KR101645992B1 - Apparatus for monitering customer complaint and computer-readable medium thereof - Google Patents

Abstract

Disclosed is a customer complaint monitoring apparatus and a computer-readable medium capable of analyzing big data and numerically managing the degree of customer's dissatisfaction.
The computer-readable medium stores instructions for causing a computer to perform the following operations: collecting customer complaint data via one or more routes; Grouping the collected customer complaint data by customers to form a customer complaint data group; Assigning weights to each of the customer complaint data in the group; And calculating an amount of complaints of the customer based on the weight.

Description

[0001] APPARATUS FOR MONITORING CUSTOMER COMPLEMENT AND COMPUTER READABLE MEDIUM THEREOF [0002]

The present invention relates to an insurance-related invention, more specifically, an invention for efficiently monitoring customer complaints using Big Data.

With the growth of the Internet and mobile industries, big data environments are emerging, meaning large, fixed or unstructured data that can be collected, managed, stored and analyzed with existing database management tools. Big data is a vast amount of data of various types generated in the digital environment, and it is considered as a core resource that can secure future competitiveness of companies and the like.

Also, extracting economically necessary value from big data can be referred to as big data technology. Utilizing Big Data can create various forms of value, such as establishing marketing strategies, optimizing processes, improving productivity and preventing fraud. Therefore, big data technology for effectively utilizing big data can be considered as a means of securing corporate competitiveness that should be considered first in the infinite competition age regardless of the type of enterprise.

Therefore, even in the insurance industry, big data can be used to secure the competitiveness of a company. For example, an insurance company can use this big data for customer management. For customer management, prompt and accurate response to customer complaints may be required. Such customer management can prevent departure of subscribers.

Customer management means resolving customer complaints to prevent customers from leaving, or managing black consumers or insurance fraud through blacklist management.

Currently, the domestic insurance industry is in a state of saturation, and it is difficult to attract new customers, so the so-called "customer dispatch" is prevalent. In such a reality, insurance companies are making enormous efforts to mobilize such personnel as dedicated departments to prevent departure of customers.

In general, if a customer makes a complaint but the appropriate action is not taken, the customer is likely to take another action, rather than repeatedly complaining in the same way over and over again. However, with the existing management system, it is impossible to manage such cases. Therefore, there have been cases where customers are managed only by the number of times of complaints, and the customer management is separated from reality.

As noted above, if you fail to maintain adequate customer care, the insurer will suffer economic loss due to customer disruption, economic loss from black consumers and insurance fraudsters, and loss of brand value due to improper response to rumor There is a need in the insurance industry for a customer management system that can prevent these damages.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-described problems, and is intended to manage complaints of customers by utilizing big data.

In addition, the present invention is intended to enable a manager to easily grasp a customer's complaint by storing the customer's complaints numerically.

In addition, the present invention is intended to provide practitioners and managers with easier understanding of customer complaints by providing appropriate feedback on the degree of numerical customer complaints.

Further, the present invention is intended to facilitate the management of the insurance company by utilizing the big data.

A computer-readable medium according to an embodiment of the present invention for realizing the above-mentioned problems is disclosed. The computer-readable medium stores instructions for causing a computer to perform the following operations: collecting customer complaints data via one or more routes; Grouping the collected customer complaint data by customers to form a customer complaint data group; Assigning weights to each of the customer complaint data in the group; And calculating an amount of complaints of the customer based on the weight.

A customer complaint monitoring device is disclosed in accordance with an embodiment of the present invention. The apparatus comprises: a receiving module for collecting customer complaints data via one or more routes; A control module for grouping the collected customer complaints data by customers to form a customer complaint data group, assigning a weight to each of the customer complaint data in the group, and calculating a degree of complaint of the customer based on the weight; And a database for storing the degree of complaints of the customer.

A computer-readable medium according to another embodiment of the present invention for realizing the above-described problems is disclosed. The computer-readable medium stores instructions for causing a computer to perform the following operations: collecting customer complaints data via one or more routes; Extracting keywords from the collected customer complaint data; Classifying the complaint type based at least in part on the extracted keyword; Grouping the collected customer complaint data by customers to form a customer complaint data group; Assigning weights to each of the customer complaint data in the group; And calculating an amount of complaints of the customer based on the weight.

A customer complaint monitoring device is disclosed in accordance with another embodiment of the present invention. The apparatus comprises: a receiving module for collecting customer complaints data via one or more routes; Extracting a keyword from the collected customer complaint data, classifying the complaint type based at least in part on the extracted keyword, grouping the collected customer complaint data for each customer to form a customer complaint data group, A control module for assigning a weight to each of the customer complaint data in the client and calculating a degree of complaint of the customer based on the weight; And a database for storing the degree of complaints of the customer.

According to an embodiment of the present invention, the complaint of the customer can be managed by utilizing the big data.

According to the embodiment of the present invention, the complaint of the customer can be numerically stored and stored, so that the manager can easily grasp the complaint of the customer.

According to one embodiment of the present invention, by providing appropriate feedback on the degree of numerical customer complaints, practitioners and managers can more easily grasp the customer complaints.

According to the embodiment of the present invention, the insurance company can easily manage the customers by utilizing the big data.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art from the following description.

Various aspects are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following examples, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will be apparent that such aspect (s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects.
Figure 1 illustrates a financial system in accordance with an embodiment of the present invention.
Figure 2 illustrates components of a server in accordance with an embodiment of the present invention.
Figure 3 illustrates information or data stored in a database in accordance with one embodiment of the present invention.
4 schematically illustrates operation of a server according to an embodiment of the present invention.
FIG. 5 illustrates an example of a keyword extraction and weighting method according to an embodiment of the present invention.
FIG. 6 illustrates an example of a keyword extraction and weighting method according to another embodiment of the present invention.
FIG. 7 illustrates a method of managing a customer according to an embodiment of the present invention.

Various embodiments and / or aspects are now described with reference to the drawings. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects. However, it will also be appreciated by those of ordinary skill in the art that such aspect (s) may be practiced without these specific details. The following description and the annexed drawings set forth in detail certain illustrative aspects of one or more aspects. It is to be understood, however, that such aspects are illustrative and that some of the various ways of practicing various aspects of the principles of various aspects may be utilized, and that the description set forth is intended to include all such aspects and their equivalents.

In addition, various aspects and features will be presented by a system that may include multiple devices, components and / or modules, and so forth. It should be understood that the various systems may include additional devices, components and / or modules, etc., and / or may not include all of the devices, components, modules, etc. discussed in connection with the drawings Must be understood and understood.

As used herein, the terms "an embodiment," "an embodiment," " an embodiment, "" an embodiment ", etc. are intended to indicate that any aspect or design described is better or worse than other aspects or designs. . As used herein, the terms 'component,' 'module,' 'system,' 'interface,' and the like generally refer to a computer-related entity and include, for example, hardware, It can mean software.

In addition, the term "or" is intended to mean " exclusive or " That is, it is intended to mean one of the natural inclusive substitutions "X uses A or B ", unless otherwise specified or unclear in context. That is, X uses A; X uses B; Or when X uses both A and B, "X uses A or B" can be applied to either of these cases. It should also be understood that the term "and / or" as used herein refers to and includes all possible combinations of one or more of the listed related items.

It is also to be understood that the term " comprises "and / or" comprising " means that the feature and / or component is present, but does not exclude the presence or addition of one or more other features, components and / It should be understood that it does not. Also, unless the context clearly dictates otherwise or to the contrary, the singular forms in this specification and claims should generally be construed to mean "one or more. &Quot;

As used herein, the terms "information" and "data" are often used interchangeably.

Figure 1 illustrates a financial system 100 in accordance with an embodiment of the present invention.

As an example, the financial system 100 herein may be a system for generating and providing insurance information and for managing customers.

