Similar customer group generation method, device and equipment, medium and program product

Abstract

The invention discloses a similar customer group generation method and device, equipment, a medium and a program product. The method comprises the following steps: receiving a similar customer group generation request sent by a customer group demander; analyzing the similar customer group generation request to obtain an analysis result of the similar customer group generation request; and based on the analysis result of the similar customer generation request and a pre-constructed customer group database, generating a similar customer group corresponding to the similar customer group generation request. According to the embodiment of the invention, the similar customer groups can be automatically generated, a large amount of labor cost can be saved, and the accuracy of the generation result can be ensured.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of artificial intelligence technology, and in particular to a method, device, equipment, medium and program product for generating similar customer groups. Background Art

[0002] Similar customer segments refer to customer groups that are identified through data analysis technology and have high similarity in certain key attributes or behavior patterns. Members of these groups usually share some common characteristics, such as demographic information (age, gender, geographic location, etc.), consumption habits, credit status, risk preferences, product usage behavior, etc. The main purpose of generating similar customer segments is to understand customer needs more accurately, optimize marketing strategies, improve customer service efficiency, and ultimately improve business performance.

[0003] Usually, similar customer groups are generated by manually setting limiting conditions in the database. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, which takes a long time and is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Summary of the invention

[0004] The present application provides a similar customer group generation method, device, equipment, medium and program product, which can automatically generate similar customer groups, which can not only save a lot of labor costs but also ensure the accuracy of the generated results.

[0005] In a first aspect, an embodiment of the present application provides a method for generating similar customer groups, the method comprising:

[0006] Receive similar customer group generation requests sent by customer group demand personnel;

[0007] Parsing the similar customer group generation request to obtain a parsing result of the similar customer generation request;

[0008] Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated.

[0009] In a second aspect, the embodiment of the present application further provides a similar customer group generation device, the device comprising: a receiving module, a parsing module and a generating module; wherein:

[0010] The receiving module is used to receive a similar customer group generation request sent by a customer group demand person;

[0011] The parsing module is used to parse the similar customer group generation request to obtain a parsing result of the similar customer generation request;

[0012] The generating module is used to generate a similar customer group corresponding to the similar customer group generating request based on the parsing result of the similar customer generating request and a pre-built customer group database.

[0013] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0014] one or more processors;

[0015] a memory for storing one or more programs,

[0016] When the one or more programs are executed by the one or more processors, the one or more processors implement the similar customer group generating method described in any embodiment of the present application.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the similar customer group generating method described in any embodiment of the present application is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the similar customer group generation method described in any embodiment of the present application.

[0019] The embodiment of the present application proposes a similar customer group generation method, device, equipment, medium and program product, which first receives a similar customer group generation request sent by a customer group demand person; then parses the similar customer group generation request to obtain the parsing result of the similar customer generation request; then generates a similar customer group corresponding to the similar customer group generation request based on the parsing result of the similar customer generation request and a pre-built customer group database. That is to say, in the technical solution of the present application, by parsing the similar customer generation request, the similar customer group corresponding to the similar customer group generation request can be automatically generated based on the parsing result of the similar customer generation request and a pre-built customer group database. In the prior art, similar customer groups are usually generated by manually setting limiting conditions in the database. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and labor-intensive, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method, device, equipment, medium and program product proposed in the embodiments of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generated results; and the technical solution of the embodiments of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a process for generating similar customer groups provided in one embodiment of the present application;

[0021] Figure 2 A schematic diagram of a process for generating similar customer groups provided in another embodiment of the present application;

[0022] Figure 3 A schematic flow chart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in an embodiment of the present application;

[0023] Figure 4 A schematic flow chart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in another embodiment of the present application;

[0024] Figure 5 A flowchart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in yet another embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of a similar customer group generating device provided in one embodiment of the present application;

[0026] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0028] Figure 1 This is a flow chart of a similar customer group generation method provided in an embodiment of the present application. The method can be executed by a similar customer group generation device or electronic device. The device or electronic device can be implemented by software and / or hardware. The device or electronic device can be integrated in any smart device with network communication function. Figure 1 As shown, the similar customer group generation method may include the following steps:

[0029] S101, receiving a similar customer group generation request sent by a customer group demand person.

