Customer Management Method and Device

Abstract

An embodiment of the present application discloses a customer management method and device. The method includes: obtaining first information of a target customer in a target area, where the first information includes basic user information of the target customer, and the target customer is any customer bound to a first real estate consultant; obtaining a target data set of the target customer based on the first information, where the target data set is data of housing sources consulted and / or viewed by the target customer; determining a target label 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 label list. By uniformly managing the customers managed by the real estate consultant, determining their labels according to the data of the housing sources consulted and / or viewed by the customers, and then adding them to the group according to the labels, the present application can recommend suitable housing sources to the customers, thereby saving the time of the real estate consultant for user management and operation and improving the customer success conversion rate.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a customer management method and apparatus. Background Art

[0002] With the development of Internet technologies, real estate transactions have gradually shifted from offline to online, and customer resources have become increasingly precious. Currently, real estate enterprises mainly rely on sales consultants to manage their respective customers, and enterprise administrators can only manage the customers of sales consultants through sales consultants. They can obtain less customer data and cannot reasonably allocate housing sources in real time according to user needs or uniformly manage customers from the perspective of the enterprise to acquire more customers. Summary of the Invention

[0003] Embodiments of this application provide a customer management method and apparatus, which can simplify the structure of the sales channel and increase the scalability of the sales channel structure.

[0004] In a first aspect, embodiments of this application provide a customer management method, and the method includes:

[0005] Obtain first information of a target customer in a target area, where the first information includes basic user information of the target customer, and the target customer is any customer bound to a first sales consultant;

[0006] Based on the first information, obtain a target data set of the target customer, where the target data set is data of housing sources consulted and / or viewed by the target customer;

[0007] Determine a target tag list of the target customer based on the first information and the target data set;

[0008] Add the target customer to at least one target group based on the target tag list.

[0009] In a second aspect, embodiments of this application provide a customer management apparatus, and the apparatus includes:

[0010] An obtaining unit, configured to obtain first information of a target customer in a target area, where the first information includes basic user information of the target customer, and the target customer is any customer bound to a first sales consultant;

[0011] The obtaining unit is further configured to obtain a target data set of the target customer based on the first information, where the target data set is data of housing sources consulted and / or viewed by the target customer;

[0012] A determining unit, configured to determine a target tag list of the target customer based on the first information and the target data set;

[0013] An adding unit, configured to add the target customers to at least one target group based on the target label list.

[0014] 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. The one or more programs are stored in the memory and configured to be executed by the processor. The programs include instructions for performing some or all of the steps described in the method according to the first aspect above.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program for electronic data exchange, where the computer program causes a computer to perform some or all of the steps described in the method according to the first aspect above.

[0016] In a fifth aspect, an embodiment of the present application provides a computer program product. The computer program product includes a non-transitory computer-readable storage medium storing a computer program, and 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 embodiments of the present application. The computer program product may be a software installation package.

[0017] The technical solution provided by the present application includes: obtaining first information of target customers in a target area, where the first information includes basic user information of the target customers, and the target customers are any customers bound to a first real estate consultant; obtaining a target data set of the target customers based on the first information, where the target data set is data of the housing sources consulted and / or viewed by the target customers; determining a target label list of the target customers based on the first information and the target data set; and adding the target customers to at least one target group based on the target label list. By uniformly managing the customers managed by the real estate consultant, determining their labels according to the data of the housing sources consulted and / or viewed by the customers, and then adding them to the groups according to the labels, the present application can recommend suitable housing sources to the customers, thereby saving the time of the real estate consultant for user management and operation and improving the customer success conversion rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings in the following description are some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0019] Figure 1 It is a schematic flowchart of a customer management method provided by an embodiment of the present application;

[0020] Figure 2 It is a schematic diagram of a sales channel hierarchical structure provided by an embodiment of the present application;

[0021] Figure 3 It is a block diagram of the functional units of a customer management device provided by an embodiment of the present application;

[0022] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0023] For those skilled in the art of this technology to better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the description of the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope protected by the present application.

[0024] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, software, product or device that includes a series of steps or units is not limited to the listed steps or units, but also includes unlisted steps or units, or other steps or units inherent to these processes, methods, products or devices.

