A marketing script generation method and device based on a crown data

Abstract

The application provides a marketing language generation method and device based on crown data, which comprises the following steps: providing preliminary marketing language information by using an AI large model, and collecting marketing data by carrying out marketing activities accordingly; counting the marketing data to determine the crown data with the top marketing performance within a specified time; obtaining the marketing language used by the salesperson corresponding to the crown data in actual marketing, and comparing it with the preliminary marketing language information to determine the distinguishing features; training and updating the AI large model based on the distinguishing features to generate second marketing language information. The application can integrate the crown data and the AI large model intelligent prediction, and realize the accurate generation and optimization of the marketing language.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of artificial intelligence, in particular to a marketing language generation method and device based on crown data. BACKGROUND

[0002] In the current business environment, the success of marketing activities often depends on multiple factors, among which the accuracy and individualization of marketing language play a crucial role. Traditional marketing language often relies on the personal experience and intuition of sales personnel, lacking scientific data support and intelligent optimization means, resulting in uneven marketing effects and difficulty in achieving the best state.

[0003] With the rapid development of artificial intelligence technology, AI large models are increasingly widely used in various fields, and their application in the marketing field has also gradually attracted attention. AI large models can deeply mine users' behavior habits, preferences and needs through big data analysis and machine learning algorithms, thereby generating more accurate and personalized marketing language. However, most AI marketing tools on the market currently only stay at the level of preliminary intelligent recommendation and automatic generation, lacking deep integration with practical experience, resulting in marketing language generated by the tools being too mechanical and template-based, lacking authenticity and persuasiveness. SUMMARY

[0004] Therefore, the embodiments of the present application provide a marketing language generation method and device based on crown data, which can integrate crown data and AI large model intelligent prediction to achieve accurate generation and optimization of marketing language.

[0005] The technical scheme of the embodiments of the present application is as follows:

[0006] In a first aspect, the embodiments of the present application provide a marketing language generation method based on crown data, which provides first marketing language information through an AI large model and generates marketing data based on the first marketing language information. The method comprises:

[0007] statistically analyzing the marketing data to obtain the crown data, wherein the crown data represents sales data with specific rankings in marketing performance within a specified time;

[0008] obtaining actual marketing language information of sales personnel corresponding to the crown data when marketing based on the first marketing language information;

[0009] determining the distinguishing feature information based on the actual marketing language information and the first marketing language information, wherein the distinguishing feature information represents the content of the actual marketing language information that is different from the first marketing language information;

[0010] The AI large model is trained and updated based on the distinguishing feature information, and second marketing language information is provided through the updated AI large model.

[0011] In a second aspect, the embodiments of the present application further provide a marketing language generation device based on top-performer data, which provides first marketing language information through an AI large model and obtains marketing data based on the first marketing language information; the device comprises:

[0012] a statistical module configured to statistically process the marketing data to obtain the top-performer data; wherein the top-performer data represents sales data of a specific ranking in marketing performance within a specified time;

[0013] an acquisition module configured to acquire actual marketing language information of a salesperson corresponding to the top-performer data when the salesperson markets based on the first marketing language information;

[0014] a determination module configured to determine distinguishing feature information based on the actual marketing language information and the first marketing language information; wherein the distinguishing feature information represents content that is different between the actual marketing language information and the first marketing language information;

[0015] an update module configured to train and update the AI large model based on the distinguishing feature information, and provide second marketing language information through the updated AI large model.

[0016] In a third aspect, the embodiments of the present application further provide an electronic device, which comprises a processor, a storage medium and a bus, the storage medium stores machine readable instructions executable by the processor, when the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the machine readable instructions to execute the marketing language generation method based on top-performer data according to any one of the first aspect.

[0017] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to execute the marketing language generation method based on top-performer data according to any one of the first aspect.