[0030] The customer group demand personnel in the embodiment of the present application refers to personnel who have a demand for generating similar customer groups, such as market researchers, bank staff, customer service personnel, product developers, etc. The similar customer group generation request in the embodiment of the present application may include only the customer group information, or may include both the customer group information and the customer group demand personnel information.

[0031] Specifically, customer information refers to information used to describe the characteristics of the customer group, including but not limited to at least one of the following: demographic characteristics, geographic location, psychological graphics, behavioral data, financial status, etc.; among which, demographic characteristics may include: age, gender, occupation, income level, educational background; geographic location may include: city level, residence type; psychological graphics may include: lifestyle, values, personality traits; behavioral data may include: purchase history, frequency of use, channel preference; financial status may include: credit score, investment tendency.

[0032] Specifically, the information of the personnel required by the customer group refers to the information used to describe the characteristics of the personnel required by the customer group, including but not limited to at least one of the following: basic personal information, identity verification information, and occupational information; among which, basic personal information may include: name, gender, date of birth, and home address; identity verification information may include: ID number, signature sample; occupational information may include: job position, job level, department, and time of employment.

[0033] S102: Parse the similar customer group generation request to obtain a parsing result of the similar customer generation request.

[0034] In this step, the similar customer group generation request may be parsed by a pre-set parsing algorithm; wherein the parsing algorithm may include but is not limited to one of the following: grammatical analysis, semantic analysis, intent recognition, parameter parsing, and context association.

[0035] In one embodiment, the information of the customer group and the information of the personnel required by the customer group can be extracted from the similar customer group generation request in a pre-set first format, and the information of the customer group and the information of the personnel required by the customer group can be determined as the parsing result of the similar customer generation request. In another embodiment, the information of the customer group can also be extracted from the similar customer group generation request in a pre-set second format, and the information of the customer group can be determined as the parsing result of the similar customer generation request.

[0036] S103 : Generate a similar customer group corresponding to the similar customer group generation request based on the parsing result of the similar customer generation request and a pre-built customer group database.

[0037] In a specific embodiment of the present application, a customer group database may be pre-built, and the customer group database may store information of multiple candidate customer groups; or the customer group database may also store information of multiple candidate customer groups and information of multiple candidate personnel; wherein one candidate personnel may correspond to one customer group or multiple customer groups. Therefore, based on the parsing result of the similar customer generation request and the pre-built customer group database, a similar customer group corresponding to the similar customer group generation request may be generated.

[0038] This application automatically generates similar customer groups, which can reduce the time and resources required to manually create and adjust target audiences, allowing marketers to work more efficiently; by identifying people with similar characteristics to existing customers, companies can find more potential customers who may be interested in products or services, thereby expanding market coverage; in addition, advertisements can be more accurately delivered to user groups that are most likely to convert, which usually results in higher click-through rates and conversion rates, thereby improving the return on investment of advertising expenditures; the automatically generated similar customer groups are based on big data analysis, making marketing decisions more scientific and reasonable, and reducing the uncertainty caused by subjective judgment.

[0039] The similar customer group generation method proposed in the embodiment of the present application first receives a similar customer group generation request sent by a customer group demand person; then parses the similar customer group generation request to obtain the parsing result of the similar customer generation request; then generates the similar customer group corresponding to the similar customer group generation request based on the parsing result of the similar customer generation request and the pre-built customer group database. That is to say, in the technical solution of the present application, the similar customer group corresponding to the similar customer group generation request can be automatically generated based on the parsing result of the similar customer generation request and the pre-built customer group database by parsing the similar customer generation request. In the prior art, similar customer groups are usually generated by manually setting limiting conditions in the database. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method proposed in the embodiment of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generation results; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0040] Figure 2 A flowchart of a method for generating similar customer groups provided in another embodiment of the present application is shown below. The above technical solution is further optimized and expanded, and can be combined with the above optional implementations. Figure 2 As shown, the similar customer group generation method may include the following steps:

[0041] S201: Receive a similar customer group generation request sent by a customer group demand person.

[0042] S202: extracting information about the customer group and information about the personnel required by the customer group from the similar customer group generation request according to a preset first format, and determining the information about the customer group and information about the personnel required by the customer group as a parsing result of the similar customer generation request.

[0043] The first format in the embodiment of the present application may be a structured format for extracting information about customer groups and information about personnel in need of customer groups in a similar customer group generation request. In one example, the first format may be a first JSON (JavaScript Object Notation) format.

