Business strategy generation method, system, device, equipment, medium and program product

Abstract

The invention provides a business strategy generation method, system and device, equipment, a medium and a program product, relates to the technical field of computers, and can solve the problem that automatic strategy generation and resource allocation cannot be realized. According to the technical scheme, at least one customer demand type for identifying each customer is generated based on customer attribute data; establishing a mapping relationship between the business target and the customer demand type, and generating a business strategy; associating one or more customer demand types for each execution resource packet, and configuring a conversion rate parameter for each execution resource packet; based on the business strategy and the customer demand type, generating at least one execution strategy and a matching relationship between a target customer identifier set corresponding to each execution strategy and an execution resource packet; generating strategy effect evaluation data based on the matching relationship and the customer behavior data of the target customer group; and updating a conversion rate parameter of the execution resource packet based on the policy effect evaluation data. The method is used for generating, executing and optimizing the business strategy.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer technology, and in particular, to a business strategy generation method, system, device, equipment, medium and program product. BACKGROUND

[0002] With the development of digital banking, financial products and services are delivered to customers in various forms such as information, activities, and benefits. At the same time, under the promotion of social media and interactive service concepts, mobile channels such as enterprise communication and mobile banking have become important tools for bank employees to conduct customer management and marketing, and have achieved remarkable results. Under the high-quality development of finance, the banking industry needs to deepen the digital management capabilities of retail customers, achieve the precision and intelligence of customer service, and thus improve the comprehensive service capabilities and market competitiveness.

[0003] Currently, the industry generally uses data analysis tools for customer insight to identify customer value and predict customer needs. However, there is often a problem of fragmented strategies and disunified actions between various business channels and execution layers, making it difficult to form a synergistic effect. The management's business strategy is difficult to efficiently and accurately decompose into executable and monitorable marketing actions, resulting in poor strategy transmission, low conversion rate of insight results, and insufficient strategy production and iteration efficiency.

[0004] However, the existing customer insight, strategy formulation, and marketing execution steps are independent and cannot achieve automatic strategy generation and resource allocation. SUMMARY

[0005] Embodiments of the present application provide a business strategy generation method, system, device, equipment, medium and program product, which can achieve automatic strategy generation and resource allocation, and improve the precision of strategy execution.

[0006] To achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions: Firstly, a business strategy generation method is provided. This method includes: acquiring customer attribute data for at least one customer, and generating customer insight data based on the customer attribute data, the customer insight data including at least one customer demand type identified for each customer; upon receiving a business objective, establishing a mapping relationship between the business objective and the customer demand types in the customer insight data to generate a business strategy; constructing multiple execution resource packages, associating each execution resource package with one or more customer demand types, and configuring initial conversion rate parameters for each execution resource package; generating at least one execution strategy based on the business strategy and the customer demand types in the customer insight data, and a matching relationship between the target customer identifier set corresponding to each execution strategy and the execution resource package; based on the matching relationship, sending the corresponding execution resource package to the target customer group indicated by each target customer identifier set; collecting customer behavior data of the target customer group towards the execution resource package, and generating strategy effectiveness evaluation data based on the customer behavior data; and updating the conversion rate parameters of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data; wherein the updated conversion rate parameters are used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0007] In the business strategy generation method of this application embodiment, by constructing an architecture with customer demand types as structured data, the entire process is automated and optimized. That is, by transforming unstructured customer attribute data into structured customer demand types and establishing a mapping relationship between them and business objectives, a unified business semantic layer is created. This enables the system to automatically understand business intent and automatically generate business strategies and execution strategies based on this mapping relationship, thereby improving the efficiency and accuracy of strategy production. Furthermore, by constructing execution resource packages that are actively associated with customer demand types, execution strategies and corresponding matching relationships are generated. When generating matching relationships, the system can quickly and accurately filter and combine optimal resources based on the key index of customer demand type, thereby achieving precise access to strategy resources. Furthermore, by updating the conversion rate parameters of the execution resource package based on strategy effectiveness evaluation data and feeding the updated parameters back into the next strategy generation, a data-driven, closed-loop reinforcement learning system was constructed. This system no longer relies on lengthy manual analysis and tuning, but can automatically correct its internal evaluation model, i.e., the conversion rate parameters, based on real-time feedback, making the next matching decision better. This achieves automated strategy generation, resource allocation, and strategy optimization for the entire system, improving the accuracy of the entire system's strategy execution.

[0008] In one possible implementation of the first aspect, the aforementioned customer insight data further includes customer relationship identification results; the aforementioned establishment of a mapping relationship between business objectives and customer demand types in the customer insight data to generate a business strategy includes: obtaining customer relationship identification results from the customer insight data, which are used to indicate the customer relationship evolution state of a customer, including low correlation state, growth state, stable state, high value state, and churn risk state; determining customer demand types associated with business objectives based on the customer relationship evolution state of the customer, to establish a mapping relationship between business objectives and customer demand types; and generating a business strategy based on the mapping relationship.

[0009] Thus, by introducing the evolution of customer relationships as a dynamic context, the mapping relationship from business objectives to customer needs is no longer static, but adaptive according to the customer's state. This solves the problem that a single mapping rule cannot meet the personalized needs of customers at different stages throughout the entire lifecycle. This dynamic mapping mechanism greatly enhances the context awareness and accuracy of strategy generation.

[0010] In another possible implementation of the first aspect, the aforementioned generation of strategy effectiveness evaluation data based on customer behavior data includes: detecting customer interaction operations triggered by matching relationships through the tracking points of the customer interface module, and obtaining corresponding customer interaction events and feedback data; integrating the customer interaction events and feedback data with the business conversion data in the business database to generate unified log data; and generating multi-stage conversion funnel data for each execution resource package based on the unified log data as strategy effectiveness evaluation data; wherein the aforementioned strategy effectiveness evaluation data includes at least one of the following: the reach rate of the execution strategy, the customer click-through rate, the customer feedback rate, and the conversion rate.

[0011] In this way, by merging and generating unified log data, the format of multi-channel data sources is unified, and front-end interaction events and back-end business conversion data are logically linked and integrated, ensuring the integrity and consistency of subsequent data analysis. Furthermore, based on the generated multi-stage conversion funnel data and other performance evaluation data, the true effectiveness of each execution resource package and execution strategy can be comprehensively and accurately quantified from macro (such as reach rate) to micro (such as the conversion rate of each resource package), providing reliable and fine-grained data input for closed-loop optimization and ensuring the accuracy of the optimization direction.

[0012] In another possible implementation of the first aspect, the aforementioned execution resource package consists of at least one execution element among business components, interaction rules, and execution content; the aforementioned construction of multiple execution resource packages includes: receiving multiple selection instructions for different execution elements; and in response to the multiple selection instructions, generating multiple different execution resource packages by combining the selected execution elements.

[0013] In this way, by receiving selection instructions and combining generation methods, the process of building execution resource packages is modularized and standardized, which can quickly combine diverse resource packages, greatly enriching the diversity of the strategy library and facilitating accurate matching of the required resources.

[0014] In another possible implementation of the first aspect, the aforementioned customer demand type is the Top-N demand type for each customer calculated by an algorithm, where parameter N in the Top-N demand type is a configurable parameter; the method further includes: adjusting the value of parameter N by an optimization algorithm based on resource constraint information of the system operating environment.

[0015] Thus, the number of customer demand type identification results (parameter N) can be dynamically adjusted based on system resource constraints. By optimizing the algorithm to adjust parameter N, a dynamic balance can be achieved between system computing power and recognition accuracy: when resources are sufficient, the recognition breadth can be increased (large N value), and when resources are scarce, the system real-time performance can be guaranteed (small N value).

