Task management method and device, electronic equipment and computer program product

Abstract

The invention discloses a task management method and device, electronic equipment and a computer program product. The task management method comprises the steps that a client grading marketing model is constructed through visual dragging according to a target marketing strategy; importing the customer list into a customer grading marketing model, and generating target marketing tasks corresponding to a plurality of target customers; distributing the target marketing task to the corresponding target processing personnel according to a preset task distribution rule; and obtaining task execution information reported by the target processing personnel, performing task summary statistics on the target marketing tasks according to the task execution information, and optimizing a target marketing strategy according to a statistical result. The method improves the accuracy of task generation, the efficiency of task allocation and the timeliness of task monitoring feedback, and can be applied to the technical field of data processing.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a task management method, apparatus, electronic device, and computer program product. Background Technology

[0002] In traditional bank marketing task management processes, branch departments regularly distribute potential customer lists to sub-branches to promote targeted marketing. However, this process has the following drawbacks: 1) Inefficient data export and manual splitting: Branch departments need to manually export all customer data from the bank's internal system (such as CRM or credit management platform) to local (such as Excel spreadsheet), and then manually filter and split the list according to the branch's jurisdiction.

[0003] 2) Task assignment relies on non-standardized channels: The broken-down lists are usually delivered to branches via email, social media or paper documents, lacking a unified task distribution mechanism.

[0004] 3) Lack of tracking mechanism in the execution process: After the branch customer manager receives the list, the branch cannot monitor key nodes such as customer visits and demand response in real time.

[0005] 4) Delayed dynamic updates and feedback: Task data cannot be updated in conjunction with the business system, and changes in customer status (such as churn warnings) require secondary manual reporting.

[0006] In summary, existing bank marketing task management methods have drawbacks such as inaccurate task generation, inefficient task allocation, and untimely task monitoring and feedback. Summary of the Invention

[0007] The purpose of this invention is to at least partially solve one of the technical problems existing in the prior art.

[0008] Therefore, one objective of this invention is to provide a task management method that improves the accuracy of task generation, the efficiency of task allocation, and the timeliness of task monitoring and feedback.

[0009] Another objective of this invention is to provide a task management device.

[0010] To achieve the above-mentioned technical objectives, the technical solutions adopted in the embodiments of the present invention include: On one hand, embodiments of the present invention provide a task management method, including the following steps: Build a customer segmentation marketing model using a visual drag-and-drop interface based on the target marketing strategy; Import the customer list into the customer segmentation marketing model to generate target marketing tasks for multiple target customers. The target marketing tasks are assigned to the corresponding target personnel according to the preset task allocation rules; Obtain the task execution information reported by the target processing personnel, summarize and statistically analyze the target marketing tasks based on the task execution information, and optimize the target marketing strategy based on the statistical results.

[0011] Furthermore, in one embodiment of the present invention, the step of constructing a customer segmentation marketing model through visual drag-and-drop based on the target marketing strategy specifically includes: Based on the target marketing strategy, generate multiple attribute dimension classification tags, multiple value dimension hierarchical tags, multiple behavioral dimension hierarchical tags, and multiple preset marketing actions; Multiple multi-dimensional label groups containing attribute dimensions, value dimensions, and behavior dimensions are generated based on the attribute dimension classification labels, the value dimension grading labels, and the behavior dimension grading labels; Using the multi-dimensional label group as the root node, and the corresponding attribute dimension classification label, value dimension hierarchical label, and behavior dimension hierarchical label as the leaf nodes of the root node, a corresponding tree structure is generated. The tree structure and preset marketing actions are displayed through a visual interface, along with preset value scoring cards and behavior scoring cards; In response to the drag-and-drop operation of the task planner on the visualization interface, the value dimension hierarchical labels of the tree structure are associated with the value scoring card, the behavior dimension hierarchical labels of the tree structure are associated with the behavior scoring card, and the multi-dimensional label group of the tree structure is associated with the corresponding preset marketing action; In response to the input operation of the task planner on the visualization interface, the value scoring rules and value grading rules of the value scoring card, as well as the behavior scoring rules and behavior grading rules of the behavior scoring card, are determined to obtain the customer grading marketing model.