[0044] In one embodiment, extracting the information of the customer group and the information of the personnel required by the customer group from the similar customer group generation request according to the pre-set first format may include the following operation steps: 1) Extracting the basic information of the similar customer group generation request: using request_id (request ID), created_at (creation time), requester (requester), purpose (purpose / purpose) and business_goal (business goal / business goal) to obtain the basic information of the similar customer group generation request. 2) Extracting the seed customer group information: reading the description and key features of the seed customer group from seed_audience (seed audience). 3) Determining the generation parameters: determining the specific algorithm, tool platform, similarity threshold and scale range for generating similar customer groups through the fields in generation_parameters (generation parameters). 4) Extracting the information of the customer group: obtaining the demographic characteristics, psychological graphics and behavioral data of the target customer group from audience_information (target audience information / audience information). 5) Extracting the information of the personnel required by the customer group: listing the personnel of different roles and the information categories they need from personnel_requirements (personnel requirements / personnel requirements).

[0045] S203: extracting customer group information from the similar customer group generation request according to a preset second format, and determining the customer group information as a parsing result of the similar customer generation request.

[0046] The second format in the embodiment of the present application may be a structured format, which is used to extract customer group information from a similar customer group generation request. In one example, the second format may be a second JSON format.

[0047] In one embodiment, extracting customer group information from a similar customer group generation request in a pre-set second format may include the following steps: 1) Extracting basic information of a similar customer group generation request: using request.id (request ID), created_at (creation time), requester (requester), purpose (purpose / purpose) and business_goal (business goal / business goal) to obtain basic information of a similar customer group generation request. 2) Extracting seed customer group information: reading the description and characteristics of the seed customer group from seed_audience (seed audience). 3) Determining generation parameters: determining the specific algorithm, tool platform, similarity threshold and scale range used to generate similar customer groups through the fields in generation_parameters (generation parameters). 4) Extracting customer group information: obtaining the demographic characteristics, psychological graphics and behavioral data of the target customer group from audience.profile (audience portrait); at the same time, the estimated customer group size and confidence level can also be obtained.

[0048] This application processes information in a structured manner to ensure data consistency and accuracy, thereby improving the efficiency and effectiveness of the entire marketing process. Standardized formats are easier to be effectively parsed by automated tools and support a higher degree of automation, such as batch processing or real-time updates.

[0049] S204: Generate a similar customer group corresponding to the similar customer group generation request based on the parsing result of the similar customer generation request and a pre-built customer group database.

[0050] In a specific embodiment of the present application, based on the parsing results of the similar customer generation request and the pre-built customer group database, a variety of methods can be used to generate similar customer groups corresponding to the similar customer group generation request. In one embodiment, the customer group demand personnel can be first matched with each candidate person based on the information of the customer group demand personnel and the information of each candidate person pre-stored in the customer group database to obtain a target person matching the customer group demand personnel; then, based on the information of the customer group and the information of each candidate customer group corresponding to the target person, the customer group can be matched with each candidate customer group corresponding to the target person to obtain a target customer group matching the customer group, and it is used as the similar customer group corresponding to the similar customer group generation request. In addition, the similar customer group corresponding to the similar customer group generation request can also be recommended to the customer group demand personnel.

[0051] In a specific example of the present application, if the similar customer group generation request includes information of multiple customer groups, then corresponding similar customer groups can be generated for the information of each customer group, and the similar customer groups corresponding to the information of all customer groups are used as the similar customer groups corresponding to the similar customer group generation request. For example, for some customer groups, similar customer groups corresponding to the similar customer group generation request can be automatically generated by machine learning; for other customer groups, similar customer groups corresponding to the similar customer group generation request can be automatically generated by using the labels of the customer groups and the labels of each candidate customer group.

[0052] By generating similar customer groups for each specific customer group, this application enables enterprises to segment the market more finely, customize marketing strategies based on the characteristics and needs of different groups, and ensure that information delivery is more relevant and effective. Each customer group has its own unique labels and characteristics. Using this information to generate similar customer groups can make marketing content more personalized, provide product or service recommendations that are highly in line with user interests, and improve user experience.