[0018] The embodiments of the present application have the following beneficial effects:

[0019] By utilizing the AI large model to preliminarily generate marketing language, and then deeply analyzing the actual marketing language in the sales crown data, through comparison and optimization, the successful communication skills and strategies are accurately refined. This process not only realizes high automation and intelligence, greatly reduces manual intervention, improves work efficiency, but also ensures the timeliness and pertinence of the marketing language. At the same time, the embodiments of the present application generate user portraits and family portraits for each customer, tailor unique marketing language, greatly enhance customer experience and satisfaction. In addition, the embodiments of the present application also have the ability of continuous optimization and iteration, through continuously collecting sales crown data and feedback, training and updating the AI large model, so that it always maintains competitiveness. Such accurate marketing language and personalized marketing strategies not only improve customer purchase intention and conversion rate, promote sales performance, but also help enterprises better adapt to market changes and customer needs, expand market share, and improve brand influence. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1 is a flowchart of steps S101-S104 provided by the embodiments of the present application;

[0022] Figure 2 is a flowchart of steps S201-S203 provided by the embodiments of the present application;

[0023] Figure 3 is a flowchart of steps S301-S302 provided by the embodiments of the present application;

[0024] Figure 4 is a flowchart of steps S401-S404 provided by the embodiments of the present application;

[0025] Figure 5 is a flowchart of steps S501-S502 provided by the embodiments of the present application;

[0026] Figure 6 is a structural schematic diagram of the marketing language generation device based on sales crown data provided by the embodiments of the present application;

[0027] Figure 7 is a structural schematic diagram of an electronic device provided by the embodiments of the present application. DETAILED DESCRIPTION

[0028] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. It should be understood that the drawings in the present application are only used for the purpose of description and illustration, 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 according to the actual proportions. The flowchart used in the present application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can not be implemented in sequence, and the steps without logical context relationship can be reversed in sequence or implemented simultaneously. In addition, one or more other operations can be added to the flowchart or one or more operations can be removed from the flowchart under the guidance of the content of the present application.

[0029] In the following description, "some embodiments" are related to a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subset of all possible embodiments, and can be combined with each other without conflict.

[0030] In addition, the described embodiments are only some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different 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 claimed present application, but only represents 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 creative labor are within the scope of protection of the present application.

[0031] In the following description, the terms "first", "second", "third" are only to distinguish similar objects, and do not represent the specific order of the objects. It can be understood that "first", "second", "third" can be interchanged in specific order or sequence as allowed, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0032] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0033] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used herein are for the purpose of describing the embodiments of the present application, not for limiting the present application.

[0034] Reference is made to Figure 1 , Figure 1is a flowchart of steps S101-S104 of the marketing script generation method based on the crown data provided by the embodiment of the present application. The steps S101-S104 shown will be described in combination with Figure 1

[0035] In step S101, the marketing data is counted to obtain the crown data; wherein the crown data represents the sales data in a specific ranking in the specified time.

[0036] In step S102, the actual marketing script information of the salesperson corresponding to the crown data is obtained when the first marketing script information is used for marketing.

[0037] In step S103, the difference feature information is determined based on the actual marketing script information and the first marketing script information; wherein the difference feature information represents the content of the actual marketing script information different from the first marketing script information.

[0038] In step S104, the AI large model is trained and updated based on the difference feature information, and the second marketing script information is provided through the updated AI large model.

[0039] Here, first, the AI large model provides the preliminary marketing script information (first marketing script information). The actual marketing activities are carried out using these preliminary marketing scripts, and the corresponding marketing data is collected. The marketing data is statistically analyzed to identify the sales data in a specific ranking (such as the crown) in the specified time, which is the crown data.

[0040] Next, the actual marketing script information used by the salesperson corresponding to the crown data in the marketing process is obtained. These actual marketing script information is verified to be effective through practice, and therefore has high reference value. By comparing the first marketing script information (the script initially provided by the AI large model) and the actual marketing script information of the crown, the difference feature information between the two is found. These difference feature information may be the key factor leading to the difference in sales performance.

[0041] Based on these difference feature information, the AI large model is trained and optimized. This includes adjusting model parameters, learning algorithms, etc., to enable the model to better understand and generate effective marketing scripts. After training, the AI large model is updated to enable it to provide new and more optimized marketing script information (second marketing script information).

[0042] The above method can form a cyclic iterative process. In each iteration, the AI large model is updated and optimized based on new crown data and difference feature information, thereby continuously improving the effectiveness and pertinence of the marketing script. ​

[0043] In some embodiments, referring to Figure 2 , Figure 2 is a flowchart of steps S201-S203 provided by the embodiments of the present application, and the sales star data obtained by counting the marketing data can be achieved by steps S201-S203, which will be described in combination with each step.

[0044] In step S201, the marketing data is cleaned and preprocessed.

[0045] In step S202, the performance indicators and the time range are determined, and the ranking data under business demand is counted according to the performance indicators and the time range.

