CN117010947A - NPS investigation method, device, equipment and storage medium based on business activity - Google Patents
NPS investigation method, device, equipment and storage medium based on business activity Download PDFInfo
- Publication number
- CN117010947A CN117010947A CN202311278339.3A CN202311278339A CN117010947A CN 117010947 A CN117010947 A CN 117010947A CN 202311278339 A CN202311278339 A CN 202311278339A CN 117010947 A CN117010947 A CN 117010947A
- Authority
- CN
- China
- Prior art keywords
- detraction
- service
- factors
- business
- analysis
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000011835 investigation Methods 0.000 title claims abstract description 32
- 230000000694 effects Effects 0.000 title claims abstract description 29
- 238000004458 analytical method Methods 0.000 claims abstract description 50
- 238000004590 computer program Methods 0.000 claims description 16
- 238000011160 research Methods 0.000 claims description 6
- 238000007621 cluster analysis Methods 0.000 claims description 5
- 230000008569 process Effects 0.000 abstract description 16
- 230000006872 improvement Effects 0.000 description 12
- 238000004891 communication Methods 0.000 description 9
- 238000010586 diagram Methods 0.000 description 9
- 238000012545 processing Methods 0.000 description 6
- 238000011156 evaluation Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005284 excitation Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000014509 gene expression Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0203—Market surveys; Market polls
Landscapes
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Finance (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses an NPS investigation method, device and equipment based on business activities and a storage medium. The method comprises the following steps: pushing a return visit roll to a derogative client at a service node, and acquiring return visit information filled in the return visit roll by the derogative client; semantically analyzing the return visit information to obtain a detraction reason, and performing custom analysis on the detraction reason to obtain a detraction factor; determining service driving factors associated with the derogatory reasons and the service nodes by using a target association model; and determining the service influence factor of the service node according to the service driving factor and the detraction factor. The embodiment of the invention can avoid errors of judging the service influence factors by manual experience and improve the improved guiding effect of NPS investigation on specific service processes.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a storage medium for NPS investigation based on business activities.
Background
NPS (Net Promoter Score net recommendation) is an index that measures the likelihood that a customer will recommend a product or service of an enterprise to others in the future, and is an important indicator for measuring customer experience. NPS was first proposed in 2003 and is now widely used in domestic financial, communications and internet industries.
Currently, NPS studies typically first screen target clients and investigate the likelihood of client recommendations by sending a questionnaire to the client. Clients are classified into "recommender", "passive" and "derogator" based on replies. The final score for the net recommendation value is obtained by subtracting the percentage of "detractors" from the percentage of "recommenders".
The NPS investigation method is often only capable of knowing the customer grading and grading reasons on a shallower table, the investigation depth is difficult to ensure, and the insight on the service improvement points and experience driving factors behind the detraction reasons is lacking. The NPS research at the present stage is remained on the macroscopic level of the whole look and feel of the client to the enterprise service, the enterprise brands and the like, the depth is not enough, and the NPS has little guidance on the improvement of the specific business process.
Disclosure of Invention
The invention provides an NPS investigation method, device, equipment and storage medium based on business activities, which are used for avoiding errors of judging business influence factors by manual experience and improving the improvement guiding effect of NPS investigation on specific business processes.
According to an aspect of the present invention, there is provided an NPS investigation method based on business activity, including:
pushing a return visit roll to a derogative client at a service node, and acquiring return visit information filled in the return visit roll by the derogative client;
semantically analyzing the return visit information to obtain a detraction reason, and performing custom analysis on the detraction reason to obtain a detraction factor;
determining service driving factors associated with the derogatory reasons and the service nodes by using a target association model;
and determining the service influence factor of the service node according to the service driving factor and the detraction factor.
