Marketing verbal skill generation method and device based on family portrait

Abstract

The invention provides a marketing verbal skill generation method and device based on a family portrait, and the method comprises the steps: responding to an input operation, and displaying an input user identifier through a human-computer interaction interface; wherein the user identifier corresponds to family portrait information of a user, and the family portrait information represents portrait information of each family member in a family where the user is located; in response to the confirmation operation, submitting a user identifier, and displaying marketing verbal skill information of a user corresponding to the user identifier; wherein the marketing verbal skill information comprises marketing verbal skill for at least one family member in a family where the user is located; the marketing efficiency can be improved.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a method and device for generating marketing scripts based on family portraits. Background Art

[0002] In the current insurance industry, traditional marketing methods face many challenges. In the process of business development, salesmen often encounter the problem of incomplete customer information, which is mainly due to limited information sources and the lack of effective integration mechanisms. In addition, the customer value assessment is often based on subjective judgment, lacking scientificity and accuracy, making it difficult for salesmen to accurately locate high-value customers, thus affecting the efficiency of visits.

[0003] At the same time, customer policy information is complex and diverse, and there is a lack of a unified management platform and standardized evaluation system between different insurance companies and products. This makes it difficult for sales staff to obtain comprehensive and accurate data support when evaluating customer protection gaps, making it difficult to optimize insurance configuration and meet customers' personalized needs.

[0004] In addition, with the rapid development of the insurance industry, massive amounts of policy data and customer data resources have been accumulated. However, these data resources have not been fully utilized and lack effective mining and analysis methods. This not only leads to a waste of data resources, but also restricts the further development of insurance marketing and customer development. Summary of the invention

[0005] In view of this, an embodiment of the present application provides a method and device for generating marketing scripts based on family portraits, which can improve marketing efficiency.

[0006] The technical solution of the embodiment of the present application is implemented as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for generating marketing words based on family portraits, the method comprising:

[0008] In response to the input operation, the input user identification is displayed through the human-computer interaction interface; wherein the user identification corresponds to the family portrait information of a user, and the family portrait information represents the portrait information of each family member in the family of the user;

[0009] In response to the confirmation operation, the user identification is submitted, and marketing speech information for the user corresponding to the user identification is displayed; wherein the marketing speech information includes marketing speech for at least one family member in the family of the user.

[0010] In a second aspect, an embodiment of the present application further provides a device for generating marketing words based on family portraits, the device comprising:

[0011] An input module, used to display the input user identification through the human-computer interaction interface in response to the input operation; wherein the user identification corresponds to the family portrait information of a user, and the family portrait information represents the portrait information of each family member in the family of the user;

[0012] The display module is used to submit the user identification in response to a confirmation operation, and display marketing speech information for the user corresponding to the user identification; wherein the marketing speech information includes marketing speech for at least one family member in the family of the user.

[0013] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: a processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium through the bus, and the processor executes the machine-readable instructions to execute the marketing script generation method based on family portraits as described in any one of the first aspects.

[0014] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method for generating marketing scripts based on family portraits as described in any one of the first aspects.

[0015] The embodiments of the present application have the following beneficial effects:

[0016] (1) Significantly improve sales staff's sales efficiency:

[0017] By introducing the AI ​​big model, it is possible to automatically generate visit scripts and recommend insurance products. This function greatly reduces the time for manual screening and preparation, allowing salespeople to focus more on communicating with customers and building relationships. In addition, the AI ​​big model can continuously optimize and adjust the scripts based on real-time feedback and historical data from customers to ensure that each visit is more accurate and efficient. This not only improves the work efficiency of salespeople, but also improves their customer satisfaction and performance.

[0018] (2) Comprehensively improve customer experience:

[0019] The AI ​​big model can generate personalized visit scripts and accurate insurance recommendations based on the customer's historical data and real-time feedback. This personalized service method can better meet the needs of customers and make customers feel valued and cared for. At the same time, the AI ​​big model can also provide detailed insurance explanations and protection plans to help customers better understand insurance products and make wise purchasing decisions. This all-round, personalized service experience not only improves customer satisfaction, but also enhances customers' trust and loyalty to insurance companies.

