CN114155004A - Customer management method and device - Google Patents
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Abstract
The embodiment of the application discloses a client management method and a client management device, wherein the method comprises the following steps: acquiring first information of a target client in the target area, wherein the first information comprises basic user information of the target client, and the target client is any client bound with the first live advisor; acquiring a target data set of a target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client; determining a target tag list of the target customer based on the first information and the target data set; adding the target customer to at least one target group based on the target tag list. According to the application, the clients managed by the employment consultant are managed in a unified mode, the labels of the clients are determined according to the data of the house sources consulted and/or checked by the clients, and then the clients are added into the groups according to the labels, so that the appropriate house sources are recommended to the clients, the time of the employment consultant for managing and operating the users can be saved, and the successful conversion rate of the clients is improved.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for customer management.
Background
With the development of internet technology, real estate transactions are gradually excessive from offline to online, and customer resources are increasingly precious. At present, real estate businesses mainly manage respective clients by a business consultant, but an enterprise administrator can only manage the clients of the business consultant through the business consultant, so that less data can be acquired from the clients, and real-time room sources cannot be reasonably distributed according to the requirements of the users, and the clients can not be uniformly managed from the perspective of the enterprises, so that more clients can be acquired.
Disclosure of Invention
The embodiment of the application provides a customer management method and a customer management device, which can simplify the structure of a sales channel and increase the expansibility of the structure of the sales channel.
In a first aspect, an embodiment of the present application provides a customer management method, where the method includes:
acquiring first information of a target client in a target area, wherein the first information comprises basic user information of the target client, and the target client is any client bound with a first live advisor;
acquiring a target data set of the target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client;
determining a target tag list for the target customer based on the first information and the target dataset;
adding the target customer to at least one target group based on the target tag list.
In a second aspect, an embodiment of the present application provides a customer management device, where the customer management device includes:
the system comprises an acquisition unit, a service providing unit and a service providing unit, wherein the acquisition unit is used for acquiring first information of a target client in a target area, the first information comprises basic user information of the target client, and the target client is any client bound with a first employment advisor;
the acquiring unit is further configured to acquire a target data set of the target client based on the first information, where the target data set is data of a house source consulted and/or viewed by the target client;
a determining unit for determining a target tag list of the target customer based on the first information and the target data set;
an adding unit, configured to add the target customer to at least one target group based on the target tag list.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, a communication interface, and one or more programs, which are stored in the memory and configured to be executed by the processor, and which include instructions for performing some or all of the steps described in the method of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to perform some or all of the steps described in the method of the first aspect.
In a fifth aspect, the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to cause a computer to perform some or all of the steps described in the method according to the first aspect of the present application. The computer program product may be a software installation package.
According to the technical scheme, first information of a target client in a target area is obtained, wherein the first information comprises basic user information of the target client, and the target client is any client bound with a first employment advisor; acquiring a target data set of a target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client; determining a target tag list of the target customer based on the first information and the target data set; adding the target customer to at least one target group based on the target tag list. According to the application, the clients managed by the employment consultant are managed in a unified mode, the labels of the clients are determined according to the data of the house sources consulted and/or checked by the clients, and then the clients are added into the groups according to the labels, so that the appropriate house sources are recommended to the clients, the time of the employment consultant for managing and operating the users can be saved, and the successful conversion rate of the clients is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of a customer management method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a hierarchical structure of a sales channel according to an embodiment of the present application;
fig. 3 is a block diagram illustrating functional units of a client management device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions of the present application, the following description is given for clarity and completeness in conjunction with the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step on the basis of the description of the embodiments of the present application belong to the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, software, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
With the increasingly fierce competition of the real estate industry, the traditional methods for acquiring the information of the buildings such as the building books and banners cannot meet the requirements of house buyers, and the digital marketing of the real estate is produced in order to meet the requirement that the house buyers want to know the information of the buildings more intuitively, conveniently and comprehensively. In the real-estate digital marketing scene, the business consultant can add the contact way of the client, and then recommend the floor and/or the house resources to the client on line according to the requirement of the client, but the ways need the business consultant to manage the client, the enterprise manager can only manage the client of the business consultant through the business consultant, the data of the client is less, the house resources can not be reasonably distributed according to the requirement of the user in real time, and the client can be uniformly managed from the view of the enterprise, so that more clients can be obtained.