1, the financial system 100 may be largely comprised of a server 110, a user terminal 120, an enterprise terminal 130, and a network 140 .

It should also be appreciated that the components that make up the financial system 100 shown in Figure 1 are illustrative and that only some of the components may constitute the financial system 100 or that additional components Or may be included in the system 100. For example, although not shown, the server 110 may include an Enterprise Application Integration (EAI) server, a customer identification information server, an insurance payment server, a call center server, a voice of customer (VOC) server, Server, and the like. For example, server 110 in accordance with an aspect of the present invention may include a call center server (not shown) so that the information and / or data collected from the server may include information and / or data collected from the call center server . In addition, the server can collect information and / or data collected from the call center server by converting from voice to a string through a Speech to Text (STT) engine. As another example, the enterprise terminal 130 may be included in the user terminal 120. In one aspect of the present invention, the information collected and generated by the server 110 may be referred to as "Big Data ".

The server 110 is capable of communicating with the user terminal 120, the enterprise terminal 130 and the Web, collecting customer complaint data, extracting and storing the customer complaint related keywords, quantifying the customer complaint data, Store, store and distribute applications for terminal applications, and design insurance products to be designed and stored, as well as information about the customer, Decision on the insurance product to be sold, generation of financial consultant education information, adjustment of the intensity of insurance payment review, and creation of recommendation information of the insurance product.

The user terminal 120 may be configured to communicate with the server 110 and / or the enterprise terminal 130, inquire about various types of information (e.g., customer identification information, customer complaint information, calculated customer complaints) Generation of contract information for the insurance product, and the like.

The enterprise terminal 130 can perform communication with the server 110 and the user terminal 120, and the like. In addition, the enterprise terminal 130 may perform the operations performed in the server 110 described above.

In addition, the network 140 may form a channel for data communication between the server 110, the user terminal 120, and the enterprise terminal 130.

The terms "system" and "network ", as used herein, are often used interchangeably.

The systems presented here are public switched telephone network (PSTN), x digital subscriber line (xDSL), rate adaptive DSL (RADSL), multi rate DSL (MDSL), very high speed DSL (VDSL) Asymmetric DSL), High Bit Rate DSL (DSL), and Local Area Network (LAN).

In addition, the system described herein can be applied to a mobile communication system such as Code Division Multi Access (CDMA), Time Division Multi Access (TDMA), Frequency Division Multi Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single Carrier- Various wireless communication systems such as other systems may be used.

The techniques described herein may be used in other systems as well as the systems mentioned above.

The network 140 according to one aspect of the present invention may be configured without discriminating the communication modes such as wired and wireless, and may be connected to various communication networks such as a personal area network (PAN), a wide area network (WAN) . Also, the network 140 may be a known World Wide Web (WWW) or may use a wireless transmission technology used for short-distance communication such as infrared (IrDA) or Bluetooth have.

Returning to Figure 1, the user terminal 120 may access the server 110 or communicate with the enterprise terminal 130 to enter into a contract for a financial instrument (e.g., an insurance instrument) , Contract information, identification information, insurance payment information, information on the insurance product to be designed, information on the insurance product to be sold, information on the strength of the FC education information and insurance payment review, customer identification information, Quot ;, " device ", " device ", etc. < / RTI >

Such a user terminal 120 may be a user equipment, a mobile, a PC, a mobile phone, a kiosk, a cellular phone, a cellular, a cellular terminal, a subscriber unit, a subscriber station, a mobile station, a terminal, a remote station, a PDA, Any device that uses a wireless connection mechanism, such as a terminal, user agent, cellular telephone, wireless telephone, Session Initiation Protocol (SIP) telephone, wireless local loop (WLL) station, And the like, but are not limited thereto.

Such a user terminal 120 may also be referred to as any device capable of using a wired connection mechanism such as a wired facsimile, a PC with a wired modem, a wired telephone, a terminal capable of wired communication, It does not.

In a further aspect of the present invention, the user terminal 120 may be used by an insurance company employee or an insurance planner (FC). In such a case, the user terminal 120 may be referred to as an enterprise terminal 130, a window terminal, an FC terminal, or the like. The present invention will be referred to as an enterprise terminal 130.

The enterprise terminal 130 may include devices capable of using a wireless connection mechanism or a wired connection mechanism of the same or similar type as the user terminal described above.

In addition, an additional terminal by another user (for example, an employee of a company other than a financial company) other than the above-mentioned user may also be included in the user terminal 120. [

In one aspect of the present invention, the enterprise terminal 130 may refer to a terminal that can be used by those associated with a financial company, such as an insurance company (e.g., FC associated with an insurance company employee and / or the insurance company). That is, according to one aspect of the present invention, a particular terminal that is relevant to an insurance company (e.g., server 110 of an insurance company) may be referred to as enterprise terminal 130. Additionally, the enterprise terminal 130 may be included in the user terminal 120.

The enterprise terminal 130 may also be a personal computer, a mobile phone, a kiosk, a cellular phone, a cellular phone, a cellular terminal, a subscriber unit, a subscriber station, a mobile station, a terminal, Such as a cellular telephone, a wireless telephone, a Session Initiation Protocol (SIP) telephone, a wireless local loop (WLL) station, a handheld device having a wireless connection capability, a wireless modem, Or any device using a wireless connection mechanism, and the like. Further, the enterprise terminal 130 may be referred to as, but is not limited to, any device capable of using a wired connection mechanism, such as a wired fax, a PC with a wired modem, a wired telephone, .

In one aspect of the present invention, the server 110 may provide different services for each of the user terminals by the various users. That is, the user terminal used by the customer and the enterprise terminal 130 used by the financial company employee are allowed to access the specific server among the plurality of insurance company servers, respectively, according to the insurance company's policy and / Or may be limited. Alternatively, the user terminal 120 may be permitted or restricted to access specific data of the insurance company server according to the policies and / or user settings of the insurance company.

For example, in accordance with the policy of the insurance company, the enterprise terminal 130 may be provided with insurance design, financial product identification and registration, customer identification, insurance claim, insurance payment, Decision about insurance product to be sold, inquiry of customer's consultation contents, inquiry about degree of numerical customer complaint, inquiry about customer complaint data, inquiry about high-risk customers who are leaving high-risk customers, inquiry about black list, FC training And may be configured to access all the data necessary to perform tasks such as generating information and adjusting the intensity of the insured payment review.

In addition, search and search result screens may be different in units of performing units of the user terminal 120 and / or the enterprise terminal 130, depending on policies and / or user settings of the insurance company.

Alternatively, a terminal used by a customer of the user terminal 120 may only access limited data such as subscription information, contract information, customer consent or authorization related information, insurance claims, insurance payment receipt, insurance product information, and / Or may be set to be.

Furthermore, a terminal used by an insurance agent (FC) may have access to the data necessary to perform tasks such as insurance design, but may be able to access certain data (e.g., data for customers other than a particular insurance planner's customer) Access may be restricted, unlike a general enterprise terminal 130 used by a financial company employee.

In an aspect of the present invention, the server 110, the user terminal 120, and the enterprise terminal 130 can transmit and receive various information through the network 140. [

For example, in accordance with the policy of the insurance company, the enterprise terminal 130 can provide various information such as insurance design, verification and registration of financial products, confirmation of the customer's identity, insurance claim, insurance payment, insurance payment, Determination of insurance product to be designed, decision on insurance product to be sold, inquiry of customer's consultation, inquiry about numerical customer complaint, inquiry about customer complaint data, inquiry of high risk customer, This can mean information needed to perform tasks such as generating inquiry FC training information and adjusting the intensity of the claims audit.