[0012] Furthermore, in one embodiment of the present invention, the step of importing the customer list into the customer segmentation marketing model to generate target marketing tasks corresponding to multiple target customers specifically includes: Based on the customer list, determine the customer attributes, customer transaction data, and customer behavior data of each target customer; Based on the customer attributes, determine the target attribute dimension classification tags corresponding to the target customer; The customer transaction data is input into the value scoring card. The target value score of the target customer is determined based on the customer transaction data and the value scoring rules. The target value dimension classification label corresponding to the target customer is determined based on the target value score and the value grading rules. The customer behavior data is input into the behavior scoring card. The target behavior score of the target customer is determined based on the customer behavior data and the behavior scoring rules. The target behavior dimension classification label corresponding to the target customer is determined based on the target behavior score and the behavior classification rules. Based on the target attribute dimension classification tags, the target value dimension classification tags, and the target behavior dimension classification tags, the tree structure is searched to obtain the target multi-dimensional tag group corresponding to the target customer; The preset marketing action is determined as the target marketing action based on the target multi-dimensional tag group, and the target marketing task is generated based on the target customer and the target marketing action.

[0013] Furthermore, in one embodiment of the present invention, before assigning the target marketing task to the corresponding target handler according to a preset task allocation rule, the method further includes: Obtain the customer profile of the target customer and determine the corresponding target marketing actions based on the target marketing task; Based on the target marketing action, a marketing script prompt template is determined, and marketing script prompt words are generated based on the customer profile and the marketing script prompt template; The marketing prompts are input into the large language model to obtain the target marketing script; Associate the target marketing script with the target marketing task.

[0014] Furthermore, in one embodiment of the present invention, before assigning the target marketing task to the corresponding target handler according to a preset task allocation rule, the method further includes: The target marketing task is pushed to the task approver through the customer relationship management system; In response to the review and confirmation operation of the task approver, the approved target marketing tasks are sorted to obtain a list of tasks to be assigned, and the list of tasks to be assigned is returned to the task planner.

[0015] Furthermore, in one embodiment of the present invention, the step of assigning the target marketing task to the corresponding target personnel according to a preset task allocation rule specifically includes: In response to the task initiation operation by the task planner, determine whether the target customer is a managed customer; When the target customer is a managed customer, the target marketing task is assigned to the account manager corresponding to the target customer based on the managed customer relationship; When the target customer is a non-management customer, the customer's location is determined, and the target marketing task is pushed to the task allocation manager in the customer's location for manual allocation.

[0016] Furthermore, in one embodiment of the present invention, the step of summarizing and statistically analyzing the target marketing task based on the task execution information, and optimizing the target marketing strategy based on the statistical results, specifically includes: The current task status of the corresponding target marketing task is determined based on the task execution information; Based on the current task status, the customer conversion rate for each attribute dimension, value dimension, behavior dimension, and marketing action is calculated. The target marketing strategy is optimized based on the customer conversion rate.

[0017] On the other hand, embodiments of the present invention provide a task management device, including: The marketing model building module is used to visually drag and drop to build customer segmentation marketing models based on target marketing strategies. The marketing task generation module is used to import the customer list into the customer segmentation marketing model and generate target marketing tasks corresponding to multiple target customers. The marketing task allocation module is used to allocate the target marketing tasks to the corresponding target personnel according to preset task allocation rules. The task summary and statistics module is used to obtain the task execution information reported by the target processing personnel, summarize and statistically analyze the target marketing tasks based on the task execution information, and optimize the target marketing strategy based on the statistical results.

[0018] On the other hand, embodiments of the present invention provide an electronic device, including: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the task management method described above.

[0019] On the other hand, embodiments of the present invention also provide a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the above-described task management method.

[0020] On the other hand, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the above-described task management method.

[0021] The advantages and beneficial effects of the present invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention: This invention, through a drag-and-drop visualization, constructs a customer segmentation marketing model based on a target marketing strategy. A customer list is imported into the model, generating target marketing tasks for multiple target customers. These tasks are then assigned to corresponding personnel according to preset task allocation rules. Task execution information reported by these personnel is obtained, and the target marketing tasks are summarized and statistically analyzed based on this information. The target marketing strategy is then optimized based on the statistical results. This invention upgrades marketing task generation from experience-driven to data-driven through the customer segmentation marketing model, achieving automated marketing task generation. Through task allocation, task summary statistics, and marketing strategy optimization, it achieves closed-loop management of the entire marketing task process, improving the accuracy of task generation, the efficiency of task allocation, and the timeliness of task monitoring and feedback. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the embodiments of the present invention are described below. It should be understood that the drawings described below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 A flowchart illustrating the steps of a task management method provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of a task management device provided in an embodiment of the present invention; Figure 3 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying 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 the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0026] The task management method provided in this invention can be applied to a terminal, a server, or software running on a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited thereto; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements the task management method, but is not limited to the above forms.

