令72时实现上述各装置实施例中各模块/单元的功能，例如图6所示模块61至64的功能。
所述保单数据处理电子设备7可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述保单数据处理电子设备可包括，但不仅限于，处理器70、存储器71。本领域技术人员可以理解，图7仅仅是保单数据处理电子设备7的示例，并不构成对保单数据处理电子设备7的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如所述保单数据处理电子设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器70可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器 (Digital Signal Processor，DSP)、专用集成电路 (Application Specific Integrated Circuit，ASIC)、现成可编程门阵列 (Field-Programmable Gate Array，FPGA) 或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
所述存储器71可以是所述保单数据处理电子设备7的内部存储单元，例如保单数据处理电子设备7的硬盘或内存。所述存储器71也可以是所述保单数据处理电子设备7的外部存储设备，例如所述保单数据处理电子设备7上配备的插接式硬盘，智能存储卡（Smart Media Card， SMC），安全数字（Secure Digital， SD）卡，闪存卡（Flash Card）等。进一步地，所述存储器71还可以既包括所述保单数据处理电子设备7的内部存储单元也包括外部存储设备。所述存储器71用于存储所述计算机可读指令以及所述保单数据处理电子设备所需的其他程序和数据。所述存储器71还可以用于暂时存储已经输出或者将要输出的数据。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述或记载的部分，可以参见其它实施例的相关描述。
在本申请所提供的实施例中，应该理解到，所揭露的装置/电子设备和方法，可以通过其它的方式实现。例如，以上所描述的装置/电子设备实施例仅仅是示意性的，例如，所述模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一计算机可读存储介质中，该计算机可读指令在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机可读指令包括计算机可读指令代码，所述计算机可读指令代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器（ROM，Read-Only Memory）、随机存取存储器（RAM，Random Access Memory）、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (20)

  1. 一种保单数据处理方法，其特征在于，包括：
    利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
    从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
    将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
    读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
  2. 如权利要求1所述的保单数据处理方法，其特征在于，在将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出之后，还包括：
    接收用户输入的客户回访结果标记指令，并根据所述客户回访结果标记指令对所述续保失败客户名单中的所述续保失败客户进行回访标记，得到客户回访数据；
    从所述客户回访数据中提取出回访标记为失联标记以及放弃续保标记的所述续保失败客户的回访失败名单；
    根据所述回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
  3. 如权利要求1所述的保单数据处理方法，其特征在于，所述将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出，包括：
    读取出所述续保失败客户名单中每个所述续保失败客户分别对应的工作人员联系方式；
    根据所述续保失败客户名单中的每个所述续保失败客户对应的个人信息以及所述保单数据，生成每个所述续保失败客户对应的客户回访信息，并通过所述工作人员联系方式，将所述续保失败客户名单以及所述客户回访信息进行输出，以使得工作人员能接收到所述续保失败客户名单中与每个所述续保失败客户对应的所述客户回访信息。
  4. 如权利要求1所述的保单数据处理方法，其特征在于，所述读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送，包括：
    读取出所述投保即将到期客户对应的所述保单数据；
    获取与所述投保即将到期客户对应的所述保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，所述N为正整数；
