Banking outlet marketing strategy generation method and related product
By acquiring customer interaction information and bank branch marketing knowledge base, and using artificial intelligence big data models to generate and retrieve target marketing strategies, the problem of relying on the subjective judgment of account managers in traditional bank branch marketing has been solved, and precise and personalized marketing strategy generation has been achieved.
Patent Information
- Application Number
- CN202510937129.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-08
- Publication Date
- 2025-10-28
AI Technical Summary
Traditional bank branch marketing methods rely on the subjective judgment of account managers, making it difficult to achieve precise and personalized marketing to customers.
By acquiring customer interaction information and the bank's branch marketing knowledge base, an initial marketing strategy is generated using a large artificial intelligence model. The strategy is then retrieved from the bank's branch marketing knowledge base, and similarity is calculated to determine the target marketing strategy.
It provides more precise and personalized marketing strategies, ensuring the feasibility of these strategies and reducing reliance on the experience of account managers.
Smart Images
Figure CN120852001A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and in particular to a method for generating marketing strategies for bank branches and related products. Background Technology
[0002] In today's digital age, with the development of fintech and the diversification of user needs, the traditional "one-size-fits-all" marketing model is no longer sufficient to meet customers' personalized needs. Therefore, it is particularly important for bank branches to provide precise marketing strategies tailored to user needs.
[0003] Currently, in actual marketing practices, bank branches generally rely on account managers proactively inquiring about customers' business needs and recommending relevant services based on their experience and current flagship products. However, this method, which depends on the account manager's subjective judgment, places high demands on their business acumen and service sensitivity, making it difficult to achieve precise and personalized marketing to customers. Summary of the Invention
[0004] This application provides a method for generating marketing strategies for bank branches and related products, which can provide customers with accurate and personalized marketing strategies.
[0005] In a first aspect, embodiments of this application provide a method for generating a bank branch marketing strategy, the method comprising:
[0006] Acquire customer interaction information and a bank branch marketing knowledge base, which includes marketing scripts, marketing product information, and bank regulations for customer marketing.
[0007] The customer interaction information is input into a preset artificial intelligence model to generate an initial marketing strategy;
[0008] Based on the customer interaction information, a search is performed in the bank's branch marketing knowledge base to obtain the target search results;
[0009] If the similarity between the initial marketing strategy and the target search result is greater than a preset first similarity, the initial marketing strategy is determined to be the target marketing strategy.
[0010] Output the target marketing strategy.
[0011] Optionally, the step of retrieving the target search results from the bank branch marketing knowledge base based on the customer interaction information includes:
[0012] The customer interaction information is vectorized to generate a first semantic vector;
[0013] The content in the bank branch marketing knowledge base is vectorized to generate multiple second semantic vectors;
[0014] Calculate the similarity between the first semantic vector and each of the plurality of second semantic vectors, and determine a target semantic vector from the plurality of second semantic vectors that has a greater than a preset second similarity.
[0015] The content in the bank branch marketing knowledge base corresponding to the target semantic vector is determined as the target retrieval result.
[0016] Optionally, the method further includes:
[0017] If the similarity between the initial marketing strategy and the target retrieval result is less than or equal to the preset first similarity, the customer interaction information is input into the artificial intelligence big model again to generate the first marketing strategy.
[0018] If the similarity between the first marketing strategy and the target search result is greater than the preset first similarity, the first marketing strategy is determined to be the target marketing strategy.
[0019] Output the target marketing strategy.
[0020] Optionally, obtaining customer interaction information includes:
[0021] Obtain audio recordings of conversations between customers and bank account managers;
[0022] Based on the aforementioned large-scale artificial intelligence model, the dialogue speech is converted into dialogue text;
[0023] Key information is extracted from the dialogue text to obtain customer interaction information.
[0024] Optionally, inputting the customer interaction information into a preset artificial intelligence model to generate an initial marketing strategy includes:
[0025] Obtain customer information corresponding to the customer from the customer relationship management system, wherein the customer relationship management system is used to store the customer information, and the customer information includes the customer's basic information and asset information;
[0026] The customer interaction information and customer information are input into the artificial intelligence big data model to generate an initial marketing strategy.
[0027] Optionally, the method further includes:
[0028] Obtain daily marketing results data from the bank, including daily successful marketing data and failed marketing data;
[0029] The marketing results data is input into the AI big model, and incremental training is performed on the AI big model based on the marketing results data.
