Method and system for realizing intelligent management and differentiation of outbound list based on data warehouse

Abstract

This invention provides a method and system for intelligent management and differentiated implementation of outbound call lists based on a data warehouse. The method includes: batch connection to an enterprise data warehouse platform; targeted retrieval of outbound call lists based on pre-configured data extraction rules; precise filtering of target lists from an intermediate table via a visual operation interface by the shift leader, associating the filtered lists with outbound call activities; automatically initiating a multi-level data cleaning mechanism after successful list association; implementing differentiated outbound call scheduling strategies based on the type of outbound call activity, including predictive and manual outbound call modes; automatically allocating lists after the shift leader confirms the allocation rules; and manually conducting outbound call operations through an integrated work interface after receiving the lists, recording outbound call process data in real time. This invention offers significant efficiency improvements, guaranteed data quality, flexible outbound call strategies, optimized resource allocation, strong system collaboration, controllable compliance risks, and scalability and maintainability.

Description

Technical Field

[0001] This invention relates to the field of computer information technology, and more specifically, to a method and system for intelligent management and differentiated implementation of outbound call lists based on data warehouses. Background Technology

[0002] A data warehouse is a strategic collection of data that provides data support for enterprise-level decision-making. It is a subject-oriented, integrated, relatively stable collection of data that reflects historical changes and is used to support management decisions.

[0003] In modern enterprise customer service, marketing, and customer follow-up scenarios, outbound calling systems have become indispensable business support tools. Traditional outbound calling models primarily employ manual outbound calling or simple automated dialing. The technical solutions typically involve: first, business personnel manually logging into the data warehouse or business system to manually export outbound call lists (usually in Excel or CSV format), and then uploading the lists to the outbound calling system via the system's import function. After receiving the lists, the outbound calling system performs simple data format validation manually. Some systems have basic deduplication functions, but usually lack a systematic data cleaning mechanism. For manual outbound calling, team leaders or supervisors manually assign calls based on experience, and agents manually dial the numbers after receiving the lists. For automated outbound calling, a simple sequential dialing strategy is used, and after connection, the call is transferred to an available agent according to preset rules. After the outbound call is completed, the agent manually records the call result, and the data is sent back to the business system.

[0004] The existing solutions still have the following shortcomings: 1) Low efficiency in list transfer. Manual export and import is time-consuming and labor-intensive, with poor data timeliness, making it difficult to meet the needs of large-scale, high-frequency outbound calling services; moreover, manual operation is prone to introducing data errors, affecting the quality of subsequent outbound calls. 2) Insufficient data cleaning capabilities. There is a lack of a systematic invalid data identification mechanism, and invalid lists such as duplicate numbers, empty numbers, and suspended numbers cannot be effectively filtered, resulting in a large number of invalid outbound calls, wasting communication resources and manpower costs. 3) Single and rigid outbound calling methods. Predictive outbound calls and manual outbound calls usually adopt independent systems or simple parallel modes, lacking a unified scheduling and control mechanism, making it impossible to flexibly switch outbound calling strategies according to business scenarios, resulting in low utilization of outbound calling resources. 4) High subjectivity in manual allocation. Manual outbound list allocation relies on the experience judgment of the shift leader, lacking scientific allocation algorithm support, which easily leads to problems such as uneven allocation and low efficiency, and is difficult to trace and optimize. 5) Poor inter-system coordination. Data warehouses, outbound calling systems, and business systems are often isolated, with data flow relying on manual intervention and unable to achieve real-time linkage and automated processing. Summary of the Invention

[0005] This invention addresses the technical problems existing in the prior art by providing a method and system for intelligent management and differentiated implementation of outbound call lists based on a data warehouse. It implements a complete data processing flow based on a data warehouse platform, including automated retrieval of outbound call lists, temporary storage in intermediate tables, intelligent cleaning and filtering, and activity association binding. The technical solution for differentiated outbound call management is based on outbound call activity type (predictive outbound calls / manual outbound calls), including automated batch dialing and real-time transfer mechanisms for predictive outbound calls, and intelligent allocation algorithms for manual outbound calls. It also integrates data linkage between the data warehouse platform, the outbound call list management system, the predictive outbound call platform, and the manual outbound call system, organically combining the above technical features into an integrated "filtering-association-cleaning-scheduling-feedback" intelligent outbound call list management system architecture.