[0027] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0028] It should be noted that in various specific embodiments of the present invention, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of the present invention require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to a confirmation page. Only after obtaining the user's separate permission or consent is the necessary user-related data for the normal operation of the embodiments of the present invention acquired.

[0029] Reference Figure 1 This invention provides a task management method, which specifically includes the following steps: S101. Construct a customer segmentation marketing model using visual drag-and-drop functionality based on the target marketing strategy; S102. Import the customer list into the customer segmentation marketing model to generate target marketing tasks for multiple target customers. S103. Assign target marketing tasks to the corresponding target personnel according to the preset task allocation rules; S104. Obtain the task execution information reported by the target processing personnel, summarize and statistically analyze the target marketing tasks based on the task execution information, and optimize the target marketing strategy based on the statistical results.

[0030] This invention upgrades marketing task generation from experience-driven to data-driven through a customer-tiered marketing model, enabling automated generation of marketing tasks. Through task allocation, task summary statistics, and marketing strategy optimization, it achieves closed-loop management of the entire marketing task process, improving the accuracy of task generation, the efficiency of task allocation, and the timeliness of task monitoring and feedback.

[0031] As an optional implementation, a customer segmentation marketing model can be constructed through visual drag-and-drop based on the target marketing strategy, specifically including: S201. Generate multiple attribute dimension classification tags, multiple value dimension hierarchical tags, multiple behavioral dimension hierarchical tags, and multiple preset marketing actions based on the target marketing strategy; S202. Generate multiple multi-dimensional label groups containing attribute dimensions, value dimensions, and behavior dimensions based on attribute dimension classification labels, value dimension hierarchical labels, and behavior dimension hierarchical labels; S203. Take the multi-dimensional label group as the root node, and take the corresponding attribute dimension classification label, value dimension hierarchical label and behavior dimension hierarchical label as the leaf nodes of the root node to generate the corresponding tree structure. S204. Display the tree structure and preset marketing actions through a visual interface, and display the preset value scoring cards and behavior scoring cards; S205. Responding to the drag-and-drop operation of the task planner in the visual interface, the value dimension hierarchical labels of the tree structure are associated with the value score card, the behavior dimension hierarchical labels of the tree structure are associated with the behavior score card, and the multi-dimensional label group of the tree structure is associated with the corresponding preset marketing action. S206. In response to the input operations of the task planner on the visual interface, determine the value scoring rules and value grading rules of the value scoring card, as well as the behavior scoring rules and behavior grading rules of the behavior scoring card, to obtain the customer grading marketing model.

[0032] Specifically, this invention transforms static customer attributes, dynamic value performance, and behavioral patterns into an operable tagging system, and uses visualization to flexibly configure marketing actions, ultimately forming an interpretable, adjustable, and executable customer-tiered marketing decision-making model. The specific process is as follows: 1. Generate a multi-dimensional tag system (attributes, value, behavior + preset marketing actions) This step is the data infrastructure construction stage of the entire system, which requires relying on the company's existing customer data platform or CRM system to complete tag extraction and hierarchical design.

[0033] (1) Attribute dimension classification labels Definition: Reflects the inherent characteristics of customers and is usually relatively stable.

[0034] 1) Example tags: Age groups: youth / middle-aged / elderly; Geographical distribution: First-tier / Second-tier / Third-tier cities; Spending power level: High / Medium / Low; Membership levels: Regular Member / VIP / Black Gold Member.

[0035] 2) Data sources: user registration information, KYC data, historical transaction summaries, etc.

[0036] 3) Generation method: After ETL cleaning, classification criteria are established by clustering, threshold partitioning or expert experience method.

[0037] (2) Value dimension hierarchical labels Definition: Measures the economic value of a customer's contribution to a business, emphasizing both "past contribution" and "potential value".