In order to solve the above problems, the present application provides a client management method, which performs unified management on clients managed by a live advisor, determines tags of the clients according to data of house sources consulted and/or checked by the clients, and further adds the tags into a group according to the tags, so as to recommend a suitable house source to the clients, thereby saving time for managing and operating users by the live advisor, and improving successful conversion rate of the clients.
Referring to fig. 1, fig. 1 is a schematic flow chart illustrating a customer management method according to an embodiment of the present application. As shown in fig. 1, the method includes the following steps.
S110, first information of a target client in a target area is obtained, wherein the first information comprises basic user information of the target client, and the target client is any client bound with a first employment advisor.
The employment consultant is the staff of the real estate enterprise, and is a comprehensive talent which guides a client to buy the real estate through the on-site service at a building selling place, promotes the sales of the building and provides the client with the specialized and consultant service of investment and employment.
In a specific implementation, in order to recommend an appropriate room source to a client as soon as possible to promote the transaction, the business consultant may enter basic information of the client currently being served into the client management system, and may further screen out a room source matching with the room purchasing requirement of the client according to the room purchasing requirement of the client to recommend to the client. The customer management system takes the management of customer data as a core, utilizes information science and technology to realize the automation of activities such as marketing, sales, service and the like, establishes a system for collecting and managing customer information and collecting and recommending house source information, and helps enterprises to realize a management marketing mode taking customers as centers.
The electronic device can acquire the client information of the target client in the target area from the database according to the input screening condition. Furthermore, the electronic device can also add, delete, modify, query and the like to the client data corresponding to the client information stored in the database. The customer information of the target customer is uploaded to the database by the employment consultant, for example, the employment consultant can upload the customer information, such as customer name, contact information, sales channel, user account number, intention floor and other information, to the database in the WeChat applet based on the WeChat applet developed by the real estate enterprise, in order to recommend a plurality of floors to the customer, or recommend one floor to a plurality of customers, and the electronic equipment can judge the target customer according to a preset judgment algorithm. The electronic equipment can also inquire out target customers stored in the database according to the screening condition, and provide targeted marketing service according to the customer information of the target customers, and the specific implementation mode is that the target customers are added into a group matched with the house purchasing requirements of the target customers, and house purchasing services such as recommendation, promotion, guidance and the like are provided for the target customers in a unified manner.
S120, acquiring a target data set of the target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client.
In the embodiment of the application, in order to obtain the room purchasing requirement of the target client, the electronic device can obtain the room source data consulted and/or checked by the target device on software or a network, and then can analyze the room purchasing requirement of the target client according to the room source data, so that accurate room source recommendation of the target client is realized, and the successful conversion rate of the client is improved.
Wherein the first information further comprises a user account of the target customer. If the business replacement advisor adds the target client by means of chat software or application program, the user account of the target client can be uploaded at the same time when uploading the user information, or the electronic equipment acquires the user account of the target client related to the business replacement advisor through the user account of the business replacement advisor.
Optionally, the obtaining a target data set of the target client based on the first information includes: acquiring a second data set associated with the user account from a third-party data platform to obtain m second data sets, wherein m is a positive integer; scoring each first data set of the m second data sets to obtain a plurality of target scores; and selecting a target score meeting preset requirements from the plurality of scores, and determining a second data set corresponding to the target score as the target data set.