[0025] Referring to "embodiment" herein means that a specific feature, structure or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.

[0026] As the competition in the real estate industry becomes increasingly fierce, traditional methods of obtaining property information such as brochures and banners can no longer meet the needs of homebuyers. To meet the requirements of homebuyers to understand property information more intuitively, conveniently, and comprehensively, digital marketing in the real estate industry has emerged. In the scenario of real estate digital marketing, a sales consultant can add the contact information of customers and then recommend properties and / or housing units to customers online according to their needs. However, these methods require sales consultants to manage their customers individually, and enterprise administrators can only manage the customers of sales consultants through sales consultants, obtaining less customer data and unable to allocate housing units reasonably according to user needs in real time or conduct unified management of customers from the perspective of the enterprise to acquire more customers.

[0027] To solve the above problems, this application proposes a customer management method. By uniformly managing the customers managed by sales consultants, determining their tags based on the data of the housing units consulted and / or viewed by the customers, and then adding them to groups according to the tags to recommend suitable housing units to the customers, the time for sales consultants to manage and operate users can be saved, and the customer success conversion rate can be improved.

[0028] Please refer to Figure 1 , Figure 1 which is a schematic flowchart of a customer management method provided by an embodiment of this application. As Figure 1 shown, the method includes the following steps.

[0029] S110. Obtain the first information of target customers in the target area, where the first information includes the basic user information of the target customers, and the target customers are any customers bound to the first sales consultant.

[0030] Among them, a sales consultant is an employee of a real estate enterprise, a comprehensive talent who guides customers to purchase real estate through on-site services at the sales office, promotes the sales of properties, and provides professional and consultative services for customers' investment and home purchase.

[0031] In specific implementation, to recommend suitable housing units to customers as soon as possible to promote transactions, a sales consultant can enter the basic information of the customers currently being served into the customer management system, and then screen out housing units that match the customers' housing purchase needs to recommend to the customers. The customer management system takes the management of customer data as the core, uses information science and technology to automate marketing, sales, service and other activities, and establishes a system for collecting and managing customer information and collecting and recommending housing unit information to help the enterprise achieve a customer-centric management and marketing model.

[0032] Among them, the electronic device can obtain the customer information of target customers in the target area from the database according to the input filtering conditions. Further, the electronic device can also perform operations such as adding, deleting, modifying, and querying the customer data corresponding to the customer information stored in the database. The customer information of the target customers is uploaded to the database by the property consultants. For example, the property consultants can, based on the WeChat mini-program developed by the real estate enterprise, upload the customer information such as customer name, contact information, sales channels, user accounts, and intended real estate properties in the WeChat mini-program. The purpose is to recommend multiple real estate properties to customers or recommend one real estate property to multiple customers. In addition, the electronic device can also judge the target customers according to a preset customer judging algorithm. The electronic device can also query the target customers that have been stored in the database according to the filtering conditions, and provide targeted marketing services according to the customer information of the target customers. The specific implementation method is to add the target customers to the groups that match their housing purchase needs, and uniformly provide housing purchase services such as recommendations, promotions, and guidance for the target customers.

[0033] S120. Based on the first information, obtain the target data set of the target customer, where the target data set is the data of the housing sources consulted and / or viewed by the target customer.

[0034] In the embodiment of the present application, in order to obtain the housing purchase needs of the target customer, the electronic device can obtain the housing source data consulted and / or viewed by the target device on the software or network, and then analyze the housing purchase needs of the target customer according to the housing source data, so as to realize the accurate housing source recommendation for the target customer and improve the customer success conversion rate.

[0035] Among them, the first information further includes the user account of the target customer. If the property consultant adds the target customer by means of a chat software or an application program, the user account of the target customer can be uploaded simultaneously when uploading the user information, or the electronic device obtains the user account of the associated target customer through the user account of the property consultant.

[0036] Optionally, the obtaining the target data set of the target customer based on the first information includes: obtaining m second data sets associated with the user account from a third-party data platform, where m is a positive integer; scoring each first data set in the m second data sets to obtain a plurality of target scores; selecting the target scores that meet the preset requirements from the plurality of scores, and determining the second data set corresponding to the target scores as the target data set.