    读取所述投保即将到期客户对应的客户联系方式，根据所述客户联系方式，进行续保资讯信息的推送，所述续保资讯信息包括所述保单到期时间、所述续保提示信息以及所述N条保险资讯。
  5. 如权利要求1所述的保单数据处理方法，其特征在于，还包括：
    当识别出所述保单到期时间大于当前时间时，判断所述保单数据中的保险金额是否大于预设金额；
    对所述保险金额大于所述预设金额的所述保单数据进行续保及时率的计算，识别出所有所述续保及时率大于预设阈值的优质客户，并得出所述优质客户对应的优质客户名单；
    生成优质客户提示信息，并将所述优质客户名单、所述优质客户对应的所述保单数据以及所述优质客户提示信息进行输出。
  6. 一种保单数据处理装置，其特征在于，包括：
    类型识别模块，用于利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
    第一名单获取模块，用于从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
    名单输出模块，用于将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
    资讯推送模块，用于读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
  7. 如权利要求6所述的保单数据处理装置，其特征在于，还包括：
    回访标记模块，由于接收用户输入的客户回访结果标记指令，并根据所述客户回访结果标记指令对所述续保失败客户名单中的所述续保失败客户进行回访标记，得到客户回访数据；
    名单生成模块，由于从所述客户回访数据中提取出回访标记为失联标记以及放弃续保标记的所述续保失败客户的回访失败名单；
    名单筛选模块，由于根据所述回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
  8. 如权利要求6所述的保单数据处理装置，其特征在于，所述名单输出模块，包括：
    联系确定子模块，用于读取出所述续保失败客户名单中每个所述续保失败客户分别对应的工作人员联系方式；
    保单输出子模块，用于根据所述续保失败客户名单中的每个所述续保失败客户对应的个人信息以及所述保单数据，生成每个所述续保失败客户对应的客户回访信息，并通过所述工作人员联系方式，将所述续保失败客户名单以及所述客户回访信息进行输出，以使得工作人员能接收到所述续保失败客户名单中与每个所述续保失败客户对应的所述客户回访信息。
  9. 如权利要求6所述的保单数据处理装置，其特征在于，所述资讯推送模块，包括：
    保单识别子模块，用于读取出所述投保即将到期客户对应的所述保单数据；
    信息生成子模块，用于获取与所述投保即将到期客户对应的所述保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，所述N为正整数；
    资讯推送子模块，用于读取所述投保即将到期客户对应的客户联系方式，根据所述客户联系方式，进行续保资讯信息的推送，所述续保资讯信息包括所述保单到期时间、所述续保提示信息以及所述N条保险资讯。
  10. 如权利要求6所述的保单数据处理装置，其特征在于，还包括：
    保额判断模块，用于当识别出所述保单到期时间大于当前时间时，判断所述保单数据中的保险金额是否大于预设金额；
    第二名单获取模块，用于对所述保险金额大于所述预设金额的所述保单数据进行续保及时率的计算，识别出所有所述续保及时率大于预设阈值的优质客户，并得出所述优质客户对应的优质客户名单；
    信息输出模块，用于生成优质客户提示信息，并将所述优质客户名单、所述优质客户对应的所述保单数据以及所述优质客户提示信息进行输出。
  11. 一种保单数据处理电子设备，其特征在于，所述保单数据处理处理电子设备包括存储器、处理器，所述存储器上存储有可在所述处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现如下步骤：
    利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
    从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
    将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
    读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
  12. 如权利要求11所述的保单数据处理电子设备，其特征在于，在将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出之后，还包括：
    接收用户输入的客户回访结果标记指令，并根据所述客户回访结果标记指令对所述续保失败客户名单中的所述续保失败客户进行回访标记，得到客户回访数据；
    从所述客户回访数据中提取出回访标记为失联标记以及放弃续保标记的所述续保失败客户的回访失败名单；
    根据所述回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