The third-party data platform can be an APP release platform, a WeChat applet, a website and other data platforms related to the house property. Because the general interest preference of the user is reflected to a certain extent by the use of the APP and the browsing of the online categories, the electronic device may acquire, through the user account of the target client, data that the user account uses or browses on the third-party platform, that is, a second data set, where the data acquired on each third-party platform corresponds to one second data set. And then, scoring each second data set, and screening effective data from the m second data sets to accurately analyze the house purchasing requirements of the target customers.
Optionally, the scoring each second data set of the m second data sets to obtain a plurality of target scores includes: respectively extracting features of the m second data sets to obtain m feature matrixes; respectively calculating difference values between a first feature matrix and the residual feature matrices in the m feature matrices to obtain m-1 difference value vectors, wherein the first feature matrix is any one feature matrix in the m feature matrices; determining the difference vector of which the number of zero elements in the m-1 difference vector is greater than a first number threshold value as a first difference vector; and determining the target score corresponding to the first difference value based on the relation between the number of the difference value vectors and the score.
After m second data sets are obtained, the data feature vector of each second data can be extracted respectively, so that a feature matrix corresponding to each second data set is obtained. The method for extracting the features may be a method for extracting the features in the prior art, and is not described herein again.
Specifically, the feature matrix in any one second data set is compared with the feature matrices of the remaining second data sets, that is, the difference between the first feature matrix and the remaining feature matrices in the m feature matrices is calculated to obtain an m-1 difference vector, and if the number of zero elements in the difference vector is greater than a first number threshold, it indicates that a strong correlation exists between the two second data sets corresponding to the difference vector.
Further, to represent the importance of the second data set, the present application may be expressed in terms of a strong association between the second data set and the remaining second data sets. Wherein the more important the second data set is, the stronger its association with the remaining second data sets is. The importance degree of any second data set is represented by counting the number of strong correlation relations between the second data set and the rest of the second data sets, namely, the score of the second data set is determined according to the number of the first difference vectors.
S130, determining a target label list of the target client based on the first information and the target data set.
Wherein the tag list comprises a plurality of first tags, and the first tags are used for indicating the user house-purchasing requirements of the target customers. Each tag may correspond to a house purchase demand of the target customer for at least one dimension of the property information of the house source, such as a geographic location, a house type area, a house type, a price range, and the like of the house source.
Further, by analyzing the first information and the target data set, the house-purchasing requirement of the target client can be obtained.
The first information comprises a target sale channel, and the target sale channel is a sale channel for establishing a binding relationship between the first employment advisor and the target client.
In another specific implementation, each real estate company has different sales channels, wherein the sales channels may change with the change of the market and the development of the real estate manufacturers, for example, when a new floor is released, the real estate company may open sales channels for distribution of a leaflet, spreading of advertisements in transportation places such as subway and public transportation in three months, and the sales channels are closed after three months. According to the sales channel of the target customer, the audience facet to which the target customer belongs can be further known.
Optionally, in the above S130, determining the target tag list of the target client based on the first information and the target data set may specifically include the following steps:
s31, classifying the target data set according to the data source to obtain a plurality of first data sets, wherein each first data set corresponds to a data source identifier.
The target data set may include a large amount of data, each data may correspond to a data source identifier, and the data source identifier may be at least one of: APP communication data, APP browsing data, web browsing data, wechat applet browsing data, and the like, which are not limited herein. In a specific implementation, the electronic device may classify the target data set according to a data source to obtain a plurality of second data sets, and each first data set corresponds to a data source identifier, so that the data may be classified according to the data source identifier.
And S32, analyzing each first data set according to the data type to obtain at least one target keyword.
Further, the data of different data sources may include data of different data types, and the data of different data types are analyzed by different data analysis methods, so that the application analyzes the data of different data types in each first data set by using different analysis methods.
