CN110544114A - Method and device for identifying and rating user group aiming at marketing preference - Google Patents
Method and device for identifying and rating user group aiming at marketing preference Download PDFInfo
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Abstract
The disclosure relates to a method, an apparatus, an electronic device and a storage medium for identifying and rating a user group aiming at marketing preferences. Wherein, the method comprises the following steps: analyzing the operator data, and identifying a user group which can be taken as marketing preference; collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform, and processing and storing the operator data; analyzing operator data stored in the data sharing platform to generate a key index of rating; and grading the user groups of the marketing preference according to a preset grading rule based on the grading key index. The method and the system identify and grade the marketing favorite user groups through analysis based on operator data, and provide accurate basis for accurate marketing.
Description
Technical Field
The present disclosure relates to the field of big data, and in particular, to a method, an apparatus, an electronic device, and a computer-readable storage medium for identifying and rating a user group for marketing preferences.
background
big data (big data) refers to a data set which cannot be captured, managed and processed by a conventional software tool within a certain time range, and is a massive, high-growth rate and diversified information asset which needs a new processing mode to have stronger decision-making power, insight discovery power and flow optimization capability. Through big data analysis, enterprises which can provide products or services for a large number of consumers can utilize big data to carry out accurate marketing; the medium and small micro-enterprises which can be in a small and beautiful mode can use big data to perform service transformation; it is possible to take full advantage of the value of large data over time for traditional enterprises that must be transformed in the face of internet pressure.
accurate marketing is an unavoidable topic in the marketing industry, but more times, people only put eyes on whether a target group is accurate or not, whether the target group rejects marketing or not is ignored, and non-negligible influence is brought to marketing without consequence, so that marketing resources are wasted, user goodness is lost, and potential customers are lost.
Accordingly, there is a need for one or more methods to address the above-mentioned problems.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method, an apparatus, an electronic device, and a computer-readable storage medium for identifying and rating a user group for marketing preferences, thereby overcoming, at least to some extent, one or more of the problems due to the limitations and disadvantages of the related art.
According to one aspect of the present disclosure, there is provided a method for identifying and rating a group of users for marketing preferences, comprising:
a user identification step, wherein operator data is analyzed to identify a user group which can be taken as marketing preference;
A data acquisition step, wherein the operator data of the user group which can be taken as marketing preference are acquired to a data sharing platform, and are processed and stored;
Analyzing the data, namely analyzing the operator data stored in the data sharing platform to generate a key index of the rating;
and a user rating step, namely rating the user groups of the marketing preference according to a preset rating rule based on the rating key index.
In an exemplary embodiment of the present disclosure, the user identifying step further includes:
the data sources of the operator data comprise short message data, call data, complaint data, e-commerce data and behavior data.
in an exemplary embodiment of the present disclosure, the data collecting step further includes:
Collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform constructed by multiple data sources, loose coupling and high heterogeneous principle;
And adopting a standardized data processing flow to process and store the operator data.
in an exemplary embodiment of the present disclosure, the data analyzing step further includes:
analyzing the short message data and the complaint data, and counting output indexes: the method comprises the following steps of (1) receiving a user number, the receiving times of activity short messages, the interaction times of the activity short messages and the related complaints times of the short messages;
analyzing the call data and the complaint data, and recording the user numbers, the call times and the complaint times of the related scenes of a satisfaction survey class, a user care class, an IVR return visit class and a customer service hotline class;
analyzing E-commerce data, and acquiring user numbers participating in E-commerce activities and participation times of different periods;
analyzing the behavior data, and acquiring the number of access times of users who actively access the service provider living room and different periods according to the URL information in the signaling data.
In an exemplary embodiment of the present disclosure, the method further comprises:
And generating a grading key index based on the analysis result of the operator data.
In an exemplary embodiment of the present disclosure, the presetting of the rating rule in the user rating step further includes:
If the marketing short message complaint or the marketing telephone complaint exists, the marketing preference grade is 1 grade;
if no complaint exists and the number of times of visit of the operator parlor in one month is 1, the marketing preference is graded to be 2 grade;
If there is no complaint and the number of times of participation in the electric business activity within one month > 1 or the number of times of visit to the operator hall within one month >3, the marketing preference is rated at level 3.
in one aspect of the present disclosure, there is provided an apparatus for identifying and rating a group of users for marketing preferences, comprising:
The user identification module is used for analyzing the operator data and identifying a user group which can be taken as marketing preference;
The data acquisition module is used for acquiring the operator data of the user group which can be taken as marketing preference into the data sharing platform, and processing and storing the operator data;
the data analysis module is used for analyzing the operator data stored in the data sharing platform and generating a key index of the rating;
and the user rating module is used for rating the user group of the marketing preference according to a preset rating rule based on the rating key index.
