CN111695015A - Customer behavior analysis method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention provides a customer behavior analysis method, a customer behavior analysis device, computer equipment and a storage medium, wherein the method comprises the following steps: the method comprises the following steps that behavior information of customers is captured by a capturing device according to preset characteristics, and a behavior database is established according to the behavior information, wherein the customers comprise transaction customers and non-transaction customers; receiving follow-up strategy information and label information of a transaction client, and establishing a transaction information base according to the follow-up strategy information and the label information; inputting face information of a transaction client through a face recognition device, and screening transaction client behavior data in a behavior database according to the face information; establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data; and capturing the real-time behavior information of the client by the capturing device according to the preset characteristics, and carrying out comparative analysis on the real-time behavior information according to the deal forecasting model. According to the scheme, the behavior of the client is analyzed in real time through modeling of behavior characteristics of the transaction client, so that business personnel can accurately and efficiently follow the client, and the working efficiency is improved.
Description
Technical Field
The present invention relates to the field of computer communications technologies, and in particular, to a method and an apparatus for analyzing client behavior, a computer device, and a storage medium.
Background
In the existing business scenario, on one hand, business personnel classify and follow up customers according to experience and subjective feeling, so that the situations of inaccuracy in customer analysis and improper use of follow-up strategies occur due to different business levels of the business personnel, and thus the situation of high-quality customer loss is easily caused; on the other hand, due to the fact that no mature behavior analysis standard, data accumulation, precipitation of methodology and the like exist, business personnel cannot learn and grow quickly, and further work efficiency is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a customer behavior analysis method, apparatus, computer device, and storage medium for solving the above technical problems.
A customer behavior analysis method, the method comprising: capturing behavior information of clients according to a preset characteristic capturing device, and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients; receiving follow-up strategy information and label information of a transaction client, and establishing a transaction information base according to the follow-up strategy information and the label information; inputting face information of a transaction client through a face recognition device, and screening transaction client behavior data in the behavior database according to the face information; establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data; and capturing real-time behavior information of the client by a capturing device according to preset characteristics, and carrying out comparative analysis on the real-time behavior information according to the deal prediction model.
In one embodiment, the capturing device captures behavior information of customers according to preset characteristics, and establishes a behavior database according to the behavior information, where the customers include transaction customers and non-transaction customers, and specifically: capturing behavior information of a client by a capturing device according to a preset characteristic; uploading the behavior information to a server for characteristic data processing to obtain behavior characteristic data of the client; and establishing a behavior database according to the behavior characteristic data.
In one embodiment, after the creating a deal forecasting model according to the deal information base and the deal customer behavior data, the method further includes: capturing real-time behavior information of a client according to a preset characteristic capturing device, updating the behavior database at regular time, and acquiring updated behavior data of the transaction client; updating the deal information base at regular time according to the follow-up strategy information and the label information received in real time to obtain an updated deal information base; and updating a deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain an updated deal forecasting model.
In one embodiment, the capturing device captures real-time behavior information of a client according to a preset feature, and performs comparative analysis on the real-time behavior information according to the deal prediction model, specifically: the grabbing device grabs the real-time behavior information of the client according to the preset characteristics; uploading the real-time behavior information to a server for characteristic data processing to obtain real-time behavior characteristic data of a client; and carrying out comparative analysis on the real-time behavior characteristic data according to the deal prediction model.
In one embodiment, the capturing device captures real-time behavior information of a client according to a preset feature, and after performing comparative analysis on the real-time behavior information according to the deal prediction model, the method further includes: judging whether the real-time behavior characteristic data conforms to the deal forecasting model; if yes, extracting target transaction customer behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model; extracting corresponding target follow-up strategy information and target label information in the transaction information base according to the target transaction client behavior data; and displaying the target follow-up strategy information and the target label information.