According to another aspect of the present invention, there is provided an NPS investigation apparatus based on business activity, including:
the return visit information acquisition module is used for pushing a return visit roll to the detraction clients at the service node and acquiring return visit information filled in the return visit roll by the detraction clients;
the detraction reason analysis module is used for semantically analyzing the return visit information to obtain detraction reasons, and performing custom analysis on the detraction reasons to obtain detraction factors;
the driving factor determining module is used for determining service driving factors associated with the derogatory reasons and the service nodes by utilizing a target association model;
and the business influence determining module is used for determining the business influence factors of the business nodes according to the business driving factors and the derogatory factors.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the business activity-based NPS investigation method of any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the NPS investigation method based on business activity according to any of the embodiments of the present invention when executed.
According to the embodiment of the invention, the NPS research of the outer client is combined into the inner specific business process, the NPS system is combined with the inner side and the outer side to automatically summarize the key business improvement factors-business influence factors for improving the NPS experience, so that the error of judging the business influence factors by manual experience is avoided, the follow-up process of evaluating and driving the business through the NPS of the client is facilitated, the experience of the client is improved, and the sales of enterprise products and services is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an NPS investigation method based on business activity according to an embodiment of the present invention;
fig. 2A is a flowchart of an NPS investigation method based on business activity according to a further embodiment of the present invention;
fig. 2B is a data flow diagram of an NPS investigation in accordance with a further embodiment of the present invention;
FIG. 2C is a schematic diagram of a two-factor excitation analysis provided in accordance with yet another embodiment of the present invention;
fig. 3 is a schematic structural diagram of an NPS investigation apparatus based on business activity according to another embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a flowchart of a NPS investigation method based on business activity, which is provided in an embodiment of the present invention, and the embodiment may be applied to a set business node after a customer completes a certain business process, where the NPS system pushes a return visit roll to a detractor customer giving negative feedback, and the detractor customer fills in a detractor reason in the return visit roll, and the NPS system determines key business improvement factors according to the detractor reason and pushes the key business improvement factors to a business responsible person of a corresponding business node. As shown in fig. 1, the method includes:
and S110, pushing a return visit roll to the derogative client at the service node, and acquiring return visit information filled in the return visit roll by the derogative client.
Wherein, the detraction clients are clients giving detraction replies to the NPS questionnaires.
Specifically, before NPS investigation, the service flow and the service node are bound, driving factors in the service flow are determined through manual qualitative service interviews, and detraction root corresponding to the service driving factors and possible expected advance maintenance of clients are performed. After the client completes the business process, the business node bound with the business process is triggered, the NPS system pushes a client questionnaire to the client, the client fills in general NPS return visit information in the client questionnaire, and the NPS system determines the detractors of the clients according to the general NPS return visit information. For the determined detraction users, the NPS system combines the business management system information to generate and push a return visit roll to the detraction clients, and collects return visit information filled in the return visit roll by the clients, wherein the return visit information records the detraction reasons input by the detraction clients on the business process.
S120, semantically analyzing the return visit information to obtain a detraction reason, and performing custom analysis on the detraction reason to obtain a detraction factor.
The return visit information comprises answer contents of open questions in the return visit volume, and the answer contents are a section of unstructured text contents input by a client.
Specifically, semantic analysis is carried out on unstructured answer content in the return visit information, and the detraction reasons of the actual expression of the clients are determined. The derogatory reasons are usually negative expressions, customer appeal analysis is performed on the derogatory reasons, the derogatory factors in the derogatory reasons are determined, and the derogatory factors are actually depth analysis results based on the NPS data of the external customer side.
S130, determining service driving factors associated with the derogatory reasons and the service nodes by using a target association model.
Specifically, the association relation among the derogatory reasons, the service nodes and the service driving factors is recorded in the target association model. In the case of service node and detraction cause determination, service driving factors associated with the current detraction cause at the service node can be determined through the target association model, and the service driving factors are actually deep analysis results of NPS data based on an internal service side.
And S140, determining the service influence factors of the service nodes according to the service driving factors and the detraction factors.
Wherein, the business impact factors are key business improvement factors which can effectively improve the customer evaluation.
Specifically, after the detraction factors and the service driving factors are respectively determined according to the detraction reasons, the two factors are integrated and analyzed, and the service influencing factors influencing the detraction evaluation given by the detraction clients are determined to serve as key factors for subsequent service improvement.