[0006] According to a first aspect of the present invention, a method for intelligent management and differentiated implementation of outbound call lists based on data warehouses is provided, comprising the following steps: Batch integration with enterprise data warehouse platforms, based on pre-configured data extraction rules, to complete the targeted retrieval of outbound call lists; The squad leader's seat uses a visual operation interface to perform precise filtering of the target list from the intermediate table, and associates and binds the filtered list with the outbound calling activity. After the list is successfully associated, a multi-level data cleaning mechanism is automatically started. Based on the type of outbound call activity, differentiated outbound call scheduling strategies are implemented for predictive outbound call mode and manual outbound call mode respectively. Among them, after the shift leader confirms the allocation rules, the list allocation is completed automatically. After the manual agent receives the list, the manual outbound call operation is carried out through the integrated work interface, and the outbound call process data is recorded in real time.

[0007] Based on the above technical solution, the present invention can also be improved as follows.

[0008] Optionally, the data extraction rules include customer group filtering conditions, data field mapping relationships, and incremental update strategies; the targeted retrieval of the outbound call list based on the pre-configured data extraction rules includes: ETL technology is used to support full and incremental extraction through configurable extraction tasks. Data compression and batch transmission are used to ensure large-scale data transmission, and transaction mechanisms are used to ensure data consistency.

[0009] Optionally, the precise filtering is a multi-dimensional combined query, including customer level, age group, and loan amount query; the association and binding of the filtered list with outbound calling activities includes: The activity types are divided into "predicted outbound call activities" and "manual outbound call activities", and the list processing path is marked according to the activity type.

[0010] Optionally, the multi-level data cleaning mechanism includes: Level 1 cleaning, also known as duplicate verification: global deduplication based on customer's unique identifier and phone number to identify multiple records of the same customer or duplicate occurrences of the same number; Secondary cleaning, namely validity verification: calling operator interfaces or based on historical outbound call data to intelligently identify and filter invalid data such as empty numbers, suspended numbers, and blacklisted numbers; The third level of cleanup, also known as compliance verification, involves filtering sensitive customers, complaining customers, and restricted lists of customers to avoid being disturbed, based on industry regulatory requirements and the company's internal compliance strategy.

[0011] Optionally, during the cleaning process, a cleaning log is generated to record the cleaning status and reasons for each entry in the list, and supports manual review and audit traceability.

[0012] Optionally, during the cleaning process, the cleaning engine operates in a dual-mode system based on a rule engine and machine learning. The rule engine supports visual configuration of cleaning rules to meet regular business needs. The machine learning mode trains a number validity prediction model using historical outbound call data to dynamically identify potentially invalid numbers and continuously improve the cleaning accuracy.

[0013] Optionally, the predictive outbound calling mode is used to push the compliant list to the predictive outbound calling platform. The predictive outbound calling platform has a built-in intelligent dialing algorithm, including the prediction of the best dialing time, the customer answering probability model, and the prediction of agent availability, to complete the automated batch dialing of customer calls. When the call is connected, the platform will transfer the call to a human agent in real time, and the agent will take over the subsequent service.

[0014] Optionally, the manual outbound call activity mode is used to provide an intelligent allocation engine and supports the following multiple allocation rule configurations, including: Average allocation algorithm: Distribute the number of names evenly according to the number of seats; Capability-weighted algorithm: Weighted allocation based on indicators such as historical conversion rate and work efficiency of agents; Customer matching algorithm: Intelligent matching based on customer characteristics and agent expertise.