The utility model provides a customer behavior analysis device, includes that the action snatchs module, information receiving module, data screening module, model building module and action analysis module, wherein: the behavior capturing module is used for capturing behavior information of clients according to a preset characteristic capturing device and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients; the information receiving module is used for receiving follow-up strategy information and label information of a transaction client and establishing a transaction information base according to the follow-up strategy information and the label information; the data screening module is used for inputting face information of a transaction client through a face recognition device and screening transaction client behavior data in the behavior database according to the face information; the model establishing module is used for establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data; the behavior analysis module is used for capturing real-time behavior information of a client according to a preset characteristic capturing device and carrying out comparative analysis on the real-time behavior information according to the deal prediction model.
In one embodiment, the apparatus further comprises a model update module: the model updating module is used for capturing real-time behavior information of the client according to a preset characteristic capturing device, updating the behavior database at regular time and acquiring updated behavior data of the transaction client; the model updating module is also used for updating the deal information base at regular time according to the follow-up strategy information and the label information received in real time to obtain an updated deal information base; and the model updating module is also used for updating a deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain an updated deal forecasting model.
In one embodiment, the apparatus further comprises an information display module: the information display module is used for judging whether the real-time behavior characteristic data conforms to the deal forecasting model; the information display module is further used for extracting target transaction client behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model if the target transaction client behavior data are consistent with the real-time behavior characteristic data; the information display module is also used for extracting corresponding target follow-up strategy information and target label information in the transaction information base according to the target transaction client behavior data; the information display module is further configured to display the target follow-up policy information and the target tag information.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the customer behavior analysis method described in the various embodiments above when executing the program.
A storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the customer behavior analysis method described in the various embodiments above.
According to the customer behavior analysis method, the customer behavior analysis device, the computer equipment and the storage medium, the business prediction model is established according to the behavior data, the follow-up strategy information and the label information of the business customer, when business personnel follow up customer service in real time, the business prediction model is used for comparing the characteristic behaviors of the customer, the guide of relevant follow-up strategies is pushed in time, the business personnel are helped to accurately and efficiently follow up the customer, and therefore the working efficiency of the business personnel is effectively improved.
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FIG. 1 is a diagram of an application scenario of a customer behavior analysis method in one embodiment;
FIG. 2 is a flow diagram that illustrates a method for analyzing customer behavior, according to one embodiment;
FIG. 3 is a block diagram showing the structure of a customer behavior analysis device according to an embodiment;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings by way of specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The customer behavior analysis method provided by the application can be applied to the application environment shown in fig. 1. The feature capture device 1 communicates with the server 2 through a network, and the server 2 communicates with the terminal 3 through the network. The server 2 can receive the client behavior information sent by the characteristic grasping device 1, and the client behavior information is processed in the server 2; the service personnel input the client follow-up information and the client label information into the terminal 3, the terminal 3 transmits the information input by the service personnel to the server 2, the server 2 processes the received information, and meanwhile, the server 2 can also push the information to the terminal 3. Each service person has a terminal 3, the terminal 3 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 2 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, there is provided a customer behavior analysis method, comprising the steps of:
s110, capturing behavior information of the clients according to the preset characteristics, and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients.
Specifically, when the feature capture device is deployed in a case, the feature capture device needs to be arranged in several important areas, such as a sand table area, a sample plate, a VR experience area, a negotiation area, and the like, wherein the feature capture device is substantially a camera, and the feature capture device can capture the face of a client, specific body movements, facial expression changes, and wearing and dressing related feature information in an all-around manner. The characteristic grabbing device uploads the grabbed client information to the cloud server, the cloud server processes the client information after receiving the client information, and then a behavior database is established according to the processed information, and the clients are divided into transaction clients and non-transaction clients according to whether the transaction is completed or not.
In one embodiment, step S110 specifically includes: capturing behavior information of a client by a capturing device according to a preset characteristic; uploading the behavior information to a server for characteristic data processing to obtain behavior characteristic data of the client; and establishing a behavior database according to the behavior characteristic data. Specifically, after behavior information of a client is captured by using the characteristic capture device, the behavior information is uploaded to the server, the server carries out data processing on the behavior information of the client to obtain behavior characteristic data of the client, and meanwhile, the residence time and visiting times of the fixed area of the client are counted to establish a behavior database.