[0016] In another possible implementation of the first aspect, the above-mentioned generation of at least one execution strategy based on the customer demand types in business strategy and customer insight data, and the matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy, includes: determining at least one target customer group scope and the strategy execution time range corresponding to each target customer group scope based on the business strategy, so as to generate at least one execution strategy; determining the associated target resource package from multiple execution resource packages based on the business objectives contained in the business strategy; and for each execution strategy, generating a matching relationship between the target customer identifier set and the target resource package corresponding to the execution strategy based on the customer demand types of customers within the target customer group scope of the execution strategy and the customer demand types associated with the target resource package.

[0017] In this way, by configuring the target customer group scope and the strategy execution time range to concretize the execution strategy, an abstract business strategy is deconstructed into multiple specific tasks that can be executed concurrently. Different strategies can be executed in parallel for different customer groups at different times, which not only achieves unified management of strategies, but also improves the flexibility of execution.

[0018] In another possible implementation of the first aspect, the aforementioned customer insight data further includes at least one of the following: customer relationship identification results and customer value prediction results; the determination of at least one target customer group range based on business strategy includes: parsing at least one of the target customer relationship stage and the target customer value range from the business strategy; based on the parsed results, identifying customers from the customer group who meet the screening criteria to form an initial customer group set, the screening criteria including at least one of the following: the customer relationship identification results conform to the target customer relationship stage; the customer value prediction results conform to the target customer value range; and dividing the initial customer group set into at least one target customer group range according to at least one segmentation strategy, the segmentation strategy being based on at least one of the following factors: different execution channels, different execution resource package types, and different geographical regions.

[0019] In this way, by analyzing business strategies to obtain screening criteria and introducing segmentation strategies (such as by channel, resource package type, region), the automated and batch generation of multi-dimensional segmented customer groups is realized, which facilitates the subsequent precise matching of refined data.

[0020] Secondly, a business strategy generation system is provided, comprising: a customer insight processing module for acquiring customer attribute data of at least one customer and generating customer insight data based on the customer attribute data, the customer insight data including at least one customer demand type identified for each customer; a business strategy processing module for establishing a mapping relationship between the business objective and the customer demand types in the customer insight data after receiving a business objective, so as to generate a business strategy; an execution resource processing module for constructing multiple execution resource packages, associating one or more customer demand types with each execution resource package, and configuring initial conversion rate parameters for each execution resource package; and an execution strategy processing module for... The system uses customer demand types from strategy and customer insight data to generate at least one execution strategy, and a matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy. A strategy execution monitoring module is used to send the corresponding execution resource package to the target customer group indicated by each target customer identifier set based on the matching relationship; and to collect customer behavior data of the target customer group towards the execution resource package, and generate strategy effectiveness evaluation data based on the customer behavior data. An iterative optimization module is used to update the conversion rate parameter of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data; wherein the updated conversion rate parameter is used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0021] It should be noted that the beneficial effects of the business strategy generation system can be found in the relevant description of the beneficial effects of the business strategy generation method mentioned above, and will not be repeated here to avoid duplication.

[0022] Thirdly, a business strategy generation apparatus is provided, comprising: an acquisition module for acquiring customer attribute data of at least one customer; a processing module for generating customer insight data based on the customer attribute data, the customer insight data including at least one customer demand type identified for each customer; the processing module further comprising, upon receiving a business objective, establishing a mapping relationship between the business objective and the customer demand types in the customer insight data to generate a business strategy; the processing module further comprising constructing multiple execution resource packages, associating one or more customer demand types with each execution resource package, and configuring initial conversion rate parameters for each execution resource package; the processing module further comprising, based on the business strategy and customer insights... The system generates at least one execution strategy based on the customer demand types in the data, and establishes a matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy. A sending module sends the corresponding execution resource package to the target customer group indicated by each target customer identifier set based on the matching relationship. An acquisition module collects customer behavior data of the target customer group regarding the execution resource package. A processing module generates strategy effectiveness evaluation data based on the customer behavior data. The processing module also updates the conversion rate parameter of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data. The updated conversion rate parameter is used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0023] It should be noted that the beneficial effects of the business strategy generation device can be found in the relevant description of the beneficial effects of the business strategy generation method mentioned above, and will not be repeated here to avoid repetition.

[0024] Fourthly, an electronic device is provided, the method comprising: a memory and at least one processor. The memory is communicatively connected to the processor. The memory is used to store computer program code, the computer program code including computer instructions. When the processor executes the computer instructions, it causes the electronic device to perform the method as described in the first aspect and any possible implementation thereof.

[0025] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer instructions. When executed by a processor, these computer instructions are used to implement the method described in the first aspect and any possible implementation thereof.

[0026] Sixthly, embodiments of this application provide a computer program product that, when run on a computer / executed by a computer's processor, implements the method described in the first aspect and any possible design thereof. The computer may be the electronic device described in the fourth aspect and any possible implementation thereof.

[0027] Understandably, the beneficial effects achieved by the system of the second aspect, the apparatus of the third aspect, the electronic equipment of the fourth aspect, the computer-readable storage medium of the fifth aspect, and the computer program product of the sixth aspect provided above can be referred to as the beneficial effects of the first aspect and any possible implementation thereof, which will not be repeated here. Attached Figure Description

[0028] Figure 1 A flowchart illustrating a business strategy generation method provided in an embodiment of this application; Figure 2 A flowchart illustrating another business strategy generation method provided in this application embodiment; Figure 3 A flowchart illustrating another business strategy generation method provided in this application embodiment; Figure 4 This application provides a schematic diagram of the structure of a business strategy generation system according to an embodiment of the present application. Figure 5 A schematic diagram of a business strategy generation device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this embodiment, unless otherwise stated, "a plurality of" means two or more.

[0030] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0031] The technical solutions provided in this application, including the collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data, comply with relevant laws and regulations and do not violate public order and good morals.

[0032] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0033] The terms / concepts involved in the embodiments of this application are explained below.

[0034] Marketing Resource Package: A combination of marketing elements accumulated from client marketing practice and business experience, encompassing all necessary marketing elements such as products, activities, and sales scripts. As the smallest unit for a single customer touchpoint in a marketing strategy, it can be configured by the user configuring the marketing strategy, allowing for customized combinations of marketing elements. For example, a product, an activity, and a sales script constitute an example of a resource package. These marketing elements can be expanded based on actual application, such as adding benefit packages.

[0035] Marketing strategy: In the course of customer management, based on business objectives and customer needs, select the target customer group, marketing scale, strategy validity period and release channels to form a marketing task that can be received and executed by the channel side.

[0036] Strategy Center: Connecting to the customer insight platform, it focuses on business objectives, customer needs, and marketing resource packages to achieve the best match between customer needs and resource packages. It also connects various outreach channels to achieve effective customer reach and conversion of marketing strategies, making it a customer management service platform.

[0037] The following description, in conjunction with the accompanying drawings, explains the business strategy generation apparatus and the business strategy generation method applicable to the business strategy generation apparatus provided in this application, through specific embodiments and application scenarios.

[0038] The business strategy generation methods, systems, devices, equipment, media, and program products provided in this application can be applied to any scenario that requires data analysis to achieve target decision-making and precise resource allocation. For example, in the e-commerce field, they can be used to realize personalized product recommendations and marketing campaigns based on user interest types; in the membership operation field, they can be used to realize differentiated rights and benefits distribution and service strategy execution based on member lifecycle status. Especially in the digital customer management scenario of the banking industry: specifically, they can be used to build a strategy center platform connecting the head office management and front-line customer managers, realizing automated, closed-loop strategy management of various business objectives, from bank-wide wealth management business promotion and precise customer acquisition for credit products to high-net-worth customer relationship maintenance.