[0020] (3) Scientifically optimize insurance configuration:

[0021] Based on the protection gap assessment of the customer's family portrait, the AI ​​big model can provide customers with scientific and reasonable insurance configuration suggestions. This function can not only help customers identify potential risks and protection needs, but also provide customers with customized insurance plans. This scientific and accurate insurance configuration suggestion not only improves the customer's protection level, but also reduces the potential risks caused by insufficient or excessive protection. At the same time, this also provides insurance companies with more accurate market positioning and product development direction.

[0022] (4) Deeply empower data resources:

[0023] AI big models can fully mine and utilize massive policy data and customer data resources, providing strong support for the digital transformation of the insurance industry. By analyzing and mining these data, AI big models can discover potential market trends and customer needs, and provide valuable references for insurance companies' business development and product innovation. At the same time, AI big models can also optimize insurance companies' operating processes and risk management, and improve overall operational efficiency and risk management levels. This deep data empowerment not only promotes the digital transformation of the insurance industry, but also brings more sustainable development momentum to insurance companies. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a flowchart of steps S101-S102 provided in an embodiment of the present application;

[0026] Figure 2 It is a flowchart of steps S201-S203 provided in an embodiment of the present application;

[0027] Figure 3 It is a flowchart of steps S301-S302 provided in an embodiment of the present application;

[0028] Figure 4 It is a flowchart of steps S401-S403 provided in an embodiment of the present application;

[0029] Figure 5 It is a flowchart of steps S501-S502 provided in an embodiment of the present application;

[0030] Figure 6a It is a schematic diagram provided by an embodiment of the present application;

[0031] Figure 6b This is one of the marketing speech interface diagrams provided in the embodiment of the present application;

[0032] Figure 6c This is the second marketing speech interface diagram provided in the embodiment of the present application;

[0033] Figure 6d This is the third marketing speech interface diagram provided in the embodiment of the present application;

[0034] Figure 6e This is the fourth marketing speech interface diagram provided by the embodiment of the present application;

[0035] Figure 7 It is a structural schematic diagram of a marketing speech generating device based on family portrait provided in an embodiment of the present application;

[0036] Figure 8 It is a schematic diagram of the composition structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0037] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0038] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0039] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0040] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0041] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0043] See also Figure 1 , Figure 1 This is a flow chart of steps S101-S102 of the method for generating marketing words based on family portraits provided in an embodiment of the present application, which will be combined with Figure 1 Steps S101-S102 are shown for explanation.

[0044] In step S101, in response to an input operation, an input user identification is displayed through a human-computer interaction interface; wherein the user identification corresponds to family portrait information of a user, and the family portrait information represents portrait information of each family member in the user's family.

[0045] When a user operates through a human-computer interaction interface (such as a computer screen, mobile phone application interface, etc.), the system will first display an input area for the user to enter his or her ID (such as user name, customer number, etc.). This user ID is the key for the system to identify and obtain the user's family portrait information. After the user enters the ID, the system will display this ID on the interface as the last step before confirmation and submission.

[0046] In step S102, in response to the confirmation operation, the user identification is submitted, and marketing speech information for the user corresponding to the user identification is displayed; wherein the marketing speech information includes marketing speech for at least one family member in the family of the user.

[0047] After the user confirms that the input is correct, he / she submits the user ID by clicking the confirmation button or other confirmation operations. After receiving the user ID, the system will generate a series of marketing words for at least one family member in the user's family based on the family portrait information. These marketing words are customized based on the family members' age, gender, interests, purchase history and other personalized information. Finally, the system will display these customized marketing words on the human-computer interaction interface for users or marketers to view and use.

[0048] In some embodiments, see Figure 2 , Figure 2 It is a flow chart of steps S201-S203 provided in an embodiment of the present application. The family portrait information can be constructed through steps S201-S203, which will be explained in combination with each step.

[0049] In step S201, user information of the user is pre-acquired; wherein the user information includes at least one of basic information, policy information, and insurance rights and interests.

[0050] In step S202, user portrait information of the user is constructed based on the user information.

[0051] In step S203, the family information of the user is determined through the user portrait information, and the family portrait information of the user is constructed based on the family information.