[0038] 1) Common indicators: RFM model breakdown: Recency, Frequency, Monetary. LTV (Local Value Forecast): Total Customer Lifetime Value; Profit contribution: Net income weighted by gross profit margin.

[0039] 2) Grading method: Use percentile methods (e.g., Top 20% is high value), Z-score standardization followed by stratification, or machine learning scorecard output segmentation.

[0040] 3) Output format: Value level → such as "extremely high value", "high value", "medium value", "low value".

[0041] (3) Behavioral dimension hierarchical labels Definition: Capturing customer activity levels and preference paths in product usage, content interaction, and service response.

[0042] 1) Typical behavioral types: Login frequency: High frequency / Medium frequency / Low frequency; Content browsing depth: shallow browsing / deep reading / conversion clicks; Marketing outreach response rate: Positive response / Average response / Silent response; Function usage breadth: full-feature users / single-feature dependents.

[0043] 2) Processing logic: Behavioral log collection → Tracking data analysis → Building behavioral sequences → Extracting key nodes and quantifying and scoring → Hierarchical classification.

[0044] (4) Pre-set marketing action library Function: Serves as a pool of target actions for subsequent matching, ensuring that all strategies can be implemented.

[0045] 1) Common movement types: Pushing coupons, sending personalized recommendation emails, initiating outbound calls to retain members, guiding participation in membership activities, and issuing reminders regarding access restrictions, etc.

[0046] 2) Management method: It is jointly formulated by the marketing department and the operations team in advance and incorporated into the unified marketing resource scheduling platform.

[0047] 2. Generate multi-dimensional tag groups (combined tag clusters) This step aims to aggregate independent dimension tags through Cartesian product or multi-condition cross-aggregation to form customer group segmentation units with business significance.

[0048] Implementation logic: Instead of simply enumerating all combinations, meaningful combination patterns are selected based on business insights or data mining results.

[0049] Technical implementation method: Rule engine driven: Business users set the combination logic (such as SQL WHERE conditions); Clustering-assisted discovery: K-means and DBSCAN are used to perform unsupervised grouping of customers and extract tag combinations in reverse; Graph Relationship Mining: Discovering high-frequency co-occurrence tag combinations through association rules (Apriori algorithm).

[0050] 3. Construct a tree structure (root node to leaf node mapping) Using the "multi-dimensional label group" generated in the previous step as the root node, and the various dimensional labels contained therein as child nodes (leaf nodes), construct one or more decision tree structures.

[0051] 4. Visual interface displays tree structure and scorecards Entering a critical stage of human-machine collaboration—concretizing abstract models into visual and operable interfaces.

[0052] The visualized content includes: (1) Left side area: Multi-dimensional tag group tree navigation panel Supports collapsing / expanding, search and location, and color-coding to indicate different priority groups; (2) Middle area: Scoring card display area Displays the field structure, weighting, and score range of the "Value Scorecard" and "Behavioral Scorecard"; (3) Right side area: List of preset marketing actions The icons for various actions (such as issuing coupons, making outbound calls, and sending push notifications) are displayed in an icon-based manner, and hovering over them allows users to view the execution cost and expected conversion rate.

[0053] 5. Drag-and-drop tags—configuration of scorecards / marketing actions This is the most innovative interactive design part of the entire process, embodying the concept of integrating low-code and intelligent decision-making.

[0054] Detailed operating procedures: (1) The task planner selects a leaf node of a multi-dimensional tag group on the interface (e.g., "Frequent login but no repeat purchase"). (2) Drag it to a rating item area of ​​the "Behavior Rating Card"; (3) The system will automatically pop up a configuration window, allowing you to set: The scoring formula for this behavior (e.g., +15 points for logging in for 7 consecutive days). Whether the warning mechanism is triggered (e.g., score will be reduced if there are no orders for 3 consecutive days).

[0055] (4) Similarly, drag the entire “multi-dimensional tag group” onto a certain “preset marketing action” (such as “issuing discount coupons”) to establish a binding relationship.

[0056] 6. Input the configured scoring rules to generate a customer segmentation marketing model. The final step is to complete the parameterization definition of the model, enabling the system to truly possess the ability to perform inference.