The above-described information may be input via the user terminal 120 and / or the enterprise terminal 130, or may be generated by the server 110. [ The above-described information may be stored in a database of the server 110 or a computer-readable storage medium. According to an aspect of the present invention, a computer-readable storage medium may be included within the user terminal 120 and / or the enterprise terminal 130.

Such storage media may include any type of storage medium in which programs and data are stored so that they can be read by a computer system. According to one aspect of the present invention, such medium may be a ROM (Read Only Memory), a RAM (Random Access Memory), a CD (Compact Disk) -ROM, a DVD Storage devices, and the like, and may also be implemented in the form of a carrier wave (e.g., transmission over the Internet). Additionally, such media may be distributed over a system coupled to the network 140 and may store computer readable codes and / or instructions in a distributed manner.

In an embodiment of the present invention, a customer, a finance company employee, and / or an insurance agent (FC), when a particular application distributed by a particular insurance company is installed and executed in the user terminal 120 and / The server 110 can communicate with the user terminal 120 and / or the enterprise terminal 130 via the network 140 so that the user can enjoy various services.

Figure 2 illustrates components of a server in accordance with an embodiment of the present invention.

A server according to an aspect of the present invention may include a call center server, a VOC server, a customer complaint management server web collection server, a Speech To Text (STT) server, and a combination thereof.

According to an aspect of the present invention, the server 110 may include a receiving module 201, a transmitting module 203, a controlling module 205, and a database 207.

The components of server 110 in FIG. 2 are exemplary and only some of the components may constitute server 110, or additional components (s) may be included in server 110 in addition to the components have.

Additionally, in accordance with an aspect of the present invention, the components shown in FIG. 2 may be components of user terminal 120 and / or enterprise terminal 130. In this case, the control module and database of the user terminal 120 and / or enterprise terminal 130 may perform at least a portion of the operations and functions of the server's control module and database, as described below. Alternatively, the receiving module of the user terminal 120 and / or the enterprise terminal 130 may receive the information and data required from the server 110. The display of the user terminal 120 and / or the enterprise terminal 130 may display information received from the server and / or generated by its control module. Alternatively, at least a portion of the components of the server 110 described below may be replaced by components of the user terminal 120 and / or enterprise terminal 130.

The receiving module 201 may receive information or data transmitted from the user terminal 120 or the enterprise terminal 130 via the network 140. [ According to an aspect of the invention, the receiving module 201 may collect customer complaints data via one or more routes. The received customer complaints data may be grouped by customer and stored in the database 207. Wherein the at least one route comprises at least one of the Internet, a telephone and a face-to-face contact route, the Internet route comprising at least one of a website provided by a financial institution, a website provided by a financial regulator, and a Social Network Service can do. For example, the customer complaints data are displayed through customer consultation information, social network service (SNS), blog, Financial Supervisory Service, and public notice received through the call center server and VOC (Voice Of Customer) server It can mean customers' complaint data. The customer's consultation data collected from the call center may be a recorded voice file and / or the recorded customer's consultation content may be a text-converted file via STT (Speech To Text) technology. Alternatively, the customer's consultation data collected from the call center may refer to output data to which voice recognition or voice analysis technology is applied in the recorded voice file.

In one aspect of the present invention, the customer complaint data includes, for example, customer call center counseling records, VOC (Voice of Customer) bulletin board data, customer complaints displayed through the customer's SNS, But not limited to, customer complaints data that may be collected on the web, such as customer complaints data submitted by the customer, customer complaints data written on the customer's blog, and the like.

As described above, the receiving module 201 receives big data from outside the company such as identification information about customers, consultation data from customers, and the like on the inside and outside server (not shown), the user terminal 120 and / 130, < / RTI > Here, the external server may include a call center server, an STT server, and / or a VOC server. The collected or received big data may be stored in the database 207.

In one aspect of the invention, an STT server is any type of software or hardware system capable of recognizing a human voice conversation, such as an STT converter, an STT converter, an STT conversion engine, and the like, . ≪ / RTI > This STT server may be replaced with a receiving module 201, replaced with a control module 205, or replaced with a database 209. Furthermore, such an STT server may be included in the server 110 or may exist independently as an external server.

The collection and reception operations of the receiving module 201 may be performed in units of a predetermined period or period (e.g., day, week, month, quarter, half or year). For example, when the predetermined period is "days ", big data related to customer complaints collected or received from customers can be classified into" daily " Thus, the collected or received big data can be evaluated, updated and / or eliminated in a predetermined period of time.

In another example, the receiving module 201 may send information (e.g., contract information, product information, customer identification information, etc.) associated with the insurance contract to an external server (not shown), a user terminal 120 and / Or via the network 140. In one embodiment,

The receiving module 201 may receive an access request message and / or an inquiry request message for various information from the user terminal 120 and / or the enterprise terminal 130.

In a further aspect of the present invention, the receiving module 201 may receive financial assets (e.g., insurance premiums) paid by the customer. Alternatively, the paid premium may be stored by a server other than the server 110 or may be processed by outsourcing.

The transmission module 203 may transmit information or data from the server 110 to the user terminal 120 or the enterprise terminal 130 via the network 140. [ The transmission module 203 may transmit a response message to the user terminal 120 or the enterprise terminal 130 in response to the request message from the user terminal 120 or the enterprise terminal 130. For example, the sending module 203 may send feedback information on an inquiry request for information related to consultation data, customer complaint data, blacklist customer data, high-risk customer data, etc. from a plurality of customers to a user terminal 120 or the enterprise terminal 130, respectively.

The information transmitted by the sending module 203 may include information retrieved from the database 207 if necessary. Further, the transmission module 203 may distribute an application including a user interface related to the insurance company to the user terminal 120 and / or the enterprise terminal 130.

The transmission module 203 and the reception module 201 of the server 110 can communicate with the user terminal 120 or the enterprise terminal 130 via the network 140 as described above.

In an aspect of the present invention, the sending module 203 and the receiving module 201 may communicate with other servers and the like via the network 140. [

The transmitting module 203 and the receiving module 201 may include a wired / wireless Internet module for network connection. For example, WLAN (Wireless LAN) (Wi-Fi), Wibro (Wireless broadband), Wimax (World Interoperability for Microwave Access), HSDPA (High Speed Downlink Packet Access) For example, a wired Internet technology may be a digital subscriber line (XDSL), a fiber to the home (FTTH), or a power line communication (PLC).

The transmitting module 203 and the receiving module 201 may transmit data to and receive data from an electronic device including a local communication module located relatively close to the server 110 including the local communication module. Bluetooth, Radio Frequency Identification (RFID), infrared data association (IrDA), Ultra Wideband (UWB), ZigBee, and the like can be used as a short range communication technology.

In one aspect of the invention, the data or information collected or received via the receiving module 201 may be stored in the database 207 or transmitted to other electronic devices in the vicinity via the local communication module.

The control module 205 may control the overall operations of the server 110. [ The control module 205 may store the data received and collected by the receiving module 201 in the database 207. In addition, the control module 205 may parse the data received by the receiving module 201 to generate feedback information and the like. Generating such feedback data may be performed by retrieving the necessary information from the database 207.

In one aspect of the present invention, the control module 205 groups collected customer complaint data by customer based on the collected information, assigns weight to each of the customer complaint data in the groups, The degree of complaint of the customer can be quantified and the degree of customer's complaint can be calculated.

For example, a customer may file a complaint with an insurance company, such as an insurance planner, payment, guarantee, or fraud. However, in general, if a customer makes a complaint, if it is not resolved, the customer may file a complaint again. However, if the problem is raised in general and the problem is not resolved, the customer can try out alternative solutions and then complain again through another route. Customers who repeatedly complain through various routes can be classified as customers with a high degree of dissatisfaction. That is, customers who complain in a way that is not the same may generally be considered to have a high degree of dissatisfaction.