[0053] The similar customer group generation method proposed in the embodiment of the present application first receives a similar customer group generation request sent by a customer group demand person; then parses the similar customer group generation request to obtain the parsing result of the similar customer generation request; then generates the similar customer group corresponding to the similar customer group generation request based on the parsing result of the similar customer generation request and the pre-built customer group database. That is to say, in the technical solution of the present application, the similar customer group corresponding to the similar customer group generation request can be automatically generated based on the parsing result of the similar customer generation request and the pre-built customer group database by parsing the similar customer generation request. In the prior art, similar customer groups are usually generated by manually setting limiting conditions in the database. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method proposed in the embodiment of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generation results; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0054] Figure 3 A flowchart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in an embodiment of the present application. Based on the above technical solution, it is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 3 As shown, the method for generating a similar customer group corresponding to a similar customer group generation request may include the following steps:

[0055] S301. According to the information of the personnel required by the customer group and the information of each candidate personnel pre-stored in the customer group database, the personnel required by the customer group is matched with each candidate personnel to obtain the target personnel matched with the personnel required by the customer group.

[0056] In this step, the similarity between the person needed by the customer group and each candidate can be calculated based on the information of the person needed by the customer group and the information of each candidate pre-saved in the customer group database, and the candidate with a similarity higher than a first predetermined threshold can be determined as the target person matching the person needed by the customer group.

[0057] S302 , matching the customer group with the candidate customer groups corresponding to the target person according to the information of the customer group and the information of the candidate customer groups corresponding to the target person, obtaining the target customer group that matches the customer group, and using it as the similar customer group corresponding to the similar customer group generation request.

[0058] In this step, the similarity between the customer group and each candidate customer group corresponding to the target person can be calculated based on the information of the customer group and the information of each candidate customer group corresponding to the target person, and the candidate customer group with a similarity higher than a second predetermined threshold is determined as the target customer group matching the customer group.

[0059] By analyzing the characteristics, behavior patterns, and needs of existing customers, this application can create a more accurate user portrait, thereby ensuring that advertising and other marketing activities can accurately reach users with the greatest potential for conversion; in addition, the use of advanced algorithms for customer matching and analysis based on large-scale data sets can reduce human bias, provide more objective market insights, and support more scientific decision-making; precise targeting means that marketing budgets and human resources can be allocated more effectively, avoiding waste on irrelevant audiences and focusing on those groups most likely to bring returns.

[0060] The similar customer group generation method proposed in the embodiment of the present application matches the customer group demand personnel with each candidate personnel according to the information of the customer group demand personnel and the information of each candidate personnel pre-stored in the customer group database, and obtains the target personnel matching the customer group demand personnel; matches the customer group with each candidate customer group corresponding to the target personnel according to the information of the customer group and the information of each candidate customer group corresponding to the target personnel, and obtains the target customer group matching the customer group. That is to say, in the technical scheme of the present application, similar customer groups corresponding to the similar customer group generation request can be automatically generated by matching the customer group demand personnel and the customer group matching. In the prior art, limiting conditions are usually manually set in the database to generate similar customer groups. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method proposed in the embodiment of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generated results; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of application.

[0061] Figure 4 A flowchart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 4 As shown, the method for generating a similar customer group corresponding to a similar customer group generation request may include the following steps:

[0062] S401, input the information of the personnel required by the customer group and the information of each candidate personnel pre-stored in the customer group database, as well as the information of the customer group and the information of each candidate customer group pre-stored in the customer group database into a pre-trained machine learning model.

[0063] S402: Generate a similar customer group corresponding to the similar customer group generation request through a machine learning model.

[0064] In a specific embodiment of the present application, based on the information of the personnel required by the customer group and the information of the various candidate personnel pre-saved in the customer group database, the characteristics of the personnel required by the customer group and the characteristics of the various candidate personnel are respectively extracted through a machine learning model; it is also possible to extract the characteristics of the customer group and the characteristics of the various candidate customer groups respectively through a machine learning model based on the information of the customer group and the information of the various candidate customer groups pre-saved in the customer group database; then, based on the characteristics of the personnel required by the customer group and the characteristics of the various candidate personnel, as well as the characteristics of the customer group and the characteristics of the various candidate customer groups, similar customer groups corresponding to the similar customer group generation request are generated.

[0065] The machine learning model in this application is able to process large amounts of complex data and extract valuable patterns and information from it, providing more accurate support for marketing activities. Machine learning algorithms can identify subtle user behavior patterns and characteristics by analyzing large amounts of historical and real-time data, thereby more accurately finding new potential customers who are similar to existing customers. In addition, machine learning models can continue to learn and optimize over time, automatically adjusting to market changes, changes in user behavior, and new business goals, maintaining long-term effectiveness.

[0066] The similar customer group generation method proposed in the embodiment of the present application first inputs the information of the customer group demand personnel and the information of each candidate personnel pre-stored in the customer group database, as well as the information of the customer group and the information of each candidate customer group pre-stored in the customer group database into a pre-trained machine learning model; then generates the similar customer group corresponding to the similar customer group generation request through the machine learning model. That is to say, in the technical solution of the present application, the similar customer group corresponding to the similar customer group generation request can be automatically generated by machine learning. In the prior art, similar customer groups are usually generated by manually setting limiting conditions in the database. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method proposed in the embodiment of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generated results; and the technical solution of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0067] Figure 5 A flowchart of a method for generating a similar customer group corresponding to a similar customer group generation request provided in another embodiment of the present application. The above technical solution is further optimized and expanded, and can be combined with the above optional implementation methods. Figure 5 As shown, the method for generating a similar customer group corresponding to a similar customer group generation request may include the following steps:

[0068] S501 . Obtain the labels of the customer group and the labels of the candidate customer groups respectively according to the information of the customer group and the information of the candidate customer groups pre-stored in the customer group database.

[0069] The labels in the embodiments of the present application may include, but are not limited to, one of the following: demographic labels, psychographic labels, and behavioral labels. For example, demographic labels may be: "35-45 years old male", "first-tier city residents"; psychographic labels may be: "investment enthusiast", "pursuing stable returns"; and behavioral labels may be: "frequent purchase of financial products", "regularly visit financial pages".

[0070] S502: Generate a similar customer group corresponding to the similar customer group generation request according to the label of the customer group and the labels of each candidate customer group.

[0071] In this step, the similarity between the label of the customer group and the labels of each candidate customer group may be calculated, and then a similar customer group corresponding to the similar customer group generation request may be generated according to the similarity; wherein the similarity may be cosine similarity or Euclidean distance.

[0072] The tags in the embodiments of the present application are key identifiers that describe user characteristics, behavior patterns, interest preferences, and other information. Using these tags for matching and analysis can significantly improve the effectiveness of marketing activities. Tags can provide detailed information about users, such as demographic characteristics, geographic location, purchase history, online behavior, etc. Based on these tags, more precise target audience groups can be created to ensure that advertising and marketing content can accurately reach the users who are most likely to be interested; the automated system can quickly match tags according to pre-set rules or machine learning algorithms, greatly improving the speed and efficiency of creating similar customer groups and reducing the time cost of manual operations.

[0073] The similar customer group generation method proposed in the embodiment of the present application first obtains the customer group label and the label of each candidate customer group respectively according to the customer group information and the information of each candidate customer group pre-stored in the customer group database; then generates the similar customer group corresponding to the similar customer group generation request according to the customer group label and the label of each candidate customer group. That is to say, in the technical scheme of the present application, the similar customer group corresponding to the similar customer group generation request can be automatically generated by the customer group label and the label of each candidate customer group. In the prior art, the limiting conditions are usually manually set in the database to generate similar customer groups. With the existing similar customer group generation method, a large number of similar customer groups need to be screened one by one, and the screening time is long, which is not only time-consuming and laborious, but also cannot guarantee the accuracy of the screening results. Therefore, compared with the prior art, the similar customer group generation method proposed in the embodiment of the present application can automatically generate similar customer groups, which can not only save a lot of labor costs, but also ensure the accuracy of the generated results; and the technical scheme of the embodiment of the present application is simple and convenient to implement, easy to popularize, and has a wider range of applications.

[0074] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data comply with relevant laws, regulations and standards in relevant regions.

[0075] Figure 6 This is a schematic diagram of the structure of a similar customer group generation device provided in one embodiment of the present application. Figure 6As shown, the similar customer group generation device includes: a receiving module 601, a parsing module 602 and a generating module 603; wherein,

[0076] The receiving module 601 is used to receive a similar customer group generation request sent by a customer group demand person;

[0077] The parsing module 602 is used to parse the similar customer group generation request to obtain a parsing result of the similar customer generation request;

[0078] The generating module 603 is used to generate a similar customer group corresponding to the similar customer group generating request based on the parsing result of the similar customer generating request and a pre-built customer group database.

[0079] The similar customer group generation device can execute the method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, please refer to the similar customer group generation method provided in any embodiment of the present application.