[0037] Among them, the third-party data platform may be a data platform such as an APP publishing platform related to real estate, a WeChat mini-program, a website URL, etc. Since the use of the APP and the browsing of online categories to a certain extent reflect the general interest preferences of users, the electronic device can obtain the data used or browsed on the third-party platform by the target customer's user account, that is, the second data set, and the data obtained on each third-party platform corresponds to a second data set. Then, each second data set is scored, and effective data is selected from the m second data sets to accurately analyze the housing purchase needs of the target customer.

[0038] Optionally, scoring each of the m second data sets to obtain multiple target scores includes: extracting features from each of the m second data sets to obtain m feature matrices; respectively calculating the differences between the first feature matrix and the remaining feature matrices in the m feature matrices to obtain m - 1 difference vectors, where the first feature matrix is any one of the m feature matrices; determining the difference vectors with the number of zero elements in the m - 1 difference vectors greater than the first quantity threshold as the first difference vectors; and determining the target scores corresponding to the number of the first differences based on the relationship between the number of difference vectors and the scores.

[0039] Among them, after obtaining the m second data sets, the data feature vectors of each second data can be extracted respectively, so as to obtain the feature matrix corresponding to each second data set. The method of feature extraction can adopt the feature extraction methods in the prior art and will not be elaborated here.

[0040] Specifically, the feature matrix in any one second data set is compared with the feature matrices of the remaining second data sets respectively, that is, the differences between the first feature matrix and the remaining feature matrices in the m feature matrices are calculated to obtain m - 1 difference vectors. If the number of zero elements in the difference vector is greater than the first quantity threshold, it indicates that there is a strong correlation between the two second data sets corresponding to the difference vector.

[0041] Furthermore, in order to show the importance degree of the second data set, the present application can represent it according to the strong correlation between the second data set and the remaining second data sets. The more important the second data set is, the stronger its correlation with the remaining second data sets is. The present application represents the importance degree of a second data set by counting the number of strong correlation relationships between any second data set and the remaining second data sets, that is, determining the score of the second data set according to the number of the first difference vectors.

[0042] S130. Determine the target label list of the target customer based on the first information and the target data set.

[0043] Among them, the label list includes a plurality of first labels, and the first labels are used to indicate the housing purchase needs of the target customer. Each label can correspond to the housing purchase needs of the target customer for at least one dimension in the housing property information, such as the geographical location of the housing, the housing type, the housing area, the housing category, the price range, etc.

[0044] Further, by analyzing the first information and the target data set, the housing purchase needs of the target customer can be obtained.

[0045] Among them, the first information includes the target sales channel, and the target sales channel is the sales channel through which the first real estate consultant establishes a binding relationship with the target customer.

[0046] In specific implementation, the sales channels opened by each real estate company are different, and with the changes in the market and the development of real estate developers, the opened sales channels may change. For example, when a new real estate project is launched, the real estate company can open sales channels such as distributing leaflets and spreading advertisements in transportation venues such as subways and buses within three months, and the sales channel will be closed after three months. The target customer's sales channel can be used to further understand the target customer's audience.

[0047] Optionally, in the above S130, determining the target label list of the target customer based on the first information and the target data set may specifically include the following steps:

[0048] S31. Classify the target data set according to the data source to obtain a plurality of first data sets, and each first data set corresponds to a data source identifier.

[0049] Among them, the target data set may include a large amount of data, and each data may correspond to a data source identifier. The data source identifier may be at least one of the following: APP communication data, APP browsing data, website page browsing data, WeChat mini-program browsing data, etc., which is not limited herein. In specific implementation, the electronic device can classify the target data set according to the 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 can be classified according to the data source.

[0050] S32. Analyze each first data set according to the data type to obtain at least one target keyword.

[0051] Further, the data from different data sources may include data of different data types, and different data analysis methods are used for data of different data types. Therefore, the present application uses different analysis methods to analyze the data of different data types in each first data set.