In one aspect of the present disclosure, there is provided an electronic device including:
A processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement a method according to any of the above.
in an aspect of the disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, realizes the method according to any one of the above.
In the identification and rating method for the marketing favorite user group in the exemplary embodiment of the disclosure, operator data is analyzed to identify the user group which can be taken as the marketing favorite; collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform, and processing and storing the operator data; analyzing operator data stored in the data sharing platform to generate a key index of rating; and grading the user groups of the marketing preference according to a preset grading rule based on the grading key index. On one hand, the method for identifying and rating the marketing favorite group breaks away from the marketing industry which focuses on the marketing target accuracy rather than the marketing rejection of the user, starts with the identification of the marketing favorite group on the basis of operator data, stores identification samples and prepares for the subsequent mining of the marketing favorite group; on the other hand, the user number and the marketing feedback indexes are analyzed and separated based on the data after standardized processing, the marketing preference user base construction and the marketing preference rating are supported, various problems brought by traditional marketing are solved, and people-oriented green marketing is really realized.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
drawings
the above and other features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 illustrates a flow chart of a method of identifying and rating a user population for marketing preferences according to an exemplary embodiment of the present disclosure;
FIG. 2 shows a schematic block diagram of an apparatus for identifying and rating a user population for marketing preferences according to an example embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure;
And
fig. 4 schematically illustrates a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the embodiments of the disclosure can be practiced without one or more of the specific details, or with other methods, components, materials, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
in the present exemplary embodiment, first, a method for identifying and rating a user group for marketing preferences is provided; referring to fig. 1, the method for identifying and rating a user population for marketing preferences may include the steps of:
a user identification step S110 of analyzing the operator data to identify a user group that can be a marketing preference;
A data acquisition step S120, in which the operator data of the user group which can be taken as marketing preference is acquired to a data sharing platform, and the operator data is processed and stored;
a data analysis step S130, analyzing the operator data stored in the data sharing platform to generate a key index of rating;
And a user rating step S140, rating the user group of the marketing preference according to a preset rating rule based on the rating key index.
In the identification and rating method for the marketing favorite user group in the exemplary embodiment of the disclosure, operator data is analyzed to identify the user group which can be taken as the marketing favorite; collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform, and processing and storing the operator data; analyzing operator data stored in the data sharing platform to generate a key index of rating; and grading the user groups of the marketing preference according to a preset grading rule based on the grading key index. On one hand, the method for identifying and rating the marketing favorite group breaks away from the marketing industry which focuses on the marketing target accuracy rather than the marketing rejection of the user, starts with the identification of the marketing favorite group on the basis of operator data, stores identification samples and prepares for the subsequent mining of the marketing favorite group; on the other hand, the user number and the marketing feedback indexes are analyzed and separated based on the data after standardized processing, the marketing preference user base construction and the marketing preference rating are supported, various problems brought by traditional marketing are solved, and people-oriented green marketing is really realized.
Next, a method of identifying and rating a user group for marketing preferences in the present exemplary embodiment will be further described.
in the user identification step S110, the operator data may be analyzed to identify a user group that may be a marketing preference.
In this exemplary embodiment, the user identifying step further includes:
The data sources of the operator data comprise short message data, call data, complaint data, e-commerce data and behavior data.
in the data collecting step S120, the operator data of the user group that can be the marketing preference can be collected into the data sharing platform, and the operator data is processed and stored.
in an embodiment of the present example, the data collecting step further comprises:
Collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform constructed by multiple data sources, loose coupling and high heterogeneous principle;
and adopting a standardized data processing flow to process and store the operator data.
in the embodiment of the present example, the operator has a plurality of data types and different types of requirements, and in order to support the data analysis requirement of the patent, data from different sources needs to be uniformly collected into a data sharing platform constructed by multiple data sources, a loose coupling principle and a high heterogeneous principle, and a normalized data processing flow is adopted to perform data processing and storage.
In the data analysis step S130, the operator data stored in the data sharing platform may be analyzed to generate a key index of rating.