S120, receiving the follow-up strategy information and the label information of the transaction client, and establishing a transaction information base according to the follow-up strategy information and the label information.
Specifically, in the actual business, business personnel records the client follow-up process of the transaction, including the strategy and method during client follow-up, correspondingly inputs label information for identifying the client which is not reached, and establishes a transaction information base according to the received transaction client follow-up strategy information and label information which are input by the business personnel.
S130, inputting face information of the transaction client through the face recognition device, and screening out transaction client behavior data in the behavior database according to the face information.
Specifically, the face information of the transaction client is entered through a face recognition device, which may be a high-speed camera, i.e., a high-speed photographing scanner. And then matching the face information of the transaction client with the client behavior data in the behavior database to acquire transaction client behavior data.
S140, establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data.
Specifically, a deal prediction model related to the deal client is established through machine learning and sample set training according to the follow-up strategy information and label information of the deal client in the deal information base and the deal client behavior data acquired in step S130.
In one embodiment, after step S140, the method further includes: capturing real-time behavior information of a client according to a preset characteristic capturing device, updating a behavior database at regular time, and acquiring updated behavior data of a transaction client; updating a transaction information base at regular time according to the follow-up strategy information and the label information received in real time to obtain an updated transaction information base; and updating the deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain the updated deal forecasting model. Specifically, key behavior characteristic indexes of the transaction clients, client follow-up strategy information and label information for identifying the clients are regularly updated every day according to the change of the newly-added transaction clients; and optimizing and upgrading the deal prediction model in a machine learning and sample set training mode, so that the accuracy of the deal prediction model is improved.
S150, capturing the real-time behavior information of the client according to a preset characteristic capturing device, and comparing and analyzing the real-time behavior information according to a deal forecasting model.
Specifically, when the service personnel follows the customer in real time to perform the service, the preset feature capture device captures the real-time behavior information of the customer, and then compares and analyzes the captured real-time behavior information of the customer according to the deal prediction model obtained in step S140 to see whether the real-time behavior information of the customer meets the behavior features of the past deal customer.
In one embodiment, step S150 specifically includes: capturing real-time behavior information of a client by a capturing device according to preset characteristics; uploading the real-time behavior information to a server for characteristic data processing to obtain real-time behavior characteristic data of a client; and carrying out comparative analysis on the real-time behavior characteristic data according to the deal prediction model. Specifically, when business personnel follow up with a client to perform business service in real time, the preset feature capture device captures real-time behavior information of the client, then the feature capture device uploads the real-time behavior information of the client to the server for processing, performs feature data processing on the real-time behavior information of the client to obtain real-time behavior feature data of the client, and then compares and analyzes the real-time behavior feature data of the client according to the deal prediction model obtained in step S140 to determine whether the real-time behavior feature data of the client conforms to relevant features in the deal prediction model.
In one embodiment, after step S150, the method further includes: judging whether the real-time behavior characteristic data conforms to a bargain prediction model; if yes, extracting target transaction client behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model; extracting corresponding target follow-up strategy information and target label information in a deal information base according to target deal client behavior data; and displaying the target follow-up strategy information and the target label information. Specifically, when a service person actually follows up, when the real-time behavior feature data of a client is compared and analyzed according to a deal forecasting model, if the judgment result of the comparison and analysis is that the real-time behavior feature data accords with the relevant features of the deal forecasting model, the server can give a follow-up strategy relevant to the service and suggestions of client classification in time in a message pushing mode on a mobile terminal used by the service person, and further help the service person to adjust and optimize a follow-up mode.
In the embodiment, the deal forecasting model is established according to the behavior data, the follow-up strategy information and the label information of the deal client, when the service personnel follow up the client service in real time, the deal forecasting model is used for comparing the characteristic behaviors of the client, the guide of the relevant follow-up strategy is pushed in time, the service personnel is helped to accurately and efficiently follow up the client, and therefore the working efficiency of the service personnel is effectively improved.