Optionally, the analyzing each first data set according to the data type to obtain at least one target keyword includes: if the data type of the first data set is a text data type, searching keywords of at least one dimension in the property information of the house source from the first data set to obtain at least one target keyword; if the data type of the first data set is an image data type, the images in the first data set are respectively cut to obtain at least one target image, a character recognition model is used for recognizing the at least one target image to obtain a plurality of characters, and the plurality of characters are respectively matched with the keywords of at least one dimension in the property information of the house source to obtain at least one target keyword.
The property information of the house source may include dimensions such as a geographic location, a house type area, a house type, and a price range. And respectively matching the data with the text data of the data type in the first data set with the related vocabulary of at least one dimension in the property information of the house source, and taking the matched vocabulary as the target keyword. If the first data set comprises images, the images are respectively cut into small images to obtain at least one target image, then a character recognition model is adopted to recognize texts in each target image, the recognized texts are matched with related words of at least one dimension in the property information of the house source, and the successfully matched texts are used as target keywords.
S33, printing the target label list on the target client based on the at least one target keyword.
After the room purchasing requirement of at least one dimension of the target client is obtained, the target tag list of the target client can be determined according to the room purchasing requirement.
S34, determining a first label of the target sales channel according to the hierarchical structure of the sales channel, wherein the first label is a label corresponding to a first search branch, and the first search branch is a branch which is searched and traversed to the target sales channel according to depth-first.
In the present application, in order to better manage the sales channels, the sales channels may be defined in a three-level structure, as shown in fig. 2. The primary structure of the method is the coarsest granularity, namely, the uppermost sales channel is defined as the primary structure, and the primary structure can comprise an online structure and an offline structure when divided according to the traditional sales channel; the tertiary structure is the finest granularity, namely, the specific sale channel at the lowest layer is defined as the tertiary structure, such as a leaflet of a certain community, a subway advertisement of a certain number of lines, a bus station advertisement of a certain station and the like; the secondary structure comprises all middle-layer sales channels at the uppermost layer and the lowermost layer, and a plurality of levels can be further included between the sales channels of the secondary structure.
And after the hierarchical structure of the sales channels is defined, the labels corresponding to the sales channels of the target customers can be determined according to the hierarchical structure. When the target sales channel is labeled, the sales channels of the primary structure can be traversed downwards to the sales channels of each tertiary structure according to the depth priority order, so that a search branch of each sales channel is obtained, and the search branch comprises a plurality of sales channels. And then tags of all sales channels included in the search branch are taken as tags of the target sales channel.
S35, adding the first label to the target label list.
In the embodiment of the application, after the target tag list is determined according to the house purchasing requirement of the target customer, the tag corresponding to the target sales channel of the target customer can be added to the target tag list, so as to better reflect the house purchasing requirement of the target customer.
S140, adding the target client to at least one target group based on the target label list.
In the embodiment of the application, in order to enable a user to contact more house sources meeting the needs of the user and improve the successful conversion rate of the user, the target user can be added into a target group matched with the house purchasing needs of the user so as to accurately and intensively recommend the house sources and send promotion activities to the user.
Optionally, the adding the target customer to at least one target group based on the target tag list includes: acquiring at least one candidate group and label information of each candidate group based on the first information to obtain at least one label information; determining a target priority level for each of the plurality of first tags based on the target data set; respectively calculating the matching degree of the label information of each candidate group and the target label list according to the target priority level to obtain at least one target matching degree; and determining a candidate group corresponding to the target matching degree greater than a preset matching degree in the at least one target matching degree as the target group.
In the application, the electronic device can label the group in advance according to the house source provided by the group for the user. For example, when a group is house source information providing a second-hand house for a user, the tag information of the group may include the second-hand house. When each house source is publicized and sold, the real estate company can market some characteristics of the house source as main selling points, such as school districts, high-grade houses, low price, large house type area and the like. The electronic equipment can determine the priority levels of the house type, the house type area, the house type and the price range in the house source information according to the main selling points of the house source, so that the housing client can recommend the house source according to the most main requirements of the user, and the working efficiency and the successful conversion rate of the client are improved.
