WO2019080414A1 - Customer label management method and system, computer device and storage medium - Google Patents

Abstract

A customer label management method and system (10), a computer device and a storage medium. By means of monitoring a call between an agent and a customer, during the process of a call between the agent and the customer, a speech recognition technique is used to recognize the content of the customer's call, and keywords associated with the basic information or behavior characteristics of a customer's voice are extracted, and the extracted keywords are then used to update the existing labels. The customer label management method and system (10), the computer device and the storage medium can realize dynamic and automatic updating and management of a customer label, thereby facilitating an agent in adjusting the label and understanding a customer's situation.

Description

Customer label management method, system, computer equipment and storage medium

The application claims the priority of the Chinese patent application entitled "Customer Label Management Method, System, Computer Equipment and Storage Medium", which is filed on October 26, 2017, the entire contents of which are hereby incorporated by reference. The way is combined in this application.

Technical field

The present application relates to the field of customer management technologies, and in particular, to a client tag management method, system, computer device, and storage medium.

Background technique

As an invisible service window for enterprises and user terminals to maintain close contact, the call center system plays an increasingly important role in product promotion, product sales, technical support, after-sales service, consulting and complaints, and plays a role in the business activities of enterprises. An increasingly important role.

The agent service is an important way for the call center system to provide services for customers. The agent service refers to the process in which the agent provides the corresponding service to the customer through the support system of the call center.

The existing agent system often adds the basic information and behavior characteristics of the customer to help the agent to conduct customer screening, management and analysis. However, if one of the customer's information changes, the system cannot update it in the first time.

Summary of the invention

The purpose of the present application is to provide a client tag management method, system, computer device and storage medium for solving the problems existing in the prior art.

To achieve the above objective, the present application provides a method for managing a customer label, including the following steps:

Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer;

Step 02: monitoring the agent's call with the client;

Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client;

Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice;

Step 05: Update or prompt to update the label corresponding to the keyword.

To achieve the above objective, the present application further provides a customer label management system, including:

A tag automatic adding module adapted to automatically add at least one tag to a customer's basic information and/or behavioral characteristics;

The call monitoring module is adapted to monitor the call between the agent and the customer;

The voice recognition module is adapted to perform voice recognition on the voice of the client when the agent talks with the client;

The keyword extraction module is adapted to extract basic information and/or behavior feature keywords identified in the voice of the client call;

A tag update module adapted to update or prompt for a tag corresponding to the updated keyword.

To achieve the above object, the present application also provides a computer device comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, the processor implementing the program to implement the following steps:

Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer;

Step 02: monitoring the agent's call with the client;

Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client;

Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice;

Step 05: Update or prompt to update the label corresponding to the keyword.

To achieve the above object, the present application also provides a computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements the following steps:

Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer;

Step 02: monitoring the agent's call with the client;

Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client;

Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice;

Step 05: Update or prompt to update the label corresponding to the keyword.

The customer label management method, system, computer equipment and storage medium provided by the application, by monitoring the call between the agent and the customer, use the voice recognition technology to identify the customer's call content during the call between the agent and the customer, and extract the customer voice. The keywords associated with their basic information or behavioral characteristics are updated with the extracted keywords. This application can realize the dynamic, automatic update and management of customer labels, and it is convenient for agents to adjust labels and understand customer conditions.

DRAWINGS

1 is a flowchart of Embodiment 1 of a client tag management method according to the present application;

2 is a schematic diagram of a program module of Embodiment 1 of a customer label management system of the present application;

3 is a schematic structural diagram of hardware of Embodiment 1 of a customer label management system of the present application;

4 is a flowchart of Embodiment 2 of a method for managing a customer label according to the present application;

FIG. 5 is a flowchart of Embodiment 3 of a method for managing a customer label according to the present application.

Detailed ways

In order to make the objects, technical solutions, and advantages of the present application more comprehensible, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present application without departing from the inventive scope are the scope of the present application.