[0039] In recent years, the banking industry has accelerated its digital transformation, with financial products reaching a broad customer base through various means such as information, activities, and benefits. Simultaneously, with the rapid popularization of social media applications, the banking industry has also introduced interactive and supportive management concepts and methods. Mobile channels such as corporate communication and mobile banking have become widely used by bank employees for customer management and marketing, yielding significant results. Under the requirements of high-quality financial development, the banking industry is further refining its customer management capabilities, deepening its digital management capabilities for retail customers, achieving more precise and intelligent customer service, and enhancing its financial product service capabilities and competitive advantage. Based on a customer-centric management philosophy, key areas for improvement include: how to accurately understand customer needs to formulate customer management strategies; how to achieve a unified management strategy from management to execution to guide marketing strategy production; and how to achieve full-process management of marketing strategies from production, release, and process monitoring to promote iterative optimization of marketing and management strategies. Currently, banks are establishing professional teams for customer insight, but the ability to promote and apply these insights across the entire bank is still insufficient, making it difficult to achieve unified strategies and coordinated operations. Therefore, it is necessary to build a comprehensive strategy system that can drive unified customer management actions across the entire bank.

[0040] While the bank's internal systems are continuously deepening their customer insight capabilities, the ability to promote and apply these insights across the entire bank is still insufficient, making it difficult to achieve a unified strategy and unified operations across the bank. It is necessary to build a strategy system that can drive the bank's customer management actions in a unified manner from top to bottom, thereby improving the conversion rate of insights and the efficiency of strategy production.

[0041] The technical solution of this application aims to establish a business process that guides the formulation of business strategies based on customer insights, thereby accelerating the production of marketing strategies, realizing the ability to manage the entire lifecycle of strategies in a unified manner across the bank, promoting the transformation and iterative optimization of customer insights, and improving the precision and intelligence of customer service. To build a unified strategy system that drives bank-wide customer management actions, this solution introduces the capabilities of mature insight platforms such as in-house customer analysis and intelligent modeling systems to achieve customer needs insights for business strategies; it builds a management platform for business and marketing strategies, realizing the management of business objectives, customer needs, full-element marketing resource packages, and marketing strategies; it optimizes the existing intelligent recommendation system within the bank to match customer needs with marketing resource packages, achieving effective customer reach through marketing strategies; it opens up channels for releasing marketing strategies with internal communication channels, and collects strategy execution results through data tracking to monitor strategy execution and customer conversion, thereby achieving iterative optimization of strategies.

[0042] This application provides a business strategy generation method, which can be applied to a business strategy generation device or an electronic device. The following illustration uses a business strategy generation device executing the method as an example. Figure 1As shown, the business strategy generation method may include the following steps 201 to 207.

[0043] Step 201: The business strategy generation device acquires customer attribute data of at least one customer and generates customer insight data based on the customer attribute data.

[0044] In some embodiments of this application, the aforementioned customer insight data includes at least one customer demand type identified for each customer.

[0045] In some embodiments of this application, the business strategy generation device can retrieve or receive pushed raw customer data in batches or in real time by calling internal database interfaces (such as customer information databases and transaction record databases) or external system application programming interfaces (APIs).

[0046] In some embodiments of this application, the business strategy generation device cleans, transforms, and extracts features from the acquired unstructured or semi-structured attribute data through a built-in data processing channel. Subsequently, it uses a preset rule engine or machine learning model (such as a classification or clustering model) to calculate the processed features and output structured insight results.

[0047] In some embodiments of this application, the cleaning described above may include processing null values ​​and removing outliers; the transformation described above may include one-hot encoding of categorical variables and standardization of numerical variables.

[0048] In some embodiments of this application, the aforementioned customer attribute data refers to the original fields obtained from the data source, which may include static attributes (such as age, occupation, and region), dynamic behaviors (such as recent login time and page browsing history), and transaction data (such as historical product purchase records and asset size).

[0049] In some embodiments of this application, the aforementioned customer insight data refers to intermediate data with clear business semantics obtained after processing the original attribute data. It is a machine-readable description of the customer's state.

[0050] In some embodiments of this application, identifying the customer need type for each customer means that the insight data is bound to a unique customer identifier and stored in a database with key-value pairs or a similar structure. The aforementioned customer need type is a key insight data point; it is a predefined, discrete enumeration value (such as financial needs, loan needs, or insurance needs), which can be categorized by rules or by analyzing customer inquiries using Natural Language Processing (NLP) models.

[0051] In some embodiments of this application, the aforementioned customer demand types can be the Top-N demand types for each customer calculated by an algorithm. It should be noted that this approach is suitable for scenarios where a customer may have multiple demand preferences simultaneously. The business strategy generation device can calculate the preference score for each customer across all predefined demand types, then sort them according to the scores, and finally select the top N demand types as the insight result for that customer.

[0052] In some embodiments of this application, the algorithm described above may be collaborative filtering, content-based recommendation algorithm, or deep learning model, with the input being the customer's historical behavior sequence and attribute features.

[0053] In some embodiments of this application, the parameter N in the Top-N demand type is a configurable parameter. The business strategy generation method provided in this application also includes the following step 301.

[0054] Step 301: The business strategy generation device adjusts the value of parameter N based on the resource constraint information of the system operating environment through an optimization algorithm.

[0055] In some embodiments of this application, the aforementioned resource constraint information refers to system metrics detected by the business strategy generation device during operation, including but not limited to CPU load, memory usage, network input / output (I / O), and database connection count.

[0056] In some embodiments of this application, the aforementioned business strategy generation device can set a central processing unit (CPU) utilization threshold (e.g., 80%). When the CPU utilization is detected to be consistently higher than this threshold, the optimization algorithm automatically lowers the parameter N (e.g., from 5 to 3) to reduce the computational load of model inference and ensure the system's real-time responsiveness. Conversely, when system resources are sufficient, the value of N is increased to provide richer insights.

[0057] In some embodiments of this application, the capabilities of customer insight platforms such as customer analysis platforms and intelligent modeling systems are introduced to identify the relationship between customers and banks, predict customer value, and mine customer needs. This yields customer relationship identification results, indicating the customer's relationship with the bank at which stage: ice-breaking phase → nurturing phase → enhancement phase → deepening phase or retention phase; customer value prediction results, for example, for customers with assets under management (AUM) between 10,000 and 20,000, the expected increase in AUM; and customer need identification results: summarizing customer need types based on business experience and calculating the top-N needs for each customer; N can be optimized to a suitable parameter based on the bank's operating scale, system resources, and application practices; in the future, intelligent algorithms can also be introduced to achieve automatic optimization.

[0058] Thus, the number of customer demand type identification results (parameter N) can be dynamically adjusted based on system resource constraints. By optimizing the algorithm to adjust parameter N, a dynamic balance can be achieved between system computing power and recognition accuracy: increasing the recognition breadth when resources are sufficient (larger N value), and ensuring system real-time performance when resources are scarce (smaller N value). This makes the system a resource-aware, resilient system, achieving a dynamic balance between computing accuracy and system load.

[0059] Step 202: After receiving the business objective, the business strategy generation device establishes a mapping relationship between the business objective and the types of customer needs in the customer insight data to generate a business strategy.

[0060] In some embodiments of this application, the aforementioned business objectives may be operational objectives. The aforementioned business strategies may be operational strategies.