[0046] In step S203, the sales data of the specific ranking is taken from the ranking data as the sales star data.

[0047] The marketing data often contains various noises and outliers, such as repeated data, missing data, error data, etc. Before counting the sales star data, these data need to be cleaned to ensure the accuracy and consistency of the data. The preprocessing steps may include data format conversion, data standardization, data normalization, etc. for subsequent analysis and statistics. The performance indicators are key indicators for measuring the sales performance of sales personnel, such as sales, sales quantity, customer satisfaction, etc. According to business demand, select appropriate performance indicators for statistics. The time range refers to the time period for counting sales performance, such as one month, one quarter, one year, etc. According to business demand and time requirements, determine the appropriate time range for statistics. After determining the performance indicators and the time range, count the marketing data according to these conditions to obtain the performance ranking data of the sales personnel. The ranking data usually includes the name of the sales personnel, the performance indicator value, the ranking, etc.

[0048] From the ranking data, according to the business demand, take the sales data of the specific ranking as the sales star data. For example, the sales data of the first ranking can be taken as the sales star data, or the sales data of the top ranking can be taken as the sales star data set.

[0049] In some embodiments, referring to Figure 3 , Figure 3 is a flowchart of steps S301-S302 provided by the embodiments of the present application, and the actual marketing script information of the sales personnel corresponding to the sales star data when marketing based on the first marketing script information can be achieved by steps S301-S302, which will be described in combination with each step.

[0050] In step S301, for the sales personnel corresponding to the sales star data, the communication voice information of the sales personnel in the marketing process is collected.

[0051] In step S302, the communication voice information is converted to obtain communication text information, and the communication text information is taken as the actual marketing script information.

[0052] Here, the communication voice information of the salesperson corresponding to the top performer data in the marketing process is collected. These voice information may come from telephone marketing, video conference, customer interview and other scenarios. The way to collect voice information can be recording equipment, telephone recording system, video conference recording, etc. Ensure that the collected voice information is clear, complete and meets the requirements of privacy policy and laws and regulations. The collected communication voice information is converted to readable communication text information. The converted communication text information is taken as the actual marketing script information of the salesperson. These text information reflects the language and expression used by the salesperson in the marketing process. The actual marketing script information can be used for subsequent analysis, comparison and optimization. By comparing the first marketing script information and the actual marketing script information, effective marketing script elements and strategies can be identified, and the AI large model can be trained and optimized.

[0053] In some embodiments, referring to Figure 4 , Figure 4 is a flowchart of steps S401-S404 provided by the embodiments of the present application. The determination of the distinguishing feature information based on the actual marketing script information and the first marketing script information can be realized by steps S401-S404, which will be described in conjunction with each step.

[0054] In step S401, the actual marketing script information is compared with the first marketing script information in content to obtain the first difference content in content, structure and expression.

[0055] In step S402, the interactive strategy used in the actual marketing script is determined and compared with the interactive strategy in the first marketing script information to determine the second difference content.

[0056] In step S403, the feedback of the salesperson and the customer is collected and taken as a reference factor.

[0057] In step S404, the distinguishing feature information is determined based on the first difference content, the second difference content and the reference factor.

[0058] Here, the actual marketing script information is compared with the first marketing script information in content. The comparison content can include the specific content, structure arrangement and expression of the script. Through comparison, the similarities and differences between the two in content can be identified, such as the use of keywords, the presentation of information, etc. These similarities and differences constitute the first difference content.

[0059] Determine the interactive strategies employed in the actual marketing script, such as the way of asking questions, response skills, emotional guidance, etc. Compare these interactive strategies with those in the first marketing script information, analyze the differences and effects of the two strategies. These differences constitute the second distinguishing content.

[0060] In addition to directly comparing the content of the script, feedback from sales personnel and customers is also needed. Sales personnel can provide information on the effectiveness of the script, customer reaction, etc.; customers can provide feedback on the acceptance and satisfaction of the script. These feedbacks serve as reference factors, helping to better understand the actual effectiveness of the script and providing important basis for determining the distinguishing feature information.

[0061] Based on the first distinguishing content, the second distinguishing content and the reference factors, conduct comprehensive analysis and judgment. Determine which distinguishing feature information plays a key role in improving marketing effectiveness, such as specific expression methods, effective interactive strategies, etc. These distinguishing feature information will be used for subsequent training and optimization of AI large model to improve the effectiveness and pertinence of marketing script.