[0052] Optionally, 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 for keywords of at least one dimension in the housing property information from the first data set to obtain the at least one target keyword; if the data type of the first data set is an image data type, performing line cutting on the images in the first data set respectively to obtain at least one target image, using an optical character recognition model to recognize the at least one target image to obtain the plurality of texts, and matching the plurality of texts with the keywords of at least one dimension in the housing property information respectively to obtain the at least one target keyword.

[0053] Among them, the above housing property information may include dimensions such as geographical location, house type, house type area, housing type, price range, etc. The data of the text data type in the first data set are respectively matched with the relevant words of at least one dimension in the housing property information, and the matched words are used as the target keywords. If the first data set includes images, the images are respectively cut into small images to obtain at least one target image, and then an optical character recognition model is used to recognize the text in each target image, and then the recognized text is matched with the relevant words of at least one dimension in the housing property information, and the successfully matched text is used as the target keyword.

[0054] S33. Mark the target customer with the target tag list based on the at least one target keyword.

[0055] Among them, after obtaining the housing purchase requirements of the target customer in at least one dimension, the target tag list of the target customer can be determined according to its housing purchase requirements.

[0056] S34. Determine the first tag of the target sales channel according to the hierarchical structure of the sales channel, where the first tag is the tag corresponding to the first search branch, and the first search branch is the branch traversed to the target sales channel according to depth-first search.

[0057] In this application, in order to better manage the sales channel, the sales channel can be defined as a three-level structure, such as Figure 2 shown. Its first-level structure is the coarsest granularity, that is, the top-level sales channel is defined as the first-level structure. For example, when divided according to the traditional sales channel, the first-level structure may include online and offline; the third-level structure is the finest granularity, that is, the specific sales channels at the bottom layer are defined as the third-level structure, such as leaflets of a certain community, subway advertisements on a certain line, bus stop advertisements at a certain station, etc.; the second-level structure includes all intermediate-level sales channels between the top layer and the bottom layer, and there may be multiple levels between the sales channels of the second-level structure.

[0058] Among them, each layer of the sales channel has its corresponding label. After defining the hierarchical structure of the sales channel, the label corresponding to the sales channel of the target customer can be determined according to its hierarchical structure. When tagging the target sales channel, it is possible to traverse downward from the sales channels of the first-level structure in the depth-first sorting order to each sales channel of the third-level structure, so as to obtain the search branch of each sales channel. The search branch includes multiple sales channels. Then, the labels of all the sales channels included in the search branch are used as the labels of the target sales channel.

[0059] S35. Add the first label to the target label list.

[0060] In the embodiment of the present application, after determining the target label list according to the housing purchase needs of the target customer, the label corresponding to the target sales channel of the target customer can also be added to the target label list to better reflect the housing purchase needs of the target customer.

[0061] S140. Add the target customer to at least one target group based on the target label list.

[0062] In the embodiment of the present application, in order to enable users to access more housing sources that meet their needs and improve the customer success conversion rate, the target customer can be added to the target group that matches their housing purchase needs, so as to accurately and centrally recommend housing sources and send promotional activities to the customers.

[0063] Optionally, adding the target customer to at least one target group based on the target label list includes: obtaining at least one candidate group and the label information of each candidate group based on the first information to obtain at least one label information; determining the target priority level of each first label in the multiple first labels according to the target data set; calculating the matching degree between the label information of each candidate group and the target label list respectively according to the target priority level to obtain at least one target matching degree; and determining the candidate group corresponding to the target matching degree greater than the preset matching degree in the at least one target matching degree as the target group.

[0064] In the present application, the electronic device can pre-tag the group according to the housing sources provided by the group to the user. For example, when the group provides housing source information of second-hand houses for the user, the label information of the group may include second-hand houses. When each housing source is being promoted and sold, the real estate company will use some features of the housing source as the main selling points for promotion, such as school district houses, high-end houses, low prices, large apartment areas, etc. The electronic device can determine the priority levels of the apartment type, apartment area, housing type, and price range in the housing source information according to the main selling points of the housing source, so that the real estate customer can recommend housing sources according to the user's most important needs, thereby improving its work efficiency and customer success conversion rate.