[0061] In some embodiments of this application, the business strategy generation device receives structured instructions through a management configuration interface or an upstream system interface. These instructions may include a target type (e.g., increasing intermediary business revenue) and target parameters (e.g., increasing by 10%). The business strategy generation device internally maintains a mapping table or configuration center, which defines the association between business objectives and one or more customer demand types. For example, the business objective of increasing deposit volume is mapped to savings demand. The business strategy generation device matches the received specific business objective with the mapping table and outputs a strategy object that can be parsed by downstream processes. This object contains target information and its associated set of demand types.

[0062] In some embodiments of this application, the above mapping relationship can be stored in the form of key-value pairs, where the key is the business target and the value is a list of customer demand types associated with it.

[0063] In some embodiments of this application, the aforementioned customer insight data also includes customer relationship identification results. Combined with... Figure 1 ,like Figure 2 As shown, step 202 above can be specifically implemented through steps 202a to 202c below.

[0064] Step 202a: After receiving the business objective, the business strategy generation device obtains the customer relationship identification results from the customer insight data.

[0065] In some embodiments of this application, the above-mentioned customer relationship identification results are used to indicate the customer relationship evolution state of the customer, which includes low association state, growth state, stable state, high value state, and churn risk state.

[0066] In some embodiments of this application, the aforementioned customer relationship evolution state can be a lifecycle stage.

[0067] In some embodiments of this application, the aforementioned low-association state can be the initial stage or the breakthrough stage. The aforementioned growth state can be the development stage or the nurturing stage. The aforementioned stable state can be the stable stage or the enhancement stage. The aforementioned high-value state can be the mature stage or the deepening stage. The aforementioned loss-risk state can be the decline stage or the retention stage.

[0068] In some embodiments of this application, the aforementioned customer relationship identification results are another key piece of insight data, typically calculated using a customer lifecycle model. This model categorizes customers into different state stages based on characteristics such as transaction frequency, amount, and time of the most recent transaction.

[0069] In some embodiments of this application, the aforementioned customer relationship evolution state is a standardized label output by the model, used to characterize the current stage of the customer-business relationship.

[0070] Step 202b: The business strategy generation device determines the types of customer needs associated with business objectives based on the customer relationship evolution status of the customer, so as to establish a mapping relationship between business objectives and customer need types.

[0071] In some embodiments of this application, the mapping table within the business strategy generation device is conditional. For example, for the same business objective of increasing customer value, when a customer is in a growth state, it is mapped to credit demand; when a customer is in a high-value state, it is mapped to wealth management demand. This is achieved by adding a conditional field (i.e., customer relationship evolution state) to the mapping table.

[0072] Step 202c: The business strategy generation device generates a business strategy based on the mapping relationship.

[0073] In some embodiments of this application, the business strategy generation device encapsulates the determined mapping relationship into a final strategy object, i.e., a business strategy.

[0074] In some embodiments of this application, management sets business objectives based on industry development and bank business strategies; at the same time, based on accumulated business experience, it establishes the relationship between business objectives and customer needs, laying the foundation for subsequent customer-centric marketing strategies; this marketing strategy can be viewed by the heads of various marketing channels, and then corresponding marketing strategies can be formulated.

[0075] Thus, by introducing the evolution of customer relationships as a dynamic context, the mapping relationship from business objectives to customer needs is no longer static, but adaptive according to the customer's state. This solves the problem that a single mapping rule cannot meet the personalized needs of customers at different stages throughout the entire lifecycle. This dynamic mapping mechanism greatly enhances the context awareness and accuracy of strategy generation.

[0076] Step 203: The business strategy generation device constructs multiple execution resource packages, associates one or more customer demand types with each execution resource package, and configures initial conversion rate parameters for each execution resource package.

[0077] In some embodiments of this application, the aforementioned execution resource package may be a marketing resource package.

[0078] In some embodiments of this application, the business strategy generation device provides a resource library management function, allowing administrators to create new resource package entities. Each resource package is stored as a record in the database, containing its unique resource package identifier. When creating or editing a resource package, the business strategy generation device uses checkboxes or a tagging system to assign one or more "customer demand type" category tags to the resource package, and this association is stored in a relational table in the database. Each resource package record has a conversion rate field, which can be manually initialized by the administrator based on historical experience or test results during the startup phase. This parameter is a floating-point number, such as 0.05 (representing a 5% conversion rate).

[0079] In some embodiments of this application, each marketing resource package is a combination of marketing elements formed by the heads of various marketing channels (execution level) within the industry, based on their business experience, combining existing products, activities, and high-quality sales scripts. It is the smallest unit of content for a single customer touchpoint in a marketing strategy. Each marketing resource package can be associated with customer needs by the head of the marketing channel as needed for subsequent marketing strategies. Depending on the characteristics of different execution channels, each marketing channel head can configure marketing resource packages independently. For example, an example of a resource package consisting of a product, an activity, and a sales script; or an example of a resource package consisting of a product group and a sales script. Each marketing resource package will have an initial conversion rate calculated by the system based on its historical customer touchpoint data, and its conversion rate will be continuously updated as the strategy package is applied in the system.

[0080] In some embodiments of this application, the aforementioned execution resource package consists of at least one execution element selected from business components, interaction rules, and execution content. The "business strategy generation device constructs multiple execution resource packages" in step 203 can be specifically implemented through steps 203a and 203b below.

[0081] Step 203a: The business strategy generation device receives multiple selection instructions for different execution elements.

[0082] In some embodiments of this application, the aforementioned execution elements may be marketing elements.

[0083] Step 203b: The business strategy generation device responds to multiple selection instructions and generates multiple different execution resource packages by combining the selected execution elements.

[0084] In some embodiments of this application, the business strategy generation device maintains independent business component libraries (such as product lists), interaction rule libraries (such as activity flows), and execution content libraries (such as script templates), realizing the modularization and configurability of resource packages. Administrators drag and drop desired elements from these libraries through a graphical interface. In response to these selection instructions, the business strategy generation device binds the identifier of the selected element to a newly created resource package identifier and persists it in storage.

[0085] In this way, by receiving selection instructions and combining generation methods, the process of building execution resource packages is modularized and standardized, which can quickly combine diverse resource packages, greatly enriching the diversity of the strategy library and facilitating accurate matching of the required resources.

[0086] Step 204: The business strategy generation device generates at least one execution strategy based on the business strategy and the customer demand types in the customer insight data, as well as the matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy.

[0087] In some embodiments of this application, the above-described execution strategy can be a marketing strategy. The above-described target customer identifier set can be a customer group list.

[0088] In some embodiments of this application, combined with Figure 1 ,like Figure 3 As shown, step 204 can be implemented through steps 204a to 204c as described below.

[0089] Step 204a: The business strategy generation device determines at least one target customer group range and the strategy execution time range corresponding to each target customer group range based on the business strategy, so as to generate at least one execution strategy.

[0090] In some embodiments of this application, the business strategy generation device generates at least one execution strategy by configuring different target customer groups and strategy execution time ranges. For example, by configuring different customer groups for region A and region B respectively, the system will generate two independent execution strategies, each of which includes its specific customer group range, time range, and target inherited from the business strategy.

[0091] In some embodiments of this application, the aforementioned customer insight data further includes at least one of the following: customer relationship identification results and customer value prediction results. The "business strategy generation device determines at least one target customer group range based on the business strategy" in step 204a can be specifically implemented through steps 204a1 to 204a3 described below.

[0092] Step 204a1: The business strategy generation device parses the business strategy to obtain at least one of the target customer relationship stage and the target customer value range.