Optionally, the determining a target priority level of each of the plurality of first tags according to the target data set includes: determining a target house purchasing demand corresponding to each target keyword in each first data set according to a mapping relation between the keyword and the house purchasing demand to obtain at least one target house purchasing demand; calculating a target expectation for each target house-buying demand in the plurality of first data sets; and determining the target priority level of the first label according to the mapping relation between the expectation and the priority level.
The electronic equipment acquires the label information printed in advance on each candidate group. Then according to the priority order of the house purchasing requirements of the users, matching the label information of each candidate group with the house type, the house type area, the house type and the price range respectively, and calculating the label information of each candidate group and the price rangeMatching degree of house purchasing requirements of users. The quantity matching degree is used for representing the quantity of matching between the label information and the house type, the house type area, the house type and the price range, and the larger the quantity matching degree is, the more the candidate group meets the requirements of the user. The number matching degree can be expressed as:
wherein N is the number of the house purchasing demands of the user, and alpha isiAnd the gamma is used for indicating whether the tag information is matched with the room purchasing requirement of the ith user, when the tag information comprises the room purchasing requirement of the ith user, the r is 1, otherwise, the r is 0.
Further, the target similarity is used for representing the similarity between the tag information and the house purchasing requirements of each user. For example, the label information includes a commercial room, the house type of the house purchasing requirement of the user is a second-hand house, and since the second-hand house may be included in the commercial room, the similarity between the label information and the house type of the house purchasing requirement of the user may be considered to be 50%. For another example, the tag information includes a room-to-room area, the area of the house type required by the user for purchasing the house is 40 square meters, and since the area of the standard room-to-room area is 40 square meters, the similarity between the tag information and the house type area required by the user for purchasing the house can be considered to be 80%.
Specifically, the matching degree may be expressed by a calculation formula as: a is1Number matching degree + a2Target similarity, wherein said a1And said a2Is a weight coefficient of the degree of matching, the1And said a2Are all positive numbers and the sum is 1. Illustratively, in an unknown scene, two weighting coefficients are typically set a priori to 1/2. The actual effect may be influenced by the propaganda house source and different for the user group, and the two weight coefficients may be adjusted according to the actual scene, for example, in the case of a selling point which is a main selling point and attractive for the propaganda house source, a2Larger set for targeted promotion; for the scene of mainly pushing some house sources of the lost building, a can be used1Larger to dig moreAnd (4) multi-purpose house purchasing clients.
In the embodiment of the application, the electronic equipment can also perform refined operation on the user, so that accurate marketing is realized. After the user using the second client joins the target group, in order to realize accurate marketing, the electronic device may join the target client who enters the group into the dynamic tag group, and screen the users who participate in the activity, so as to prepare for subsequent accurate touch. For example, the preferential activity of a certain villa building can be set through conditions, and the rules and links of the preferential activity can be sent to the users joining the label group of the 'high-end houses'.
Specifically, the electronic device may use the house purchasing demand of the user as a tag of the target client, then screen out all house source information matched with the tag in the tag list from a database including the house source information, and send the screened house source information to the target client for the user to browse. Wherein the house source information in the database is valid house source information (i.e. house source that the user can rent or buy or sell at present) or house source information that is currently active.
For example, the electronic device may subsequently adjust its tag list according to the house source information in the target client browsing database, so as to achieve accurate marketing. For example, if the house sources viewed by the target client are second-hand houses in the three rooms and two halls, the two rooms and one halls in the tag list can be modified into the three rooms and two halls.