[0030] Optionally, the bank branch marketing knowledge base is a regularly updated bank branch marketing knowledge base;
[0031] The regular update method includes supplementing or replacing newly added marketing scripts, marketing product information, and bank regulations information.
[0032] Secondly, embodiments of this application provide an apparatus for generating marketing strategies for bank branches, comprising:
[0033] The data acquisition module is used to acquire customer interaction information and the bank branch marketing knowledge base, which includes marketing scripts, marketing product information and bank regulations information for customer marketing.
[0034] The strategy generation module is used to input the customer interaction information into a preset artificial intelligence big data model to generate an initial marketing strategy;
[0035] The information retrieval module is used to search the bank branch marketing knowledge base based on the customer interaction information to obtain the target retrieval results;
[0036] The strategy determination module is used to determine the initial marketing strategy as the target marketing strategy when the similarity between the initial marketing strategy and the target retrieval result is greater than a preset first similarity.
[0037] The strategy output module is used to output the target marketing strategy.
[0038] Thirdly, embodiments of this application provide an electronic device, the device including: a processor, a memory, and a system bus;
[0039] The processor and the memory are connected via the system bus;
[0040] The memory is used to store a program, which includes instructions that, when executed by the processor, cause the processor to perform any of the implementation steps of the above-described method for generating bank branch marketing strategies.
[0041] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program, which, when executed by a terminal device, implements any of the implementation steps of the method for generating the above-mentioned bank branch marketing strategy.
[0042] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:
[0043] In this embodiment, customer interaction information and a bank branch marketing knowledge base are acquired. The bank branch marketing knowledge base includes marketing scripts, product information, and bank regulations. Next, the customer interaction information is input into a pre-defined AI model, which generates an initial marketing strategy. Then, the customer interaction information is searched within the constructed bank branch marketing knowledge base to obtain target search results. Finally, if the similarity between the initial marketing strategy and the target search results is greater than a preset first similarity, the initial marketing strategy becomes the target marketing strategy, and the target marketing strategy is output. Therefore, this solution, by inputting customer interaction information into a pre-defined AI model to combine with the customer's actual needs, provides a more accurate and personalized initial marketing strategy; and by searching the customer interaction information in the constructed bank branch marketing knowledge base and calculating the similarity between the search results and the initial marketing strategy, the executability of the initial marketing strategy is effectively guaranteed. Compared to the existing method where account managers recommend services to customers based on experience, this solution can provide customers with more accurate and personalized marketing strategies. Attached Figure Description
[0044] Figure 1 A flowchart illustrating a method for generating a bank branch marketing strategy, as provided in this application embodiment;
[0045] Figure 2 A schematic diagram illustrating the overall framework of a method for generating a bank branch marketing strategy, provided in an embodiment of this application;
[0046] Figure 3 This is a schematic diagram of a device for generating marketing strategies for bank branches, provided in an embodiment of this application. Detailed Implementation
[0047] As mentioned earlier, in actual marketing processes, bank branches rely on account managers to proactively engage with customers to understand their needs and recommend relevant financial services based on their experience and the bank's current flagship products, thereby achieving marketing objectives. However, this traditional marketing approach heavily depends on the account manager's accumulated marketing experience and subjective judgment, requiring a high level of business acumen and service sensitivity, making it difficult to provide precise and personalized services to each customer.
[0048] Furthermore, while some existing bank branches have integrated customer information stored in their customer relationship management systems, allowing bank account managers to inquire about customers' intentions upon their arrival and then recommend products based on that information, this process still relies heavily on the account manager's subjective judgment and fails to provide truly personalized service.
[0049] To address the aforementioned issues, this application provides a method for generating a bank branch marketing strategy. First, customer interaction information and a bank branch marketing knowledge base are acquired. This knowledge base stores marketing scripts, product information, and bank regulations for customer marketing. Then, the customer interaction information is used as input to a preset artificial intelligence model to generate an initial marketing strategy. Next, the customer interaction information is retrieved from the bank branch marketing knowledge base to obtain target search results. Finally, the similarity between the initial search strategy and the target search results is calculated. If the calculated similarity is greater than a preset first similarity, the initial marketing strategy is determined as the target marketing strategy, and the target marketing strategy is output.
[0050] As can be seen, this solution inputs customer interaction information into a pre-set AI model to provide a more accurate and personalized initial marketing strategy based on the customer's actual needs. Then, it retrieves this customer interaction information from a pre-built bank branch marketing database and calculates the similarity between the search results and the initial marketing strategy, effectively ensuring the executability of the initial marketing strategy. Compared to existing technologies where account managers recommend services to customers based on experience, this solution provides customers with more accurate and personalized marketing strategies.
[0051] It should be noted that the embodiments of this application do not limit the executing entity of the method for generating bank branch marketing strategies. For example, the method for generating bank branch marketing strategies in the embodiments of this application can be applied to information processing devices such as servers or terminal devices. The server can be a standalone server, a cluster server, or a cloud server. The terminal device can be an electronic device such as a smartphone, computer, personal digital assistant (PDA), or tablet computer.
[0052] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0053] Figure 1 A flowchart illustrating a method for generating a bank branch marketing strategy, as provided in this application embodiment. (In conjunction with...) Figure 1 As shown, the method for generating the bank branch marketing strategy may include the following steps S101-S105.
[0054] S101: Obtain customer interaction information and the bank branch marketing knowledge base, which includes marketing scripts, marketing product information, and bank regulations for customer marketing.
[0055] In this embodiment of the application, the customer interaction information is obtained by acquiring the voice dialogue between the customer and the bank's account manager, converting the voice dialogue into text dialogue based on a preset artificial intelligence model, and then extracting key information from the text dialogue.
[0056] It should be noted that the bank branch marketing knowledge base obtained in this application embodiment includes marketing scripts, marketing product information, and bank regulations information used for customer marketing. Specifically, marketing scripts refer to the standardized and strategic expressions used by bank account managers when serving customers. These scripts help improve communication efficiency and marketing effectiveness between account managers and customers. Marketing product information refers to information covering the key elements of various financial products offered by the bank, including but not limited to the product name, features, target customer groups, revenue structure, risk level, and application process. It is mainly used to describe product characteristics. Bank regulations information refers to the internal regulations of the bank branch that bank account managers must follow when marketing products to ensure that marketing activities are carried out legally and compliantly.
[0057] Furthermore, the bank branch marketing knowledge base in this application embodiment is a knowledge base that can be updated regularly, supporting the supplementation or replacement of newly added marketing scripts, marketing product information and bank regulations information, so as to maintain the timeliness of the bank branch marketing knowledge base.
[0058] S102: Input customer interaction information into a pre-set artificial intelligence big data model to generate an initial marketing strategy.
[0059] In this embodiment, the first step is to obtain customer information from the customer relationship management system (CRM). It should be noted that the CRM system is primarily used to store and manage customer-related data, typically including basic customer information and asset information. Basic information includes the customer's name, gender, age, and contact information, while asset information includes the customer's account balance, financial product holdings, investment records, and loan-related information.
[0060] Next, the acquired customer information and customer interaction information are input into a pre-set artificial intelligence model, which then generates an initial marketing strategy.
[0061] It should be noted that the large-scale artificial intelligence model used in this application embodiment can undergo incremental training daily. Specifically, it acquires daily marketing results data from banks, including both successful and unsuccessful marketing data. This marketing results data is then input into the large-scale artificial intelligence model for incremental training, thereby continuously optimizing the model's marketing strategy generation capabilities and improving the accuracy of the generated marketing strategies.
[0062] S103: Based on customer interaction information, perform a search in the bank's branch marketing knowledge base to obtain the target search results.
[0063] In this embodiment, customer interaction information is first vectorized to generate a first semantic vector. Then, for marketing scripts, marketing product information, and bank regulations in the bank branch marketing knowledge base, each specific entry in each category is vectorized to generate multiple corresponding second semantic vectors. Next, the similarity between the first semantic vector and each second semantic vector is calculated, and vectors with a similarity greater than a preset second similarity are selected from the multiple second semantic vectors as target semantic vectors. Finally, the specific entry in the bank branch marketing knowledge base corresponding to the target semantic vector is determined as the target retrieval result.
[0064] It should be noted that there can be multiple target semantic vectors selected, and each target semantic vector corresponds to a specific entry under a certain content category in the bank's branch marketing knowledge base, such as a specific sales pitch, a product information item, or a bank regulation. When there are multiple target semantic vectors, these target semantic vectors can be aggregated, sorted, or filtered to ultimately determine the corresponding target retrieval results.
[0065] S104: If the similarity between the initial marketing strategy and the target search results is greater than the preset first similarity, the initial marketing strategy is determined to be the target marketing strategy.
[0066] In this embodiment, when the similarity between the initial marketing strategy and the target search result is greater than a preset first similarity, the initial marketing strategy can be determined as the target marketing strategy. When the similarity between the initial marketing strategy and the target search result is less than or equal to the preset first similarity, the customer interaction information needs to be input into the artificial intelligence big data model again, and the model will regenerate the first marketing strategy.
[0067] Subsequently, if the similarity between the first marketing strategy and the target search result is greater than a preset first similarity, then the first marketing strategy is determined as the target marketing strategy. Conversely, if the similarity between the first marketing strategy and the target search result is still less than or equal to the preset first similarity, then the AI model needs to continue generating a second marketing strategy and calculate the similarity between the second search strategy and the target search result.
[0068] It should be noted that the embodiments of this application do not limit the number of times the artificial intelligence big model generates marketing strategies. The artificial intelligence big model can continuously generate new marketing strategies until the similarity between the generated marketing strategy and the target retrieval result is greater than the preset first similarity, at which point the iteration process stops.
[0069] S105: Output target marketing strategy.
[0070] After the target marketing strategy is determined, the AI big data model outputs the target marketing strategy. It should be noted that the target marketing strategy in this embodiment can be determined by the initial marketing strategy, the first marketing strategy, or other marketing strategies generated in subsequent iterations.
[0071] Based on the relevant content of steps S101-S105 above, it is known that in this embodiment, customer interaction information and a bank branch marketing knowledge base are obtained. This knowledge base includes marketing scripts, marketing product information, and bank regulations for marketing to customers. Customer interaction information is used as input to a preset artificial intelligence model to generate an initial marketing strategy. Then, the customer interaction information is searched in the bank branch marketing knowledge base to obtain target search results. The similarity between the initial search strategy and the target search results is calculated. When the calculated similarity is greater than a preset first similarity, the initial marketing strategy is determined as the target marketing strategy, and the target marketing strategy is output. It can be seen that this solution, by inputting customer interaction information into a preset artificial intelligence model to combine with the actual needs of customers, provides a more accurate and personalized initial marketing strategy; and by searching the customer interaction information in the constructed bank branch marketing knowledge base and calculating the similarity between the search results and the initial marketing strategy, the executability of the initial marketing strategy is effectively guaranteed. Compared with the existing technology where account managers recommend services to customers based on experience, this solution can provide customers with more accurate and personalized marketing strategies.
[0072] Furthermore, for ease of understanding, the embodiments of this application can also be combined with Figure 2 This paper introduces the generation method of bank branch marketing strategies from the perspective of the overall framework.
[0073] Figure 2 A schematic diagram illustrating the overall framework of a method for generating a bank branch marketing strategy, as provided in this application embodiment, in conjunction with... Figure 2As shown, when a customer arrives at a bank branch, the bank's video camera captures the customer's image in real time. When the customer authorizes facial recognition, the camera can directly identify the customer and issue a number. If the customer does not authorize facial recognition, bank staff will remind them to swipe their ID card at the queuing machine to obtain a number. After obtaining the number, the system combines the customer's information stored in the bank's customer information system and simultaneously pushes this information to the account manager's marketing PAD and SMS message to remind the account manager to prepare to serve the customer.
[0074] When an account manager is meeting with a client, the first step is to access the AI-powered big data model deployed on the account manager's marketing PAD. This model converts the voice conversation between the account manager and the client into text, automatically extracting key interaction points from the text. The extracted interaction points are then input into the AI-powered big data model, which, combined with customer-related data from the bank's marketing knowledge base and customer relationship management system, outputs targeted marketing strategies.
[0075] Once the AI-powered big data model generates a marketing strategy, account managers can recommend corresponding marketing products to customers based on that strategy. If a customer chooses to conduct the transaction through mobile banking, they can leave the store immediately after completion, and the account manager needs to record the marketing results simultaneously. If a customer chooses to conduct the transaction at the counter, the counter staff will conduct secondary marketing based on the marketing strategy provided by the AI-powered big data model and record the corresponding marketing results.
[0076] Furthermore, the marketing results recorded by account managers and counter staff need to be fed back into the AI big data model for subsequent incremental training, so as to continuously optimize the accuracy of the marketing strategies output by the AI big data model.
[0077] Furthermore, based on the method for generating bank branch marketing strategies provided in the above embodiments, this application embodiment can also provide a device for generating bank branch marketing strategies. The device for generating bank branch marketing strategies will now be described in conjunction with the embodiments and accompanying drawings.
[0078] It should be noted that the customer information (including but not limited to customer voice conversations, customer interaction information, customer basic information, and customer asset information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved in this application are all information and data authorized by the customer or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.
[0079] Figure 3 This is a schematic diagram of a device for generating marketing strategies for bank branches, provided as an embodiment of this application. (Combined with...) Figure 3 As shown in the embodiment of this application, the bank branch marketing strategy generation device 300 may include:
[0080] The data acquisition module 301 is used to acquire customer interaction information and the bank branch marketing knowledge base, which includes marketing scripts, marketing product information and bank regulations information for customer marketing.
[0081] The strategy generation module 302 is used to input the customer interaction information into a preset artificial intelligence big data model to generate an initial marketing strategy;
[0082] Information retrieval module 303 is used to perform a retrieval in the bank branch marketing knowledge base based on the customer interaction information to obtain the target retrieval result;
[0083] The strategy determination module 304 is used to determine the initial marketing strategy as the target marketing strategy when the similarity between the initial marketing strategy and the target retrieval result is greater than a preset first similarity.
[0084] The strategy output module 305 is used to output the target marketing strategy.
[0085] Optionally, the information retrieval module 303 is specifically used for:
[0086] The customer interaction information is vectorized to generate a first semantic vector;
[0087] The content in the bank branch marketing knowledge base is vectorized to generate multiple second semantic vectors;
[0088] Calculate the similarity between the first semantic vector and each of the plurality of second semantic vectors, and determine a target semantic vector from the plurality of second semantic vectors that has a greater than a preset second similarity.
[0089] The content in the bank branch marketing knowledge base corresponding to the target semantic vector is determined as the target retrieval result.
[0090] The bank branch marketing strategy generation device 300 further includes:
[0091] The first marketing strategy generation module is used to input the customer interaction information into the artificial intelligence big model again to generate the first marketing strategy when the similarity between the initial marketing strategy and the target retrieval result is less than or equal to the preset first similarity.
[0092] The first marketing strategy determination module is used to determine the first marketing strategy as the target marketing strategy when the similarity between the first marketing strategy and the target retrieval result is greater than the preset first similarity.
[0093] The target strategy output module is used to output the target marketing strategy.
[0094] Optionally, the data acquisition module 301 is specifically used for:
[0095] Obtain audio recordings of conversations between customers and bank account managers;
[0096] Based on the aforementioned large-scale artificial intelligence model, the dialogue speech is converted into dialogue text;
[0097] Key information is extracted from the dialogue text to obtain customer interaction information.
[0098] Optionally, the strategy generation module 302 is specifically used for:
[0099] Obtain customer information corresponding to the customer from the customer relationship management system, wherein the customer relationship management system is used to store the customer information, and the customer information includes the customer's basic information and asset information;
[0100] The customer interaction information and customer information are input into the artificial intelligence big data model to generate an initial marketing strategy.
[0101] Optionally, the bank branch marketing strategy generation device 300 further includes:
[0102] The marketing data acquisition module is used to acquire the bank's daily marketing results data, which includes daily successful marketing data and failed marketing data.
[0103] The model training module is used to input the marketing results data into the artificial intelligence big model and perform incremental training on the artificial intelligence big model based on the marketing results data.
[0104] Optionally, the bank branch marketing knowledge base is a regularly updated bank branch marketing knowledge base;
[0105] The regular update method includes supplementing or replacing newly added marketing scripts, marketing product information, and bank regulations information.
[0106] Furthermore, embodiments of this application also provide an electronic device, including: a processor, a memory, and a system bus;
[0107] The processor and the memory are connected via the system bus;
[0108] The memory is used to store one or more programs, the one or more programs including instructions that, when executed by the processor, cause the processor to perform any of the implementation steps of the above-described method for generating bank branch marketing strategies.
[0109] Furthermore, embodiments of this application also provide a computer-readable storage medium for storing a computer program, which, when executed by a terminal device, implements any of the implementation steps in the above-described method for generating bank branch marketing strategies.
[0110] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network communication device such as a media gateway, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application. It should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on describing the differences from other embodiments. The same or similar parts between the various embodiments can be referred to mutually.
[0111] The system disclosed in the embodiments is described in a relatively simple manner because it corresponds to the method disclosed in the embodiments. For relevant details, please refer to the method section.
[0112] It should also be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0113] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for generating a marketing strategy for bank branches, characterized in that, The method includes: Acquire customer interaction information and a bank branch marketing knowledge base, which includes marketing scripts, marketing product information, and bank regulations for customer marketing. The customer interaction information is input into a preset artificial intelligence model to generate an initial marketing strategy; Based on the customer interaction information, a search is performed in the bank's branch marketing knowledge base to obtain the target search results; If the similarity between the initial marketing strategy and the target search result is greater than a preset first similarity, the initial marketing strategy is determined to be the target marketing strategy. Output the target marketing strategy.
2. The method according to claim 1, characterized in that, The step of retrieving the target search results from the bank branch marketing knowledge base based on the customer interaction information includes: The customer interaction information is vectorized to generate a first semantic vector; The content in the bank branch marketing knowledge base is vectorized to generate multiple second semantic vectors; Calculate the similarity between the first semantic vector and each of the plurality of second semantic vectors, and determine a target semantic vector from the plurality of second semantic vectors that has a greater than a preset second similarity. The content in the bank branch marketing knowledge base corresponding to the target semantic vector is determined as the target retrieval result.
3. The method according to claim 1, characterized in that, The method further includes: If the similarity between the initial marketing strategy and the target retrieval result is less than or equal to the preset first similarity, the customer interaction information is input into the artificial intelligence big model again to generate the first marketing strategy. If the similarity between the first marketing strategy and the target search result is greater than the preset first similarity, the first marketing strategy is determined to be the target marketing strategy. Output the target marketing strategy.
4. The method according to claim 1, characterized in that, The acquisition of customer interaction information includes: Obtain audio recordings of conversations between customers and bank account managers; Based on the aforementioned large-scale artificial intelligence model, the dialogue speech is converted into dialogue text; Key information is extracted from the dialogue text to obtain customer interaction information.
5. The method according to claim 1, characterized in that, The step of inputting the customer interaction information into a preset artificial intelligence big data model to generate an initial marketing strategy includes: Obtain customer information corresponding to the customer from the customer relationship management system, wherein the customer relationship management system is used to store the customer information, and the customer information includes the customer's basic information and asset information; The customer interaction information and customer information are input into the artificial intelligence big data model to generate an initial marketing strategy.
6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Obtain daily marketing results data from the bank, including daily successful marketing data and failed marketing data; The marketing results data is input into the AI big model, and incremental training is performed on the AI big model based on the marketing results data.
7. The method according to claim 1, characterized in that, The bank branch marketing knowledge base is a regularly updated bank branch marketing knowledge base; The regular update method includes supplementing or replacing newly added marketing scripts, marketing product information, and bank regulations information.
8. A device for generating marketing strategies for bank branches, characterized in that, include: The data acquisition module is used to acquire customer interaction information and the bank branch marketing knowledge base, which includes marketing scripts, marketing product information and bank regulations information for customer marketing. The strategy generation module is used to input the customer interaction information into a preset artificial intelligence big data model to generate an initial marketing strategy; The information retrieval module is used to search the bank branch marketing knowledge base based on the customer interaction information to obtain the target retrieval results; The strategy determination module is used to determine the initial marketing strategy as the target marketing strategy when the similarity between the initial marketing strategy and the target retrieval result is greater than a preset first similarity. The strategy output module is used to output the target marketing strategy.
9. An electronic device, characterized in that, The device includes: a processor, a memory, and a system bus; The processor and the memory are connected via the system bus; The memory is used to store a program, the program including instructions that, when executed by the processor, cause the processor to perform the steps of the method for generating a bank branch marketing strategy according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed by a terminal device, implements the steps of the method for generating a bank branch marketing strategy as described in any one of claims 1 to 7.