[0093] In some embodiments of this application, the business strategy generation device parses the business strategy object, extracts the customer demand type determined by the mapping relationship, and further derives the target customer state required to achieve the goal according to predefined rules. For example, the business goal of activating dormant customers is parsed as the target customer relationship stage = churn risk state.

[0094] Step 204a2: Based on the results obtained from the analysis, the business strategy generation device identifies customers that meet the screening criteria from the customer group to form an initial customer group set.

[0095] In some embodiments of this application, the above screening criteria include at least one of the following: the customer relationship identification result matches the target customer relationship stage; the customer value prediction result matches the target customer value range.

[0096] In some embodiments of this application, the business strategy generation device performs a database query operation, and based on at least one of the target customer relationship stage and the target customer value range obtained through parsing, filters out all customer identifiers that meet the conditions from the customer insight table to form an initial list of customer identifiers.

[0097] Step 204a3: The business strategy generation device divides the initial customer group set into at least one target customer group range according to at least one segmentation strategy.

[0098] In some embodiments of this application, the above-mentioned partitioning strategy is based on at least one of the following factors: different execution channels, different execution resource package types, and different geographical regions.

[0099] In some embodiments of this application, the business strategy generation device further segments the initial customer group set according to a segmentation strategy. For example, based on geographical attributes, the initial customer group is divided into customer groups in region A, customer groups in region B, etc.; or based on resource package suitability, it is divided into customer groups suitable for resource package A, customer groups suitable for resource package B, etc. Finally, multiple customer group subsets are output.

[0100] In this way, by analyzing business strategies to obtain screening criteria and introducing segmentation strategies (such as by channel, resource package type, region), the automated and batch generation of multi-dimensional segmented customer groups is realized, which facilitates the subsequent precise matching of refined data.

[0101] Step 204b: The business strategy generation device determines the associated target resource package from multiple execution resource packages based on the business objectives contained in the business strategy.

[0102] In some embodiments of this application, the business strategy generation device performs a query in the resource package library based on the customer demand type associated with the business strategy, filters out all resource packages associated with the same demand type, and forms a target resource package candidate set.

[0103] Step 204c: For each execution strategy, the business strategy generation device generates a matching relationship between the target customer identifier set and the target resource package corresponding to the execution strategy, based on the customer demand type of customers within the target customer group range of the execution strategy and the customer demand type associated with the target resource package.

[0104] In some embodiments of this application, for a target customer group under an execution strategy, the business strategy generation device iterates through each customer in the group to obtain their customer demand type; simultaneously, it iterates through each resource package in the target resource package candidate set to obtain its associated customer demand type. When the demand types of the two groups overlap, the resource package is considered suitable for that customer. Finally, the device outputs a matching matrix or matching list for this execution strategy, clearly recording which customer subset should use which resource package(s).

[0105] In some embodiments of this application, the creation process of each marketing strategy is as follows: Each marketing channel manager selects the business objectives from the business strategies issued by their own and their superiors, and combines factors such as the reachable customer range of the channel, the strategy execution method (mass messaging, phone calls), and channel marketing costs to configure the business scale and the strategy execution time range, thus completing the production of a marketing strategy. The system then incorporates the capabilities of existing intelligent recommendation systems within the industry, automatically calculating the optimal "customer list - marketing resource package" matching relationship for the current marketing strategy based on the configuration information of the marketing strategy, including business objectives, target customer range, and the number of marketing customers (the number of customers in the target customer range multiplied by the business scale), combined with the obtained customer-demand relationship and marketing resource package-demand relationship. The calculation is based on the historical conversion rate of customers on the corresponding resource packages. The above process is generally completed in T+1, and its output is the association data between customers and resource packages. This data will be used by the channel side to query the marketing strategy task, and then by the corresponding execution-side user to execute the strategy.

[0106] In this way, by configuring the target customer group scope and the strategy execution time range to concretize the execution strategy, an abstract business strategy is deconstructed into multiple specific tasks that can be executed concurrently. Different strategies can be executed in parallel for different customer groups at different times, which not only achieves unified management of strategies, but also improves the flexibility of execution.

[0107] Step 205: The business strategy generation device sends the corresponding execution resource package to the target customer group indicated by each target customer identifier set based on the matching relationship.

[0108] In some embodiments of this application, the business strategy generation device calls the channel gateway service that is connected to various channels (such as enterprise communication software, SMS platform, APP push), takes the target customer identifier set and execution resource package content in the matching relationship as parameters, and sends them through the API provided by the channel to trigger the channel to execute specific outreach actions.

[0109] In some embodiments of this application, after the marketing strategy reaches its start time, the distribution channels configured for the marketing strategy can query the strategy task and support channel-side personnel in viewing and executing it. For example, account managers can view marketing strategies on the enterprise side, obtain marketing strategies associated with their managed clients, and send the resource packages included in the marketing strategy to clients via enterprise messages. During strategy execution, the channel side will collect data such as marketing strategy reach, customer clicks, and customer feedback through event tracking, and combine this with product sales data from in-house product components to statistically analyze funnel data such as reach, feedback, intent, and conversion for each resource package in the marketing strategy, thereby achieving effectiveness evaluation of the marketing strategy.

[0110] Step 206: The business strategy generation device collects customer behavior data of the target customer group on the execution resource package, and generates strategy effectiveness evaluation data based on the customer behavior data.

[0111] In some embodiments of this application, the business strategy generation device initiates a data detection and collection process to capture customer feedback related to the execution of this strategy from the data flowing back from various channels.

[0112] In some embodiments of this application, step 206 can be specifically implemented by steps 206a to 206c as described below.

[0113] Step 206a: The business strategy generation device detects customer interaction operations triggered by matching relationships through the embedded points of the customer interface module, and obtains the corresponding customer interaction events and feedback data.

[0114] In some embodiments of this application, the business strategy generation device pre-embeds a software development kit (SDK) on the channel side (such as a webpage or an app). When a customer clicks on a link or content issued by the strategy, the SDK captures the event and sends it to the device's log collector, carrying information such as a resource package identifier, a customer identifier, and a timestamp.

[0115] Step 206b: The business strategy generation device integrates customer interaction events and feedback data with business conversion data in the business database to generate unified log data.

[0116] In some embodiments of this application, the business strategy generation device initiates a data cleaning and association task to perform association queries between the front-end embedded logs and the back-end business database (such as a transaction record table). Through customer identifiers and resource package identifiers, the customer's click behavior is associated with whether or not a transaction is ultimately completed, forming a complete unified log containing full-link information.

[0117] Step 206c: The business strategy generation device generates multi-stage conversion funnel data for each execution resource package based on unified log data, which serves as strategy effectiveness evaluation data.

[0118] In some embodiments of this application, the above-mentioned strategy effectiveness evaluation data includes at least one of the following: the reach rate of the implemented strategy, the customer click-through rate, the customer feedback rate, and the conversion rate.

[0119] In some embodiments of this application, the business strategy generation device aggregates and calculates unified log data, groups it according to resource package identifiers and execution strategy identifiers, and then, based on the event types in the logs, counts the number of each resource package in the stages such as reach-click-inquiry-transaction, and then calculates the conversion rate of each stage. These quantitative indicators are packaged into a strategy effectiveness evaluation report.

[0120] In this way, by merging and generating unified log data, the format of multi-channel data sources is unified, and front-end interaction events and back-end business conversion data are logically linked and integrated, ensuring the integrity and consistency of subsequent data analysis. Furthermore, based on the generated multi-stage conversion funnel data and other performance evaluation data, the true effectiveness of each execution resource package and execution strategy can be comprehensively and accurately quantified from macro (such as reach rate) to micro (such as the conversion rate of each resource package), providing reliable and fine-grained data input for closed-loop optimization and ensuring the accuracy of the optimization direction.

[0121] Step 207: The business strategy generation device updates the conversion rate parameters of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data.

[0122] In some embodiments of this application, the updated conversion rate parameter is used to generate a matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0123] In some embodiments of this application, the business strategy generation device directly updates the conversion rate parameter field of the resource package record in the resource library (e.g., from 5% to 4.5%) based on the latest calculated conversion rate (e.g., the actual conversion rate of resource package A in this activity is 4.5%). When the business strategy generation device performs matching, such as regenerating the matching relationship between the target customer identifier set and the target resource package corresponding to the execution strategy, it can query these updated and more accurate conversion rate parameters and tend to recommend resource packages with better historical performance to the customer group, thereby achieving closed-loop optimization.

[0124] In some embodiments of this application, the business strategy generation device may use an exponential smoothing method to update the conversion rate parameter of the corresponding execution resource package in the matching relationship. For example, the new conversion rate parameter is calculated based on the smoothing factor, the latest conversion rate, and the original conversion rate parameter.

[0125] In the business strategy generation method of this application embodiment, by constructing an architecture with customer demand types as structured data, the entire process is automated and optimized. That is, by transforming unstructured customer attribute data into structured customer demand types and establishing a mapping relationship between them and business objectives, a unified business semantic layer is created. This enables the system to automatically understand business intent and automatically generate business strategies and execution strategies based on this mapping relationship, thereby improving the efficiency and accuracy of strategy production. Furthermore, by constructing execution resource packages that are actively associated with customer demand types, execution strategies and corresponding matching relationships are generated. When generating matching relationships, the system can quickly and accurately filter and combine optimal resources based on the key index of customer demand type, thereby achieving precise access to strategy resources. Furthermore, by updating the conversion rate parameters of the execution resource package based on strategy effectiveness evaluation data and feeding the updated parameters back into the next strategy generation, a data-driven, closed-loop reinforcement learning system was constructed. This system no longer relies on lengthy manual analysis and tuning, but can automatically correct its internal evaluation model, i.e., the conversion rate parameters, based on real-time feedback, making the next matching decision better. This achieves automated strategy generation, resource allocation, and strategy optimization for the entire system, improving the accuracy of the entire system's strategy execution.

[0126] Figure 4 This is a schematic diagram of the structure of a business strategy generation system provided in an embodiment of this application, such as... Figure 4 As shown, the business strategy generation system 80 includes: a customer insight processing module 81, a business strategy processing module 82, an execution resource processing module 83, an execution strategy processing module 84, a strategy execution monitoring module 85, and an iterative optimization module 86.

[0127] The aforementioned customer insight processing module 81 is used to acquire customer attribute data of at least one customer and generate customer insight data based on the customer attribute data. The customer insight data includes at least one customer need type identified for each customer.

[0128] The aforementioned business strategy processing module 82 is used to establish a mapping relationship between the business objectives and the types of customer needs in the customer insight data after receiving the business objectives, so as to generate business strategies.

[0129] The aforementioned execution resource processing module 83 is used to construct multiple execution resource packages, associate one or more customer demand types with each execution resource package, and configure initial conversion rate parameters for each execution resource package.

[0130] The aforementioned execution strategy processing module 84 is used to generate at least one execution strategy based on the business strategy and the customer demand type in the customer insight data, as well as the matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy.

[0131] The aforementioned strategy execution monitoring module 85 is used to send corresponding execution resource packages to the target customer groups indicated by each target customer identifier set based on the matching relationship; and to collect customer behavior data of the target customer groups on the execution resource packages, and generate strategy effectiveness evaluation data based on the customer behavior data.

[0132] The aforementioned iterative optimization module 86 is used to update the conversion rate parameter of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data; wherein, the updated conversion rate parameter is used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0133] For example, to build a unified strategy system that can drive bank-wide customer operations, a strategy center business platform is created, which includes the following functional modules: 1. Customer Insight Management: Utilizing customer insight platforms such as customer analytics platforms and intelligent modeling systems, this system enables the identification of customer-bank relationships, customer value prediction, and customer needs analysis. The results include: customer relationship identification (e.g., the customer-bank relationship's current stage: ice-breaking phase → nurturing phase → enhancement phase → deepening phase or retention phase); customer value prediction (e.g., for customers with AUM between 10,000 and 20,000, the expected AUM increase); and customer needs identification (summarizing customer needs based on business experience and calculating the top-N needs for each customer). N can be optimized to a suitable parameter based on the bank's operating scale, system resources, and application practices. Future implementation may also incorporate intelligent algorithms for automatic optimization.

[0134] 2. Business Strategy Management: Management sets business objectives based on industry development and the bank's business strategy. At the same time, based on accumulated business experience, it establishes the relationship between business objectives and customer needs, laying the foundation for subsequent customer-centric marketing strategies. This marketing strategy can be reviewed by the heads of various marketing channels, who can then formulate corresponding marketing strategies. See step 4 below for details.

[0135] 3. Marketing Resource Management: This includes multiple instantiated marketing resource packages. Each marketing resource package is a combination of marketing elements created by the heads of various marketing channels (execution level) within the industry, based on their business experience and combining existing products, activities, and high-quality sales scripts. It is the smallest unit of content for a single customer reach in a marketing strategy. Each marketing resource package can be associated with the requirements in step 2 by the head of the marketing channel as needed for subsequent marketing strategies. Depending on the characteristics of different execution channels, each marketing channel head can configure marketing resource packages independently. For example, a product, an activity, and a sales script constitute an instance of a resource package; a product group and a sales script constitute an instance of a resource package. The system will calculate an initial conversion rate for each marketing resource package based on its historical customer reach data, and continuously update its conversion rate as the strategy package is applied within the system.

[0136] 4. Marketing Strategy Management: This includes multiple instantiated marketing strategies. The creation process for each marketing strategy is as follows: Each marketing channel manager selects the operational objectives from the business strategies issued by their own and their superiors. They then combine this with factors such as the channel's reachable customer base, strategy execution methods (mass messaging, phone calls), and channel marketing costs to configure the operational scale and strategy execution timeframe, thus completing the production of a marketing strategy. The system then leverages the capabilities of the industry's existing intelligent recommendation system to automatically calculate the optimal "customer list - marketing resource package" matching relationship for the current marketing strategy based on the strategy's configuration information, including operational objectives, target customer base, and the number of marketing customers (the number of customers within the target customer base multiplied by the operational scale). This is combined with the customer-demand relationship and the marketing resource package-demand relationship obtained in steps 1 and 2 above. The calculation is based on the customer's historical conversion rate on the corresponding resource package. This process is generally completed on T+1 day, and its output is the association data between customers and resource packages. This data will be used by the channel side to query the marketing strategy task, and then by the corresponding execution-side user to execute the strategy.

[0137] 5. Strategy Execution Monitoring: Once a marketing strategy reaches its start time, the designated distribution channels for that strategy can access the strategy task and allow channel personnel to view and execute it. For example, account managers can view marketing strategies on the enterprise side, obtain marketing strategies associated with their managed clients, and send the resource packages included in the marketing strategy to clients via enterprise messaging. During strategy execution, the channel side will collect data such as marketing strategy reach, customer clicks, and customer feedback through event tracking. This data, combined with sales data from in-house product components, will be used to compile funnel data for each resource package within the marketing strategy, including reach, feedback, intent, and conversion, thereby enabling the evaluation of the marketing strategy's effectiveness.