[0015] According to a second aspect of the present invention, a system for intelligent management and differentiated implementation of outbound call lists based on data warehouses is provided, comprising: The data warehouse linkage module is used to connect to enterprise data warehouse platforms in batches and complete the targeted retrieval of outbound call lists based on pre-configured data extraction rules. The intelligent data cleaning module enables the team leader to perform precise filtering of the target list from the intermediate table through a visual operation interface, and associate and bind the filtered list with the outbound calling activities. After the list is successfully associated, the system automatically starts a multi-level data cleaning mechanism. The differentiated outbound call scheduling module executes differentiated outbound call scheduling strategies for predictive outbound call mode and manual outbound call mode based on the outbound call activity type. Specifically, after the shift leader confirms the allocation rules, the list allocation is completed automatically. After the manual agent receives the list, they carry out manual outbound call operations through the integrated work interface and record outbound call process data in real time.

[0016] According to a third aspect of the present invention, an electronic device is provided, including a memory and a processor, wherein the processor is used to execute a computer program stored in the memory to implement a method for intelligent management and differentiation of outbound call lists based on a data warehouse.

[0017] The technical effects and advantages of this invention are as follows: This invention provides a method and system for intelligent management and differentiated implementation of outbound call lists based on a data warehouse. Through automated linkage between the data warehouse platform and the outbound call system, the time required to obtain the call list is reduced from hours to minutes, significantly improving business response speed. An integrated "screening-association-cleaning" process is constructed, effectively filtering invalid lists through a multi-level cleaning mechanism, significantly saving communication and labor costs. Predictive outbound call patterns improve agent utilization through intelligent algorithms; manual outbound call patterns replace experience-based judgment with scientific allocation algorithms, achieving fair and efficient allocation of human resources.

[0018] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the steps of the method for intelligent management and differentiated implementation of outbound call lists based on data warehouses provided in this embodiment of the invention; Figure 2 This is a system architecture design diagram for intelligent management and differentiated implementation of outbound call lists based on data warehouse, provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the modules of a system for intelligent management and differentiation of outbound call lists based on a data warehouse, provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be noted that, in this embodiment of the invention, the index warehouse platform serves as the authoritative data source for the outbound call list.

[0022] Agent utilization rate is a metric that measures the effective working time of agents. It is calculated as (effective call duration + post-call processing time) / total login time. This invention significantly improves this metric through a predictive outbound call mechanism.

[0023] Understandably, given the deficiencies in the background technology, this invention provides a method for intelligent management and differentiated implementation of outbound call lists based on data warehouses, comprising the following steps: Step S1: Data warehouse linkage and automated list collection, batch connection to the enterprise data warehouse platform, and targeted retrieval of outbound call lists based on pre-configured data extraction rules; The data extraction rules include customer group screening criteria, data field mapping relationships, and incremental update strategies.

[0024] In this embodiment, firstly, the data warehouse platform is automatically and batch-connected through a preset timed task or real-time triggering mechanism; Deep integration with the data warehouse platform is achieved using ETL (Extract-Transform-Load) technology. Configuration-based extraction tasks support both full and incremental extraction modes; data compression and batch transmission technologies ensure the efficiency and stability of large-scale data transmission; and a transaction mechanism ensures data consistency, supporting retries on failures and resumption of interrupted transmissions.

[0025] Finally, the retrieved data is first temporarily stored in the system's intermediate table to achieve stable and efficient collection of the list data source, completely eliminating the inefficient mode of traditional manual export and import.

[0026] It should be noted that ETL (Extract-Transform-Load) is short for data extraction, transformation, and loading, and is a core step in building a data warehouse. In this invention, it is used to automate the migration of outbound call lists from the data warehouse to the outbound call system.

[0027] Step S2: Intelligent list processing and activity association. The team leader performs precise filtering of the target list from the intermediate table through the visual operation interface, and associates and binds the filtered list with the outbound calling activity. After the list is successfully associated, the system automatically starts a multi-level data cleaning mechanism. It should be noted that the Team Leader Seat is a management position within the outbound call team, responsible for functions such as list allocation, quality monitoring, and team management. This invention provides the Team Leader Seat with a visual interface for list management and allocation.

[0028] A staging table is a table in a database used for temporary data storage, typically serving as a transit point for data flow. In this invention, it is used to temporarily store the raw outbound call list data retrieved from the data warehouse, facilitating subsequent processing.

[0029] The precise filtering includes: supporting multi-dimensional combined queries, such as customer level, age group, loan amount, etc.