[0065] Optionally, determining the target priority level of each first label among the multiple first labels according to the target data set includes: determining the target housing purchase demand corresponding to each target keyword in each first data set according to the mapping relationship between the keyword and the housing purchase demand, to obtain at least one target housing purchase demand; calculating the target expectation of each target housing purchase demand in the multiple first data sets; and determining the target priority level of the first label according to the mapping relationship between the expectation and the priority level.

[0066] Wherein, the electronic device obtains the label information pre-labeled for each candidate group. Then, in the order of the priority level of the user's housing purchase demand, the label information of each candidate group is respectively matched with the house type, house type area, housing type, and price range, and the matching degree between each label information and the user's housing purchase demand is calculated. The quantity matching degree is used to represent the number of matches between the label information and the house type, house type area, housing type, and price range, and the larger the quantity matching degree, the more the candidate group meets the user's needs. The quantity matching degree can be expressed as: Where N is the quantity in the user's housing purchase demand, and α i is the priority level of the i-th user housing purchase demand, and γ is used to represent whether the label information matches the i-th user housing purchase demand. When the label information includes the i-th user housing purchase demand, r takes 1, otherwise it takes 0.

[0067] Further, the target similarity is used to represent the similarity between the label information and each user housing purchase demand. For example, if the label information includes commercial housing and the housing type of the user's housing purchase demand is second-hand housing, since commercial housing may include second-hand housing, it can be considered that the similarity between the label information and the housing type of this user housing purchase demand is 50%. Another example is that if the label information includes one bedroom and one living room and the house type area of the user's housing purchase demand is 40 square meters, since the standard area of one bedroom and one living room is 40 square meters, it can be considered that the similarity between the label information and the house type area of this user housing purchase demand is 80%.

[0068] Specifically, the matching degree can be expressed by the calculation formula: a 1 *quantity matching degree + a 2 *target similarity, where a 1 and a 2 are the weight coefficients of the matching degree, and a 1 and a 2All are positive numbers, and their sum is 1. Exemplarily, in an unknown scenario, generally two weight coefficients are set a priori to 1 / 2. The actual effect may be affected by the promoted housing sources and the different user groups. The two weight coefficients can be adjusted according to the actual scenario. For example, in a scenario where the promoted housing source has a prominent and attractive selling point, a 2 can be set larger for targeted promotion; in a scenario where some unsalable property listings are promoted, then a 1 can be set larger to discover more potential home-buying customers.

[0069] In the embodiment of the present application, the electronic device can also carry out refined operation on users to achieve precise marketing. After a user using the second client joins the target group, in order to achieve precise marketing, the electronic device can add the target customers who join the group to the dynamic tag group, screen the users participating in the activity, and prepare for subsequent precise reach. For example, for a preferential activity of a certain villa property, through condition setting, the rules and links of the preferential activity can be sent to the users who join the "high-end housing" tag group.

[0070] Specifically, the electronic device can use the user's housing purchase demand as a tag for the target customer, and then screen out all housing source information that matches the tags in the tag list from the database including housing source information, and send the screened housing source information to the target customer for the user to view. The housing source information in the database is valid housing source information (that is, housing that the user can currently rent or buy) or housing source information with current activities.

[0071] Exemplarily, the electronic device can subsequently adjust its tag list according to the target customer's browsing of the housing source information in the database to achieve precise marketing. For example, if the housing sources viewed by the target customer are all second-hand houses with three bedrooms and two living rooms, then the two bedrooms and one living room in the tag list can be modified to three bedrooms and two living rooms.

[0072] It can be seen that the present application proposes a customer management method, which obtains the first information of target customers in the target area. The first information includes the basic user information of the target customers, and the target customers are any customers bound to the first real estate consultant; based on the first information, obtain the target data set of the target customers, and the target data set is the data of the housing sources consulted and / or viewed by the target customers; determine the target tag list of the target customers based on the first information and the target data set; add the target customers to at least one target group based on the target tag list. The present application uniformly manages the customers managed by the real estate consultant, determines their tags according to the data of the housing sources consulted and / or viewed by the customers, and then adds them to the group according to the tags to recommend suitable housing sources to the customers, thereby saving the time of the real estate consultant for user management and operation and improving the customer success conversion rate.