[0057] The input content includes: (1) Value scoring rules: Weight settings for each indicator (can be adjusted via slider); Fraction calculation methods (linear mapping, piecewise function, sigmoid normalization).

[0058] (2) Value classification rules: Define the value level corresponding to the total score range: 90–100: Extremely high value; 70–89: High value; 50–69: Medium; <50: Low value.

[0059] (3) The behavioral scoring rules are set in a similar manner to the grading rules.

[0060] The above steps encapsulate complex statistical models into an intuitive user interface, enabling users to build customer segmentation marketing models through visual drag-and-drop functionality based on target marketing strategies.

[0061] As an optional implementation, the customer list is imported into a customer segmentation marketing model to generate target marketing tasks corresponding to multiple target customers, which specifically include: S301. Determine the customer attributes, customer transaction data, and customer behavior data of each target customer based on the customer list; S302. Determine the target attribute dimension classification tags corresponding to the target customers based on customer attributes; S303. Input customer transaction data into the value scoring card, determine the target value score of the target customer based on the customer transaction data and value scoring rules, and determine the target value dimension classification label corresponding to the target customer based on the target value score and value grading rules. S304. Input customer behavior data into the behavior scoring card, determine the target behavior score of the target customer based on the customer behavior data and behavior scoring rules, and determine the target behavior dimension classification label corresponding to the target customer based on the target behavior score and behavior classification rules. S305. Based on the target attribute dimension classification tags, target value dimension classification tags, and target behavior dimension classification tags, search the tree structure to obtain the target multi-dimensional tag group corresponding to the target customer; S306. Determine the corresponding preset marketing actions as target marketing actions based on the target multi-dimensional tag group, and generate target marketing tasks based on target customers and target marketing actions.

[0062] Specifically, based on the customer list, the customer attributes, transaction data, and behavior data of each target customer are determined and input into the customer segmentation marketing model. Based on customer attributes, the corresponding target attribute dimension classification labels for each target customer are determined. Customer transaction data is input into a value scoring card, and based on the transaction data and value scoring rules, the target value score for each target customer is determined. Based on the target value score and value segmentation rules, the corresponding target value dimension classification labels for each target customer are determined. Customer behavior data is input into a behavior scoring card, and based on the behavior data and behavior scoring rules, the target behavior score for each target customer is determined. Based on the target behavior score and behavior segmentation rules, the corresponding target behavior dimension classification labels for each target customer are determined. Based on the target attribute dimension classification labels, target value dimension classification labels, and target behavior dimension classification labels, a tree structure is searched to obtain the target multi-dimensional label group corresponding to each target customer. Based on the target multi-dimensional label group, the corresponding preset marketing actions are determined as target marketing actions, and target marketing tasks are generated based on the target customer and the target marketing actions.

[0063] As a further optional implementation, before assigning the target marketing task to the corresponding target handler according to the preset task allocation rules, the method further includes: S401. Obtain customer profiles of target customers and determine corresponding target marketing actions based on target marketing tasks; S402. Based on the marketing script prompt template determined by the target marketing action, generate marketing script prompt words based on customer profile and marketing script prompt template; S403. Input the marketing script prompts into the large language model to obtain the target marketing script; S404. Link target marketing language to target marketing tasks.

[0064] Specifically, embodiments of the present invention generate marketing scripts using a large language model and associate them with marketing tasks. The specific process is as follows: 1. Obtain customer profiles & determine marketing actions (1) Integration of portrait data Data sources: Integrating CRM system transaction records, social media behavior (such as browsing / liking), questionnaires and third-party data (such as industry reports), covering demographic attributes (age / occupation), behavioral preferences (purchase frequency), and pain point needs (such as "cumbersome system integration").

[0065] Profile modeling: Use a 9-dimensional framework (background, challenge, goal, quotes, etc.) to structure tags, or build according to four types of models (goal-oriented / role-perspective / attractive / fictional).

[0066] (2) Marketing action matching Task breakdown: Select actions based on marketing goals (e.g., new product promotion / customer retention): Potential customer acquisition → Personalized email outreach; High-value customer maintenance → VIP exclusive discounts.

[0067] Decision logic: Automatic matching via a rule engine (e.g., IF "Customer value level = A" THEN "Send customized gift package").