Therefore, in one aspect of the present invention, the customer complaint data collected through various routes are grouped according to the customer's identification information in order to calculate the degree of complaint of each customer.

For example, customer complaint data collected from routes such as Social Network Service (SNS), Blog, Call Center, Voice of Customer (VOC), Financial Supervisory Service and National Ombudsman are grouped according to customer identification information do. Therefore, according to the identification information of the customer A, the customer's complaint data can be grouped into the customer B's complaint data according to the identification information of the customer B. In this case, the customer A's complaint data group and the customer B's complaint data group are divided into groups, and each group of data in the group is given a weight according to a predetermined criterion. Or < RTI ID = 0.0 > B < / RTI >

For example, a score can be given to A customer's complaint data grouped according to the customer identification information, and the degree of A customer's complaint can be expressed numerically. For example, a customer has complained that "the insurance agent does not explain the terms of the contract" through the call center, but if the complaint is not resolved, the customer A may again complain through the call center, The Korean National Newspaper on the Blue House homepage may leave a post saying, "The insurance agent does not explain the contents of the contract". In this case, since the second complaint through the National Newspaper and the first complaint through the call center are different from the collection route on the server, the second complaint can be weighted because it can be judged to be a more positive complaint. For example, if the degree of customer complaint for the first complaint is one point, the second complaint is weighted and can be quantified to 1.5 points. Therefore, A customer can be classified as "customer with 2.5 points of dissatisfaction index with the insurance agent" through two complaints.

On the other hand, in case A customer customer complains that "the insurance agent does not explain the contents of the contract" 2 times through the call center, the customer's complaint route is the same, so the customer complaint about each complaint is 1 In point A, a customer can be classified as a "customer with two points of dissatisfaction with the insurance agent" through two complaints. A more detailed description of the quantification will be given later. Also, the description of the method of calculating the degree of customer complaints is only an example, and appropriate analysis methods that can calculate the degree of customer's dissatisfaction are included in the scope of the present invention.

In addition, the control module 205 may perform control related to the retrieval and storage of the big data in the receiving module 201 and the database 207. [ Such big data may include, for example, various data stored in the call center.

In addition, the control module 205 may determine the collection level of the big data. For example, the collection level of the big data may be classified into three types, i.e., classification of external data and internal data, classification of fixed data and unstructured data, number of predetermined keywords, capacity of data to be collected, Etc.), and the like. In addition, the control module 205 can determine the level of analysis of the big data.

In a further aspect of the present invention, the control module 205 of the server 110 may generate a program associated with an application service covering insurance products. The user terminal 120 or the enterprise terminal 130 can receive and execute the corresponding application generated by the control module 205 of the server 110. [ The control module 205 may generate a message to install the application if the corresponding application is not installed in the user terminal or the enterprise terminal and may transmit the message through the transmission module 203. [ In addition, the control module 3205 may generate a message to update to a new version of the application if the corresponding application of the user equipment is an old version. In one aspect of the invention, the functions of the control module 205 described above may be implemented by an independent server (e.g., a service platform server and / or an update server, etc.) separate from the server 110.

In further aspects of the present invention, if a particular application associated with the insurance company and / or certain actions of a particular application are billed as a fee, a payment is normally made through a billing module (not shown) The application may be provided. Additionally, such a billing module may be integrated into the control module 205 of the server 110, or may be coupled with the control module 205, so that it may be operatively associated with the control module 205. Further, the functions of the billing module described above may be implemented by independent servers (e.g., a billing server and / or a payment server, etc.) separate from the server 110. [

Additionally, the server 110 may include an EAI server. Therefore, through this EAI server, the insurance company can organically integrate all the related applications within the insurance company and establish an environment to centrally integrate, manage and use necessary information. When the server 110, the payment server, the billing server, and / or the database have different platforms, these servers can be integratedly managed through the EAI server.

In one aspect of the present invention, the database 207 may be coupled with other components of the server 110. In addition, the database 207 may include one or a plurality of databases. Optionally or alternatively, a database containing part or all of the information may be located within user terminal 120 or enterprise terminal 130. In addition, the database 207 may exist independently from the server 110. In this case, the database 207 may be capable of communicating with the server 110.

In further aspects of the present invention, the database 207 stores various information needed to provide to the user terminal 120 or the enterprise terminal 130, .

In an aspect of the present invention, the database 207 may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD Or XD memory), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read- Memory, a magnetic memory, a magnetic disk, and an optical disk. In a further aspect of the present invention, the server 110 may operate in association with web storage that performs the storage function of the database 207 on the Internet.

The various embodiments described herein may be embodied in a recording medium or storage medium readable by a computer or similar device using, for example, software, hardware, or a combination thereof.

For example, in accordance with a hardware implementation, the embodiments described herein may be implemented as application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs) may be implemented using at least one of field programmable gate arrays, processors, controllers, micro-controllers, microprocessors, and electrical units for performing other functions. The embodiments described herein may be implemented by the control module 207 itself.

In another example, according to a software implementation, embodiments such as the procedures and functions described herein may be implemented with separate software modules. Each of the software modules may perform one or more of the functions and operations described herein. Software code may be implemented in a software application written in a suitable programming language. The software code may be stored in the database 207 and executed by the control module 205.

3 illustrates information or data stored in the database 207 in accordance with one embodiment of the present invention.

3, the database 207 according to one aspect of the present invention includes customer complaint data 311, 312, ..., and 313 grouped in units of customer identification information 310, The high-risk group data 320, the blacklist data 330, and the keyword data 340 may be stored in the user database 314, 315, ..., and 316, respectively. The information or data shown in FIG. 3 is exemplary only, and additional information or data may also be stored in the database 207.

Additionally, in accordance with an aspect of the present invention, the database shown in FIG. 3 may be included in user terminal 120 and / or enterprise terminal 130. In this case, the above-described data and information may be generated through the control module of the user terminal 120 and / or the enterprise terminal 130, or data and information may be received from the server through the receiving module.

The database 207 according to one aspect of the present invention may collect information or data from the external server, the user terminal 120 and / or the enterprise terminal 130 through the receiving module 201 or directly. As described above, the database 207 (or the receiving module 201 may collect various types of data in various ways).

The information or data stored in the database 207 may include both formatted data, semi-structured data, and unstructured data.

Here, the formatted data refers to formatted data, and may include, for example, data stored in a fixed field. Also, unstructured data refers to unformatted data, and may include various data that can be obtained through a search such as, for example, an image, a moving picture, a text file, and the like. In addition, semi-structured data is not a fixed field but may include XML or HTML text as data including metadata, schema, and the like. Further, the internal data refers to data used by the users of the user terminal 120 or the enterprise terminal 130, and the external data may refer to data used by users other than the terminals 120 and 130.

The information or data stored in the database 207 may include both document data and non-document data.

Here, the document data may mean data in which the data itself can be understood by human cognitive ability. In addition, non-document data may be meaningful in a single unit, and may mean data that may have hidden meaning through a particular pattern when clustered.

The database 207 in accordance with an aspect of the present invention may be comprised of one or more subcomponents. For example, although not shown, the database 207 may include a form data collection unit, a semi-structured data collection unit, an unstructured data collection unit, an internal data collection unit, an external data collection unit, a document data collection unit, And / or a data integration unit.

Referring to FIG. 3, the customer complaints data 311 are grouped according to the keyword, and the customer complaint types 314 are grouped and stored in units of the customer identifying information 310. The customer identification information 310 may have a customer complaint type 314 as a subgroup.

In one aspect of the present invention, information or data to be secured among data collected and stored in the database 207 may be encrypted and stored.