According to the embodiment of the invention, the NPS research of the outer client is combined into the inner specific business process, the NPS system is combined with the inner side and the outer side to automatically summarize the key business improvement factors-business influence factors for improving the NPS experience, so that the error of judging the business influence factors by manual experience is avoided, the follow-up process of evaluating and driving the business through the NPS of the client is facilitated, the experience of the client is improved, and the sales of enterprise products and services is improved.
Fig. 2A is a flowchart of an NPS investigation method based on business activity according to another embodiment of the present invention, where the present embodiment is optimized and improved based on the foregoing embodiment. As shown in fig. 2A, the method includes:
s210, combining the business driving factors, the customer portraits and the customer management information to establish a target association model among the business nodes, the driving factors and the detraction reasons.
Specifically, the NPS system obtains customer portraits and customer management information from the unified customer information system, obtains business process information from the insurance business management system, and obtains each business driving factor determined manually. The NPS performs statistical analysis on the business driving factors, the customer portraits and the customer operation information, determines the association relationship among the business nodes, the driving factors and the derogatory reasons, and completes the establishment of a target association model.
And S220, pushing a return visit roll to the derogative client at the service node, and acquiring return visit information filled in the return visit roll by the derogative client.
S230, carrying out semantic analysis on answer texts of open questions in the return visit information according to historical data to obtain detraction reasons of the service nodes; and carrying out root cause analysis, expectation analysis and cluster analysis on the derogatory reasons to obtain derogatory factors.
Specifically, the history data is imported into the NPS system, and the NPS system performs semantic analysis on answer texts of the open questions with reference to the history data (such as the history detraction reasons), so that more accurate detraction reasons can be obtained. After the customer semantic analysis module finishes semantic analysis, the customer appeal analysis module performs cluster analysis, root cause information analysis and prediction information analysis on the detraction causes, and the detraction factors in the detraction causes are determined.
S240, determining service driving factors associated with the derogatory reasons and the service nodes by using a target association model.
S250, classifying and combining the service driving factors and the derogatory factors to obtain the service influencing factors of the service nodes.
In particular, although the service driving factor and the detracting factor are the deep parsing results of the NPS data by the inner side and the outer side, the two may still have repetitive or similar problems, for example, a certain service driving factor is also a detracting factor at the same time or is quite similar to a certain detracting factor. The business driving factors and the derogatory factors are required to be classified and combined, and the classified and combined business driving factors and derogatory factors are used as final business influencing factors, so that the business influencing factors are combined and tidied, and the guiding accuracy and effectiveness of the follow-up business influencing factors on the improvement of the business process are improved.
S260, carrying out influence sorting on the business influence factors based on a double-factor theory, and generating an influence analysis report in a preset format according to the business influence factors subjected to influence sorting; and pushing the impact analysis report to a business responsible person of the business node through a supervision system.
Specifically, after determining the service impact factors, the NPS system sorts the service impact factors according to the impact degree of the customer evaluation based on the double factor theory, generates an impact analysis report of an electronic version based on a preset format by using the sorted service impact factors, and the impact analysis report can be sent to a service responsible person of a service node through a supervision system connected with the NPS system.
Fig. 2B is an exemplary data flow diagram of an NPS investigation in accordance with a further embodiment of the present invention. When the NPS system researches the NPS, the NPS system refers to the detraction reasons of the client side and additionally refers to the service driving factors of the service side, so that the service influencing factors for improving the service can be obtained.
Illustratively, FIG. 2C is a schematic diagram of a two-factor excitation analysis provided in accordance with yet another embodiment of the present invention. The influence degree of each business influence factor can be ranked according to the specific positions and the relative positions of the business influence factors in different quadrants.
According to the embodiment of the invention, after the service influence factors are determined, the analysis report is timely pushed to the service responsible person, so that the service improvement efficiency is improved.