[0073] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process on the method side. It can be understood that in order for a network device to implement the above functions, it includes the corresponding hardware structure and / or software module for executing each function. Those skilled in the art should easily realize that, combining the units and algorithm steps of each example described in the embodiments provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is executed in the way of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0074] Please refer to Figure 3 , Figure 3 which is a functional unit composition block diagram of a customer management device 300 provided by an embodiment of the present application. The device 300 includes a data acquisition unit 310, a determination unit 320, and an addition unit 330; wherein,

[0075] The acquisition unit 310 is used to acquire first information of a target customer in a target area. The first information includes basic user information of the target customer, and the target customer is any customer bound to a first real estate consultant;

[0076] The acquisition unit 310 is further used to acquire a target data set of the target customer based on the first information. The target data set is data of housing sources consulted and / or viewed by the target customer;

[0077] The determination unit 320 is used to determine a target label list of the target customer based on the first information and the target data set;

[0078] The addition unit 330 is used to add the target customer to at least one target group based on the target label list.

[0079] Optionally, the first information includes a target sales channel, and the target sales channel is a sales channel through which the first real estate consultant establishes a binding relationship with the target customer;

[0080] In terms of determining the target label list of the target customer based on the first information and the target data set, the determination unit 320 specifically is used for:

[0081] Classify the target data set according to the data source to obtain a plurality of first data sets, and each first data set corresponds to a data source identifier; analyze each first data set according to the data type to obtain at least one target keyword;

[0082] Apply the target tag list to the target customer based on the at least one target keyword; determine a first tag of the target sales channel according to the hierarchical structure of the sales channels, where the first tag is the tag corresponding to a first search branch, and the first search branch is the branch traversed to the target sales channel by depth-first search; add the first tag to the target tag list.

[0083] 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, find keywords of at least one dimension in the housing property information from the first data set to obtain the at least one target keyword; if the data type of the first data set is an image data type, perform row cutting on the images in the first data set to obtain at least one target image, use an optical character recognition model to recognize the at least one target image to obtain the multiple pieces of text, and match the multiple pieces of text with the keywords of at least one dimension in the housing property information respectively to obtain the at least one target keyword.

[0084] Optionally, the first information further includes the user account of the target customer.

[0085] In terms of obtaining the target data set of the target customer, the obtaining unit 310 is specifically configured to: obtain m second data sets associated with the user account from a third-party data platform, where m is a positive integer; score each first data set in the m second data sets to obtain multiple target scores; select a target score that meets the preset requirements from the multiple scores, and determine the second data set corresponding to the target score as the target data set.

[0086] Optionally, in terms of scoring each of the m second data sets to obtain multiple target scores, the obtaining unit 310 is specifically configured to: perform feature extraction on the m second data sets respectively to obtain m feature matrices; calculate the differences between the first feature matrix and the remaining feature matrices in the m feature matrices respectively to obtain m - 1 difference vectors, where the first feature matrix is any one of the m feature matrices; determine a difference vector with the number of zero elements greater than a first quantity threshold in the m - 1 difference vectors as a first difference vector; determine the target score corresponding to the quantity of the first difference based on the relationship between the quantity of the difference vectors and the scores.

[0087] Optionally, the tag list includes multiple first tags, and the first tags are used to indicate the user's housing purchase needs of the target customer.

[0088] In terms 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: obtain at least one candidate group and the tag information of each candidate group based on the first information, so as to obtain at least one piece of tag information; determine the target priority level of each first tag in the multiple first tags according to the target data set; calculate the matching degree between the tag information of each candidate group and the target tag list respectively according to the target priority level, so as to obtain at least one target matching degree; and determine the candidate group corresponding to the target matching degree greater than the preset matching degree in the at least one target matching degree as the target group.

[0089] Optionally, in terms of determining the target priority level of each first tag in the multiple first tags according to the target data set, the adding unit 330 is specifically configured to: determine the target housing purchase demand corresponding to each target keyword in each first data set according to the mapping relationship between the keyword and the housing purchase demand, so as to obtain at least one target housing purchase demand; calculate the target expectation of each target housing purchase demand in the multiple first data sets; and determine the target priority level of the first tag according to the mapping relationship between the expectation and the priority level.

[0090] It should be understood that the device 300 here is embodied in the form of functional units. The term "unit" here may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a dedicated processor or a group of processors, etc.) for executing one or more software or firmware programs, a memory, a combined logic circuit and / or other suitable components supporting the described functions. In an alternative example, those skilled in the art can understand that the device 300 may specifically be the in-vehicle device in the above embodiment, and the device 300 may be used to execute each process and / or step corresponding to the in-vehicle device in the above method embodiment. To avoid repetition, it will not be described in detail here.

[0091] The device 300 of each of the above solutions has the function of implementing the corresponding steps executed by the in-vehicle device in the above method; the function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions, and respectively executes the transceiver operations and related processing operations in each method embodiment.

[0092] In the embodiments of the present application, the device 300 may also be a chip or a chip system, for example: a system on chip (SoC). Correspondingly, the transceiver unit may be the transceiver circuit of the chip, which is not limited herein.

[0093] Please refer to Figure 4 ,Figure 4 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. 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 are configured to be executed by the one or more processors.

[0094] The above program includes instructions for performing the following steps: obtaining first information of a target customer in a target area, the first information including basic user information of the target customer, and the target customer being any customer bound to a first real estate consultant; based on the first information, obtaining a target data set of the target customer, the target data set being data of housing sources consulted and / or viewed by the target customer; determining a target label 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 label list.

[0095] Among them, all relevant contents of each scenario involved in the above method embodiment can be cited to the function description of the corresponding functional module, and will not be elaborated here.

[0096] It should be understood that the above memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A part of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the device type.

[0097] In the embodiment of the present application, the processor of the above device may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0098] It should be understood that the "at least one" involved in the embodiments of the present application refers to one or more, and the "multiple" refers to two or more. The "and / or" describes the association relationship of associated objects and indicates that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone, where A and B may be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. The "following at least one (item)" or its similar expression refers to any combination of these items, including any combination of single item (item) or plural items (items). For example, at least one (item) of a, b, or c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c may be single or multiple.

[0099] In addition, unless otherwise stated, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects and are not used to limit the order, time sequence, priority, or importance of multiple objects. For example, the first information and the second information are only used to distinguish different information, rather than indicating differences in the content, priority, sending order, or importance of these two types of information.

[0100] In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by the hardware processor, or executed and completed by the combination of the hardware and software units in the processor. The software unit can be located in mature storage media in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. This storage media is located in the memory, and the processor executes the instructions in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0101] The 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 the computer to execute some or all of the steps of any of the methods recorded in the above method embodiments.

[0102] The embodiments of the present application also provide a computer program product. The above computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the above computer program is operable to enable the computer to execute some or all of the steps of any of the methods recorded in the above method embodiments. The computer program product can be a software installation package.

[0103] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0104] In the above embodiments, the descriptions of the respective embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] In several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0106] The units described as separate components above may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple 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 this application.

[0107] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0108] If the above integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a TRP, etc.) to execute all or part of the steps of the methods in various embodiments of this application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0109] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program. This program can be stored in a computer-readable memory, and the memory can include: flash drives, ROM, RAM, magnetic disks, or optical discs, etc.

[0110] The above has introduced the embodiments of this application in detail. Specific examples are used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A customer management method, characterized in that, the method includes: Obtain the first information of the target customer in the target area, where the first information includes the basic user information of the target customer, and the target customer is any customer bound to the first real estate consultant; Based on the first information, obtain the target data set of the target customer, where the target data set is the data of the housing sources consulted and / or viewed by the target customer; Determine the target label list of the target customer based on the first information and the target data set; Add the target customer to at least one target group based on the target label list; the first information includes the target sales channel, and the target sales channel is the sales channel through which the first real estate consultant establishes a binding relationship with the target customer; The determining the target label list of the target customer based on the first information and the target data set includes: Classify the target data set according to the data source to obtain multiple first data sets, and each first data set corresponds to a data source identifier; Analyze each first data set according to the data type to obtain at least one target keyword; Mark the target customer with the target label list based on the at least one target keyword; According to the hierarchical structure of the sales channel, determine the first label of the target sales channel, where the first label is the label corresponding to the first search branch, and the first search branch is the branch that traverses to the target sales channel according to depth-first search; Add the first label to the target label list.