The control module 205 extracts the keyword from the customer complaint data 311 collected by the SNS (Social Network Service), the blog, the call center, the VOC (Voice of Customer), the Financial Supervisory Service, And a customer complaint type 314 matching the corresponding keyword. In addition, each customer complaint data is grouped into customer identification information units and stored. For example, if the customer A posts an article in the SNS, such as "The insurance planner is annoyed waiting for a delay in appointment time ", the server collects it in the customer complaint data 311. It is the unstructured data that the above-mentioned "the insurance planner in charge is delayed waiting for the appointment time" and analyzes it to extract keywords such as "insurance planner", "appointment time" and "late". Based on the extracted keyword, the post on the SNS that the "customer's insurance planner is late in the appointment time is annoying" is classified as the customer complaint type 314 of "insurance planner complaint" based on the extracted keyword, The customer complaint data 311 of the customer A may be stored. Each stored customer complaint data 311 may be weighted and stored based on at least one of a collection route of customer complaint data, a generation term of customer complaint data, and a type of customer complaint. Also, the numerical level of customer complaints can be stored according to customer identification information. The degree of customer complaint can be stored as a total score according to the customer identification information, a customer identification information, and a total score according to the type of customer complaint. For example, if the total complaint score of a particular customer A is 15, then the customer A may have complained about the types of complaints such as "dissatisfaction with premiums on renewal", "dissatisfied insurance planner", " have. In this case, the customer A may have complained about 8 complaints about "dissatisfied with the increase in premiums upon renewal", 5 dissatisfied with "dissatisfied with payment of insurance premiums", and 2 complaints about "unsatisfied with insurance planner in charge". In this case, the user terminal 120 and / or the enterprise terminal 130 can inquire about the total degree of complaint of the customer A and the degree of complaint of each type of the customer A.

In one aspect of the present invention, the high-risk group data 320 is data for classifying and storing the customer's identification information into a high-risk group when the numerical level of customer complaints exceeds a predetermined threshold. In this case, the high-risk group can mean a group of customers who are likely to leave or a group of customers likely to raise complaints to related institutions such as the National Newspaper and Financial Supervisory Service. When the degree of customer complaint is calculated, when the collection route is different for the customer complaint data classified by each complaint type unit and the customer identification information, the weight is given as the generation term of the customer complaint data is shorter. The degree of customer complaints can be quantified by the classified customer complaint type unit. For example, if a particular customer complains about the same type of complaint through different routes, we give a larger weight to that collected through the same route, and the shorter the generation term of customer complaint data The customer may be classified as a high-risk customer if a large weight is given and the weighted calculated customer complaint level exceeds a predetermined threshold.

For example, if a customer complains about the types of complaints such as "dissatisfied with the increase in premium at the time of renewal", "dissatisfied with the insurance planner in charge", "dissatisfied with payment of insurance premium" We determine the collection route of each complaint data and assign a weight to the customer complaint data. If a complaint is raised through SNS or a call center regarding "dissatisfaction with premium increase at the time of renewal", 1 point is given to SNS and 1.5 points to call center, and A customer has 2.5 complaints about " It can be a customer with. In this case, when a customer raises the "complaints of rising insurance premiums on renewal" to SNS first and then raises "dissatisfied insurance planner" through the call center, each complaint has different collection routes but different types of customer complaints, And can be numerically expressed only by the basic scores. In this case, the shorter the generation term of the customer complaint data, the larger the weight can be given. This type of customer complaint classification and weighting for each type allows customer A to be classified as the customer with the highest premium on renewal, and customer A When the degree of complaint exceeds a predetermined threshold, the customer's identification information is classified as a high-risk group (320) customers who are likely to move away from the reason of "premium increase at the time of renewal" or to file a complaint with the Financial Supervisory Service .

In an aspect of the present invention, the blacklist data 320 indicates data for classifying and storing the customer identification information into a black list when the numerical level of customer complaints exceeds a preset threshold value. In this case, the blacklist can be applied to a customer who has a high risk of insurance fraud, a customer who is at high risk of damaging the image of an insurance company such as a black consumer, a customer who has complained many complaints, And a customer who finds a part that can not be used. When the degree of customer complaint is quantified, the customer complaint data classified on the basis of the identification information of each customer are different from each other in the collection roots of the customer complaint data, and when the generation term of the customer complaint data is short, Weight is assigned. For example, if a particular customer complains about a different type, on a different route, with a short term, then we collect a higher weight than if it were collected on the same route, or the complaint filed was long And if the weighted quantified customer complaint level exceeds a predetermined threshold value, it can be classified as a black list.

For example, if customer A complains about the types of "complaints of rising premiums on renewals", "complaints of insurance planners in charge", "payment of premiums", etc., the customer complaints data may be referred to as " Data " Then, the collection route of each complaint data is determined and weighted. And, when the generation term of each customer complaint data is short, additional weight is given. A If a customer complains about a "complaint of rising insurance premiums" on the SNS, a complaint to the call center about a "complainant in charge of the insurance agent in charge", and a complaint about "dissatisfied payment" with a VOC, Since the collection route is different for SNS, call center, and VOC, the degree of customer dissatisfaction is 1 point in basic complaint, 1.5 points in weight in the second complaint, and 2 points in weight in the third complaint Points. In this case, the first complaint and the second complaint have a term of one week, and the second and third complaints have a term of one month, the second complaint is additionally given a weight of 0.5, and the third complaint A weight of 0.3 points can be further given to A to quantify the level of customer complaints. In this case, the degree of complaint of customer A can be calculated as 1+ (1.5 + 0.5) + (2 + 0.3) = 5.3. When the degree of customer A's complaint exceeds a predetermined threshold, the customer's identification information can be classified as a black list 330. [

4 schematically illustrates operation of a server according to an embodiment of the present invention.

As shown in FIG. 4, customer complaint data 410 may be generated via a route such as the Internet, telephone, and face-to-face contact. The Internet route may include at least one of a website provided by a financial company, a website provided by a financial supervisory institution, and a social network service (SNS). Of these, the customer's consultation data recorded in the telephone call at the call center can be textized through the STT function. In addition, the customer complaints data 410 may be generated through an appropriate route through which the customer can express their dissatisfaction.

As shown in FIG. 4, the server can collect customer complaints data that can be collected through a suitable route through the receiving module 201. [ The receiving module 201 crawls a bulletin board, an SNS, and the like and collects customer complaint data (430). The receiving module 201 monitors (430) the number of exposures of customer complaints and collects customer complaints data. The collected data can be grouped into customer identification information units based on customer identification information and stored for each customer. The collected data can be stored in the database for a specific period of time. Also, information or data to be secured among the collected data may be encrypted and stored.

In one aspect of the invention, the degree of customer dissatisfaction may be calculated 450 in the collected customer complaint data 410. The collected customer complaints data 410 are grouped into customer identification information units and stored as a data group indicating complaints of the respective customers for each customer. The grouped customer complaint data may be customer complaint data of the corresponding customer. The control module 205 of the server assigns weights to each customer complaint data based on at least one of the type of customer complaint, the collection route of the customer complaint data, and the generation term of the customer complaint data, The degree of complaints of the customer can be calculated.

For example, in one aspect of the invention, in the case where the A customer complains twice to the call center on the grounds of "insurer misstatement ", the two call center counseling records can be each customer complaint data. Since each customer complaint data is expressed through the same route (call center) for the same type ("insurer explanation of insolvency"), each customer's degree of dissatisfaction can be a weightless basic score of 1 point, and A The customer's numerical customer complaint level can be 2 points with 1 + 1 = 2. However, if customer A makes two complaints to the call center on the grounds of "insurer explanation of insolvency", if they protest the call center and protest through the VOC bulletin board, the call center counseling record and bulletin board writing can become customer complaints data. In this case, for each customer complaint data, the number of customer complaints expressed in the call center counseling record is 1 point, which is the basic score, while the VOC bulletin board is customer complaint data expressed through different routes from the call center, , And the customer's numerical customer complaint level can be 1 + 1.5 = 2.5.