[0138] 6. Iterative optimization of marketing resources and marketing strategies: The performance data from step 5 is used to update the historical conversion rate of the marketing resource package in step 3 above. On the other hand, it can guide managers and channel managers to optimize the configuration of steps 2, 3, and 4 above, thereby optimizing business strategies, marketing resource packages, and marketing strategies.

[0139] This solution integrates various insight platforms, activity databases, product databases, and marketing outreach channels within the bank to build a unified strategy system that drives bank-wide customer management actions. It enables a full-process customer management service workflow encompassing customer insight, business and marketing strategy formulation, strategy execution and monitoring, and strategy iteration and optimization. This enhances managers' strategic coordination and guidance capabilities, empowers frontline staff to improve the accuracy of marketing execution, and improves the quality and efficiency of customer management.

[0140] It should be noted that the explanation of the business strategy generation system 80 can be found in the relevant description of the business strategy generation method in the above embodiments. To avoid repetition, it will not be repeated here.

[0141] Figure 5 This is a schematic diagram of the structure of a business strategy generation device provided in an embodiment of this application, such as... Figure 5 As shown, the business strategy generation device 100 includes: an acquisition module 101, a processing module 102, and a sending module 103.

[0142] The aforementioned acquisition module 101 is used to acquire customer attribute data of at least one customer. The aforementioned processing module 102 is used to generate customer insight data based on customer attribute data, the customer insight data including at least one customer need type identified for each customer; The aforementioned processing module 102 is also used to establish a mapping relationship between the business objectives and the types of customer needs in the customer insight data after receiving the business objectives, so as to generate business strategies. The aforementioned processing module 102 is also used to construct multiple execution resource packages, associate one or more customer demand types with each execution resource package, and configure initial conversion rate parameters for each execution resource package; The aforementioned processing module 102 is also used to generate at least one execution strategy based on the customer demand types in the business strategy and customer insight data, as well as the matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy; The aforementioned sending module 103 is used to send the corresponding execution resource package to the target customer group indicated by each target customer identifier set based on the matching relationship; the acquisition module 101 is also used to collect customer behavior data of the target customer group on the execution resource package; The aforementioned processing module 102 is also used to generate strategy effectiveness evaluation data based on customer behavior data; The aforementioned processing module 102 is also used to update the conversion rate parameter of the corresponding execution resource package in the matching relationship based on the strategy effectiveness evaluation data; wherein, the updated conversion rate parameter is used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

[0143] In some embodiments of this application, the aforementioned customer insight data further includes customer relationship identification results; the aforementioned processing module 102 is specifically used for: acquiring customer relationship identification results from the customer insight data, wherein the customer relationship identification results are used to indicate the customer relationship evolution state of the customer, the customer relationship evolution state includes low correlation state, growth state, stable state, high value state, and churn risk state; determining the customer demand type associated with business objectives based on the customer relationship evolution state of the customer, so as to establish a mapping relationship between business objectives and customer demand types; and generating business strategies based on the mapping relationship.

[0144] In some embodiments of this application, the processing module 102 is specifically used to: detect customer interaction operations triggered by matching relationships through the tracking points of the customer interface module, and obtain the corresponding customer interaction events and feedback data; integrate the customer interaction events and feedback data with the business conversion data in the business database to generate unified log data; and generate multi-stage conversion funnel data for each execution resource package based on the unified log data as strategy effectiveness evaluation data; wherein, the above-mentioned strategy effectiveness evaluation data includes at least one of the following: the reach rate of the execution strategy, the customer click rate, the customer feedback rate, and the conversion rate.

[0145] In some embodiments of this application, the execution resource package is composed of at least one execution element among business components, interaction rules, and execution content; the processing module 102 is specifically used to: receive multiple selection instructions for different execution elements; and in response to the multiple selection instructions, generate multiple different execution resource packages by combining the selected execution elements.

[0146] In some embodiments of this application, the aforementioned customer demand type is the Top-N demand type for each customer calculated by an algorithm, and the parameter N in the Top-N demand type is a configurable parameter; the aforementioned processing module 102 is also used to adjust the value of parameter N by an optimization algorithm based on the resource constraint information of the system operating environment.

[0147] In some embodiments of this application, the processing module 102 is specifically used to: determine at least one target customer group range and the strategy execution time range corresponding to each target customer group range based on the business strategy, so as to generate at least one execution strategy; determine the associated target resource package from multiple execution resource packages based on the business objectives contained in the business strategy; and for each execution strategy, generate a matching relationship between a target customer identifier set and a target resource package corresponding to the execution strategy based on the customer demand type of customers within the target customer group range of the execution strategy and the customer demand type associated with the target resource package.

[0148] In some embodiments of this application, the aforementioned customer insight data further includes at least one of the following: customer relationship identification results and customer value prediction results; the aforementioned processing module 102 is specifically used for: parsing at least one of the target customer relationship stage and the target customer value range from the business strategy; based on the parsed results, determining customers that meet the screening conditions from the customer group to form an initial customer group set, the screening conditions including at least one of the following: the customer relationship identification results conform to the target customer relationship stage; the customer value prediction results conform to the target customer value range; and dividing the initial customer group set into at least one target customer group range according to at least one segmentation strategy, the segmentation strategy being based on at least one of the following factors: different execution channels, different execution resource package types, and different geographical regions.

[0149] It should be noted that the explanation of the business strategy generation device 100 can be found in the relevant description of the business strategy generation device in the above embodiments, and will not be repeated here to avoid repetition. The business strategy generation device provided in this application embodiment can achieve the same effect as the above-described business strategy generation method. The effect of the business strategy generation method can be found in the relevant description of the effect of the above-described embodiments of the business strategy generation method, and will not be repeated here to avoid repetition.

[0150] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6 As shown, the electronic device 500 includes: a memory 501, a transceiver 502, and at least one processor 503.

[0151] Transceiver 502 is used to interact with other devices to send and receive data.

[0152] The memory 501 is used to store computer program code, which includes computer instructions. These computer instructions run in the aforementioned electronic device to implement the method shown in the above-described method embodiments. For example, the memory may include high-speed random access memory (RAM), and may also include non-volatile memory (NVM), such as at least one disk storage device, or a USB flash drive, portable hard drive, read-only memory, magnetic disk, or optical disk, etc.

[0153] Processor 503 can be a general-purpose processor, including a Central Processing Unit (CPU), a network processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. Processor 503 can also be other general-purpose processors. The general-purpose processor can be a microprocessor or any conventional processor.

[0154] The memory 501, transceiver 502, and processor 503 are communicatively connected. For example, the memory 501 and transceiver 502 can be connected to the processor 503 via a system bus and communicate with each other. The system bus can be a peripheral component interconnect (PCI) bus, an extended industry standard architecture (EISA) bus, an industry standard architecture (ISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the figure, but this does not mean that there is only one bus or one type of bus.

[0155] Optionally, the memory 501 can be either standalone or integrated with the processor 503. When the memory 501 is set up independently, it is connected to the processor 503 via a system bus.

[0156] This application also provides a chip for executing instructions, which is used to execute the technical solution of the business strategy generation method in the above embodiments.

[0157] This application also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed by a processor, they are used to implement the technical solution of the business strategy generation method described in the above embodiments. Specifically, when the computer instructions are executed by a processor, the electronic device can execute the technical solution of the business strategy generation method described in the above embodiments.

[0158] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the business strategy generation method in the above embodiments.

[0159] The aforementioned computer-readable storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The computer-readable storage media can be any available medium accessible to a general-purpose or special-purpose computer.