[0062] In some embodiments, the AI large model queries the family information of the user by receiving a user identification representing the user's portrait, and generates a corresponding family portrait based on the family information, and generates first marketing script information based on the family portrait; wherein the first marketing script information includes marketing script for each family member of the user.

[0063] Here, the AI large model first receives a user identification representing the user's portrait. This identification may be the user's ID, mobile phone number, email address, etc., which is used to uniquely identify a user. Using the received user identification, the AI large model queries the family information of the user in the database. These information may include the names, ages, genders, occupations, interests, consumption habits, etc. of the family members. Based on the queried family information, the AI large model generates a corresponding family portrait. The family portrait is a data model that integrates the characteristics of family members, reflecting the overall situation and needs of family members. The AI large model generates first marketing script information based on the family portrait. This process may involve steps such as selection of script templates, replacement of keywords, insertion of personalized information, etc. Importantly, the first marketing script information not only targets the user himself, but also includes marketing script for each family member of the user. This requires the AI large model to understand and analyze the relationship and demand differences between family members to provide more accurate marketing information.

[0064] In some embodiments, see Figure 5 , Figure 5is a flowchart of steps S501-S502 provided by the embodiment of the present application, and the method further comprises steps S501-S502, which will be described in combination with each step.

[0065] In step S501, a marketing activity is determined based on a business requirement, and a marketing feature is extracted from the marketing activity; wherein the marketing feature represents a feature associated with the marketing activity.

[0066] In step S502, the AI large model is trained and updated based on the marketing feature.

[0067] According to the business requirement, determine the marketing activities to be carried out. These activities may include promotional activities, new product launches, member day activities, etc., aiming to attract user attention, increase sales or enhance brand influence. From the determined marketing activities, extract the features associated with the activities, which constitute the marketing feature set. Marketing features may include the theme, time, target user group, discount strength, marketing channel, etc. of the activity. The process of extracting marketing features requires a deep understanding of the core elements and goals of marketing activities to ensure that the extracted features can accurately reflect the characteristics and needs of the activities. Based on the extracted marketing features, the AI large model is trained. The training process may include data preprocessing, model selection, parameter adjustment, performance evaluation, etc. Through training, the AI large model can learn the correlation between marketing features and marketing effects, so as to generate more accurate and effective marketing copy. After training, the AI large model is updated to make it better adapt to business requirements in actual application.

[0068] In some embodiments, the AI large model automatically discontinues the dependency relationship of the marketing feature after the marketing activity ends.

[0069] Here, the marketing activity enters the end stage after completing according to the predetermined plan or goal. This means that the duration of the activity has reached, or the goal of the activity (such as sales, attention, etc.) has been achieved. After monitoring the end of the marketing activity, the AI large model automatically discontinues the dependency relationship of the previously extracted marketing features. This means that the model will no longer generate marketing copy or make other decisions based on these features. This step involves internal logic adjustment of the model, such as deleting or disabling algorithm modules, weight parameters, etc. related to these features. After discontinuing the dependency relationship of the marketing feature, the AI large model may need to be further adjusted and optimized. This may include re-evaluating the performance of the model, updating the training data of the model, introducing new features, etc. These adjustments and optimizations aim to ensure that the model can continue to adapt to changes in business requirements and market environment, maintain its competitiveness and effectiveness.

[0070] In summary, the embodiment of the present application has the following beneficial effects:

[0071] By utilizing the AI large model to preliminarily generate marketing language, and then deeply analyzing the actual marketing language in the sales crown data, through comparison and optimization, the successful communication skills and strategies are accurately refined. This process not only realizes high automation and intelligence, greatly reduces manual intervention, improves work efficiency, but also ensures the timeliness and pertinence of the marketing language. At the same time, the embodiments of the present application generate user portraits and family portraits to tailor unique marketing language for each customer, greatly enhancing customer experience and satisfaction. In addition, the embodiments of the present application also have the ability of continuous optimization and iteration, by continuously collecting sales crown data and feedback, training and updating the AI large model, so that it always maintains competitiveness. Such accurate marketing language and personalized marketing strategies not only improve customer purchase intention and conversion rate, promote sales performance, but also help enterprises better adapt to market changes and customer needs, expand market share, and improve brand influence.