Also, for example, in an aspect of the present invention, in the case where the customer A makes two complaints on Feb. 15 and Mar. 18 on the basis of "insurer explanation for failure" to the call center, Lt; / RTI > Also, in case B-customer appeals to the call center twice on Feb. 15 and Feb. 18 for "insurer explanation of insolvency", the two call center counseling records can be individual customer complaint data. Each customer complaint data can be grouped into A customer's complaint data and B customer complaint data based on the customer identification information. In the grouped customer A's complaint data, each protest of the customer A has a term of about one month, and in the group B customer's complaint data, each of the customer B protests has a three-day term. In this case, the B client's protest on February 18 may be given a higher weighting than the A client's March 18 protest, because B client has protested to the customer center with shorter term than A client. The weights can be assigned differently depending on the terms such as day, week, month, quarter, semester, year. In this case, the shorter the term, the larger the weight can be given.

Further, in an aspect of the present invention, a weight given when the collection route of the customer complaint data is different and a weight given when the generation term of the customer complaint data is short can be given independently. For example, with respect to the customer complaint data, the weight according to whether the collection routes are different from each other and the weight according to the generation term can be given to the customer complaint data.

Further, in one aspect of the present invention, the customer complaint data can be grouped into the customer identification information unit, and the customer identification information can have the customer complaint type as the sub group. Accordingly, weights may be assigned to each customer complaint type 314, 315, ..., 316 in the data group grouped by each customer identification information 310. [ For example, in customer A's customer complaint data group, a weight can be given based only on the customer complaints data group of the "responsible insurance agent complaints" type, the customer complaint collection route, and the complaint occurrence term. In addition, the weight may be given based on the customer complaint collection route and the customer complaint generation term in the group of customer complaint data grouped and grouped based only on the customer identification information regardless of the customer complaint type.

FIG. 5 illustrates an example of a keyword extraction and weighting method according to an embodiment of the present invention.

As shown in FIG. 5, customer complaint data may be collected in the VOC bulletin board 510, the SNS 520, and the like. The collected customer complaint data may be grouped based on the customer identification information 511, 521. [ As shown in the figure, based on the customer identification information 511, 521, each customer complaint data 510, 520 can be grouped into customer customer complaint data.

The collected customer complaints data may be formal data, irregular data, and the example shown in this figure may be unstructured data. The control module 205 of the server 110 analyzes the irregular customer complaint data and extracts keywords from each data.

For example, if you are a subscriber to the actual expenses insured (510) collected from the VOC bulletin board shown in Figure 5, the subscription fee has almost doubled since it was two years since the subscription was renewed this time. Customer complaint data 513 can be obtained. At this time, it is possible to extract the keyword by analyzing the customer complaint data. In the above example, keywords such as " actual expenses insurance ", "subscriber "," update ", " The control module 203 may match the type of the keyword with the customer complaint data. The control module 203 may classify the category of the customer complaints data 513 as "premium on increase in premium on renewal" based, in part, on the extracted keyword. In addition, it may be the second customer complaint data 523, which is collected on the SNS shown in FIG. 5 (520), "What is my insurance renewed? In the above example, "insurance", "renewal", "payment fee", "jumped?" Etc. may be keywords. At this time, since the control module is the same customer as the customer 511 who expressed the dissatisfaction with the VOC bulletin board 510 by the customer identification information 521, the second customer complaint data 523 is also the complaint of the " Data. Therefore, the control module 203 can classify the category of the second customer complaints data 523 as "premium on increase in insurance fee on renewal" based, in part, on the extracted keyword. Collected customer complaint data is grouped into A customer's complaint data, and among A customer's complaint data, it can be classified as complaint data of the type of "increase in insurance premium at the time of renewal ".

And the level of customer complaints can be quantified. The degree of customer dissatisfaction can be weighted when the collection route of each customer complaint data is different. The degree of each customer's complaint can be quantified by taking the weight into consideration. For example, the first customer complaint data 513 may be numerically expressed as a base score of the degree of complaints for the type of "increase in the premium raised at the time of renewal ". The degree of complaints of the second customer complaint data 523 can be quantified to 1.5 points by giving a weight of 0.5 because the second customer complaint data is collected through the SNS 520 which is a different route from the first customer complain data . Therefore, the complaint level of the customer can be 1 + 1.5 = 2.5 points. The customer may be stored as a customer with 2.5 points of dissatisfaction with the "premium raised at the time of renewal".

In one embodiment of the present invention, the weights may be given in consideration of the generation term of the customer complaint data. In this example, the first customer complaint data occurred 2014.4.8 and the second customer complaint data occurred on 2014.4.10 and has a two-day term, so that the second customer complaint data has a higher weighting value than when it has a longer term . For example, if we have an occurrence term within one week, we have a weight of 0.5, and if we have an occurrence term within one month, a weight of 0.3 can be given. Based on the weighting, at least in part, the level of customer complaints can be quantified. The specific values of the above-mentioned weights are illustrative, and appropriate weights for quantifying the degree of customer's complaints may be included in the scope of the present invention. The above-described method of calculating the level of customer complaints is exemplary and a suitable method for calculating the level of customer complaints may be included in the present invention.

Also, in an embodiment of the present invention, when the numerical level of customer's level of dissatisfaction exceeds a preset threshold, the customer's identification information may be classified as high risk group 320. [

FIG. 6 illustrates an example of a method of extracting keywords and assigning weights according to an embodiment of the present invention.

As shown in FIG. 6, customer complaint data may be collected in the VOC bulletin board 610, the SNSs 620 and 630, and the like. The collected customer complaint data can be grouped based on the customer identification information 611, 621, and 631. [ 6, each customer complaint data 610, 620, and 630 can be grouped into customer customer complaint data based on the customer identification information 611, 621, and 631. [

The collected customer complaints data may be formal data, irregular data, and the example shown in this figure may be unstructured data. The control module 205 of the server 110 analyzes the irregular customer complaint data and extracts keywords from each data.

For example, I am a subscriber of the actual expenses insurance collected from the VOC bulletin board shown in FIG. 6 (610) .It is 2 years since my subscription, Customer complaint data 613. The control module 205 may classify it into the first customer complaint data 613 of the customer A based on the customer identification information 611. [ At this time, it is possible to extract the keyword by analyzing the customer complaint data. In the above example, keywords such as " actual expenses insurance ", "subscriber "," update ", " The control module 203 may match the type of the keyword with the customer complaint data. The control module 203 may classify the category of the customer complaints data 513 as "premium on increase in premium on renewal" based, in part, on the extracted keyword. In addition, (620) "Having a drink in the cafe and waiting for the insurance FC ..." collected on the SNS shown in FIG. 6 can be the second customer complaint data 623. The control module can classify the second customer complaint data 623 of the customer A based on the customer identification information 621. [ In the above example, keywords such as "another "," insurance FC ", "waiting" Accordingly, the control module 203 may classify the category of the second customer complaints data 523 as "responsible insurance planner complaint" based, in part, on the extracted keywords. (630) collected on another SNS shown in FIG. 6, "It is a little bit tricky." I went to the hospital and received the actual expenses insurance. Customer complaint data 633 can be obtained. The control module 203 can classify the third customer complaint data 633 of the customer A based on the customer identification information 631. [ In the above example, keywords such as "actual expenses insurance", "benefit", "insurance money", "examination", "earned money" Accordingly, the control module 203 may classify the type of the third customer complaint data as "claim payment dispute complaint" based, in part, on the extracted keyword. Also, the degree of customer complaints may be calculated without classifying the customer complaint type.