2. The method according to claim 1, characterized in that, 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, find the keywords of at least one dimension in the housing source attribute information from the first data set to obtain the at least one target keyword; If the data type of the first data set is an image data type, perform row cutting on the images in the first data set to obtain at least one target image, use an optical character recognition model to recognize the at least one target image to obtain the multiple texts, and match the multiple texts with the keywords of at least one dimension in the housing source attribute information respectively to obtain the at least one target keyword.

3. The method according to claim 1 or 2, characterized in that, the first information further includes the user account of the target customer; the obtaining the target data set of the target customer based on the first information includes: Obtain m second data sets associated with the user account from a third-party data platform, where m is a positive integer; Score each first data set in the m second data sets to obtain multiple target scores; Select the target scores that meet the preset requirements from the multiple scores, and determine the second data sets corresponding to the target scores as the target data sets.

4. The method according to claim 3, characterized in that, Performing scoring on each of the m second data sets to obtain a plurality of target scores, including: Performing feature extraction on the m second data sets respectively to obtain m feature matrices; Calculating the differences between the first feature matrix and the remaining feature matrices in the m feature matrices respectively to obtain m - 1 difference vectors, where the first feature matrix is any one of the m feature matrices; Determining the difference vectors with the number of zero elements in the m - 1 difference vectors greater than the first quantity threshold as the first difference vectors; Determining the target score corresponding to the number of the first differences based on the relationship between the number of difference vectors and the score.

5. The method according to claim 4, wherein, The label list includes a plurality of first labels, and the first labels are used to indicate the housing purchase needs of the target customers; The adding the target customer to at least one target group based on the target label list includes: Obtaining at least one candidate group and the label information of each candidate group based on the first information to obtain at least one label information; Determining the target priority level of each first label in the plurality of first labels according to the target data set; Calculating the matching degree between the label information of each candidate group and the target label list respectively according to the target priority level to obtain at least one target matching degree; Determining the candidate groups corresponding to the target matching degrees greater than the preset matching degree in the at least one target matching degree as the target groups.

6. The method according to claim 5, wherein, The determining the target priority level of each first label in the plurality of first labels according to the target data set includes: Determining the target housing purchase needs corresponding to each target keyword in each first data set according to the mapping relationship between keywords and housing purchase needs to obtain at least one target housing purchase need; Calculating the target expectation of each target housing purchase need in the plurality of first data sets; Determining the target priority level of the first label according to the mapping relationship between the expectation and the priority level.

7. A customer management device, wherein, The device includes: An obtaining unit, configured to obtain the first information of a target customer in a target area, where the first information includes the basic user information of the target customer, and the target customer is any customer bound to a first real estate consultant; The obtaining unit is further configured to obtain the target data set of the target customer based on the first information, where the target data set is the data of the housing sources consulted and / or viewed by the target customer; A determining unit, configured to determine the target label 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 label list; The first information includes a target sales channel, and the target sales channel is the sales channel through which the first real estate consultant establishes a binding relationship with the target customer; In terms of determining the target label list of the target customer based on the first information and the target data set, the determining unit is specifically configured to: Classify the target data set according to the data source to obtain a plurality of first data sets, and each first data set corresponds to a data source identifier; analyze each first data set according to the data type to obtain at least one target keyword; Based on the at least one target keyword, assign the target label list to the target customer; according to the hierarchical structure of the sales channels, determine the first label of the target sales channel, where the first label is the label corresponding to the first search branch, and the first search branch is the branch that traverses to the target sales channel according to depth-first search; add the first label to the target label list.

8. An electronic device, Characterized in that, The electronic device includes a processor, a memory, and a communication interface. The memory stores one or more programs, and the one or more programs are executed by the processor. The one or more programs include instructions for performing the steps in the method according to any one of claims 1-6.

9. 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 execute the steps of the method according to any one of claims 1-6.

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