It can be seen that the present application provides a client management method, obtaining first information of a target client in a target area, where the first information includes basic user information of the target client, and the target client is any client bound with a first employment advisor; acquiring a target data set of a target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client; determining a target tag list of the target customer based on the first information and the target data set; adding the target customer to at least one target group based on the target tag list. According to the application, the clients managed by the employment consultant are managed in a unified mode, the labels of the clients are determined according to the data of the house sources consulted and/or checked by the clients, and then the clients are added into the groups according to the labels, so that the appropriate house sources are recommended to the clients, the time of the employment consultant for managing and operating the users can be saved, and the successful conversion rate of the clients is improved.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the network device comprises corresponding hardware structures and/or software modules for performing the respective functions in order to realize the above functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Referring to fig. 3, fig. 3 is a block diagram illustrating functional units of a client management apparatus 300 according to an embodiment of the present application, where the apparatus 300 includes a data obtaining unit 310, a determining unit 320, and an adding unit 330; wherein the content of the first and second substances,
the obtaining unit 310 is configured to obtain first information of a target client in a target area, where the first information includes basic user information of the target client, and the target client is any client bound with a first employment advisor;
the obtaining unit 310 is further configured to obtain a target data set of the target client based on the first information, where the target data set is data of a room source consulted and/or viewed by the target client;
the determining unit 320 is configured to determine a target tag list of the target customer based on the first information and the target data set;
the adding unit 330 is configured to add the target customer to at least one target group based on the target tag list.
Optionally, the first information includes a target sales channel, and the target sales channel is a sales channel for establishing a binding relationship between the first employment advisor and the target client;
in determining the target tag list of the target client based on the first information and the target data set, the determining unit 320 is specifically configured to:
classifying the target data set according to data source identifiers to obtain a plurality of first data sets, wherein each first data set corresponds to one data source identifier; analyzing each first data set according to the data type to obtain at least one target keyword;
printing the target label list on the target client based on the at least one target keyword; determining a first label of the target sales channel according to the hierarchical structure of the sales channel, wherein the first label is a label corresponding to a first search branch, and the first search branch is a branch which is searched and traversed to the target sales channel according to depth-first search; adding the first tag to the target tag list.
Optionally, in terms of analyzing each first data set according to the data type to obtain at least one target keyword, the determining unit 320 is specifically configured to: if the data type of the first data set is a text data type, searching keywords of at least one dimension in the property information of the house source from the first data set to obtain at least one target keyword; if the data type of the first data set is an image data type, the images in the first data set are respectively cut to obtain at least one target image, a character recognition model is used for recognizing the at least one target image to obtain a plurality of characters, and the plurality of characters are respectively matched with the keywords of at least one dimension in the property information of the house source to obtain at least one target keyword.
Optionally, the first information further includes a user account of the target customer;
in terms of acquiring the target data set of the target client, the acquiring unit 310 is specifically configured to: acquiring a second data set associated with the user account from a third-party data platform to obtain m second data sets, wherein m is a positive integer; scoring each first data set of the m second data sets to obtain a plurality of target scores; and selecting a target score meeting preset requirements from the plurality of scores, and determining a second data set corresponding to the target score as the target data set.
Optionally, in respect of scoring each second data set of the m second data sets to obtain a plurality of target scores, the obtaining unit 310 is specifically configured to: respectively extracting features of the m second data sets to obtain m feature matrixes; respectively calculating difference values between a first feature matrix and the residual feature matrices in the m feature matrices to obtain m-1 difference value vectors, wherein the first feature matrix is any one feature matrix in the m feature matrices; determining the difference vector of which the number of zero elements in the m-1 difference vector is greater than a first number threshold value as a first difference vector; and determining the target score corresponding to the first difference value based on the relation between the number of the difference value vectors and the score.
Optionally, the tag list includes a plurality of first tags, and the first tags are used for indicating the user house-buying requirements of the target customer;
in respect of adding the target customer to at least one target group based on the target tag list, the adding unit 330 is specifically configured to: acquiring at least one candidate group and label information of each candidate group based on the first information to obtain at least one label information; determining a target priority level for each of the plurality of first tags based on the target data set; respectively calculating the matching degree of the label information of each candidate group and the target label list according to the target priority level to obtain at least one target matching degree; and determining a candidate group corresponding to the target matching degree greater than a preset matching degree in the at least one target matching degree as the target group.