Fig. 3 is a schematic structural diagram of an NPS investigation apparatus based on business activity according to another embodiment of the present invention. As shown in fig. 3, the apparatus includes:
a return visit information obtaining module 310, configured to push a return visit roll to a derogative client at a service node, and obtain return visit information filled in the return visit roll by the derogative client;
the detraction reason analysis module 320 is configured to semantically analyze the return information to obtain a detraction reason, and perform customer complaint analysis on the detraction reason to obtain a detraction factor;
a driving factor determining module 330, configured to determine a service driving factor associated with the derogatory reason and the service node using a target association model;
and a service impact determination module 340, configured to determine a service impact factor of the service node according to the service driving factor and the derogatory factor.
The NPS investigation device based on the business activity provided by the embodiment of the invention can execute the NPS investigation method based on the business activity provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Optionally, the derogatory reason analysis module 320 includes:
the detraction reason analysis unit is used for carrying out semantic analysis on the answer text of the open questions in the return visit information according to the historical data to obtain the detraction reason of the service node;
and the derogatory factor acquisition unit is used for carrying out root cause analysis, expected analysis and cluster analysis on the derogatory reasons to obtain the derogatory factors.
Optionally, the apparatus further includes:
and the association model building module is used for building a target association model among the service nodes, the driving factors and the detraction reasons by combining the service driving factors, the customer portraits and the customer operation information.
Optionally, the service influence determining module is specifically configured to classify and combine the service driving factor and the detracting factor to obtain a service influence factor of the service node.
Optionally, the apparatus further includes:
the analysis report generation module is used for carrying out influence sorting on the business influence factors based on a double-factor theory and generating an influence analysis report in a preset format according to the business influence factors subjected to the influence sorting;
and the analysis report pushing module is used for pushing the influence analysis report to the business responsible person of the business node through a supervision system.
The NPS investigation device based on the business activity further described can also execute the NPS investigation method based on the business activity provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 4 shows a schematic diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM) 42, a Random Access Memory (RAM) 43, etc., in which the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 may also be stored. The processor 41, the ROM 42 and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
Various components in electronic device 40 are connected to I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, an optical disk, or the like; and a communication unit 49 such as a network card, modem, wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 41 may be various general and/or special purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 41 performs the various methods and processes described above, such as NPS research methods based on business activity.
In some embodiments, the business activity-based NPS investigation method may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more of the steps of the business activity-based NPS investigation method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to perform the business activity-based NPS investigation method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a customer, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a customer; and a keyboard and pointing device (e.g., a mouse or trackball) by which a client can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a customer; for example, feedback provided to the customer may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the customer may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a client computer having a graphical client interface or a web browser through which a client can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. An NPS investigation method based on business activity, the method comprising:
pushing a return visit roll to a derogative client at a service node, and acquiring return visit information filled in the return visit roll by the derogative client;
semantically analyzing the return visit information to obtain a detraction reason, and performing custom analysis on the detraction reason to obtain a detraction factor;
determining service driving factors associated with the derogatory reasons and the service nodes by using a target association model;
and determining the service influence factor of the service node according to the service driving factor and the detraction factor.
2. The method of claim 1, wherein said semantically analyzing said return information for a detraction cause and performing a customer complaint analysis of said detraction cause for a detraction factor comprises:
carrying out semantic analysis on answer texts of open questions in the return visit information according to historical data to obtain detraction reasons of the service nodes;
and carrying out root cause analysis, expectation analysis and cluster analysis on the derogatory reasons to obtain derogatory factors.
3. The method of claim 1, wherein prior to determining the service driver associated with the derogatory cause and the service node using a target association model, further comprising:
and combining the business driving factors, the customer portraits and the customer management information to establish a target association model among the business nodes, the driving factors and the detraction reasons.
4. The method of claim 1, wherein said determining a traffic impact factor for the traffic node based on the traffic driver and the derogation factor comprises:
and classifying and combining the service driving factors and the derogatory factors to obtain the service influencing factors of the service nodes.
5. The method of claim 1, wherein after determining the traffic impact factor for the traffic node, further comprising:
the business influence factors are subjected to influence sorting based on a double-factor theory, and an influence analysis report in a preset format is generated according to the business influence factors subjected to influence sorting;
and pushing the impact analysis report to a business responsible person of the business node through a supervision system.