In an embodiment of the present example, the data analyzing step further comprises:
analyzing the short message data and the complaint data, and counting output indexes: the method comprises the following steps of (1) receiving a user number, the receiving times of activity short messages, the interaction times of the activity short messages and the related complaints times of the short messages;
Analyzing the call data and the complaint data, and recording the user numbers, the call times and the complaint times of the related scenes of a satisfaction survey class, a user care class, an IVR return visit class and a customer service hotline class;
analyzing E-commerce data, and acquiring user numbers participating in E-commerce activities and participation times of different periods;
analyzing the behavior data, and acquiring the number of access times of users who actively access the service provider living room and different periods according to the URL information in the signaling data.
in an embodiment of the present example, the method further comprises:
And generating a grading key index based on the analysis result of the operator data.
In the embodiment of the present example, the short message data and the complaint data are analyzed, and the output index is counted: the method comprises the following steps of (1) receiving a user number, the receiving times of activity short messages, the interaction times of the activity short messages and the related complaints times of the short messages; analyzing the call data and the complaint data, and recording the user numbers, the call times and the complaint times of the related scenes of a satisfaction survey class, a user care class, an IVR return visit class and a customer service hotline class; analyzing E-commerce data, and acquiring user numbers participating in E-commerce activities and participation times of different periods; analyzing the behavior data, and acquiring the number of access times of users who actively access the service provider living room and different periods according to the URL information in the signaling data.
in the user rating step S140, the user group of the marketing preference may be rated according to a preset rating rule based on the rating key indicator.
in this exemplary embodiment, the presetting of the rating rule in the user rating step further includes:
if the marketing short message complaint or the marketing telephone complaint exists, the marketing preference grade is 1 grade;
If no complaint exists and the number of times of visit of the operator parlor in one month is 1, the marketing preference is graded to be 2 grade;
If there is no complaint and the number of times of participation in the electric business activity within one month > 1 or the number of times of visit to the operator hall within one month >3, the marketing preference is rated at level 3.
In the embodiment of the present example, the marketing preference user library is constructed based on the user data obtained by the analysis, and all the related users are marketing preference users. And (4) carrying out marketing preference rating (marketing short message complaints, marketing telephone complaints, number of times of participating in E-commerce activities and number of times of visiting in a living room of an operator) based on the matched indexes, wherein the rating is 3 grade, 2 grade and 1 grade from high to low. The rating rules are as follows:
If the marketing short message complaint or the marketing telephone complaint exists, the marketing preference grade is 1 grade;
If no complaint exists and the number of times of visit of the operator parlor in one month is 1, the marketing preference is graded to be 2 grade;
if there is no complaint and the number of times of participation in the electric business activity within one month > 1 or the number of times of visit to the operator hall within one month >3, the marketing preference is rated at level 3.
in the embodiment of the example, the marketing preference group mining is used as a target to perform multidimensional and multi-view operator data analysis, and the required data is defined to be short message data, call data, e-commerce data and behavior data. Based on different sources and formats of data, the data are required to be uniformly collected and processed to a data sharing platform constructed by multiple data sources, loose coupling and high heterogeneous principle, and a standardized data processing flow is applied to carry out data processing and storage. Aiming at the data after standardized processing, the user number and the marketing feedback indexes are analyzed and separated, the construction of a marketing preference user base and the marketing preference rating are supported, various problems brought by the traditional marketing are solved, and people-oriented green marketing is really realized.
It should be noted that although the various steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that these steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
further, in the present exemplary embodiment, there is also provided an identification and rating apparatus for a user group of marketing preferences. Referring to fig. 2, the apparatus 200 for identifying and rating a user group for marketing preferences may include: a user identification module 210, a data collection module 220, a data analysis module 230, and a user rating module 240. Wherein:
a user identification module 210, configured to analyze operator data and identify a user group that can be a marketing preference;
the data acquisition module 220 is configured to acquire operator data of the user group that can serve as the marketing preference into a data sharing platform, and perform data processing and storage on the operator data;
a data analysis module 230, configured to analyze operator data stored in the data sharing platform, and generate a key index of rating;
and the user rating module 240 is configured to rate the user group of the marketing preference according to a preset rating rule based on the rating key index.
the specific details of the above-mentioned identifying and rating device module for the user group of the marketing preference have been described in detail in the corresponding identifying and rating method for the user group of the marketing preference, and therefore are not described herein again.