In one embodiment, as shown in fig. 3, there is provided a customer behavior analysis apparatus 200, which includes a behavior crawling module 201, an information receiving module 202, a data filtering module 203, a model building module 204, and a behavior analysis module 205, wherein:
the behavior capturing module 201 is used for capturing behavior information of clients according to a preset characteristic capturing device and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients;
the information receiving module 202 is configured to receive follow-up policy information and tag information of a transaction client, and establish a transaction information base according to the follow-up policy information and the tag information;
the data screening module 203 is used for inputting face information of a transaction client through the face recognition device and screening transaction client behavior data in the behavior database according to the face information;
the model establishing module 204 is used for establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data;
the behavior analysis module 205 is configured to capture real-time behavior information of the client according to a preset feature capture device, and perform comparative analysis on the real-time behavior information according to a deal prediction model.
In one embodiment, the behavior capturing module 201 is further configured to capture behavior information of the client according to a preset feature capturing device; uploading the behavior information to a server for characteristic data processing to obtain behavior characteristic data of the client; and establishing a behavior database according to the behavior characteristic data.
In one embodiment, the apparatus further comprises a model update module, wherein: the model updating module is used for capturing real-time behavior information of the client according to a preset characteristic capturing device, updating a behavior database at regular time and acquiring updated behavior data of the transaction client; the model updating module is also used for updating the transaction information base at regular time according to the follow-up strategy information and the label information received in real time to obtain the updated transaction information base; the model updating module is also used for updating the deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain the updated deal forecasting model.
In one embodiment, the behavior analysis module 205 is further configured to capture real-time behavior information of the client according to a preset feature capture device; uploading the real-time behavior information to a server for characteristic data processing to obtain real-time behavior characteristic data of a client; and carrying out comparative analysis on the real-time behavior characteristic data according to the deal prediction model.
In one embodiment, the apparatus further comprises an information display module, wherein: the information display module is used for judging whether the real-time behavior characteristic data conforms to the bargain prediction model; the information display module is also used for extracting target transaction client behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model if the target transaction client behavior data are consistent with the real-time behavior characteristic data; the information display module is also used for extracting corresponding target follow-up strategy information and target label information in the deal information base according to the target deal customer behavior data; the information display module is further used for displaying the target follow-up strategy information and the target label information.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing the configuration template and also used for storing target webpage data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a customer behavior analysis method.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment, there is also provided a storage medium storing a computer program comprising program instructions which, when executed by a computer, which may be part of the above-mentioned customer behaviour analysis apparatus, cause the computer to perform the method according to the preceding embodiment.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device or distributed across a network of computing devices, and optionally they may be implemented in program code executable by a computing device, such that they may be stored on a computer storage medium (ROM/RAM, magnetic disks, optical disks) and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The foregoing is a more detailed description of the present invention that is presented in conjunction with specific embodiments, and the practice of the invention is not to be considered limited to those descriptions. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A customer behavior analysis method, comprising:
capturing behavior information of clients according to a preset characteristic capturing device, and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients;
receiving follow-up strategy information and label information of a transaction client, and establishing a transaction information base according to the follow-up strategy information and the label information;
inputting face information of a transaction client through a face recognition device, and screening transaction client behavior data in the behavior database according to the face information;
establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data;
and capturing real-time behavior information of the client by a capturing device according to preset characteristics, and carrying out comparative analysis on the real-time behavior information according to the deal prediction model.
2. The method according to claim 1, wherein the capturing device captures behavior information of customers according to preset characteristics, and establishes a behavior database according to the behavior information, wherein the customers comprise transaction customers and non-transaction customers, specifically:
capturing behavior information of a client by a capturing device according to a preset characteristic;
uploading the behavior information to a server for characteristic data processing to obtain behavior characteristic data of the client;
and establishing a behavior database according to the behavior characteristic data.