6. An NPS research device based on business activity, the device comprising:
the return visit information acquisition module is used for pushing a return visit roll to the detraction clients at the service node and acquiring return visit information filled in the return visit roll by the detraction clients;
the detraction reason analysis module is used for semantically analyzing the return visit information to obtain detraction reasons, and performing custom analysis on the detraction reasons to obtain detraction factors;
the driving factor determining module is used for determining service driving factors associated with the derogatory reasons and the service nodes by utilizing a target association model;
and the business influence determining module is used for determining the business influence factors of the business nodes according to the business driving factors and the derogatory factors.
7. The apparatus according to claim 6, wherein said detraction cause analysis module comprises:
the detraction reason analysis unit is used for carrying out semantic analysis on the answer text of the open questions in the return visit information according to the historical data to obtain the detraction reason of the service node;
and the derogatory factor acquisition unit is used for carrying out root cause analysis, expected analysis and cluster analysis on the derogatory reasons to obtain the derogatory factors.
8. The apparatus of claim 6, wherein the apparatus further comprises:
and the association model building module is used for building a target association model among the service nodes, the driving factors and the detraction reasons by combining the service driving factors, the customer portraits and the customer operation information.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the business activity-based NPS investigation method of any of claims 1-5.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the business activity-based NPS investigation method of any of claims 1-5 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311278339.3A CN117010947B (en) | 2023-10-07 | 2023-10-07 | NPS investigation method, device, equipment and storage medium based on business activity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311278339.3A CN117010947B (en) | 2023-10-07 | 2023-10-07 | NPS investigation method, device, equipment and storage medium based on business activity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117010947A true CN117010947A (en) | 2023-11-07 |
| CN117010947B CN117010947B (en) | 2024-01-09 |
Family
ID=88563954
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311278339.3A Active CN117010947B (en) | 2023-10-07 | 2023-10-07 | NPS investigation method, device, equipment and storage medium based on business activity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117010947B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107835231A (en) * | 2017-10-19 | 2018-03-23 | 平安科技（深圳）有限公司 | The processing method and terminal device of feedback information |
| WO2018201699A1 (en) * | 2017-05-05 | 2018-11-08 | 平安科技（深圳）有限公司 | Customer evaluation method, device, and apparatus, and computer readable storage medium |
| CN108776857A (en) * | 2018-04-22 | 2018-11-09 | 平安科技（深圳）有限公司 | NPS short messages method of investigation and study, system, computer equipment and storage medium |
| CN109086446A (en) * | 2018-08-20 | 2018-12-25 | 阳光保险集团股份有限公司 | NPS customer experience management method, device and computer readable storage medium |
| CN109685560A (en) * | 2018-12-17 | 2019-04-26 | 泰康保险集团股份有限公司 | Big data processing method, device, medium and electronic equipment |
| CN110765776A (en) * | 2019-10-11 | 2020-02-07 | 阳光财产保险股份有限公司 | Method and device for generating return visit labeling sample data |
| CN111582731A (en) * | 2020-05-11 | 2020-08-25 | 拉扎斯网络科技（上海）有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN114693325A (en) * | 2020-12-29 | 2022-07-01 | 中国联合网络通信集团有限公司 | User public praise intelligent guarantee method and device based on neural network |
| US20220358529A1 (en) * | 2021-05-10 | 2022-11-10 | Microsoft Technology Licensing, Llc | Net promoter score uplift for specific verbatim topic derived from user feedback |
| CN115601080A (en) * | 2022-08-25 | 2023-01-13 | 蒲惠智造科技股份有限公司（Cn） | Industrial big data management method and system based on cloud server and storage medium |
| CN115601047A (en) * | 2022-09-23 | 2023-01-13 | 阿里巴巴（中国）有限公司（Cn） | Data processing method and device |
-
2023