[0052] Before building family portrait information, you first need to collect various types of user information. This information includes but is not limited to: Basic information: such as the user's name, age, gender, contact information, occupation, income and other basic information. Policy information: Details of insurance products that the user has purchased or is considering, including insurance type, insured amount, coverage period, premium, etc. Insurance rights and interests: The various rights and interests that users enjoy based on the purchased insurance policies, such as claims services, value-added services, renewal discounts, etc. Build user portrait information: Based on pre-acquired user information, use data analysis, machine learning and other technical means to build the user's personal portrait information.

[0053] User portrait information is a digital representation that integrates multiple aspects of a user's characteristics and can comprehensively reflect the user's personal characteristics, consumption habits, risk preferences, etc.

[0054] The user's family structure is further determined through key features in the user's portrait information (such as the user's marital status, children, etc.). Family information may include the number of family members, age distribution, occupation distribution, health status, etc. Based on the determined family information and combined with other relevant features in the user's portrait information, the user's family portrait information is constructed. Family portrait information is a database or data set that integrates the portrait information of each family member in the user's family, which can comprehensively reflect the various characteristics of family members.

[0055] In some embodiments, see Figure 3 , Figure 3 It is a flow chart of steps S301-S302 provided in an embodiment of the present application. The construction of the user portrait information of the user based on the user information can be achieved through steps S301-S302, which will be explained in combination with each step.

[0056] In step S301, index information is extracted from the user information; wherein the index information includes at least one of a basic information index, a transaction behavior index, a service experience index and an external information index.

[0057] In step S302, the indicator information is integrated and processed to obtain the user portrait information.

[0058] Here, from the pre-acquired user information, index information that can reflect user characteristics, behaviors, preferences, etc. is extracted. These index information include but are not limited to: Basic information index: such as the user's name, age, gender, contact information, occupation, income, etc., which is the basis for building a user portrait.

[0059] Transaction behavior indicators: such as users’ purchase frequency, purchase amount, purchase time, purchase channel, etc., can reflect users’ consumption habits and behavior patterns.

[0060] Service experience indicators: such as user satisfaction with the product, complaints, return and exchange rates, etc., can reflect the user's evaluation and attitude towards the service.

[0061] External information indicators: such as user behavior on social media, interest tags, credit scores, etc., can supplement and improve user portraits.

[0062] Integrate and process indicator information: Integrate and process the extracted indicator information, including data cleaning, deduplication, normalization, classification and other processing.

[0063] Using data analysis, data mining and other technical means, we conduct in-depth analysis and mining of indicator information to extract valuable user characteristics and behavior patterns. Based on the analysis results, we build user portrait information, including multiple dimensions such as user personalized characteristics, consumption preferences, and risk levels.

[0064] In some embodiments, see Figure 4 , Figure 4 It is a flow chart of steps S401-S403 provided in an embodiment of the present application. Determining the family information of the user through the user portrait information and constructing the family portrait information of the user based on the family information can be achieved through steps S401-S403, which will be explained in combination with each step.

[0065] In step S401, basic family information is obtained from the user portrait information.

[0066] In step S402, the insurance policy information associated with each family member is obtained based on the basic family information, and the family financial information is constructed based on the insurance policy information. The protection status of each family member is obtained, and the family protection information is constructed based on the protection status.

[0067] In step S403, the family portrait information is constructed based on the family basic information, family financial information and family security information.

[0068] Here, basic information related to the user's family is extracted from the user portrait information, such as the user's marital status, number of children, name, age, gender, etc. This information constitutes the basic framework of the family portrait, and is an important basis for subsequent analysis of family member-related insurance policy information, and construction of family financial information and family protection information.

[0069] Based on the basic family information, obtain the insurance policy information associated with each family member. Insurance policy information usually includes insurance type, insured amount, premium, etc., which can reflect the insurance consumption capacity of family members and the financial status of the family. Summarize and analyze the insurance policy information to construct family financial information, including the family's total insured amount, total premium, insurance consumption ratio, etc., to assess the family's financial health and insurance consumption capacity. Also based on the basic family information, obtain the protection status of each family member. The protection status includes the type of insurance products purchased, the scope of protection, the protection period, etc., which can reflect the protection level and risk tolerance of family members. Integrate and analyze the protection status to construct family protection information, including the family's protection coverage, protection gaps, protection priorities, etc., to assess the family's protection needs and risk status.

[0070] Based on basic family information, family financial information and family security information, a comprehensive family portrait is constructed. Family portrait information is a comprehensive and systematic description of family characteristics, including family structure, financial status, security needs and other dimensions. This family portrait information can serve as an important basis for subsequent precision marketing, risk management, customer service and other decision-making.

[0071] In some embodiments, see Figure 5 , Figure 5 It is a flow chart of steps S501-S502 provided in an embodiment of the present application. The marketing speech information can be obtained through steps S501-S502, which will be explained in conjunction with each step.

[0072] In step S501, at least one Prompt word is constructed based on the family portrait information; wherein the Prompt word represents context information that matches the family portrait information.

[0073] In step S502, the family portrait information and the at least one prompt word are input into the AI ​​big model as input information, and the marketing speech information is returned through the AI ​​big model.

[0074] Here, at least one Prompt word is constructed based on the family portrait information. These prompt words are intended to represent context information that matches the family portrait information, that is, they can summarize or reflect the key features or requirements in the family portrait.

[0075] The family portrait information and at least one constructed prompt word are input into the AI ​​big model as input information. The AI ​​big model is a large neural network model that has been trained with a large amount of data and has strong text generation and comprehension capabilities. It can generate text content related to the input information. After receiving the input information, the AI ​​big model will perform internal processing and calculation, and then return marketing speech information that matches the family portrait information. These marketing speech information are generated based on the key features and needs in the family portrait and the guidance of the prompt word, so they are highly targeted and personalized.

[0076] In some embodiments, the AI ​​big model includes at least one insurance type protection classification plug-in, and the at least one insurance type protection classification plug-in is constructed based on the label information corresponding to each insurance type. The AI ​​big model obtains at least one insurance type that matches the family portrait information through the at least one insurance type protection classification plug-in, and generates the marketing speech information based on the at least one insurance type.

[0077] The AI ​​big model contains at least one insurance protection classification plug-in. These plug-ins are built based on the label information corresponding to each insurance type, and are designed to help the AI ​​big model better understand and classify different insurance types. Label information may include key features such as the type of insurance (such as life insurance, health insurance, property insurance, etc.), coverage, target audience, etc. Through this label information, the insurance protection classification plug-in can accurately identify and classify different insurance types. When the AI ​​big model receives family portrait information as input, it uses the insurance protection classification plug-in to analyze and match the insurance types most relevant to the family portrait information. This process involves comprehensive consideration of multiple dimensions of family portrait information, such as the age, occupation, health status, financial status, etc. of family members, to determine which insurance types best meet the needs and preferences of the family.

[0078] Once the insurance types that match the family profile information are determined, the AI ​​model will generate corresponding marketing information based on these insurance types. The marketing information may include key information such as detailed introduction of the insurance type, coverage, premium calculation method, purchase process, etc., aiming to help family members better understand and choose the insurance products that suit them.

[0079] See also Figure 6a , Figure 6a It is a schematic diagram provided by the embodiment of the present application, such as Figure 6a As shown in the figure, when a user interacts with the big model, the big model will first receive the information input by the user and trigger the various steps of the service layer. The service layer will perform portrait analysis, speech generation, and insurance type recommendation steps based on the user information and the data provided by the data layer. Finally, the results of these steps will be displayed in the form of marketing speech. The specific marketing speech is as follows Figure 6b-6e shown.

[0080] In some embodiments, the method further comprises:

[0081] Obtain feedback information from the user and actual sales data for the user;

[0082] The AI ​​big model is updated based on the feedback information and the actual sales data.

[0083] Here, the AI ​​big model generates text content for dialogue with users, i.e., marketing language, to explain the recommended insurance products or answer users' questions. After receiving this information, users will give feedback, such as satisfaction ratings, opinions on the recommended insurance products, etc. In addition to direct feedback from users, the method also involves collecting actual sales data for the user. This data may include whether the user purchased the recommended insurance products, the type of insurance purchased, the purchase amount, etc.

[0084] Based on user feedback and actual sales data, the AI ​​model will be iteratively updated. The update process includes adjusting model parameters, optimizing algorithms, updating insurance protection classification plug-ins, etc., to improve the accuracy and effectiveness of the model.

[0085] In summary, the embodiments of the present application have the following beneficial effects:

[0086] (1) Significantly improve sales staff's sales efficiency:

[0087] By introducing the AI ​​big model, it is possible to automatically generate visit scripts and recommend insurance products. This function greatly reduces the time for manual screening and preparation, allowing salespeople to focus more on communicating with customers and building relationships. In addition, the AI ​​big model can continuously optimize and adjust the scripts based on real-time feedback and historical data from customers to ensure that each visit is more accurate and efficient. This not only improves the work efficiency of salespeople, but also improves their customer satisfaction and performance.

[0088] (2) Comprehensively improve customer experience:

[0089] The AI ​​big model can generate personalized visit scripts and accurate insurance recommendations based on the customer's historical data and real-time feedback. This personalized service method can better meet the needs of customers and make customers feel valued and cared for. At the same time, the AI ​​big model can also provide detailed insurance explanations and protection plans to help customers better understand insurance products and make wise purchasing decisions. This all-round, personalized service experience not only improves customer satisfaction, but also enhances customers' trust and loyalty to insurance companies.

[0090] (3) Scientifically optimize insurance configuration:

[0091] Based on the protection gap assessment of the customer's family portrait, the AI ​​big model can provide customers with scientific and reasonable insurance configuration suggestions. This function can not only help customers identify potential risks and protection needs, but also provide customers with customized insurance plans. This scientific and accurate insurance configuration suggestion not only improves the customer's protection level, but also reduces the potential risks caused by insufficient or excessive protection. At the same time, this also provides insurance companies with more accurate market positioning and product development direction.

[0092] (4) Deeply empower data resources:

[0093] AI big models can fully mine and utilize massive policy data and customer data resources, providing strong support for the digital transformation of the insurance industry. By analyzing and mining these data, AI big models can discover potential market trends and customer needs, and provide valuable references for insurance companies' business development and product innovation. At the same time, AI big models can also optimize insurance companies' operating processes and risk management, and improve overall operational efficiency and risk management levels. This deep data empowerment not only promotes the digital transformation of the insurance industry, but also brings more sustainable development momentum to insurance companies.

[0094] Based on the same inventive concept, the embodiments of the present application also provide a marketing script generation device based on family portraits corresponding to the marketing script generation method based on family portraits in the first embodiment. Since the principle of solving the problem by the device in the embodiments of the present application is similar to the above-mentioned marketing script generation method based on family portraits, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0095] like Figure 7 As shown, Figure 7 : is a schematic diagram of the structure of a marketing speech generation device 700 based on family portraits provided in an embodiment of the present application. The marketing speech generation device 700 based on family portraits includes:

[0096] The input module 701 is used to display the input user identification through the human-computer interaction interface in response to the input operation; wherein the user identification corresponds to the family portrait information of a user, and the family portrait information represents the portrait information of each family member in the family of the user;

[0097] The display module 702 is used to submit the user identification in response to the confirmation operation, and display the marketing speech information for the user corresponding to the user identification; wherein the marketing speech information includes the marketing speech for at least one family member in the family of the user.

[0098] Those skilled in the art should understand that Figure 7 The implementation functions of each unit in the illustrated device 700 for generating marketing speech based on family portraits can be understood by referring to the relevant description of the aforementioned method for generating marketing speech based on family portraits. Figure 7 The functions of each unit in the marketing speech generation device 700 based on family portraits shown can be implemented by a program running on a processor, or by a specific logic circuit.

[0099] In a possible implementation, the family portrait information is constructed in the following manner:

[0100] Pre-acquire user information of the user; wherein the user information includes at least one of basic information, policy information, and insurance rights;

[0101] Constructing user portrait information of the user based on the user information;

[0102] The family information of the user is determined through the user portrait information, and the family portrait information of the user is constructed based on the family information.

[0103] In a possible implementation, constructing user portrait information of the user based on the user information includes:

[0104] Extracting index information from the user information; wherein the index information includes at least one of a basic information index, a transaction behavior index, a service experience index, and an external information index;

[0105] The indicator information is integrated and processed to obtain the user portrait information.

[0106] In a possible implementation, determining the family information of the user through the user portrait information, and constructing the family portrait information of the user based on the family information includes:

[0107] Obtaining basic family information from the user portrait information;

[0108] Based on the basic family information, obtain the insurance policy information associated with each family member, and construct the family financial information according to the insurance policy information, and obtain the protection status of each family member, and construct the family protection information according to the protection status;

[0109] The family portrait information is constructed based on the family basic information, family financial information and family security information.

[0110] In a possible implementation, the marketing speech information is obtained in the following manner:

[0111] Constructing at least one Prompt word based on the family portrait information; wherein the Prompt word represents context information matching the family portrait information;

[0112] The family portrait information and the at least one prompt word are input into the AI ​​big model as input information, and the marketing speech information is returned through the AI ​​big model.

[0113] In one possible implementation, the AI ​​big model includes at least one insurance protection classification plug-in, and the at least one insurance protection classification plug-in is constructed based on the label information corresponding to each insurance type. The AI ​​big model obtains at least one insurance type that matches the family portrait information through the at least one insurance protection classification plug-in, and generates the marketing speech information based on the at least one insurance type.

[0114] In a possible implementation, the method further includes:

[0115] Obtain feedback information from the user and actual sales data for the user;

[0116] The AI ​​big model is updated based on the feedback information and the actual sales data.

[0117] The above-mentioned marketing speech generation device based on family portrait has the following beneficial effects:

[0118] (1) Significantly improve sales staff's sales efficiency:

[0119] By introducing the AI ​​big model, it is possible to automatically generate visit scripts and recommend insurance products. This function greatly reduces the time for manual screening and preparation, allowing salespeople to focus more on communicating with customers and building relationships. In addition, the AI ​​big model can continuously optimize and adjust the scripts based on real-time feedback and historical data from customers to ensure that each visit is more accurate and efficient. This not only improves the work efficiency of salespeople, but also improves their customer satisfaction and performance.

[0120] (2) Comprehensively improve customer experience:

[0121] The AI ​​big model can generate personalized visit scripts and accurate insurance recommendations based on the customer's historical data and real-time feedback. This personalized service method can better meet the needs of customers and make customers feel valued and cared for. At the same time, the AI ​​big model can also provide detailed insurance explanations and protection plans to help customers better understand insurance products and make wise purchasing decisions. This all-round, personalized service experience not only improves customer satisfaction, but also enhances customers' trust and loyalty to insurance companies.

[0122] (3) Scientifically optimize insurance configuration:

[0123] Based on the protection gap assessment of the customer's family portrait, the AI ​​big model can provide customers with scientific and reasonable insurance configuration suggestions. This function can not only help customers identify potential risks and protection needs, but also provide customers with customized insurance plans. This scientific and accurate insurance configuration suggestion not only improves the customer's protection level, but also reduces the potential risks caused by insufficient or excessive protection. At the same time, this also provides insurance companies with more accurate market positioning and product development direction.

[0124] (4) Deeply empower data resources:

[0125] AI big models can fully mine and utilize massive policy data and customer data resources, providing strong support for the digital transformation of the insurance industry. By analyzing and mining these data, AI big models can discover potential market trends and customer needs, and provide valuable references for insurance companies' business development and product innovation. At the same time, AI big models can also optimize insurance companies' operating processes and risk management, and improve overall operational efficiency and risk management levels. This deep data empowerment not only promotes the digital transformation of the insurance industry, but also brings more sustainable development momentum to insurance companies.

[0126] like Figure 8 As shown, Figure 8 The present invention provides a schematic diagram of the structure of an electronic device 800 according to an embodiment of the present invention. The electronic device 800 includes:

[0127] A processor 801, a storage medium 802 and a bus 803, wherein the storage medium 802 stores machine-readable instructions executable by the processor 801. When the electronic device 800 is running, the processor 801 communicates with the storage medium 802 via the bus 803, and the processor 801 executes the machine-readable instructions to perform the steps of the method for generating marketing scripts based on family portraits described in the embodiment of the present application.

[0128] In actual application, the components in the electronic device 800 are coupled together via bus 803. It is understood that bus 803 is used to realize the connection and communication between these components. Bus 803 includes not only a data bus, but also a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 8 Various buses are labeled as bus 803.

[0129] The above electronic device has the following beneficial effects:

[0130] (1) Significantly improve sales staff's sales efficiency:

[0131] By introducing the AI ​​big model, it is possible to automatically generate visit scripts and recommend insurance products. This function greatly reduces the time for manual screening and preparation, allowing salespeople to focus more on communicating with customers and building relationships. In addition, the AI ​​big model can continuously optimize and adjust the scripts based on real-time feedback and historical data from customers to ensure that each visit is more accurate and efficient. This not only improves the work efficiency of salespeople, but also improves their customer satisfaction and performance.

[0132] (2) Comprehensively improve customer experience:

[0133] The AI ​​big model can generate personalized visit scripts and accurate insurance recommendations based on the customer's historical data and real-time feedback. This personalized service method can better meet the needs of customers and make customers feel valued and cared for. At the same time, the AI ​​big model can also provide detailed insurance explanations and protection plans to help customers better understand insurance products and make wise purchasing decisions. This all-round, personalized service experience not only improves customer satisfaction, but also enhances customers' trust and loyalty to insurance companies.

[0134] (3) Scientifically optimize insurance configuration:

[0135] Based on the protection gap assessment of the customer's family portrait, the AI ​​big model can provide customers with scientific and reasonable insurance configuration suggestions. This function can not only help customers identify potential risks and protection needs, but also provide customers with customized insurance plans. This scientific and accurate insurance configuration suggestion not only improves the customer's protection level, but also reduces the potential risks caused by insufficient or excessive protection. At the same time, this also provides insurance companies with more accurate market positioning and product development direction.

[0136] (4) Deeply empower data resources:

[0137] AI big models can fully mine and utilize massive policy data and customer data resources, providing strong support for the digital transformation of the insurance industry. By analyzing and mining these data, AI big models can discover potential market trends and customer needs, and provide valuable references for insurance companies' business development and product innovation. At the same time, AI big models can also optimize insurance companies' operating processes and risk management, and improve overall operational efficiency and risk management levels. This deep data empowerment not only promotes the digital transformation of the insurance industry, but also brings more sustainable development momentum to insurance companies.

[0138] The embodiment of the present application also provides a computer-readable storage medium, which stores executable instructions. When the executable instructions are executed by at least one processor 801, the method for generating marketing scripts based on family portraits described in the embodiment of the present application is implemented.

[0139] In some embodiments, the storage medium can be a magnetic random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface storage, an optical disk, or a compact disc read-only memory (CDROM); it can also be various devices including one or any combination of the above memories.

[0140] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0141] As an example, executable instructions may, but do not necessarily, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, for example, in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (for example, files storing one or more modules, subroutines, or code portions).

[0142] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0143] The computer-readable storage medium has the following advantages:

[0144] (1) Significantly improve sales staff's sales efficiency:

[0145] By introducing the AI ​​big model, it is possible to automatically generate visit scripts and recommend insurance products. This function greatly reduces the time for manual screening and preparation, allowing salespeople to focus more on communicating with customers and building relationships. In addition, the AI ​​big model can continuously optimize and adjust the scripts based on real-time feedback and historical data from customers to ensure that each visit is more accurate and efficient. This not only improves the work efficiency of salespeople, but also improves their customer satisfaction and performance.

[0146] (2) Comprehensively improve customer experience:

[0147] The AI ​​big model can generate personalized visit scripts and accurate insurance recommendations based on the customer's historical data and real-time feedback. This personalized service method can better meet the needs of customers and make customers feel valued and cared for. At the same time, the AI ​​big model can also provide detailed insurance explanations and protection plans to help customers better understand insurance products and make wise purchasing decisions. This all-round, personalized service experience not only improves customer satisfaction, but also enhances customers' trust and loyalty to insurance companies.

[0148] (3) Scientifically optimize insurance configuration:

[0149] Based on the protection gap assessment of the customer's family portrait, the AI ​​big model can provide customers with scientific and reasonable insurance configuration suggestions. This function can not only help customers identify potential risks and protection needs, but also provide customers with customized insurance plans. This scientific and accurate insurance configuration suggestion not only improves the customer's protection level, but also reduces the potential risks caused by insufficient or excessive protection. At the same time, this also provides insurance companies with more accurate market positioning and product development direction.

[0150] (4) Deeply empower data resources:

[0151] AI big models can fully mine and utilize massive policy data and customer data resources, providing strong support for the digital transformation of the insurance industry. By analyzing and mining these data, AI big models can discover potential market trends and customer needs, and provide valuable references for insurance companies' business development and product innovation. At the same time, AI big models can also optimize insurance companies' operating processes and risk management, and improve overall operational efficiency and risk management levels. This deep data empowerment not only promotes the digital transformation of the insurance industry, but also brings more sustainable development momentum to insurance companies.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed methods and electronic devices can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0153] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a platform server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard drives, ROM, RAM, magnetic disks, or optical disks.

[0156] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for generating marketing words based on family portraits, characterized in that: The method comprises: In response to the input operation, the input user identification is displayed through the human-computer interaction interface; wherein the user identification corresponds to the family portrait information of a user, and the family portrait information represents the portrait information of each family member in the family of the user; In response to the confirmation operation, the user identification is submitted, and marketing speech information for the user corresponding to the user identification is displayed; wherein the marketing speech information includes marketing speech for at least one family member in the family of the user.

2. The method according to claim 1, characterized in that The family portrait information is constructed in the following way: Pre-acquire user information of the user; wherein the user information includes at least one of basic information, policy information, and insurance rights; Constructing user portrait information of the user based on the user information; The family information of the user is determined through the user portrait information, and the family portrait information of the user is constructed based on the family information.

3. The method according to claim 2, characterized in that The constructing user portrait information of the user based on the user information includes: Extracting index information from the user information; wherein the index information includes at least one of a basic information index, a transaction behavior index, a service experience index, and an external information index; The indicator information is integrated and processed to obtain the user portrait information.

4. The method according to claim 2, characterized in that: The determining the family information of the user through the user portrait information, and constructing the family portrait information of the user based on the family information, includes: Obtaining basic family information from the user portrait information; Based on the basic family information, obtain the insurance policy information associated with each family member, and construct the family financial information according to the insurance policy information, and obtain the protection status of each family member, and construct the family protection information according to the protection status; The family portrait information is constructed based on the family basic information, family financial information and family security information.

5. The method according to claim 1, characterized in that The marketing speech information is obtained in the following ways: Constructing at least one Prompt word based on the family portrait information; wherein the Prompt word represents context information matching the family portrait information; The family portrait information and the at least one prompt word are input into the AI ​​big model as input information, and the marketing speech information is returned through the AI ​​big model.

6. The method according to claim 1, characterized in that The AI ​​big model includes at least one insurance protection classification plug-in, and the at least one insurance protection classification plug-in is constructed based on the label information corresponding to each insurance type. The AI ​​big model obtains at least one insurance type that matches the family portrait information through the at least one insurance protection classification plug-in, and generates the marketing speech information based on the at least one insurance type.

7. The method according to claim 5 or 6, characterized in that: The method further comprises: Obtain feedback information from the user and actual sales data for the user; The AI ​​big model is updated based on the feedback information and the actual sales data.

8. A marketing speech generation device based on family portrait, characterized in that: The device comprises: An input module, used to display the input user identification through the human-computer interaction interface in response to the input operation; wherein the user identification corresponds to the family portrait information of a user, and the family portrait information represents the portrait information of each family member in the family of the user; The display module is used to submit the user identification in response to a confirmation operation, and display marketing speech information for the user corresponding to the user identification; wherein the marketing speech information includes marketing speech for at least one family member in the family of the user.

9. An electronic device, characterized in that: include: A processor, a storage medium and a bus, wherein the storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to execute the method for generating marketing scripts based on family portraits as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the method for generating marketing speech based on family portraits as described in any one of claims 1 to 7 is executed.

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