3. The method of claim 1, wherein after establishing a deal predictive model based on the deal information base and the deal customer behavior data, further comprising:
capturing real-time behavior information of a client according to a preset characteristic capturing device, updating the behavior database at regular time, and acquiring updated behavior data of the transaction client;
updating the deal information base at regular time according to the follow-up strategy information and the label information received in real time to obtain an updated deal information base;
and updating a deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain an updated deal forecasting model.
4. The method according to claim 1, wherein the capturing device captures real-time behavior information of the client according to a preset feature, and performs comparative analysis on the real-time behavior information according to the deal prediction model, specifically:
the grabbing device grabs the real-time behavior information of the client according to the preset characteristics;
uploading the real-time behavior information to a server for characteristic data processing to obtain real-time behavior characteristic data of a client;
and carrying out comparative analysis on the real-time behavior characteristic data according to the deal prediction model.
5. The method of claim 1, wherein the capturing device captures real-time behavior information of the client according to a preset feature, and after performing comparative analysis on the real-time behavior information according to the deal prediction model, the method further comprises:
judging whether the real-time behavior characteristic data conforms to the deal forecasting model;
if yes, extracting target transaction customer behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model;
extracting corresponding target follow-up strategy information and target label information in the transaction information base according to the target transaction client behavior data;
and displaying the target follow-up strategy information and the target label information.
6. The utility model provides a customer behavior analysis device which characterized in that snatchs module, information receiving module, data screening module, model building module and behavior analysis module including the action, wherein:
the behavior capturing module is used for capturing behavior information of clients according to a preset characteristic capturing device and establishing a behavior database according to the behavior information, wherein the clients comprise transaction clients and non-transaction clients;
the information receiving module is used for receiving follow-up strategy information and label information of a transaction client and establishing a transaction information base according to the follow-up strategy information and the label information;
the data screening module is used for inputting face information of a transaction client through a face recognition device and screening transaction client behavior data in the behavior database according to the face information;
the model establishing module is used for establishing a transaction prediction model according to the transaction information base and the transaction customer behavior data;
the behavior analysis module is used for capturing real-time behavior information of a client according to a preset characteristic capturing device and carrying out comparative analysis on the real-time behavior information according to the deal prediction model.
7. The apparatus of claim 6, further comprising a model update module to:
the model updating module is used for capturing real-time behavior information of the client according to a preset characteristic capturing device, updating the behavior database at regular time and acquiring updated behavior data of the transaction client;
the model updating module is also used for updating the deal information base at regular time according to the follow-up strategy information and the label information received in real time to obtain an updated deal information base;
and the model updating module is also used for updating a deal forecasting model according to the updated deal customer behavior data and the updated deal information base to obtain an updated deal forecasting model.
8. The apparatus of claim 6, further comprising an information display module:
the information display module is used for judging whether the real-time behavior characteristic data conforms to the deal forecasting model;
the information display module is further used for extracting target transaction client behavior data which are consistent with the real-time behavior characteristic data in the transaction prediction model if the target transaction client behavior data are consistent with the real-time behavior characteristic data;
the information display module is also used for extracting corresponding target follow-up strategy information and target label information in the transaction information base according to the target transaction client behavior data;
the information display module is further configured to display the target follow-up policy information and the target tag information.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A storage medium having a computer program stored thereon, the computer program, when being executed by a processor, realizing the steps of the method of any one of claims 1 to 5.