- 2023-10-07 CN CN202311278339.3A patent/CN117010947B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018201699A1 (en) * | 2017-05-05 | 2018-11-08 | 平安科技（深圳）有限公司 | Customer evaluation method, device, and apparatus, and computer readable storage medium |
| CN107835231A (en) * | 2017-10-19 | 2018-03-23 | 平安科技（深圳）有限公司 | The processing method and terminal device of feedback information |
| CN108776857A (en) * | 2018-04-22 | 2018-11-09 | 平安科技（深圳）有限公司 | NPS short messages method of investigation and study, system, computer equipment and storage medium |
| CN109086446A (en) * | 2018-08-20 | 2018-12-25 | 阳光保险集团股份有限公司 | NPS customer experience management method, device and computer readable storage medium |
| CN109685560A (en) * | 2018-12-17 | 2019-04-26 | 泰康保险集团股份有限公司 | Big data processing method, device, medium and electronic equipment |
| CN110765776A (en) * | 2019-10-11 | 2020-02-07 | 阳光财产保险股份有限公司 | Method and device for generating return visit labeling sample data |
| CN111582731A (en) * | 2020-05-11 | 2020-08-25 | 拉扎斯网络科技（上海）有限公司 | Data processing method and device, electronic equipment and readable storage medium |
| CN114693325A (en) * | 2020-12-29 | 2022-07-01 | 中国联合网络通信集团有限公司 | User public praise intelligent guarantee method and device based on neural network |
| US20220358529A1 (en) * | 2021-05-10 | 2022-11-10 | Microsoft Technology Licensing, Llc | Net promoter score uplift for specific verbatim topic derived from user feedback |
| CN115601080A (en) * | 2022-08-25 | 2023-01-13 | 蒲惠智造科技股份有限公司（Cn） | Industrial big data management method and system based on cloud server and storage medium |
| CN115601047A (en) * | 2022-09-23 | 2023-01-13 | 阿里巴巴（中国）有限公司（Cn） | Data processing method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117010947B (en) | 2024-01-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2018538587A (en) | Risk assessment method and system | |
| US11275748B2 (en) | Influence score of a social media domain | |
| WO2014193399A1 (en) | Influence score of a brand | |
| WO2013160904A1 (en) | Influence scores for social media profiles | |
| US11062350B2 (en) | Method, apparatus, and device for monitoring promotion status data, and non-volatile computer storage medium | |
| CN112269918B (en) | Information recommendation method, device, equipment and storage medium | |
| CN114090601B (en) | Data screening method, device, equipment and storage medium | |
| CN112017062A (en) | Resource limit distribution method and device based on guest group subdivision and electronic equipment | |
| US10394804B1 (en) | Method and system for increasing internet traffic to a question and answer customer support system | |
| US20180322125A1 (en) | Itemset determining method and apparatus, processing device, and storage medium | |
| US20230085599A1 (en) | Method and device for training tag recommendation model, and method and device for obtaining tag | |
| CN117010947B (en) | NPS investigation method, device, equipment and storage medium based on business activity | |
| US10853820B2 (en) | Method and apparatus for recommending topic-cohesive and interactive implicit communities in social customer relationship management | |
| CN113612777B (en) | Training method, flow classification method, device, electronic equipment and storage medium | |
| CN113495841B (en) | Compatibility detection method, device, equipment, storage medium and program product | |
| CN115202847A (en) | Task scheduling method and device | |
| CN113360672A (en) | Methods, apparatus, devices, media and products for generating a knowledge graph | |
| CN114172725B (en) | Illegal website processing method and device, electronic equipment and storage medium | |
| CN116628167B (en) | Response determination method and device, electronic equipment and storage medium | |
| CN117574146B (en) | Text classification labeling method, device, electronic equipment and storage medium | |
| CN117633226A (en) | Classification method and device, storage medium and electronic equipment | |
| CN113327133A (en) | Data recommendation method, data recommendation device, electronic equipment and readable storage medium | |
| CN118312663A (en) | Method and device for determining article information, electronic equipment and storage medium | |
| CN115965276A (en) | Index set determination method and device, electronic equipment and storage medium | |
| CN113344405A (en) | Method, apparatus, device, medium, and product for generating information based on knowledge graph |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |