Optimization system and method applied to marketing channel

Abstract

The invention discloses an optimization system and method applied to a marketing channel, and particularly relates to the technical field of marketing management. The system comprises a data acquisition module, a data processing module, an optimization analysis module, a channel distribution module and an execution monitoring module. The data acquisition module acquires channel input cost, output effect and market environment data from a plurality of heterogeneous data sources; the data processing module is used for cleaning, integrating and standardizing the original data; the optimization analysis module generates a channel resource allocation scheme based on a preset mathematical programming model; the channel distribution module converts the scheme into an executable instruction; and the execution monitoring module executes the instruction and collects feedback data in real time to form closed-loop optimization. The system realizes automatic configuration and dynamic adjustment of marketing channel resources, and is mainly used for solving the problem of multi-channel collaborative optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of marketing management, and more particularly, to an optimization system and method applied to a marketing channel. BACKGROUND

[0002] The field of marketing channel management has undergone an evolution from traditional offline channels to explosive growth of digital channels since the 1990s. With the rapid popularization of Internet technology after 2010, the structure of enterprise marketing channels has become increasingly complex, forming a diversified pattern of online and offline, self-owned and third-party, and paid and natural traffic coexisting. In recent years, channel analysis technology based on big data has gradually become an industry standard, and enterprises have begun to use basic data statistics tools and business intelligence platforms to evaluate channel effectiveness, and channel management is transitioning from experience-driven to data-driven.

[0003] However, the existing technology has obvious deficiencies: first, the channel data of most enterprises is scattered in various independent systems, forming data islands and lacking effective integration mechanisms; second, the commonly used channel analysis tools are mostly based on static historical data for retrospective reporting, and cannot provide forward-looking optimization suggestions; third, traditional channel allocation decisions still rely on human experience and lack the support of scientific quantitative decision-making models; finally, there is a significant lag between the execution and effect monitoring of channel strategies, and it usually takes several days or even weeks to discover execution deviations, resulting in missed market opportunities and resource waste.

[0004] Therefore, in view of the above problems, an optimization system and method applied to a marketing channel are proposed. The core problems to be solved are: how to realize the automatic integration and standardized processing of marketing channel data; how to establish a scientific channel resource optimization allocation model; how to automatically convert the optimization scheme into executable instructions; and how to realize real-time monitoring and feedback of channel execution effect. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide an optimization system and method applied to a marketing channel to solve the problems raised in the background art.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an optimization system applied to a marketing channel, comprising: a data acquisition module for acquiring original data related to a marketing channel from a plurality of data sources, the original data including channel input cost data, channel output effect data, and market environment data; a data processing module connected to the data collection module, configured to clean, integrate and format the raw data to generate standardized channel data, the data processing module comprising a data deduplication unit and a data conversion unit; an optimization analysis module connected to the data processing module, configured to receive the standardized channel data and perform calculation based on a preset optimization model to generate an optimization result comprising a channel resource allocation scheme, the optimization analysis module comprising a model operation unit configured to perform linear programming or integer programming calculation; a channel allocation module connected to the optimization analysis module, configured to receive the optimization result and convert the channel resource allocation scheme into executable channel configuration instructions; an execution monitoring module connected to the channel allocation module and the data collection module, configured to execute the channel configuration instructions to a predetermined marketing channel and collect channel feedback data after the execution of the instructions in real time, and transmit the channel feedback data back to the data collection module.

[0007] Optionally, the channel input cost data collected by the data collection module comprises at least one of advertising cost, personnel cost and commission ratio, the channel output effect data comprises at least one of customer conversion rate, sales and customer lifetime value, and the market environment data comprises at least one of market competition intensity index and macroeconomic indicators.

[0008] Optionally, the data processing module further comprises a data verification unit configured to perform compliance verification on the cleaned data based on a predefined rule range, the rule range comprising a valid numerical value range of 0 to 1,000,000 for numerical data and a predefined optional value set for enumeration data.

[0009] Optionally, the preset optimization model in the optimization analysis module takes minimization of total channel input cost or maximization of total channel output effect as an objective function, and takes at least one of channel input budget constraint, single channel minimum input constraint and total channel resource constraint as a constraint condition.

[0010] Optionally, the optimization analysis module further comprises a model updating unit configured to adjust the objective function coefficient or constraint condition parameter of the preset optimization model in a periodic manner according to the channel feedback data transmitted back by the execution monitoring module.

[0011] Optionally, the channel allocation module further comprises an instruction verification unit configured to verify the logical consistency and executability of the channel resource allocation scheme before converting the scheme, and select to accept, reject or modify the scheme according to the verification result.

[0012] Optionally, the execution monitoring module further comprises an alarm unit configured to generate and send an alarm information when the deviation of the channel feedback data from the expected effect data exceeds a preset threshold range of 10% to 30%.

[0013] An optimization method applied to a marketing channel, applied to the system described above, the method implements the following steps, including: S1, acquiring marketing channel related raw data from multiple data sources through the data acquisition module; S2, cleaning, integrating and formatting the raw data through the data processing module to generate standardized channel data; S3, receiving the standardized channel data through the optimization analysis module and calculating based on the preset optimization model to generate an optimization result containing a channel resource allocation scheme; S4, receiving the optimization result through the channel allocation module and converting the channel resource allocation scheme into executable channel configuration instructions; S5, executing the channel configuration instructions to the predetermined marketing channel through the execution monitoring module, and collecting channel feedback data after the execution of the instructions in real time, and returning the channel feedback data to the data acquisition module for subsequent data processing and optimization analysis.

[0014] Optionally, the cleaning, integration and formatting of the raw data include: adopting rule-based data deduplication processing to delete or merge duplicate records; and uniformly converting the data format, converting the cost data into RMB amount representation, and converting the percentage data into decimal representation between 0 and 1.

[0015] Technical effects and advantages of the present application: Compared with the prior art, the present application realizes the full life cycle automatic management of the marketing channel resources by constructing a closed-loop optimization system integrating data acquisition, processing, optimization, allocation and monitoring. Specifically, the system automatically acquires raw data from multiple heterogeneous data sources through the data acquisition module, cleans and standardizes the data through the data processing module, calculates the optimal allocation scheme through the optimization analysis module using a mathematical programming model, converts the executable instructions through the channel allocation module, and finally executes and monitors the feedback in real time through the execution monitoring module, forming a continuous optimization closed loop. This innovation completely changes the traditional mode of relying on manual and dispersed decision-making, realizes end-to-end automation from data to decision-making to execution, and has the advantages of greatly improving the efficiency and response speed of channel management, shortening the strategy adjustment period from several days or weeks to several hours, ensuring the high consistency of decision-making and execution, and reducing errors and costs caused by manual intervention.

[0016] Compared with the prior art, the application realizes the automatic, standardized and trusted processing of multi-source channel data by embedding a rule engine-based data verification unit in the designed data processing module. The unit performs compliance verification on the cleaned data through predefined rule ranges (such as the valid value range of 0 to 1000000), and adopts a multi-level verification strategy (including single-field verification, multi-field association verification and cross-data source consistency verification) to ensure data quality. This innovation solves the problem of diverse original data sources, different formats and uneven quality, and its advantage is to provide highly reliable and standardized input data for subsequent optimization analysis, avoid the risk of "garbage in, garbage out", and ensure the scientificity and accuracy of the final optimization decision, laying a solid foundation for high-quality data analysis.

[0017] Compared with the prior art, the application realizes the dynamic self-adaptive optimization of channel resource allocation strategy by enabling the optimization analysis module to have a model self-updating capability based on feedback data. The system periodically adjusts the objective function coefficients (such as channel effect coefficients) or constraint condition parameters of the optimization model through the model updating unit using the latest channel effect data returned by the execution monitoring module in an incremental learning manner. This innovation enables the mathematical model to continuously learn the latest changes in the market, and its advantage is that it overcomes the disadvantage of traditional static models that quickly lag behind the market once they are developed, improving the applicability and precision of the optimization scheme in a rapidly changing market environment, so that the allocation of channel resources can continuously approach the optimal state, maximizing the return on investment.