[0072] Based on the same inventive concept, the embodiments of the present application also provide a marketing language generation device based on sales crown data corresponding to the marketing language generation method based on sales crown data in the first embodiment. Since the principle of solving problems in the device of the embodiments of the present application is similar to the above-mentioned marketing language generation method based on sales crown data, the implementation of the device can be referred to the implementation of the method, and the repeated parts will not be described again.

[0073] As shown in Figure 6 , the structure schematic diagram of the marketing language generation device 600 based on sales crown data provided by the embodiments of the present application is shown in Figure 6 . The AI large model provides first marketing language information and performs marketing based on the first marketing language information to obtain marketing data. The marketing language generation device 600 based on sales crown data includes:

[0074] The statistical module 601 is configured to statistically process the marketing data to obtain the sales crown data. The sales crown data represents sales data of a specific ranking in marketing performance within a specified time.

[0075] The acquisition module 602 is configured to acquire actual marketing language information of a salesperson corresponding to the sales crown data when the salesperson performs marketing based on the first marketing language information.

[0076] The determination module 603 is configured to determine the distinguishing feature information based on the actual marketing language information and the first marketing language information. The distinguishing feature information represents the content of the actual marketing language information that is different from the first marketing language information.

[0077] The update module 604 is configured to train and update the AI large model based on the distinguishing feature information, and provide second marketing language information through the updated AI large model.

[0078] Those skilled in the art should understand that, Figure 6 The implementation functions of each unit in the illustrated marketing script generation device 600 based on the crown data can be understood with reference to the foregoing related description of the marketing script generation method based on the crown data. Figure 6 The functions of each unit in the illustrated marketing script generation device 600 based on the crown data can be implemented by a program running on a processor, or by a specific logic circuit.

[0079] In a possible implementation, the statistical module 601 performs statistics on the marketing data to obtain the crown data, including:

[0080] Data cleaning and preprocessing are performed on the marketing data;

[0081] The performance indicators and the time range are determined, and the ranking data under the business demand is counted according to the performance indicators and the time range;

[0082] The sales data of the specific ranking is taken from the ranking data as the crown data.

[0083] In a possible implementation, the acquisition module 602 acquires actual marketing script information of a salesperson corresponding to the crown data when the salesperson performs marketing based on the first marketing script information, including:

[0084] For the salesperson corresponding to the crown data, communication voice information of the salesperson in the marketing process is collected;

[0085] The communication voice information is processed to obtain communication text information, and the communication text information is taken as the actual marketing script information.

[0086] In a possible implementation, the determination module 603 determines the distinguishing feature information based on the actual marketing script information and the first marketing script information, including:

[0087] The actual marketing script information and the first marketing script information are compared in content to obtain first distinguishing content in content, structure, and expression manner;

[0088] An interactive strategy adopted in the actual marketing script is determined, and compared with the interactive strategy in the first marketing script information to determine second distinguishing content;

[0089] Feedback opinions of salespersons and customers are collected, and the feedback opinions are taken as reference factors;

[0090] The first distinguishing content, the second distinguishing content, and the reference factors are used to determine the distinguishing feature information.

[0091] In one possible implementation, the AI ​​big data model queries the user's family information by receiving a user identifier that represents the user profile, generates a corresponding family profile based on the family information, and generates first marketing script information based on the family profile; wherein, the first marketing script information includes marketing scripts for each family member of the user.

[0092] In one possible implementation, the update module 604 further includes:

[0093] Marketing activities are determined based on business needs, and marketing features are extracted from the marketing activities; wherein, the marketing features represent characteristics associated with the marketing activities;

[0094] The AI ​​model is trained and updated based on the aforementioned marketing features.

[0095] In one possible implementation, the AI ​​big model automatically terminates its dependency on the marketing features after the marketing campaign ends.

[0096] The aforementioned marketing script generation device based on top sales data initially generates marketing scripts using an AI model, then deeply analyzes the actual marketing scripts within the top sales data. Through comparison and optimization, it accurately extracts successful communication skills and strategies. This process not only achieves a high degree of automation and intelligence, significantly reducing manual intervention and improving work efficiency, but also ensures the timeliness and relevance of the marketing scripts. Furthermore, this embodiment generates user profiles and family profiles to tailor unique marketing scripts for each customer, greatly enhancing customer experience and satisfaction. In addition, this embodiment has the capability for continuous optimization and iteration, constantly collecting top sales data and feedback to train and update the AI ​​model, ensuring its continued competitiveness. This precise marketing script and personalized marketing strategy not only increases customer purchase intention and conversion rates, promoting sales performance, but also helps companies better adapt to market changes and customer needs, expand market share, and enhance brand influence.