[0068] 2. Generate marketing script prompts (1) Template design Structured templates: Dynamic text containing variable slots, for example: "Dear [Name], regarding your [Industry], we have noticed the [Pain Point Keywords] issue. Our [Product Name], through [Feature Highlights], can help you achieve your [Goals]." Style matching: Select the communication style based on customer tags (such as business-like and serious / friendly and interactive).

[0069] (2) Prompt word synthesis Variable population: Inject profile tags (such as "Industry = Finance", "Pain Point = Data Dispersion") into the template.

[0070] Enhancement instructions: Add generation requirements (such as "limit to 80 characters" or "add a sense of urgency") to improve the quality of LLM output.

[0071] 3. Generate target dialogue using a large model. (1) LLM calls and optimization Model selection: Use an industry-optimized model (such as DeepSeek / GPT-4). Parameter setting example: params={temperature=0.7,max_tokens=150,presence_penalty=0.5}.

[0072] Generation strategy: Batch generation: Submit multiple sets of prompts at once to obtain the script library; A / B testing optimization: Generate 2-3 versions of the sales script for the same customer and select the best one for deployment.

[0073] (2) Manual verification Compliance screening: Exaggerated statements (such as "absolutely number one") are prohibited; Enhance emotional impact: Add emojis or contextualized stories to increase emotional resonance.

[0074] 4. Script Relevance and Task Deployment (1) System cascading Metadata binding: Associate the script ID with the task ID, supporting automatic invocation by customer group.

[0075] Channel adaptation: Convert the length / format of the dialogue (e.g., email version → SMS version).

[0076] (2) Effect tracking closed loop Event tracking design: Monitoring the click-through rate and conversion rate of the message; Profile iteration: Feed back the profile tag library with the features of high-conversion phrases (such as "includes data comparison").

[0077] As a further optional implementation, before assigning the target marketing task to the corresponding target handler according to the preset task allocation rules, the method further includes: S501. Push target marketing tasks to task approvers through the customer relationship management system; S502. In response to the review and confirmation operation of the task approver, sort the approved target marketing tasks, obtain the list of tasks to be assigned, and return the list of tasks to be assigned to the task planner.

[0078] Specifically, task approval is required before marketing tasks are assigned, and the specific process is as follows: 1. Initial review: The task approver shall conduct the review in the Task Center - Task Approval. The initial reviewer shall be the head of the department or the manager of the branch retail department.

[0079] 2. Final review: The task approver shall conduct the review in the Task Center - Task Approval. The final reviewer shall be a deputy general manager of the department or a deputy branch manager or above.

[0080] After the final review is completed, a task number will be generated. The task planner will then contact the relevant business department at the branch to initiate a data retrieval request in the NOA system, attaching the task number. The retail data team in the data mining department will then retrieve the data and import it into the system. One day after the data mining department imports the data, the task planner can start distributing the data.

[0081] As a further optional implementation, the target marketing tasks are assigned to corresponding target personnel according to preset task allocation rules, which specifically includes: S601. In response to the task initiation operation by the task planner, determine whether the target customer is a managed customer. S602. When the target customer is a managed customer, the target marketing task is assigned to the account manager corresponding to the target customer according to the managed customer relationship. S603. When the target customer is a non-management customer, determine the customer's location and push the target marketing task to the task allocation supervisor in the customer's location for manual allocation.

[0082] Specifically, after the task is initiated, managed clients are automatically assigned by the system based on their managed client relationships. Non-managed clients can be manually assigned by branch managers or sub-branch managers according to the initially set assignment method. For example: for clients who prioritize wealth management, the client list is first assigned to a wealth management client manager; if there is no assigned wealth management client manager, it is assigned to a private banking client manager; if there is no assigned wealth management client manager, it is assigned to a direct banking client manager; if there is no assigned direct banking client manager, it is assigned to a branch manager for manual assignment. Manual assignment can be done by assigning to all client managers (wealth management, client managers, etc.) within the branch's jurisdiction. Branch managers / sub-branch managers log in to the CRM (Customer Relationship Management) system—Task Center—List Assignment, select the list task to be assigned, click Assign to enter the assignment page, select the client and client manager, and click Assign.