[0030] Associating the filtered list with outbound calling activities includes: classifying the activity types into two categories, "predictive outbound calling activities" and "manual outbound calling activities," and marking the list processing path according to the activity type.

[0031] The system automatically initiates a multi-level data cleaning mechanism, including: Level 1 cleaning (duplicate verification): Global deduplication is performed based on the customer's unique identifier (such as customer number, ID card number) and phone number to identify multiple records of the same customer or duplicate occurrences of the same number; Second-level cleaning (validity verification): By calling the operator's interface or based on historical outbound call data, it intelligently identifies and filters invalid data such as empty numbers, suspended numbers, and blacklisted numbers; Level 3 Cleaning (Compliance Verification): Based on industry regulatory requirements and the company's internal compliance strategy, filter restricted lists such as sensitive customers, complaining customers, and customers to be avoided.

[0032] During the cleaning process, the system generates a cleaning log, which records the cleaning status and reasons for each entry in the list, and supports manual review and audit traceability.

[0033] In this embodiment, the cleaning engine operates in a dual-mode system based on a rules engine and machine learning. The rules engine supports visual configuration of cleaning rules to meet regular business needs; the machine learning mode trains a number validity prediction model using historical outbound call data to dynamically identify potentially invalid numbers and continuously improve cleaning accuracy.

[0034] It should be noted that a rule engine is a component embedded in an application used to separate business rules from the application code, enabling business decisions based on predefined rules. In this invention, it is used to configure and execute data cleaning rules.

[0035] Step S3: Differentiated outbound call control and execution. Based on the type of outbound call activity, a differentiated outbound call scheduling strategy is executed for predictive outbound call mode and manual outbound call mode. Finally, after the shift leader confirms the allocation rules, the system automatically completes the list allocation. After the manual agent receives the list, they carry out manual outbound call operations through the integrated work interface and record the outbound call process data in real time.

[0036] It should be noted that Predictive Dialer is an automated dialing system that uses algorithms to predict the probability of customers answering calls and the availability of agents. It improves outbound calling efficiency by automating batch dialing and transferring calls to human agents after they are connected.

[0037] For the predictive outbound calling model, a compliant list is pushed to the predictive outbound calling platform. The platform incorporates intelligent dialing algorithms (including optimal dialing time prediction, customer answer probability models, and agent availability prediction) to automate batch dialing of customer calls. Once a call is connected, the platform immediately transfers the call to a human agent for subsequent service. This model, through a collaborative mechanism of "machine pre-dialing + human service," significantly improves outbound call connection rates and the effective working hours of human agents.

[0038] For manual outbound calling, an intelligent allocation engine is provided, supporting multiple allocation rule configurations, including: Average allocation algorithm: Distribute the number of names evenly according to the number of seats; Capability-weighted algorithm: Weighted allocation based on indicators such as agent historical conversion rate and work efficiency; Customer matching algorithm: intelligent matching based on customer characteristics and agent expertise (e.g., assigning high-value customers to senior agents).

[0039] This invention incorporates a scheduling decision tree, which automatically selects the optimal outbound calling strategy based on multi-dimensional factors such as activity type, customer characteristics, and resource status. It supports flexible strategy configuration and A / B testing, facilitating continuous business optimization.

[0040] It should be noted that A / B testing is a comparative experimental method that compares the performance metrics of different versions by randomly assigning users to different versions in order to determine the optimal solution. This invention is used for the continuous optimization of outbound calling strategies.

[0041] On the other hand, embodiments of the present invention propose a system for intelligent management and differentiated implementation of outbound call lists based on data warehouse linkage, specifically as follows: Figure 1 As shown, this system adopts a layered architecture design, which includes the following from bottom to top: Data layer: Enterprise-level data warehouse, storing basic customer information, asset information, phone number status, etc. Service layer: Intelligent management and control system for outbound call lists, realizing core functions such as list retrieval, cleaning, storage, and scheduling; Access layer: Standardized API interface to achieve seamless integration with data warehouse platform, predictive outbound calling platform, and manual outbound calling system; Application layer: Supervisor's workstation, human agent's workstation, and predictive outbound call platform interface.