It should be noted that although several modules or units of the identification and rating apparatus 200 for a user population of marketing preferences are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
in addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 300 according to such an embodiment of the invention is described below with reference to fig. 3. The electronic device 300 shown in fig. 3 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
as shown in fig. 3, electronic device 300 is embodied in the form of a general purpose computing device. The components of electronic device 300 may include, but are not limited to: the at least one processing unit 310, the at least one memory unit 320, a bus 330 connecting different system components (including the memory unit 320 and the processing unit 310), and a display unit 340.
wherein the storage unit stores program code that is executable by the processing unit 310 to cause the processing unit 310 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification. For example, the processing unit 310 may perform steps S110 to S140 as shown in fig. 1.
the storage unit 320 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)3201 and/or a cache memory unit 3202, and may further include a read only memory unit (ROM) 3203.
the storage unit 320 may also include a program/utility 3204 having a set (at least one) of program modules 3205, such program modules 3205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
Bus 330 may be one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
The electronic device 300 may also communicate with one or more external devices 370 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 300, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 300 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 350. Also, the electronic device 300 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 360. As shown, network adapter 360 communicates with the other modules of electronic device 300 via bus 330. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 300, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
referring to fig. 4, a program product 400 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (9)
1. a method for identifying and rating a population of users for marketing preferences, the method comprising:
A user identification step, wherein operator data is analyzed to identify a user group which can be taken as marketing preference;
A data acquisition step, wherein the operator data of the user group which can be taken as marketing preference are acquired to a data sharing platform, and are processed and stored;
Analyzing the data, namely analyzing the operator data stored in the data sharing platform to generate a key index of the rating;
and a user rating step, namely rating the user groups of the marketing preference according to a preset rating rule based on the rating key index.
2. The method of claim 1, wherein the user identifying step further comprises:
The data sources of the operator data comprise short message data, call data, complaint data, e-commerce data and behavior data.
3. The method of claim 1, wherein the data acquisition step further comprises:
collecting the operator data of the user group which can be taken as marketing preference into a data sharing platform constructed by multiple data sources, loose coupling and high heterogeneous principle;
And adopting a standardized data processing flow to process and store the operator data.
4. the method of claim 1, wherein the data analysis step further comprises:
Analyzing the short message data and the complaint data, and counting output indexes: the method comprises the following steps of (1) receiving a user number, the receiving times of activity short messages, the interaction times of the activity short messages and the related complaints times of the short messages;
Analyzing the call data and the complaint data, and recording the user numbers, the call times and the complaint times of the related scenes of a satisfaction survey class, a user care class, an IVR return visit class and a customer service hotline class;
analyzing E-commerce data, and acquiring user numbers participating in E-commerce activities and participation times of different periods;
Analyzing the behavior data, and acquiring the number of access times of users who actively access the service provider living room and different periods according to the URL information in the signaling data.
5. The method of claim 4, wherein the method further comprises:
And generating a grading key index based on the analysis result of the operator data.
6. The method of claim 1, wherein presetting the rating rule in the user rating step further comprises:
If the marketing short message complaint or the marketing telephone complaint exists, the marketing preference grade is 1 grade;
if no complaint exists and the number of times of visit of the operator parlor in one month is 1, the marketing preference is graded to be 2 grade;
if there is no complaint and the number of times of participation in the electric business activity within one month > 1 or the number of times of visit to the operator hall within one month >3, the marketing preference is rated at level 3.
7. An apparatus for identifying and rating a population of users for marketing preferences, the apparatus comprising:
The user identification module is used for analyzing the operator data and identifying a user group which can be taken as marketing preference;
The data acquisition module is used for acquiring the operator data of the user group which can be taken as marketing preference into the data sharing platform, and processing and storing the operator data;
the data analysis module is used for analyzing the operator data stored in the data sharing platform and generating a key index of the rating;
And the user rating module is used for rating the user group of the marketing preference according to a preset rating rule based on the rating key index.
8. An electronic device, comprising
a processor; and
a memory having computer readable instructions stored thereon which, when executed by the processor, implement the method of any of claims 1 to 6.