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| CN112365281A (en) * | 2020-10-28 | 2021-02-12 | 国网冀北电力有限公司计量中心 | Power customer service demand analysis method and device |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127525A (en) * | 2016-06-27 | 2016-11-16 | 浙江大学 | A kind of TV shopping Method of Commodity Recommendation based on sorting algorithm |
| CN106921945A (en) * | 2017-02-22 | 2017-07-04 | 上海斐讯数据通信技术有限公司 | The positioning shopping guide method and system of a kind of commodity |
| TWM549914U (en) * | 2017-07-05 | 2017-10-01 | Far Eastern Int Bank | Intelligent sales forecasting system |
| CN108073671A (en) * | 2017-04-12 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus and electronic equipment |
| CN108510321A (en) * | 2018-03-23 | 2018-09-07 | 北京焦点新干线信息技术有限公司 | A kind of construction method and device of house property user portrait |
| CN108564432A (en) * | 2018-03-16 | 2018-09-21 | 北京墨丘科技有限公司 | A kind of consumption demand management method, system and electronic equipment |
| CN109242523A (en) * | 2018-07-06 | 2019-01-18 | 成都正合云智数据科技有限公司 | A kind of building purchasers being exclusively used in real estate's sales industry draw a portrait method and its realization device |
| CN109615129A (en) * | 2018-12-05 | 2019-04-12 | 重庆锐云科技有限公司 | Real estate client's conclusion of the business probability forecasting method, server and computer storage medium |
| CN109615128A (en) * | 2018-12-05 | 2019-04-12 | 重庆锐云科技有限公司 | Real estate client's conclusion of the business probability forecasting method, device and server |
| CN109919006A (en) * | 2019-01-23 | 2019-06-21 | 深圳壹账通智能科技有限公司 | Expression detection method, device, electronic equipment and storage medium |
| CN110163675A (en) * | 2019-05-17 | 2019-08-23 | 湖南城市学院 | A kind of Price Evaluation of Real Estate method and system |
| CN110770779A (en) * | 2017-10-13 | 2020-02-07 | 美的集团股份有限公司 | Method and system for providing personalized live information exchange |
-
2020
- 2020-06-04 CN CN202010500330.2A patent/CN111695015A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127525A (en) * | 2016-06-27 | 2016-11-16 | 浙江大学 | A kind of TV shopping Method of Commodity Recommendation based on sorting algorithm |
| CN106921945A (en) * | 2017-02-22 | 2017-07-04 | 上海斐讯数据通信技术有限公司 | The positioning shopping guide method and system of a kind of commodity |
| CN108073671A (en) * | 2017-04-12 | 2018-05-25 | 北京市商汤科技开发有限公司 | Business object recommends method, apparatus and electronic equipment |
| TWM549914U (en) * | 2017-07-05 | 2017-10-01 | Far Eastern Int Bank | Intelligent sales forecasting system |
| CN110770779A (en) * | 2017-10-13 | 2020-02-07 | 美的集团股份有限公司 | Method and system for providing personalized live information exchange |
| CN108564432A (en) * | 2018-03-16 | 2018-09-21 | 北京墨丘科技有限公司 | A kind of consumption demand management method, system and electronic equipment |
| CN108510321A (en) * | 2018-03-23 | 2018-09-07 | 北京焦点新干线信息技术有限公司 | A kind of construction method and device of house property user portrait |
| CN109242523A (en) * | 2018-07-06 | 2019-01-18 | 成都正合云智数据科技有限公司 | A kind of building purchasers being exclusively used in real estate's sales industry draw a portrait method and its realization device |
| CN109615129A (en) * | 2018-12-05 | 2019-04-12 | 重庆锐云科技有限公司 | Real estate client's conclusion of the business probability forecasting method, server and computer storage medium |
| CN109615128A (en) * | 2018-12-05 | 2019-04-12 | 重庆锐云科技有限公司 | Real estate client's conclusion of the business probability forecasting method, device and server |
| CN109919006A (en) * | 2019-01-23 | 2019-06-21 | 深圳壹账通智能科技有限公司 | Expression detection method, device, electronic equipment and storage medium |
| CN110163675A (en) * | 2019-05-17 | 2019-08-23 | 湖南城市学院 | A kind of Price Evaluation of Real Estate method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112365281A (en) * | 2020-10-28 | 2021-02-12 | 国网冀北电力有限公司计量中心 | Power customer service demand analysis method and device |
| CN112365281B (en) * | 2020-10-28 | 2024-05-14 | 国网冀北电力有限公司计量中心 | Power customer service demand analysis method and device |
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