The customer label management method, system, computer equipment and storage medium provided by the application are suitable for the daily conversation between the agent and the client. By monitoring the call between the agent and the client, the present application uses the voice recognition technology to identify the customer's call content during the call between the agent and the client, and extracts keywords related to the basic information or behavior characteristics of the customer voice, and then The extracted keywords update the existing tags. This application can realize the dynamic, automatic update and management of customer labels, and it is convenient for agents to adjust labels and understand customer conditions.

Embodiment 1

Referring to FIG. 1, a customer label management method of this embodiment includes the following steps:

In step 01, at least one tag is automatically added to the basic information and/or behavior characteristics of the customer.

In this step, the basic information label may be added according to the pre-stored basic information of the customer, and the behavior characteristic label is added according to the collected behavior characteristics of the customer, and at least one basic information label and at least one behavior characteristic label constitute the multi-dimensional label of the customer. The basic information of the target customer includes age, gender, region, education level, income, etc. The behavior characteristics include the type of business used, the business validity period, the customer source, the operating frequency, and the operation time.

The age labels added to the customer's basic information include, for example, 18-25 years old, 26-35 years old, 36-45 years old, 46-50 years old, 51 years old and above. The gender labels include male and female, and the regional labels include, for example, Beijing and Shanghai. , Guangdong, etc., cultural level labels include, for example, elementary school, junior high school, high school, undergraduate, master's degree, doctoral degree, and so on. The income label includes, for example, 0-3 million, 30,000-50,000, 50,000-100,000, 100,000-200,000- 300,000, 300,000-500,000-500-100,000 or more. The business type labels added to the customer's behavioral characteristics include, for example, the property insurance business, life insurance business, medical business, comprehensive business (financial business), etc., and the business validity period label includes, for example, one month before the effective one, and within 1-6 months. Effective, effective within 6-12 months, effective before 1-3 years, beginning before 3 years, expiring within 1 month, expiring within 1-6 months, expiring within 6-12 months, 1 - Expiration within 3 years, expiration after 3 years, customer source labels include, for example, pure orphans, referrals, active consultation, etc. Operating frequency labels include, for example, more than once a day, more than once a week, more than once a month, and quarterly. More than one time, the operation time label includes, for example, 6:00-9:00, 9:00-12:00, 12:00-17:00, 17:00-20:00, 20:00-0:00, 0: 00-6:00.

Step 02, monitoring the agent's call with the customer.

In this step, after the system adds multiple tags to the basic information and behavior characteristics of the customer, the tags are not automatically managed and updated, and the client's call is monitored in real time, if the agent is detected to talk to the client. Then, the speech recognition, keyword extraction, and the like of the subsequent steps are performed to facilitate management and update of multiple tags of the client.

In step 03, when the agent makes a call with the client, the voice of the client is voice-recognized.

In this step, the system initiates this step and subsequent steps once it detects that the agent is talking to the client. The voice recognition for the customer may include all the call voices during the client's call, and then extract the keywords from all the recognized texts through subsequent steps; or may only identify those that are needed, for example, when the agent deliberately asks some basic information or When the behavior is characterized, the customer's answer is voice-recognized; all voices can be recognized in real time while the customer is speaking, but only useful words and sentences are recorded.

In step 04, basic information and/or behavior feature keywords identified in the customer voice are extracted.

In this step, through the semantic recognition technology, keywords related to basic information or behavior characteristics are extracted in the recognized voice characters. Regarding the semantic recognition technology itself, the prior art has given a lot of records and enlightenment, and the present application does not describe it. In the process of extracting keywords, several scenarios can be preset to facilitate the extraction of keywords: First, during the process of the call between the agent and the client, the basic information or behavior of the agent is prompted by the prompting on the display screen. Features are asked, when the customer answers these questions, the content of the call can be used as the source of the extracted keywords; second, the agent actively wants to ask questions about basic information or behavioral characteristics, then the agent first selects this basic in the system. The information or behavior characteristics are to be updated, such as a mouse click or stay in the space of the occupation column, and then ask the customer whether the work has changed during the call. After the mouse operation, the system extracts keywords from the call content when the customer answers the question.