[0097] like Figure 7 As shown, Figure 7 This is a schematic diagram of the composition structure of the electronic device 700 provided in the embodiments of this application. The electronic device 700 includes:

[0098] The processor 701, the storage medium 702 storing machine readable instructions executable by the processor 701, and the bus 703 for communication between the processor 701 and the storage medium 702 when the electronic device 700 is running, the processor 701 executes the machine readable instructions to perform the steps of the marketing script generation method based on the crown data according to the embodiments of the present application.

[0099] In actual application, various components in the electronic device 700 are coupled together through the bus 703. It can be understood that the bus 703 is used to realize the connection and communication between the components. In addition to the data bus, the bus 703 also includes a power bus, a control bus and a status signal bus. However, in order to clearly illustrate the present application, various buses are marked as bus 703 in the Figure 7 .

[0100] The above electronic device generates marketing scripts by using AI large models, then deeply analyzes the actual marketing scripts in the crown data, and through comparison and optimization, accurately refines successful communication skills and strategies. This process not only realizes high automation and intelligence, greatly reduces manual intervention, improves work efficiency, but also ensures the timeliness and pertinence of the marketing scripts. At the same time, the embodiments of the present application generate user portraits and family portraits to tailor unique marketing scripts for each customer, greatly enhancing customer experience and satisfaction. In addition, the embodiments of the present application also have the ability of continuous optimization and iteration, by continuously collecting crown data and feedback, training and updating the AI large model, so that it always maintains competitiveness. Such accurate marketing scripts and personalized marketing strategies not only improve customer purchase intention and conversion rate, promote sales performance, but also help enterprises better adapt to market changes and customer needs, expand market share, and improve brand influence.

[0101] The embodiments of the present application also provide a computer readable storage medium, the storage medium stores executable instructions, when the executable instructions are executed by at least one processor 701, the marketing script generation method based on the crown data according to the embodiments of the present application is realized.

[0102] In some embodiments, the storage medium can be a ferromagnetic 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 disc, or a compact disc read only memory (CD ROM), and the like. It can also be various devices including one or any combination of the above memories.

[0103] In some embodiments, the executable instructions can be in the form of programs, software, modules, scripts, or code, written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and can 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.

[0104] By way of example, the executable instructions can, but need not, correspond directly with a file in a file system, can be stored in a portion of a file that holds other programs or data, e.g., one or more scripts stored in a hypertext markup language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files, e.g., files that store one or more modules, sub programs, or portions of code.

[0105] By way of example, the executable instructions can be stored on the storage medium of a stand-alone device or within a device that employs the storage medium, e.g., a server computer, a client computer, or a desktop, laptop or other platforms operating systems or computers or the like. As one specific example, the executable instructions can be programmed into ROM. The storage medium can be removable from the stand-alone device or device employing such storage medium.

[0106] The above computer-readable storage medium preliminarily generates marketing language by utilizing the AI large model, then deeply analyzes the actual marketing language in the sales crown data, and accurately refines successful communication skills and strategies through comparison and optimization. This process not only realizes high automation and intelligence, greatly reduces manual intervention, and improves work efficiency, but also ensures the timeliness and pertinence of the marketing language. At the same time, the embodiments of the application generate user portraits and family portraits to customize unique marketing language for each customer, greatly enhancing customer experience and satisfaction. In addition, the embodiments of the application also have the ability of continuous optimization and iteration, and by continuously collecting sales crown data and feedback, the AI large model is trained and updated, so that it always maintains competitiveness. Such accurate marketing language and personalized marketing strategies not only improve customer purchase intention and conversion rate, promote sales performance, but also help enterprises better adapt to market changes and customer needs, expand market share, and improve brand influence.

[0107] In several embodiments provided in the present application, it should be understood that the disclosed method and electronic device can be implemented in other ways. The above-described device embodiments are only illustrative, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, 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 or direct coupling or communication connection between the displayed or discussed components can be through some interface, indirect coupling or communication connection of the devices or units, which can be electrical, mechanical or other forms.