[0083] The account manager logs into the CRM system – My Homepage, enters the Daily Work Tasks module – clicks the List Tasks tab – selects the corresponding task, enters the task processing list page – selects the customer – clicks Process, dials to contact the customer – fills in feedback – completes and processes the next task.

[0084] As an optional implementation method, the target marketing tasks are summarized and statistically analyzed based on the task execution information, and the target marketing strategy is optimized based on the statistical results, which specifically includes: S701. Determine the current task status of the corresponding target marketing task based on the task execution information; S702. Based on the current task status, calculate the customer conversion rate for each attribute dimension, each value dimension, each behavioral dimension, and each marketing action. S703. Optimize target marketing strategies based on customer conversion rates.

[0085] Specifically, embodiments of the present invention periodically statistically analyze the current task status (in progress, converted, completed marketing but not converted) of each target marketing task, obtain the customer conversion rate of each attribute dimension, each value dimension, each behavioral dimension, and each marketing action, and then optimize the target marketing strategy based on the customer conversion rate, so as to provide more accurate attribute dimension classification tags, value dimension grading tags, behavioral dimension grading tags, and preset marketing actions when the next marketing task is generated.

[0086] The method steps of the embodiments of the present invention have been described above. It can be understood that the embodiments of the present invention upgrade marketing task generation from experience-driven to data-driven through a customer-tiered marketing model, realizing the automated generation of marketing tasks. Through task allocation, task summary statistics, and marketing strategy optimization, the entire process of marketing tasks is managed in a closed loop, improving the accuracy of task generation, the efficiency of task allocation, and the timeliness of task monitoring and feedback.

[0087] Reference Figure 2 This invention provides a task management device, comprising: The marketing model building module is used to visually drag and drop to build customer segmentation marketing models based on target marketing strategies. The marketing task generation module is used to import the customer list into the customer segmentation marketing model and generate target marketing tasks for multiple target customers. The marketing task allocation module is used to assign target marketing tasks to corresponding target personnel according to preset task allocation rules; The task summary and statistics module is used to obtain task execution information reported by target processing personnel, summarize and statistically analyze target marketing tasks based on task execution information, and optimize target marketing strategies based on statistical results.

[0088] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0089] Reference Figure 3 This invention provides an electronic device, comprising: At least one processor; At least one memory for storing at least one program; When the above-mentioned at least one program is executed by the above-mentioned at least one processor, the above-mentioned at least one processor implements the above-mentioned task management method.

[0090] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0091] This invention also provides a computer-readable storage medium storing a processor-executable computer program that, when executed by a processor, implements the task management method described above.

[0092] A computer-readable storage medium according to an embodiment of the present invention can execute a task management method provided in an embodiment of the present invention, and can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0093] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the task management method described above.

[0094] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0095] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0096] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0097] The terms "first," "second," "third," "fourth," etc. (if present) in the specification and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0098] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the aforementioned blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0099] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the aforementioned functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0100] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0102] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the aforementioned program can be printed, because the aforementioned program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0103] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0104] In the foregoing description of this specification, references to terms such as "one embodiment," "another embodiment," or "some embodiments" indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0105] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0106] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A task management method, characterized in that, Includes the following steps: Build a customer segmentation marketing model using a visual drag-and-drop interface based on the target marketing strategy; Import the customer list into the customer segmentation marketing model to generate target marketing tasks for multiple target customers. The target marketing tasks are assigned to the corresponding target personnel according to the preset task allocation rules; Obtain the task execution information reported by the target processing personnel, summarize and statistically analyze the target marketing tasks based on the task execution information, and optimize the target marketing strategy based on the statistical results.

2. The task management method according to claim 1, characterized in that, The process of constructing a customer segmentation marketing model based on a target marketing strategy through visual drag-and-drop functionality specifically includes: Based on the target marketing strategy, generate multiple attribute dimension classification tags, multiple value dimension hierarchical tags, multiple behavioral dimension hierarchical tags, and multiple preset marketing actions; Multiple multi-dimensional label groups containing attribute dimensions, value dimensions, and behavior dimensions are generated based on the attribute dimension classification labels, the value dimension grading labels, and the behavior dimension grading labels; Using the multi-dimensional label group as the root node, and the corresponding attribute dimension classification label, value dimension hierarchical label, and behavior dimension hierarchical label as the leaf nodes of the root node, a corresponding tree structure is generated. The tree structure and preset marketing actions are displayed through a visual interface, along with preset value scoring cards and behavior scoring cards; In response to the drag-and-drop operation of the task planner on the visualization interface, the value dimension hierarchical labels of the tree structure are associated with the value scoring card, the behavior dimension hierarchical labels of the tree structure are associated with the behavior scoring card, and the multi-dimensional label group of the tree structure is associated with the corresponding preset marketing action; In response to the input operation of the task planner on the visualization interface, the value scoring rules and value grading rules of the value scoring card, as well as the behavior scoring rules and behavior grading rules of the behavior scoring card, are determined to obtain the customer grading marketing model.