Optionally, in terms of determining the target priority level of each of the plurality of first tags according to the target data set, the adding unit 330 is specifically configured to: determining a target house purchasing demand corresponding to each target keyword in each first data set according to a mapping relation between the keyword and the house purchasing demand to obtain at least one target house purchasing demand; calculating a target expectation for each target house-buying demand in the plurality of first data sets; and determining the target priority level of the first label according to the mapping relation between the expectation and the priority level.
It should be understood that the apparatus 300 herein is embodied in the form of a functional unit. The term "unit" herein may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a processor (e.g., a shared, dedicated, or group processor) and memory that execute one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that support the described functionality. In an optional example, it may be understood by those skilled in the art that the apparatus 300 may be specifically an on-board device in the foregoing embodiment, and the apparatus 300 may be configured to perform each process and/or step corresponding to the on-board device in the foregoing method embodiment, and in order to avoid repetition, details are not described here again.
The device 300 of each scheme has the functions of realizing the corresponding steps executed by the vehicle-mounted equipment in the method; the functions can be realized by hardware, and the functions can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the above functions, and performs transceiving operations and related processing operations in the respective method embodiments, respectively.
In an embodiment of the present application, the apparatus 300 may also be a chip or a chip system, such as: system on chip (SoC). Correspondingly, the transceiver unit may be a transceiver circuit of the chip, and is not limited herein.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure, where the electronic device includes: one or more processors, one or more memories, one or more communication interfaces, and one or more programs; the one or more programs are stored in the memory and configured to be executed by the one or more processors.
The program includes instructions for performing the steps of: acquiring first information of a target client in a target area, wherein the first information comprises basic user information of the target client, and the target client is any client bound with a first live advisor; acquiring a target data set of the target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client; determining a target tag list for the target customer based on the first information and the target dataset; adding the target customer to at least one target group based on the target tag list.
All relevant contents of each scene related to the method embodiment may be referred to the functional description of the corresponding functional module, and are not described herein again.
It will be appreciated that the memory described above may include both read-only memory and random access memory, and provides instructions and data to the processor. The portion of memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In the embodiment of the present application, the processor of the above apparatus may be a Central Processing Unit (CPU), and the processor may also be other general processors, Digital Signal Processors (DSP), Application Specific Integrated Circuits (ASIC), Field Programmable Gate Arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It is to be understood that reference to "at least one" in the embodiments of the present application means one or more, and "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
And, unless stated to the contrary, the embodiments of the present application refer to the ordinal numbers "first", "second", etc., for distinguishing a plurality of objects, and do not limit the sequence, timing, priority, or importance of the plurality of objects. For example, the first information and the second information are different information only for distinguishing them from each other, and do not indicate a difference in the contents, priority, transmission order, importance, or the like of the two kinds of information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The steps of a method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software elements in a processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in a memory, and a processor executes instructions in the memory, in combination with hardware thereof, to perform the steps of the above-described method. To avoid repetition, it is not described in detail here.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially or partially contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a memory and includes several instructions for causing a computer device (which may be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash disk, ROM, RAM, magnetic or optical disk, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A customer management method, the method comprising:
acquiring first information of a target client in a target area, wherein the first information comprises basic user information of the target client, and the target client is any client bound with a first live advisor;
acquiring a target data set of the target client based on the first information, wherein the target data set is data of a house source consulted and/or viewed by the target client;
determining a target tag list for the target customer based on the first information and the target dataset;
adding the target customer to at least one target group based on the target tag list.