[0080] An embodiment of the present invention also provides a computer program product.

[0081] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer program products, which can include one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor, which can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0082] Figure 7 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, referring to Figure 7 , Figure 7 The electronic device 12 shown is only an example and should not limit the functions and scope of use of the embodiments of the present application. Figure 7As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0083] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0084] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0085] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 7 not shown, usually called a "hard drive"). Although Figure 7 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0086] A program / utility 40 having a set (at least one) of program modules 46 may be stored, for example, in the memory 28, such program modules 46 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 46 generally perform the functions and / or methods of the embodiments described herein.

[0087] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although Figure 7 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0088] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a similar customer group generation method provided in an embodiment of the present invention: receiving a similar customer group generation request sent by a customer group demand person; parsing the similar customer group generation request to obtain a parsed result of the similar customer generation request; and generating a similar customer group corresponding to the similar customer group generation request based on the parsed result of the similar customer generation request and a pre-built customer database.

[0089] The embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, a similar customer group generation method provided by all the embodiments of the present invention is implemented: receiving a similar customer group generation request sent by a customer group demand person; parsing the similar customer group generation request to obtain the parsing result of the similar customer generation request; based on the parsing result of the similar customer generation request and a pre-built customer group database, generating a similar customer group corresponding to the similar customer group generation request. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electronic device, device or device of electrical, magnetic, optical, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer readable storage medium may be any tangible medium that can contain or store a program for use by or in connection with an instruction executing electronic device, apparatus, or device.

[0090] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction-executing electronic device, apparatus, or device.

[0091] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0092] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for generating similar customer groups, characterized in that: The method comprises: Receive similar customer group generation requests sent by customer group demand personnel; Parsing the similar customer group generation request to obtain a parsing result of the similar customer generation request; Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated.

2. The method according to claim 1, characterized in that The similar customer group generation request is parsed to obtain a parsing result of the similar customer generation request, including: Extracting the information of the customer group and the information of the personnel required by the customer group from the similar customer group generation request in a preset first format, and determining the information of the customer group and the information of the personnel required by the customer group as the parsing result of the similar customer generation request; or The customer group information is extracted from the similar customer group generation request according to a preset second format, and the customer group information is determined as the parsing result of the similar customer generation request.

3. The method according to claim 2, characterized in that Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated, including: According to the information of the personnel required by the customer group and the information of each candidate personnel pre-stored in the customer group database, the personnel required by the customer group is matched with each candidate personnel to obtain a target person who matches the personnel required by the customer group; According to the information of the customer group and the information of each candidate customer group corresponding to the target person, the customer group is matched with each candidate customer group corresponding to the target person to obtain a target customer group matching the customer group, and the target customer group is used as the similar customer group corresponding to the similar customer group generation request.

4. The method according to claim 2, characterized in that: Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated, including: Inputting the information of the personnel required by the customer group and the information of each candidate personnel pre-stored in the customer group database, as well as the information of the customer group and the information of each candidate customer group pre-stored in the customer group database into a pre-trained machine learning model; A similar customer group corresponding to the similar customer group generation request is generated through the machine learning model.

5. The method according to claim 2, characterized in that: Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated, including: According to the information of the customer group and the information of each candidate customer group pre-stored in the customer group database, respectively obtaining a label of the customer group and a label of each candidate customer group; A similar customer group corresponding to the similar customer group generation request is generated according to the label of the customer group and the labels of each candidate customer group.

6. The method according to claim 1, characterized in that Based on the parsing result of the similar customer generation request and a pre-built customer group database, a similar customer group corresponding to the similar customer group generation request is generated, including: If the similar customer group generation request includes information of multiple customer groups, corresponding similar customer groups are generated for the information of each customer group, and similar customer groups corresponding to the information of all customer groups are used as similar customer groups corresponding to the similar customer group generation request.

7. A similar customer group generation device, characterized in that: The device comprises: a receiving module, a parsing module and a generating module; wherein, The receiving module is used to receive a similar customer group generation request sent by a customer group demand person; The parsing module is used to parse the similar customer group generation request to obtain a parsing result of the similar customer generation request; The generating module is used to generate a similar customer group corresponding to the similar customer group generating request based on the parsing result of the similar customer generating request and a pre-built customer group database.

8. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the similar customer group generating method according to any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the similar customer group generating method as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the similar customer group generation method according to any one of claims 1 to 6.

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