3. The task management method according to claim 2, characterized in that, The step of importing the customer list into the customer segmentation marketing model to generate target marketing tasks corresponding to multiple target customers specifically includes: Based on the customer list, determine the customer attributes, customer transaction data, and customer behavior data of each target customer; Based on the customer attributes, determine the target attribute dimension classification tags corresponding to the target customer; The customer transaction data is input into the value scoring card. The target value score of the target customer is determined based on the customer transaction data and the value scoring rules. The target value dimension classification label corresponding to the target customer is determined based on the target value score and the value grading rules. The customer behavior data is input into the behavior scoring card. The target behavior score of the target customer is determined based on the customer behavior data and the behavior scoring rules. The target behavior dimension classification label corresponding to the target customer is determined based on the target behavior score and the behavior classification rules. Based on the target attribute dimension classification tags, the target value dimension classification tags, and the target behavior dimension classification tags, the tree structure is searched to obtain the target multi-dimensional tag group corresponding to the target customer; The preset marketing action corresponding to the target multi-dimensional tag group is determined as the target marketing action, and the target marketing task is generated based on the target customer and the target marketing action.

4. The task management method according to claim 1, characterized in that, Before assigning the target marketing task to the corresponding target handler according to the preset task allocation rules, the method further includes: Obtain the customer profile of the target customer and determine the corresponding target marketing actions based on the target marketing task; Based on the marketing script prompt template determined by the target marketing action, and based on the customer profile and the marketing script prompt template, marketing script prompt words are generated; The marketing prompts are input into the large language model to obtain the target marketing script; Associate the target marketing script with the target marketing task.

5. The task management method according to claim 1, characterized in that, Before assigning the target marketing task to the corresponding target handler according to the preset task allocation rules, the method further includes: The target marketing task is pushed to the task approver through the customer relationship management system; In response to the review and confirmation operation of the task approver, the approved target marketing tasks are sorted to obtain a list of tasks to be assigned, and the list of tasks to be assigned is returned to the task planner.

6. The task management method according to claim 1, characterized in that, The step of assigning the target marketing task to the corresponding target personnel according to the preset task allocation rules specifically includes: In response to the task initiation operation by the task planner, determine whether the target customer is a managed customer; When the target customer is a managed customer, the target marketing task is assigned to the account manager corresponding to the target customer based on the managed customer relationship; When the target customer is a non-management customer, the customer's location is determined, and the target marketing task is pushed to the task allocation manager in the customer's location for manual allocation.

7. A task management method according to any one of claims 1 to 6, characterized in that, The step of summarizing and statistically analyzing the target marketing tasks based on the task execution information, and optimizing the target marketing strategy based on the statistical results, specifically includes: The current task status of the corresponding target marketing task is determined based on the task execution information; Based on the current task status, the customer conversion rate for each attribute dimension, value dimension, behavior dimension, and marketing action is calculated. The target marketing strategy is optimized based on the customer conversion rate.

8. A task management device, characterized in that, include: The marketing model building module is used to build customer segmentation marketing models through visual drag-and-drop based on target marketing strategies. The marketing task generation module is used to import the customer list into the customer segmentation marketing model and generate target marketing tasks corresponding to multiple target customers. The marketing task allocation module is used to allocate the target marketing tasks to the corresponding target personnel according to preset task allocation rules. The task summary and statistics module is used to obtain the task execution information reported by the target processing personnel, summarize and statistically analyze the target marketing tasks based on the task execution information, and optimize the target marketing strategy based on the statistical results.

9. An electronic device, characterized in that, include: At least one processor; At least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a task management method as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements a task management method as described in any one of claims 1 to 7.

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