Customer complaints can be quantified. In the case where the collection route of each customer complaint data is different, the shorter the generation term, the more the customer complaint degree can be weighted. The degree of individual customer complaints can be calculated by taking the weights into consideration. For example, the first customer complaint data 613 may be numerically expressed as a basic score of the degree of complaints. Since the second customer complaint data 623 is collected as a route different from the first customer complaint data 613, the degree of complaint can be numerically expressed by 1.5 points to which the weight 0.5 is assigned. Since the third customer complaint data 633 is collected as a route different from the first customer complaint data 613 and the second customer complaint data 623, the degree of complaint can be numerically expressed by two points to which one weight is assigned. Therefore, the complaint level of the customer can be 1 + (1 + 0.5) + (1 + 1) = 4.5.

The weights may be given in consideration of the generation term of the customer complaint data. In the example above, the first customer complaint data 613 occurred at 2014.4.8 and the second customer complaint data 623 occurred at 2014.4.10, so that the first customer complaint data and the second customer complain data were stored for two days It has an occurrence term. The third customer complaint data 633 occurs on May 13, 2013, and the second customer complaint data and the third customer complain data have an occurrence term of about one month. For example, since the generation term with the previous customer complaint data 613 is two days in the second customer complaint data 623, the degree of complaint is additionally given by adding a weight of 0.5 point, and the third customer complaint data 633 ), Since the generation term with the previous customer complaint data 623 is about one month, the degree of complaint can be calculated by additionally giving a weight of 0.3. The weight by the generation term may be additionally given to the weight according to the collection route, or it may be given independently. Therefore, the complaint level of the customer can be 1 + (1 + 0.5 + 0.5) + (1 + 1 + 0.3) = 5.3. And may quantify the degree of customer complaint based at least in part on the weights. The specific values of the above-mentioned weights are illustrative, and appropriate weights for quantifying the degree of customer's complaints may be included in the scope of the present invention. The above-described method of calculating the level of customer complaints is exemplary and a suitable method for calculating the level of customer complaints may be included in the present invention.

Also, in one embodiment of the present invention, when the numerical level of the customer's level of dissatisfaction exceeds a preset threshold, the customer's identification information may be classified into the black list 330. [

The server 110, the user terminal 120, the company terminal 130, and the like may perform one or more feedback operations when the numerical level of customer complaints exceeds a preset threshold value according to an exemplary embodiment of the present invention.

According to one embodiment of the present invention, the feedback operation comprises the steps of: providing a notice to the user terminal about the customer; sorting the quantified customer complaint level based on a numerical value; Sorting the customer complaints based on the numeric values, and enforcing the insurance claims payout audit for the customer.

According to one embodiment of the present invention, an insurance agent, an internal person (hereinafter, referred to as a user) can easily identify a customer with a high degree of dissatisfaction by sorting the customer complaint score based on the numerical value. And can provide a feedback operation that sorts the customer identification information in the order of higher customer complaint scores.

For example, if customer A's complaint is 6.5, customer B's customer complaint is 10, customer C's customer's complaint is 4.5, customer D's customer's customer complaint is 15, , D customer, B customer, A customer, C customer, so that the user can easily recognize the customer with high dissatisfaction. Table 1 below is an example of the result of summarizing customer identification information in descending order of customer complaint score. The user can easily understand that customer D is the customer with the greatest dissatisfaction.

Customer identification information Complaint Score D 15 B 10 A 6.5 C 4.5

According to another embodiment of the present invention, the customer's complaint score can be sorted based on the score of each type so that the user can easily recognize a customer having a high level of complaints according to each type. It is possible to provide a feedback operation for sorting the customer identification information in the order of the highest complaint score for a particular complaint type.

For example, "A complaint by insurance planner" of A customer is 5.5, "Complaints about payment of payment is 7", "A complaint about insurance payment disbursement" is 15, and "A complaint by insurance planner" , "Complaints on payment of fees" 10 points, "dissatisfaction with the payment of insurance claims" 4 points, "customer complaints from insurance planners" 8 points, "payment complaints" about 6 points, If the degree is 10, the users are most likely to complain about each type of complaint by sorting in the order of high level of dissatisfaction based on "insurer complaints", "complaints of payment fees", or " Making it easier for customers to find out. Table 2 below is an example of the results when sorting customer identification information in ascending order of insurance agent complaint scores. The user can easily grasp that customer C is the customer with the greatest dissatisfaction with the insurance planner.

Customer identification information Insurance agent complaint Payment Complaint Claim payment dispute C 8 6 10 A 5.5 7 15 B One 10 4

According to another embodiment of the present invention, the degree of customer complaints quantified on the basis of the type of customer complaint is sorted based on the numerical value, so that the user can easily find out what the particular customer is most displeased with have. You can provide feedback that aligns the type of customer complaint in the order of highest customer complaint score.

For example, if customer A's "dissatisfied with insurance planner" is 5.5, "disbursement complaint" is 7, and "insurance disbursement audit complaint" is 15, The complaints by type of complaint can be sorted in the following order: "Claims Complaints Now,""DisbursementFees," and "Insurance Planner Complaints." The following is an example of the results when sorting customer complaint types in ascending order of customer A's complaint scores. The user can easily grasp that customer A's main complaint is a claim payment audit.

Customer identification information Types of customer complaints A
Claim payment dispute Payment Complaint Insurance agent complaint 15 7 5.5

In addition, according to another embodiment of the present invention, when the numerical level of customer complaints exceeds the threshold value, it is possible to provide a feedback operation for enhancing the insurance payment inspection for the customer. For example, a customer who has a degree of dissatisfaction above a threshold level with respect to the type of "insurance payment review " may be an insurance fraudster and may provide an action to enforce the insurance claim assessment for that customer.

According to another embodiment of the present invention, when the numerical value of the customer complaint exceeds the threshold value, the user terminal 120 or the enterprise terminal 130 can provide a notification about the corresponding customer identification information.

FIG. 7 illustrates a method of managing a customer according to an embodiment of the present invention.

7 may also be included in one aspect of the present invention, and the steps of some of the steps shown in FIG. 7 may be omitted. In addition, at least some of the steps shown in FIG. 7 may be performed by the user terminal 120 and / or the enterprise terminal 130.

Referring to FIG. 7, a server 110 according to an aspect of the present invention may collect (710) customer complaints data from customers via one or more routes. As described above, the at least one route includes at least one of the Internet, telephone, and face-to-face contact routes, and the Internet route includes a website provided by a financial company, a website provided by a financial regulator, Or the like. The collected customer complaint data can be consultation records of call center, VOC, etc., expression of emotions through SNS, comparison analysis of insurance products through blogs, and the like.

In one aspect of the present invention, the server 110 may extract 720 keywords from each of the customer complaint data in the groups. Since the customer complaint data collected as described above is atypical data, the keyword can be extracted through analysis to classify the customer complaint type. At this time, it is possible to store a word meaningful in the insurance industry in advance in the database as a keyword. Keywords can be used not only to find words that match keywords in customer complaints data, but also various big data analysis techniques to find meaning in unstructured data.

In one aspect of the present invention, the server 110 may classify (730) the type of customer complaints data based at least in part on the extracted keywords. Through the analysis of the unformatted data as described above, the customer complaint type of the customer complaint data can be classified. For example, when keywords such as "insurance planner" and "delayed appointment time" are extracted from the customer complaint data, the customer complaint data may be classified into the "insurance planner related complaint type"

In one aspect of the present invention, the server 110 may group the collected customer complaint data by customer to form groups of customer complaint data (740). The customer complaint data collected as described above can be grouped based on the customer identification information and classified into the customer complaint data group for each customer. The customer identification information group can also have the customer complaint type as a subgroup.