  13. 如权利要求11所述的保单数据处理电子设备，其特征在于，所述将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出，包括：
    读取出所述续保失败客户名单中每个所述续保失败客户分别对应的工作人员联系方式；
    根据所述续保失败客户名单中的每个所述续保失败客户对应的个人信息以及所述保单数据，生成每个所述续保失败客户对应的客户回访信息，并通过所述工作人员联系方式，将所述续保失败客户名单以及所述客户回访信息进行输出，以使得工作人员能接收到所述续保失败客户名单中与每个所述续保失败客户对应的所述客户回访信息。
  14. 如权利要求11所述的保单数据处理电子设备，其特征在于，所述读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送，包括：
    读取出所述投保即将到期客户对应的所述保单数据；
    获取与所述投保即将到期客户对应的所述保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，所述N为正整数；
    读取所述投保即将到期客户对应的客户联系方式，根据所述客户联系方式，进行续保资讯信息的推送，所述续保资讯信息包括所述保单到期时间、所述续保提示信息以及所述N条保险资讯。
  15. 如权利要求11所述的保单数据处理电子设备，其特征在于，还包括：
    当识别出所述保单到期时间大于当前时间时，判断所述保单数据中的保险金额是否大于预设金额；
    对所述保险金额大于所述预设金额的所述保单数据进行续保及时率的计算，识别出所有所述续保及时率大于预设阈值的优质客户，并得出所述优质客户对应的优质客户名单；
    生成优质客户提示信息，并将所述优质客户名单、所述优质客户对应的所述保单数据以及所述优质客户提示信息进行输出。
  16. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，其特征在于，所述计算机可读指令被至少一个处理器执行时实现如下步骤：
    利用大数据分析工具，根据客户的保单数据中的保单到期时间，对所述客户进行客户类型划分，识别出所述保单到期时间小于或等于当前时间的续保失败客户，以及所述保单到期时间与当前时间的差值小于预设时间的投保即将到期客户，所述保单到期时间是指每个客户对应的所有保单数据中，保单有效期的保单起始时间与当前时间最接近的一份保单数据中的保单到期时间；
    从所述续保失败客户对应的所述保单数据中提取出所述续保失败客户的个人信息数据，并根据所述个人信息数据生成续保失败客户名单；
    将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出；
    读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送。
  17. 如权利要求16所述的计算机可读存储介质，其特征在于，在将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出之后，还包括：
    接收用户输入的客户回访结果标记指令，并根据所述客户回访结果标记指令对所述续保失败客户名单中的所述续保失败客户进行回访标记，得到客户回访数据；
    从所述客户回访数据中提取出回访标记为失联标记以及放弃续保标记的所述续保失败客户的回访失败名单；
    根据所述回访失败名单对存储的客户名单数据进行筛选，并将得出活跃投保名单进行存储。
  18. 如权利要求16所述的计算机可读存储介质，其特征在于，所述将所述续保失败客户对应的所述续保失败客户名单，以及所述续保失败客户对应的所述保单数据进行输出，包括：
    读取出所述续保失败客户名单中每个所述续保失败客户分别对应的工作人员联系方式；
    根据所述续保失败客户名单中的每个所述续保失败客户对应的个人信息以及所述保单数据，生成每个所述续保失败客户对应的客户回访信息，并通过所述工作人员联系方式，将所述续保失败客户名单以及所述客户回访信息进行输出，以使得工作人员能接收到所述续保失败客户名单中与每个所述续保失败客户对应的所述客户回访信息。
  19. 如权利要求16所述的计算机可读存储介质，其特征在于，所述读取所述投保即将到期客户的客户联系方式，根据所述投保即将到期客户对应的所述保单数据生成续保资讯信息，并根据所述客户联系方式对所述续保资讯信息进行推送，包括：
    读取出所述投保即将到期客户对应的所述保单数据；
    获取与所述投保即将到期客户对应的所述保单数据相关的，且与当前时间最为接近的N条保险资讯，并生成续保提示信息，所述N为正整数；
    读取所述投保即将到期客户对应的客户联系方式，根据所述客户联系方式，进行续保资讯信息的推送，所述续保资讯信息包括所述保单到期时间、所述续保提示信息以及所述N条保险资讯。
  20. 如权利要求16所述的计算机可读存储介质，其特征在于，还包括：
    当识别出所述保单到期时间大于当前时间时，判断所述保单数据中的保险金额是否大于预设金额；
    对所述保险金额大于所述预设金额的所述保单数据进行续保及时率的计算，识别出所有所述续保及时率大于预设阈值的优质客户，并得出所述优质客户对应的优质客户名单；
    生成优质客户提示信息，并将所述优质客户名单、所述优质客户对应的所述保单数据以及所述优质客户提示信息进行输出。
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