9. a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 6.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201040852A (en) * | 2009-05-13 | 2010-11-16 | Alibaba Group Holding Ltd | Method and system of evaluating online marketing efficiency |
| CN103810623A (en) * | 2014-03-04 | 2014-05-21 | 深圳市远行科技有限公司 | Real-time automatic marketing method and system |
| CN105741134A (en) * | 2016-01-26 | 2016-07-06 | 北京百分点信息科技有限公司 | Method and apparatus for applying cross-data-source marketing crowds to marketing |
| CN105869035A (en) * | 2016-04-07 | 2016-08-17 | 中国联合网络通信集团有限公司 | Mobile user credit evaluation method and apparatus |
| CN106372959A (en) * | 2016-08-22 | 2017-02-01 | 广州图灵科技有限公司 | Internet-based user access behavior digital marketing system and method |
| CN106934754A (en) * | 2016-12-31 | 2017-07-07 | 南京真云计算科技有限公司 | User evaluation |
| CN107230103A (en) * | 2017-05-22 | 2017-10-03 | 江苏德胜智业信息技术有限公司 | A kind of precision marketing system |
| CN107256496A (en) * | 2017-05-27 | 2017-10-17 | 上海非码网络科技有限公司 | Customer management method and system, server based on multi-platform data |
| CN107392682A (en) * | 2017-09-13 | 2017-11-24 | 沈阳东知科技有限公司 | A kind of customer information processing system and processing method by all kinds of means |
| CN107464142A (en) * | 2017-08-09 | 2017-12-12 | 南京甄视智能科技有限公司 | Method, the user's portrait system for system of being drawn a portrait based on marketing feedback result back feeding user |
| CN107527240A (en) * | 2016-08-18 | 2017-12-29 | 南京坦道信息科技有限公司 | A kind of operator's industry product Praise effect identification system and method |
| CN107784526A (en) * | 2017-12-04 | 2018-03-09 | 北京铭嘉实咨询有限公司 | Precision Marketing Method based on user characteristics behavior |
| CN108053261A (en) * | 2017-12-27 | 2018-05-18 | 网智天元科技集团股份有限公司 | Marketing message processing method and system |
| CN109003127A (en) * | 2018-07-06 | 2018-12-14 | 国网福建省电力有限公司 | A kind of credit rating method based on Electricity customers data |
| CN109412832A (en) * | 2018-09-10 | 2019-03-01 | 中国联合网络通信集团有限公司 | User service providing method and system |
| CN109902966A (en) * | 2019-03-06 | 2019-06-18 | 中国联合网络通信集团有限公司 | A kind of user ranking method and system |
-
2019
- 2019-08-16 CN CN201910756968.XA patent/CN110544114A/en active Pending
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| TW201040852A (en) * | 2009-05-13 | 2010-11-16 | Alibaba Group Holding Ltd | Method and system of evaluating online marketing efficiency |
| CN103810623A (en) * | 2014-03-04 | 2014-05-21 | 深圳市远行科技有限公司 | Real-time automatic marketing method and system |
| CN105741134A (en) * | 2016-01-26 | 2016-07-06 | 北京百分点信息科技有限公司 | Method and apparatus for applying cross-data-source marketing crowds to marketing |
| CN105869035A (en) * | 2016-04-07 | 2016-08-17 | 中国联合网络通信集团有限公司 | Mobile user credit evaluation method and apparatus |
| CN107527240A (en) * | 2016-08-18 | 2017-12-29 | 南京坦道信息科技有限公司 | A kind of operator's industry product Praise effect identification system and method |
| CN106372959A (en) * | 2016-08-22 | 2017-02-01 | 广州图灵科技有限公司 | Internet-based user access behavior digital marketing system and method |
| CN106934754A (en) * | 2016-12-31 | 2017-07-07 | 南京真云计算科技有限公司 | User evaluation |
| CN107230103A (en) * | 2017-05-22 | 2017-10-03 | 江苏德胜智业信息技术有限公司 | A kind of precision marketing system |
| CN107256496A (en) * | 2017-05-27 | 2017-10-17 | 上海非码网络科技有限公司 | Customer management method and system, server based on multi-platform data |
| CN107464142A (en) * | 2017-08-09 | 2017-12-12 | 南京甄视智能科技有限公司 | Method, the user's portrait system for system of being drawn a portrait based on marketing feedback result back feeding user |
| CN107392682A (en) * | 2017-09-13 | 2017-11-24 | 沈阳东知科技有限公司 | A kind of customer information processing system and processing method by all kinds of means |
| CN107784526A (en) * | 2017-12-04 | 2018-03-09 | 北京铭嘉实咨询有限公司 | Precision Marketing Method based on user characteristics behavior |
| CN108053261A (en) * | 2017-12-27 | 2018-05-18 | 网智天元科技集团股份有限公司 | Marketing message processing method and system |
| CN109003127A (en) * | 2018-07-06 | 2018-12-14 | 国网福建省电力有限公司 | A kind of credit rating method based on Electricity customers data |
| CN109412832A (en) * | 2018-09-10 | 2019-03-01 | 中国联合网络通信集团有限公司 | User service providing method and system |
| CN109902966A (en) * | 2019-03-06 | 2019-06-18 | 中国联合网络通信集团有限公司 | A kind of user ranking method and system |
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Application publication date: 20191206 |