Step 05: Update or prompt to update the label corresponding to the keyword.

In this step, after extracting the keyword of the customer basic information or behavior characteristics, the system may automatically update the corresponding label, or may list the extracted keywords after the keyword is extracted or after the call ends, prompting the agent to be seated. Whether the agent operation updates the label. When the same type of tag extracts two or more keywords, the agent may also be prompted to select a keyword to update the tag.

Referring to FIG. 2, a customer label management system is shown. In this embodiment, the customer label management system 10 can be divided into one or more program modules, and one or more program modules are stored in a storage medium. And executed by one or more processors to complete the application, and the above-described client tag management method can be implemented. A program module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function, and are more suitable than the program itself to describe the execution of the customer tag management system 10 in a storage medium. The following description will specifically describe the functions of each program module of this embodiment:

The tag automatic adding module 11 is adapted to automatically add at least one tag to the basic information and/or behavior characteristics of the client.

The tag automatic addition module 11 may include a basic information tag adding sub-module adapted to add a basic information tag according to the customer's pre-stored basic information and a behavior feature tag adding sub-module adapted to add a behavior feature tag according to the collected customer's behavior characteristics. The basic information tag adding submodule is adapted to add a basic information tag according to the customer's pre-stored basic information, and the behavior feature tag adding sub-module is adapted to add the behavior feature tag according to the collected customer's behavior characteristics.

The call monitoring module 12 is adapted to monitor the call between the agent and the client.

The voice recognition module 13 is adapted to perform voice recognition on the voice of the client when the agent talks with the client.

The keyword extraction module 14 is adapted to extract basic information and/or behavior feature keywords identified in the voice of the client call. The keyword extraction module 14 is further adapted to obtain a selection of the agent for the basic information or the behavioral feature to be updated, and extract keywords associated with the selected basic information or behavioral features in the customer speech after the agent selection.

The tag update module 15 is adapted to update or prompt the tag corresponding to the updated keyword.

The customer label management system may further include a prompting module 16 adapted to prompt the agent to query the basic information or behavior characteristics of the client; and is also suitable for listing the extracted client during the conversation with the client or after the call ends. Basic information and/or behavioral feature keywords, and whether the agent is updated or updated by the agent to select keywords; it is also suitable for prompting the agent whether to use two or more tags as the tag set of the screening target client; The agent uses two or more tags as the tag set of the screening target client, and prompts whether the agent will push the tag set with the frequency greater than the preset threshold to the system or other agents.

The embodiment further provides a computer device, such as a smart phone, a tablet computer, a notebook computer, a desktop computer, a rack server, a blade server, a tower server or a rack server (including a stand-alone server, or A server cluster consisting of multiple servers). The computer device 20 of this embodiment includes at least but not limited to: a memory 21 and a processor 22 communicably connected to each other through a system bus, as shown in FIG. It is noted that FIG. 3 shows only computer device 20 having components 21-22, but it should be understood that not all illustrated components may be implemented and that more or fewer components may be implemented instead.

In this embodiment, the memory 21 (ie, the readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), Read only memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, and the like. In some embodiments, memory 21 may be an internal storage unit of computer device 20, such as a hard disk or memory of computer device 20. In other embodiments, the memory 21 may also be an external storage device of the computer device 20, such as a plug-in hard disk equipped on the computer device 20, a smart memory card (SMC), and a secure digital (Secure Digital, SD) card, flash card, etc. Of course, the memory 21 can also include both internal storage units of the computer device 20 as well as external storage devices thereof. In this embodiment, the memory 21 is generally used to store an operating system installed in the computer device 20 and various types of application software, such as the program code of the customer tag management system 10 of the second embodiment. Further, the memory 21 can also be used to temporarily store various types of data that have been output or are to be output.

Processor 22 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 22 is typically used to control the overall operation of the computer device 20. In this embodiment, the processor 22 is configured to run program code or process data stored in the memory 21, such as running the customer tag management system 10 and the like.

The embodiment further provides a computer readable storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory (for example, SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), and only Read memory (ROM), electrically erasable programmable read only memory (EEPROM), programmable read only memory (PROM), magnetic memory, magnetic disk, optical disk, server, App application store, etc., on which a computer program is stored. The program implements the corresponding function when executed by the processor. The computer readable storage medium of this embodiment is used to store the client tag management system 10, and when executed by the processor, implements the client tag management method of the first embodiment.

Embodiment 2

Referring to FIG. 4, the customer label management method of this embodiment is based on the first embodiment, and includes the following steps:

In step 01, at least one tag is automatically added to the basic information and/or behavior characteristics of the customer.

In this step, the basic information label may be added according to the pre-stored basic information of the customer, and the behavior characteristic label is added according to the collected behavior characteristics of the customer, and at least one basic information label and at least one behavior characteristic label constitute the multi-dimensional label of the customer. The basic information of the target customer includes age, gender, region, education level, income, etc. The behavior characteristics include the type of business used, the business validity period, the customer source, the operating frequency, and the operation time.

Step 02, monitoring the agent's call with the customer.

In this step, after the system adds multiple tags to the basic information and behavior characteristics of the customer, the tags are not automatically managed and updated, and the client's call is monitored in real time, if the agent is detected to talk to the client. Then, the speech recognition, keyword extraction, and the like of the subsequent steps are performed to facilitate management and update of multiple tags of the client.

In step 03, when the agent makes a call with the client, the voice of the client is voice-recognized.

In this step, the system initiates this step and subsequent steps once it detects that the agent is talking to the client. The voice recognition for the customer may include all the call voices during the client's call, and then extract the keywords from all the recognized texts through subsequent steps; or may only identify those that are needed, for example, when the agent deliberately asks some basic information or When the behavior is characterized, the customer's answer is voice-recognized; all voices can be recognized in real time while the customer is speaking, but only useful words and sentences are recorded.

In step 04, basic information and/or behavior feature keywords identified in the customer voice are extracted.

In this step, through the semantic recognition technology, keywords related to basic information or behavior characteristics are extracted in the recognized voice characters. Regarding the semantic recognition technology itself, the prior art has given a lot of records and enlightenment, and the present application does not describe it. In the process of extracting keywords, several scenarios can be preset to facilitate the extraction of keywords: First, during the process of the call between the agent and the client, the basic information or behavior of the agent is prompted by the prompting on the display screen. Features are asked, when the customer answers these questions, the content of the call can be used as the source of the extracted keywords; second, the agent actively wants to ask questions about basic information or behavioral characteristics, then the agent first selects this basic in the system. The information or behavior characteristics are to be updated, such as a mouse click or stay in the space of the occupation column, and then ask the customer whether the work has changed during the call. After the mouse operation, the system extracts keywords from the call content when the customer answers the question.

Step 05: Update or prompt to update the label corresponding to the keyword.

In this step, after extracting the keyword of the customer basic information or behavior characteristics, the system may automatically update the corresponding label, or may list the extracted keywords after the keyword is extracted or after the call ends, prompting the agent to be seated. Whether the agent operation updates the label. When the same type of tag extracts two or more keywords, the agent may also be prompted to select a keyword to update the tag.

In step 06, it is suggested whether the agent uses two or more tags as the tag set of the screening target client.

In this step, after the system is updated or the agent selects the label corresponding to the updated keyword, in order to facilitate the agent to manage and filter the client, the agent may be prompted whether to perform label set processing on the updated label, that is, two or two. More than one label is used as a label set for screening target customers. For example, the agent will have a label of 26-35 years old, undergraduate label, comprehensive gold business, 20:00-0:00 as a label set according to the prompt, and presets have these The customer of the label is "white collar".

Embodiment 3

In step 01, at least one tag is automatically added to the basic information and/or behavior characteristics of the customer.

In this step, the basic information label may be added according to the pre-stored basic information of the customer, and the behavior characteristic label is added according to the collected behavior characteristics of the customer, and at least one basic information label and at least one behavior characteristic label constitute the multi-dimensional label of the customer. The basic information of the target customer includes age, gender, region, education level, income, etc. The behavior characteristics include the type of business used, the business validity period, the customer source, the operating frequency, and the operation time.

Step 02, monitoring the agent's call with the customer.

In this step, after the system adds multiple tags to the basic information and behavior characteristics of the customer, the tags are not automatically managed and updated, and the client's call is monitored in real time, if the agent is detected to talk to the client. Then, the speech recognition, keyword extraction, and the like of the subsequent steps are performed to facilitate management and update of multiple tags of the client.

In step 03, when the agent makes a call with the client, the voice of the client is voice-recognized.

In this step, the system initiates this step and subsequent steps once it detects that the agent is talking to the client. The voice recognition for the customer may include all the call voices during the client's call, and then extract the keywords from all the recognized texts through subsequent steps; or may only identify those that are needed, for example, when the agent deliberately asks some basic information or When the behavior is characterized, the customer's answer is voice-recognized; all voices can be recognized in real time while the customer is speaking, but only useful words and sentences are recorded.

In step 04, basic information and/or behavior feature keywords identified in the customer voice are extracted.

In this step, through the semantic recognition technology, keywords related to basic information or behavior characteristics are extracted in the recognized voice characters. Regarding the semantic recognition technology itself, the prior art has given a lot of records and enlightenment, and the present application does not describe it. In the process of extracting keywords, several scenarios can be preset to facilitate the extraction of keywords: First, during the process of the call between the agent and the client, the basic information or behavior of the agent is prompted by the prompting on the display screen. Features are asked, when the customer answers these questions, the content of the call can be used as the source of the extracted keywords; second, the agent actively wants to ask questions about basic information or behavioral characteristics, then the agent first selects this basic in the system. The information or behavior characteristics are to be updated, such as a mouse click or stay in the space of the occupation column, and then ask the customer whether the work has changed during the call. After the mouse operation, the system extracts keywords from the call content when the customer answers the question.

Step 05: Update or prompt to update the label corresponding to the keyword.

In this step, after extracting the keyword of the customer basic information or behavior characteristics, the system may automatically update the corresponding label, or may list the extracted keywords after the keyword is extracted or after the call ends, prompting the agent to be seated. Whether the agent operation updates the label. When the same type of tag extracts two or more keywords, the agent may also be prompted to select a keyword to update the tag.

Step 06: Obtain an agent set of two or more tags as a target client of the screening target, prompt the agent whether to modify the tag set, and prompt the agent whether to push the tag set whose frequency is greater than a preset threshold to the system or other agents.

In this step, after the system is updated or the agent selects the tag corresponding to the updated keyword, in order to facilitate the agent to manage and filter the client, the agent may first obtain the tag set of the agent for two or more tags as the screening target client. After updating the label, the agent may be prompted whether to modify the label set, and whether the agent will push the label set whose frequency is greater than the preset threshold to the system or other agents. For example, the agent has a label of 26-35 years old, undergraduate label, comprehensive gold business, 20:00-0:00 as a label set, and the customer who has these labels at the same time is a "white collar" and talks with a customer. After finishing and updating the label, it is found that the operation time label is not suitable for one of the "white collar" label sets from 20:00-0:00, then the label in the label set can be adjusted at this time according to the prompt; meanwhile, if the agent thinks that he is himself The set of tags is set up to help manage and analyze other customers, and the agent can push the form of the tag set to the system or other agents according to the prompts.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims (20)

A customer label management method, comprising the steps of: Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer; Step 02: monitoring the agent's call with the client; Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client; Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice; Step 05: Update or prompt to update the label corresponding to the keyword. The client tag management method according to claim 1, wherein the step 01 comprises adding a basic information tag according to the pre-stored basic information of the client, and adding a behavior feature tag according to the collected behavior characteristics of the client. The client tag management method according to claim 1, wherein the step 04 further comprises prompting the agent to query the basic information or behavior characteristics of the client. The client tag management method according to claim 1, wherein the step 04 further comprises: obtaining a selection of the agent to update the basic information or the behavior feature, and extracting the basic information or behavior selected from the client voice after the agent selection. Keywords associated with features. The customer label management method according to claim 1, wherein the step 05 includes listing the extracted customer basic information and/or behavior characteristic keywords during the conversation with the client or after the conversation ends, and prompting Whether the agent is updated or for the agent to select keywords to update. The client tag management method according to claim 1, wherein the client tag management method further comprises a step 06 of prompting the agent whether to use two or more tags as the tag set of the screening target client. The client tag management method according to claim 1, wherein the client tag management method further comprises a step 06 of acquiring a tag set of the agent as the screening target client by the agent, and prompting the agent whether to modify the tag. The set of tags, and whether the agent will push the tag set with a frequency greater than the preset threshold to the system or other agents. A customer label management system, characterized in that it comprises: A tag automatic adding module adapted to automatically add at least one tag to a customer's basic information and/or behavioral characteristics; The call monitoring module is adapted to monitor the call between the agent and the customer; The voice recognition module is adapted to perform voice recognition on the voice of the client when the agent talks with the client; The keyword extraction module is adapted to extract basic information and/or behavior feature keywords identified in the voice of the client call; A tag update module adapted to update or prompt for a tag corresponding to the updated keyword. A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, the processor implementing the program to implement the following steps: Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer; Step 02: monitoring the agent's call with the client; Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client; Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice; Step 05: Update or prompt to update the label corresponding to the keyword. The computer device according to claim 9, wherein the step 01 comprises adding a basic information tag according to the pre-stored basic information of the client, and adding a behavior feature tag according to the collected behavior characteristics of the client. The computer device of claim 9 wherein step 04 further comprises prompting the agent to query the customer's basic or behavioral characteristics. The computer device according to claim 9, wherein the step 04 further comprises: obtaining a selection of the agent to update the basic information or the behavior feature, and extracting the relevant basic information or behavior characteristics in the customer voice after the agent selection is extracted. Key words. The computer device according to claim 9, wherein the step 05 includes listing the extracted customer basic information and/or behavior characteristic keywords during the conversation with the client or after the call ends, and prompting whether the agent is present. Update or for the agent to select keywords to update. The computer device according to claim 9, wherein the client tag management method further comprises a step 06 of prompting the agent whether to use two or more tags as the tag set of the screening target client. A computer readable storage medium having stored thereon a computer program, wherein the program is executed by a processor to implement the following steps: Step 01, automatically adding at least one label to the basic information and/or behavior characteristics of the customer; Step 02: monitoring the agent's call with the client; Step 03: When the agent makes a call with the client, perform voice recognition on the voice of the client; Step 04, extracting basic information and/or behavior feature keywords identified in the customer voice; Step 05: Update or prompt to update the label corresponding to the keyword. The computer readable storage medium of claim 15 wherein step 01 includes adding a base information tag based on the customer's pre-stored basic information and adding a behavior feature tag based on the collected customer's behavioral characteristics. The computer readable storage medium of claim 15 wherein step 04 further comprises prompting the agent to query the customer's basic or behavioral characteristics. The computer readable storage medium according to claim 15, wherein the step 04 further comprises: obtaining a selection of the agent to the basic information or the behavior feature to be updated, and extracting the selected basic information in the customer voice after the agent selection or Keywords associated with behavioral characteristics. The computer readable storage medium of claim 15 wherein step 05 includes listing the extracted customer basic information and/or behavioral feature keywords during or after the agent's call with the client, and Indicate whether the agent is updated or for the agent to select keywords to update. The computer readable storage medium according to claim 15, wherein the client tag management method further comprises a step 06 of prompting the agent whether to use two or more tags as the tag set of the screening target client.

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