[0160] An exemplary computer-readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Of course, the computer-readable storage medium can also be a component of the processor. The processor and the computer-readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the computer-readable storage medium can exist as discrete components in an electronic control unit or main control device; this application does not limit this.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0162] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0163] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0164] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0165] It should be understood that the steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0166] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0167] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A service policy generation method characterized by comprising: include: Obtain customer attribute data for at least one customer, and generate customer insight data based on the customer attribute data, wherein the customer insight data includes at least one customer need type identified for each customer; After receiving the business objectives, a mapping relationship is established between the business objectives and the types of customer needs in the customer insight data to generate business strategies; Build multiple execution resource packages, associate one or more customer demand types with each execution resource package, and configure initial conversion rate parameters for each execution resource package; Based on the business strategy and the customer demand types in the customer insight data, at least one execution strategy is generated, as well as the matching relationship between the target customer identifier set and the execution resource package corresponding to each execution strategy; Based on the matching relationship, send the corresponding execution resource package to the target customer group indicated by each target customer identifier set; Collect customer behavior data of the target customer group regarding the execution resource package, and generate strategy effectiveness evaluation data based on the customer behavior data; Based on the strategy effectiveness evaluation data, the conversion rate parameter of the corresponding execution resource package in the matching relationship is updated; wherein, the updated conversion rate parameter is used to generate the matching relationship between the target customer identifier set and the execution resource package when regenerating the execution strategy.

2. The service policy generation method according to claim 1, characterized by, The customer insight data also includes customer relationship identification results; The process of establishing a mapping relationship between the business objectives and the types of customer needs in the customer insight data to generate business strategies includes: Obtain customer relationship identification results from the customer insight data. The customer relationship identification results are used to indicate the customer relationship evolution status of the customer. The customer relationship evolution status includes low correlation status, growth status, stable status, high value status, and churn risk status. Based on the customer relationship evolution status of the customer, determine the types of customer needs associated with the business objectives, so as to establish a mapping relationship between the business objectives and customer need types; The business strategy is generated based on the mapping relationship.

3. The service policy generation method of claim 1, wherein, The strategy effectiveness evaluation data generated based on the customer behavior data includes: By embedding data points in the customer interface module, the system detects customer interaction operations triggered by the matching relationship and obtains the corresponding customer interaction events and feedback data. The customer interaction events and feedback data are integrated with the business conversion data in the business database to generate unified log data; Based on the unified log data, multi-stage conversion funnel data for each execution resource package is generated as strategy effectiveness evaluation data. The strategy effectiveness evaluation data includes at least one of the following: reach rate of the implemented strategy, customer click rate, customer feedback rate, and conversion rate.

4. The service policy generation method of claim 1, wherein, The execution resource package consists of at least one execution element among business components, interaction rules, and execution content; The construction of multiple execution resource packages includes: Receive multiple selection instructions for different execution elements; In response to the multiple selection instructions, multiple different execution resource packages are generated by combining the selected execution elements.

5. The service policy generation method of claim 1, wherein, The customer demand type is a Top-N demand type of each customer calculated by an algorithm, and a parameter N in the Top-N demand type is a configurable parameter; The method further comprises: Based on the resource constraint information of the system running environment, the value of the parameter N is adjusted by an optimization algorithm.

6. The service policy generation method of claim 1, wherein, The matching relationship between the target customer identification set corresponding to each execution strategy and the execution resource package is generated based on the business strategy and the customer demand type in the customer insight data, comprising: Based on the business strategy, at least one target customer group range and the corresponding strategy execution time range of each target customer group range are determined to generate at least one execution strategy; Based on the business target contained in the business strategy, the associated target resource package is determined from the plurality of execution resource packages; For each execution strategy, the matching relationship between the target customer identification set corresponding to the execution strategy and the target resource package is generated based on the customer demand type of the customers in the target customer group range of the execution strategy and the customer demand type associated with the target resource package.

7. The service policy generation method according to claim 6, characterized by, The customer insight data further comprises at least one of the following: customer relationship identification result and customer value prediction result; The determination of at least one target customer group range based on the business strategy comprises: At least one of the target customer relationship stage and the target customer value range is parsed from the business strategy; Based on the parsed result, customers meeting the screening conditions are determined from the customer group to form an initial customer group set, and the screening conditions comprise at least one of the following: the customer relationship identification result meets the target customer relationship stage; and the customer value prediction result meets the target customer value range; According to at least one division strategy, the initial customer group set is divided into the at least one target customer group range, and the division strategy is based on at least one of the following factors: different execution channels, different execution resource package types, and different geographical areas.

8. A service policy generation system characterized by comprising: Comprise: A customer insight processing module is configured to obtain customer attribute data of at least one customer, and generate customer insight data based on the customer attribute data, wherein the customer insight data comprises at least one customer demand type identified for each customer; A business strategy processing module is configured to, after receiving a business target, establish a mapping relationship between the business target and the customer demand type in the customer insight data to generate a business strategy; An execution resource processing module is configured to construct a plurality of execution resource packages, associate one or more customer demand types with each execution resource package, and configure an initial conversion rate parameter for each execution resource package; An execution strategy processing module is configured to, based on the business strategy and the customer demand type in the customer insight data, generate at least one execution strategy, and a matching relationship between a target customer identification set corresponding to each execution strategy and an execution resource package; An execution strategy processing module is configured to, based on the business strategy and the customer demand type in the customer insight data, generate at least one execution strategy, and a matching relationship between a target customer identification set corresponding to each execution strategy and an execution resource package; The policy execution monitoring module is configured to send a corresponding execution resource package to a target customer group indicated by each target customer identification set based on the matching relationship; and collect customer behavior data of the target customer group on the execution resource package, and generate policy effectiveness evaluation data based on the customer behavior data. The iterative optimization module is configured to update a conversion rate parameter of a corresponding execution resource package in the matching relationship based on the policy effectiveness evaluation data; and wherein the updated conversion rate parameter is used to generate the matching relationship between the target customer identification set and the execution resource package when the execution strategy is regenerated.

9. A service policy generation apparatus characterized by comprising: The method comprises: The acquisition module is configured to acquire customer attribute data of at least one customer. The processing module is configured to generate customer insight data based on the customer attribute data, wherein the customer insight data comprises at least one customer demand type identified for each customer. The processing module is further configured to establish a mapping relationship between the business target and the customer demand type in the customer insight data to generate a business strategy after receiving the business target. The processing module is further configured to construct a plurality of execution resource packages, associate one or more customer demand types with each execution resource package, and configure an initial conversion rate parameter for each execution resource package. The processing module is further configured to generate at least one execution strategy based on the business strategy and the customer demand type in the customer insight data, and a matching relationship between a target customer identification set corresponding to each execution strategy and an execution resource package. The sending module is configured to send a corresponding execution resource package to a target customer group indicated by each target customer identification set based on the matching relationship. The acquisition module is further configured to collect customer behavior data of the target customer group on the execution resource package. The processing module is further configured to generate policy effectiveness evaluation data based on the customer behavior data. The processing module is further configured to update a conversion rate parameter of a corresponding execution resource package in the matching relationship based on the policy effectiveness evaluation data; and wherein the updated conversion rate parameter is used to generate the matching relationship between the target customer identification set and the execution resource package when the execution strategy is regenerated.

10. An electronic device, comprising: The method comprises: The memory and at least one processor are in communication connection; the memory is used to store computer program codes, the computer program codes comprise computer instructions; when the processor executes the computer instructions, the electronic device executes the business strategy generation method in any one of claims 1-7.

11. A computer readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, the computer instructions are executed by the processor to implement the business strategy generation method in any one of claims 1-7.

12. A computer program product, characterised in that, When the computer program product is running on the computer / is executed by the processor of the computer, the business strategy generation method in any one of claims 1-7 is implemented.

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