2. The method of claim 1, wherein the first information comprises a target sales channel, the target sales channel being a sales channel for the first employment advisor to establish a binding relationship with the target customer;
the determining a target tag list for the target customer based on the first information and the target dataset comprises:
classifying the target data set according to data source identifiers to obtain a plurality of first data sets, wherein each first data set corresponds to one data source identifier;
analyzing each first data set according to the data type to obtain at least one target keyword;
printing the target label list on the target client based on the at least one target keyword;
determining a first label of the target sales channel according to the hierarchical structure of the sales channel, wherein the first label is a label corresponding to a first search branch, and the first search branch is a branch which is searched and traversed to the target sales channel according to depth-first search;
adding the first tag to the target tag list.
3. The method of claim 2, wherein analyzing each first data set according to data type to obtain at least one target keyword comprises:
if the data type of the first data set is a text data type, searching keywords of at least one dimension in the property information of the house source from the first data set to obtain at least one target keyword;
if the data type of the first data set is an image data type, the images in the first data set are respectively cut to obtain at least one target image, a character recognition model is used for recognizing the at least one target image to obtain a plurality of characters, and the plurality of characters are respectively matched with the keywords of at least one dimension in the property information of the house source to obtain at least one target keyword.
4. A method according to claim 2 or 3, wherein the first information further comprises a user account number of the target customer;
the obtaining a target data set of the target customer based on the first information comprises:
acquiring a second data set associated with the user account from a third-party data platform to obtain m second data sets, wherein m is a positive integer;
scoring each first data set of the m second data sets to obtain a plurality of target scores;
and selecting a target score meeting preset requirements from the plurality of scores, and determining a second data set corresponding to the target score as the target data set.
5. The method of claim 4, wherein scoring each of the m second data sets results in a plurality of goal scores, comprising:
respectively extracting features of the m second data sets to obtain m feature matrixes;
respectively calculating difference values between a first feature matrix and the residual feature matrices in the m feature matrices to obtain m-1 difference value vectors, wherein the first feature matrix is any one feature matrix in the m feature matrices;
determining the difference vector of which the number of zero elements in the m-1 difference vector is greater than a first number threshold value as a first difference vector;
and determining the target score corresponding to the first difference value based on the relation between the number of the difference value vectors and the score.
6. The method of claim 5, wherein the tag list comprises a plurality of first tags, the first tags being used to indicate the user's room-purchasing requirements of the target customer;
the adding the target customer to at least one target group based on the target tag list comprises:
acquiring at least one candidate group and label information of each candidate group based on the first information to obtain at least one label information;
determining a target priority level for each of the plurality of first tags based on the target data set;
respectively calculating the matching degree of the label information of each candidate group and the target label list according to the target priority level to obtain at least one target matching degree;
and determining a candidate group corresponding to the target matching degree greater than a preset matching degree in the at least one target matching degree as the target group.
7. The method of claim 6, wherein determining a target priority level for each of the plurality of first tags based on the target data set comprises:
determining a target house purchasing demand corresponding to each target keyword in each first data set according to a mapping relation between the keyword and the house purchasing demand to obtain at least one target house purchasing demand;
calculating a target expectation for each target house-buying demand in the plurality of first data sets;
and determining the target priority level of the first label according to the mapping relation between the expectation and the priority level.
8. A customer management apparatus, the apparatus comprising:
the system comprises an acquisition unit, a service providing unit and a service providing unit, wherein the acquisition unit is used for acquiring first information of a target client in a target area, the first information comprises basic user information of the target client, and the target client is any client bound with a first employment advisor;
the acquiring unit is further configured to acquire a target data set of the target client based on the first information, where the target data set is data of a house source consulted and/or viewed by the target client;
a determining unit for determining a target tag list of the target customer based on the first information and the target data set;
an adding unit, configured to add the target customer to at least one target group based on the target tag list.
9. An electronic device, comprising a processor, a memory, and a communication interface, the memory storing one or more programs, and the one or more programs being executable by the processor, the one or more programs including instructions for performing the steps in the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program causes a computer to perform the steps of the method according to any one of claims 1-7.
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