[0042] Furthermore, the service layer of this system includes three core modules: a data warehouse linkage module, an intelligent data cleaning module, and a differentiated outbound call scheduling module; among them, The data warehouse linkage module is used to connect to enterprise data warehouse platforms in batches and complete the targeted retrieval of outbound call lists based on pre-configured data extraction rules. The intelligent data cleaning module enables the team leader to perform precise filtering of the target list from the intermediate table through a visual operation interface, and associate and bind the filtered list with the outbound calling activities. After the list is successfully associated, the system automatically starts a multi-level data cleaning mechanism. The differentiated outbound call scheduling module executes differentiated outbound call scheduling strategies for predictive outbound call mode and manual outbound call mode based on the outbound call activity type. Specifically, after the shift leader confirms the allocation rules, the list allocation is completed automatically. After the manual agent receives the list, they carry out manual outbound call operations through the integrated work interface and record outbound call process data in real time.

[0043] It is understood that the system for intelligent management and differentiation of outbound call lists based on data warehouse linkage provided by the present invention corresponds to the method for intelligent management and differentiation of outbound call lists based on data warehouse linkage provided in the foregoing embodiments. The relevant technical features of the system for intelligent management and differentiation of outbound call lists based on data warehouse linkage can be referred to the relevant technical features of the method for intelligent management and differentiation of outbound call lists based on data warehouse linkage, and will not be repeated here.

[0044] Through the above technical solutions, the present invention has the following technical effects: Significant efficiency improvements: Through the automated linkage between the data warehouse platform and the outbound calling system, the time to obtain lists has been reduced from hours to minutes, supporting real-time processing of large-scale lists and greatly improving business response speed.

[0045] Data quality assurance: We have built an integrated "screening-association-cleaning" process, which effectively filters invalid lists through a multi-level cleaning mechanism. It is expected to reduce invalid outbound calls by 30%-50%, significantly saving communication and labor costs.

[0046] Flexible outbound calling strategies: The differentiated outbound calling scheduling mechanism based on activity type balances the efficiency of predictive outbound calling (automated batch processing) with the flexibility of manual outbound calling (refined service), adapting to diverse business scenarios such as telemarketing, customer service, and follow-up calls.

[0047] Resource allocation optimization: The predictive outbound call mode improves agent utilization through intelligent algorithms (expected effective call duration to increase by more than 40%); the manual outbound call mode replaces experience-based judgment with scientific allocation algorithms to achieve fair and efficient allocation of human resources.

[0048] Strong system synergy: Breaks down data silos, enables data flow between data warehouse, outbound calling system, and business system, supports real-time feedback of outbound calling data and strategy optimization, and forms a self-reinforcing intelligent outbound calling system.

[0049] Compliance risks are controllable: The built-in compliance verification mechanism automatically filters sensitive customers and restricted lists, meets industry regulatory requirements, and reduces corporate compliance risks.

[0050] Scalability and maintainability: It adopts a modular and configurable design, which supports flexible adjustment of cleaning rules, allocation algorithms and outbound call strategies, facilitating rapid business iteration and continuous system optimization.

[0051] This invention provides a physical structure of an electronic device, comprising: a processor, a communication interface, a memory, and a communication bus, wherein the processor, communication interface, and memory communicate with each other via the communication bus. The processor invokes logical instructions from the memory to execute a method for intelligent management and differentiated implementation of outbound call lists based on data warehouse linkage. Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part 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 the present 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.

[0052] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units 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 achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0053] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0054] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0055] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent management and differentiated implementation of outbound call lists based on data warehouses, characterized in that, Includes the following steps: Batch integration with enterprise data warehouse platforms, based on pre-configured data extraction rules, to complete the targeted retrieval of outbound call lists; The squad leader's seat uses a visual operation interface to perform precise filtering of the target list from the intermediate table, and associates and binds the filtered list with the outbound calling activity. After the list is successfully associated, a multi-level data cleaning mechanism is automatically started. Based on the type of outbound call activity, differentiated outbound call scheduling strategies are implemented for predictive outbound call mode and manual outbound call mode respectively. Among them, after the shift leader confirms the allocation rules, the list allocation is completed automatically. After the manual agent receives the list, the manual outbound call operation is carried out through the integrated work interface, and the outbound call process data is recorded in real time.

2. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 1, characterized in that, The data extraction rules include customer group screening criteria, data field mapping relationships, and incremental update strategies. The targeted retrieval of the outbound call list based on the pre-configured data extraction rules includes: ETL technology is used to support full and incremental extraction through configurable extraction tasks. Data compression and batch transmission are used to ensure large-scale data transmission, and transaction mechanisms are used to ensure data consistency.

3. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 1, characterized in that, The precise filtering is a multi-dimensional combination query, including customer level, age group and loan amount query; The process of associating and binding the filtered list with the outbound calling activity includes: The activity types are divided into "predicted outbound call activities" and "manual outbound call activities", and the list processing path is marked according to the activity type.

4. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 1, characterized in that, The multi-level data cleaning mechanism includes: Level 1 cleaning, also known as duplicate verification: global deduplication based on customer's unique identifier and phone number to identify multiple records of the same customer or duplicate occurrences of the same number; Secondary cleaning, namely validity verification: calling operator interfaces or based on historical outbound call data to intelligently identify and filter invalid data such as empty numbers, suspended numbers, and blacklisted numbers; The third level of cleanup, also known as compliance verification, involves filtering sensitive customers, complaining customers, and restricted lists of customers to avoid being disturbed, based on industry regulatory requirements and the company's internal compliance strategy.

5. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 4, characterized in that, During the cleaning process, a cleaning log is generated to record the cleaning status and reasons for each entry in the list, and supports manual review and audit traceability.

6. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses, as described in claim 4, is characterized in that... During the cleaning process, the cleaning engine operates in a dual-mode system based on a rules engine and machine learning. The rules engine supports visual configuration of cleaning rules to meet regular business needs. The machine learning mode trains a number validity prediction model using historical outbound call data to dynamically identify potentially invalid numbers and continuously improve the cleaning accuracy.

7. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 1, characterized in that, The predictive outbound calling mode is used to push the compliant list to the predictive outbound calling platform. The predictive outbound calling platform has a built-in intelligent dialing algorithm, including the prediction of the best dialing time, the customer answering probability model, and the agent idleness prediction, to complete the automated batch dialing of customer calls; when the call is connected, the platform will transfer the call to a human agent in real time, and the agent will take over the subsequent service.

8. The method for intelligent management and differentiated implementation of outbound call lists based on data warehouses according to claim 1, characterized in that, The manual outbound calling activity mode is used to provide an intelligent allocation engine and supports the following multiple allocation rule configurations, including: Average allocation algorithm: Distribute the number of names evenly according to the number of seats; Capability-weighted algorithm: Weighted allocation based on indicators such as historical conversion rate and work efficiency of agents; Customer matching algorithm: Intelligent matching based on customer characteristics and agent expertise.

9. A system for intelligent management and differentiated implementation of outbound call lists based on data warehouses, characterized in that: include: The data warehouse linkage module is used to connect to enterprise data warehouse platforms in batches and complete the targeted retrieval of outbound call lists based on pre-configured data extraction rules. The intelligent data cleaning module enables the team leader to perform precise filtering of the target list from the intermediate table through a visual operation interface, and associate and bind the filtered list with the outbound calling activities. After the list is successfully associated, the system automatically starts a multi-level data cleaning mechanism. The differentiated outbound call scheduling module executes differentiated outbound call scheduling strategies for predictive outbound call mode and manual outbound call mode based on the outbound call activity type. Specifically, after the shift leader confirms the allocation rules, the list allocation is completed automatically. After the manual agent receives the list, they carry out manual outbound call operations through the integrated work interface and record outbound call process data in real time.

10. An electronic device, characterized in that, The system includes a memory and a processor, wherein the processor is used to execute a computer program stored in the memory to implement the method for intelligent management and differentiation of outbound call lists based on a data warehouse as described in any one of claims 1 to 8.

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