[0108] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0109] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0110] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a nonvolatile computer readable storage medium executable by a processor. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a platform server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk, and various media that can store program codes.

[0111] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for generating marketing scripts based on crown data, characterized by, The method involves using an AI large-scale model to provide initial marketing script information and then conducting marketing based on that information to obtain marketing data; the method includes: The sales champion data is obtained by statistically analyzing the marketing data; wherein, the sales champion data represents the sales data that ranks in a specific position in marketing performance within a specified time period; Obtain the actual marketing script information of the salesperson corresponding to the top sales data when conducting marketing based on the first marketing script information; Based on the actual marketing script information and the first marketing script information, distinguishing feature information is determined; wherein, the distinguishing feature information characterizes the content that distinguishes the actual marketing script information from the first marketing script information; The AI ​​model is trained and updated based on the distinguishing feature information, and the updated AI model provides second marketing script information. The step of determining the distinguishing feature information based on the actual marketing script information and the first marketing script information includes: The actual marketing script information is compared with the first marketing script information to obtain the first difference in content, structure and expression. Determine the interaction strategy used in the actual marketing script and compare it with the interaction strategy in the first marketing script information to determine the second difference content; Collect feedback from sales staff and customers, and use this feedback as a reference factor; The distinguishing feature information is determined based on the first distinguishing content, the second distinguishing content, and the reference factors.

2. The method of claim 1, wherein, The process of statistically analyzing the marketing data to obtain the top sales performer data includes: The marketing data is cleaned and preprocessed. Determine performance indicators and time ranges, and statistically analyze ranking data based on the performance indicators and time ranges to meet business needs; The sales data of a specific ranking are taken from the ranking data and used as the top sales data.

3. The method of claim 1, wherein, The step of obtaining the actual marketing script information of the salesperson corresponding to the top sales data when conducting marketing based on the first marketing script information includes: For the sales personnel corresponding to the top sales performers, collect their communication voice information during the marketing process; The voice communication information is converted into text communication information, which is then used as the actual marketing script information.

4. The method of claim 1, wherein, The AI ​​big data model queries the user's family information by receiving the user identifier that represents the user profile, and generates a corresponding family profile based on the family information. Based on the family profile, it generates first marketing script information. The first marketing script information includes marketing scripts for each family member of the user.

5. The method of claim 1, wherein, The method further includes: Marketing activities are determined based on business needs, and marketing features are extracted from the marketing activities; wherein, the marketing features represent characteristics associated with the marketing activities; The AI ​​model is trained and updated based on the aforementioned marketing features.

6. The method of claim 5, wherein, The AI ​​big data model automatically terminates its dependence on the marketing features after the marketing campaign ends.

7. A marketing script generating apparatus based on a crown data, characterized by, The device provides initial marketing script information through an AI large-scale model and generates marketing data based on that initial marketing script information; the device includes: A statistics module configured to perform statistics on the marketing data to obtain the top-performer data, wherein the top-performer data represents sales data of a salesperson who has a specific ranking in marketing performance within a specified time period; An acquisition module configured to acquire actual marketing script information of the salesperson when the salesperson performs marketing based on the first marketing script information corresponding to the top-performer data; A determination module configured to determine distinguishing feature information based on the actual marketing script information and the first marketing script information, wherein the distinguishing feature information represents content that is different between the actual marketing script information and the first marketing script information, and the determination of the distinguishing feature information based on the actual marketing script information and the first marketing script information comprises: comparing the actual marketing script information and the first marketing script information in terms of content to obtain first distinguishing content in terms of content, structure, and expression manner; determining an interactive strategy used in the actual marketing script and comparing the interactive strategy with an interactive strategy in the first marketing script information to determine second distinguishing content; collecting feedback opinions of the salesperson and the customer and taking the feedback opinions as reference factors; and determining the distinguishing feature information based on the first distinguishing content, the second distinguishing content, and the reference factors; An update module configured to train and update the AI large model based on the distinguishing feature information and provide second marketing script information through the updated AI large model.

8. An electronic device, comprising: The method comprises the following steps: A processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, the processor and the storage medium communicate through the bus when the electronic device is running, and the processor executes the machine-readable instructions to perform the marketing script generation method based on top-performer data according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is executed by the processor to perform the marketing script generation method based on top-performer data according to any one of claims 1 to 6.

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