In one aspect of the invention, the server 110 may weight each of the customer complaints data in the groups (750). As described above, the weights may be given based on at least one of a type of customer complaint, a collection root of customer complaint data, and an occurrence term of customer complaint data. In this case, as the collection route of the customer complaint data is varied and the generation term of the customer complaint data is shorter, the weight can be given a higher weight. In this case, the weight may be given in consideration of whether the collection path of the customer complaint data is different or the generation term of the customer complaint data is short for each customer identification information group. According to another aspect of the present invention, the weight may be given in consideration of whether the collection path of the customer complaint data is different or the generation term of the customer complaint data is short for each type of customer complaint, which is a subgroup of the customer identification information group.

In an aspect of the present invention, the server 110 may calculate the degree of complaint of the customer based on the weight (760). As described above, the calculation may be a method of summing the degree of customer complaints of each customer complaint data. Alternatively, a combination analysis, multiple regression analysis, and the like can be used. The above-described methods and the like are merely illustrative, and appropriate methods for quantifying the level of customer complaints in customer complaints data may also be included within the scope of the present invention.

In an aspect of the present invention, the numerical customer complaint level may be stored in the database 207 (770). As described above, the customer complaint data can be grouped and stored for each customer based on the customer identification information 310, and the customer identification information can include the customer complaint type 314 as a subgroup. Also, the database 207 may include customer identification information 320 classified as a high-risk group, customer identification information 330 classified into a black list, keyword data 340, and the like. Depending on the degree of customer dissatisfaction, customer identification information data may be classified as blast and / or high risk.

In an aspect of the invention, the server 110 may perform a feedback operation 780 if the quantified customer complaint level exceeds a predetermined threshold.

For example, if repeatedly raising a problem for the same type of customer complaint, the customer's identifying information may be classified as high risk customer 320. [

Also, for example, if the same customer repeatedly raises a problem with a short term through different routes, the identification information of the customer may be classified as the black list customer 330. [

In addition, for example, when the numerical value of the customer complaint exceeds a preset threshold value, the user terminal 120 and / or the enterprise terminal 130 can provide notification about the identification information of the customer. In addition, the degree of the customer's dissatisfaction can be displayed on the basis of the customer identification information, the type of customer complaint, and / or the degree of customer's complaint so that the user can easily grasp the customer's complaint. The above description is merely an example, and an appropriate method for allowing a user, a manager, and the like to easily grasp the degree of customer complaints quantified is also included in the scope of the present invention.

Also, taking into account the types of customer complaints, it may be possible to strengthen the claims audit for customers with high levels of dissatisfaction. For example, a customer who has a high frequency of inspections and has a strong dissatisfaction with the insurance payment review process, evidence documents, etc. may be an insurance fraudster, so he can strengthen the insurance payment audit for the customer.

The information related to the feedback operation may be transmitted to the user terminal 120 or the enterprise terminal 130 through the transmission module of the server 110. [

In one or more exemplary implementations, the functions presented herein may be implemented in hardware, software, firmware, or a combination thereof. When implemented in software, the functions may be stored on or transmitted via one or more instructions or code on a computer readable medium. Computer-readable media includes computer storage media and communication media including any medium for facilitating transfer of a computer program from one place to another. The storage medium may be any general purpose computer or any available medium that can be accessed by a special purpose computer. By way of example, and not limitation, such computer-readable media can comprise any form of computer readable medium, such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage media, magnetic disk storage media or other magnetic storage devices, But not limited to, a general purpose computer, a special purpose computer, a general purpose processor, or any other medium that can be accessed by a particular processor. In addition, any connection means may be considered as a computer-readable medium. For example, if the software is transmitted from a web site, server, or other remote source over wireless technologies such as coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or infrared radio, and microwave, Wireless technologies such as cable, fiber optic cable, twisted pair, DSL, or infrared radio, and microwave may be included within the definition of such medium. The discs and discs used here include compact discs (CDs), laser discs, optical discs, DVDs, floppy discs, and Blu-ray discs where disc plays the data magnetically, As shown in FIG. The combinations may also be included within the scope of computer readable media.

Those skilled in the art will appreciate that the various illustrative elements, components, logical blocks, modules, and algorithm steps described above may be implemented as electronic hardware, computer software, or combinations thereof. In order to clarify the interchangeability of hardware and software, various illustrative components, blocks, modules and steps have been described in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement these functions in varying ways for each particular application, but such implementation decisions are not necessarily outside the scope of the invention.

The various illustrative logical blocks and modules described in connection with the present disclosure may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, Or transistor logic, discrete hardware components, or any combination designed to implement the functions described herein. A general purpose processor may be a microprocessor; In an alternative embodiment, such a processor may be an existing processor, controller, microcontroller, or state machine. A processor may be implemented as a combination of computing devices, such as, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or a combination of such configurations.

For a hardware implementation, the various illustrative logic, logic blocks, and modules of processing units described in connection with the aspects disclosed herein may be implemented with one or more application specific integrated circuits (ASICs), digital signal processors (DSPs) Such as digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), discrete gate or transistor logic, discrete hardware components, general purpose processors, Controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. The general purpose processor may be a microprocessor, but, in the alternative, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented in a combination of computing devices (e.g., a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other suitable configuration). Additionally, at least one processor may include one or more modules capable of implementing one or more of the steps and / or operations described herein.

In addition, various aspects or features described herein may be implemented as a method, apparatus, or article of manufacture using standard programming and / or engineering techniques. Moreover, steps and / or operations of a method or algorithm described in connection with the aspects disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. Additionally, in some aspects, steps or acts of a method or algorithm may be present as a machine-readable medium, or as a combination of at least one or any combination of codes or instructions on a computer-readable medium, It can be integrated into computer program stuff. The term article of manufacture as used herein is intended to encompass a computer program accessible from any suitable computer-readable device or medium.

The description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features presented herein.

Claims (15)

22. A computer-readable medium,
The computer-readable medium stores instructions that cause a computer to perform the following operations:
Collecting one or more customer complaints data for a particular customer via one or more routes;
Weighting each of said one or more customer complaints data based on a collection root and an occurrence term of customer complaint data; And
Calculating a degree of complaint of the specific customer based on the weight;
/ RTI >
Wherein the collection route of the customer complaint data includes an Internet collection route, a telephone collection route and a face-to-face contact collection route,
The Internet collection route is determined differently depending on the type of the web site,
Wherein the weighting operation assigns a greater weight to the one or more customer complaint data when the one or more customer complain data is collected through the same route when the one or more customer complain data is collected through the same route, And a weighting operation,
Computer-readable medium. The method according to claim 1,
The act of collecting the customer complaint data comprises:
Converting the voice data into text through a Speech-to-Text (STT) engine when the collection route of the customer complaint data is a telephone collection route; And
Determining the voice data as the customer complaint data if a preset keyword is extracted from the converted text; / RTI >
Computer-readable medium. The method according to claim 1,
Providing the user with a notification of the particular customer if the calculated degree of complaint exceeds a predetermined threshold level;
≪ / RTI >
Computer-readable medium. As a customer complaint monitoring device,
A receiving module that collects one or more customer complaints data for a particular customer via one or more routes;
A control module that weights each of the one or more customer complaint data based on a collection route and an occurrence term of customer complaint data and calculates a degree of complaint of the specific customer based on the weight; And
A database for storing the calculated degree of complaint;
Lt; / RTI >
Wherein the collection route of the customer complaint data includes an Internet collection route, a telephone collection route and a face-to-face contact collection route,
The Internet collection route is determined differently depending on the type of the web site,
Wherein the control module grants a larger weight when the one or more customer complaint data is collected through different routes and collects a larger weight when the one or more customer complaint data is collected through the same route, Lt; RTI ID = 0.0 >
Customer complaint monitoring device.
delete delete delete delete delete delete delete delete delete delete delete

Images