[0018] Compared with the prior art, the application ensures the executability of the optimization scheme and the controllability of the execution process by introducing instruction verification and real-time alarm mechanisms in the channel allocation and execution links. The instruction verification unit automatically verifies the logical consistency and executability of the allocation scheme before conversion, preventing the issuance of invalid or conflicting instructions; the alarm unit monitors the execution deviation in real time, and generates multi-level alarms as soon as the feedback data deviates from the expected value by more than 10% to 30% of the preset threshold. The advantage of this innovation is that it builds a safe, reliable and automated execution system that can prevent errors before execution and timely detect and warn problems during execution, reducing the risk of resource waste and opportunity loss caused by scheme defects or abnormal execution, and ensuring the smooth landing of the optimization strategy. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 The system framework diagram of the application. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings of the embodiments of the application Figure 1The technical solutions in the embodiments of the present application are clearly and completely described. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the scope of protection of the present application.

[0021] Embodiment one: An optimization system applied to a marketing channel, comprising: A data acquisition module, configured to acquire original data related to the marketing channel from enterprise internal resource planning systems, customer relationship management systems, third-party advertising platform data interfaces, and public market data sources through application programming interface calling, wherein the original data includes channel input cost data, channel output effect data, and market environment data, the data acquisition process adopts a token-based identity verification mechanism to ensure data access security, and data caching technology is used to temporarily store the collected data to cope with network transmission interruption; A data processing module connected with the data acquisition module through a data bus, configured to clean, integrate, and format the original data using a distributed data processing framework, including using a rule engine-based data deduplication unit to eliminate duplicate records, using a data conversion unit to map data fields from different sources to a unified data model, and using a data quality evaluation algorithm to verify the integrity of the processed data, and finally generating standardized channel data meeting predetermined mode requirements; An optimization analysis module connected with the data processing module through a high-bandwidth data interface, configured to load the standardized channel data using in-memory computing technology, and call a built-in mathematical programming solver to execute a pre-set optimization model calculation, the model operation unit supports linear programming, integer programming, and mixed integer programming algorithms, and can handle both continuous variables and discrete decision problems, and after calculation, an optimization result data set containing a specific channel resource allocation scheme is generated; Wherein, E j =(R j ^α*C j ^β) / (L j ^γ*ln(1+V j )); E j : the comprehensive performance score of the jth channel.

[0022] R j : the return on investment of the jth channel (calculation method: total profit generated by the channel / total input cost of the channel).

[0023] C jCustomer conversion rate of the jth channel (calculation method: the number of customers who completed a purchase in this channel / the total number of visits or leads in this channel).

[0024] L j Customer acquisition cost of the jth channel (calculation method: total investment cost of this channel / number of new customers brought by this channel).

[0025] V j Customer lifetime value of the jth channel (predicted total value that this channel's customers may bring in the future based on their historical purchase behavior).

[0026] α, β, γ: adjustable weight index (default value can be set to 1, allowing enterprises to adjust the importance of different indicators according to strategic goals).

[0027] This formula is used to comprehensively quantify the effectiveness E j of the jth marketing channel. It combines multiple key indicators, aiming to break through the limitations of traditional single-dimensional focus (such as sales or ROI), providing accurate and comprehensive input basis for subsequent optimization allocation. Among them, R j represents the return on investment of the jth channel, C j represents the customer conversion rate of the jth channel, L j represents the customer acquisition cost of the jth channel, V j represents the customer lifetime value of the jth channel. α, β, γ are adjustable weight indexes, used to balance the influence degree of different indicators on the final effectiveness value.

[0028] Channel allocation module, through message queue mechanism, receives the optimization results output by the optimization analysis module, uses business rule engine to convert mathematical optimization results into operation instruction set that specific channel management system can recognize, including but not limited to advertisement budget adjustment instruction, channel personnel configuration instruction and commission structure modification instruction; Execution monitoring module, through enterprise service bus, is bidirectionally connected with the channel allocation module and the data collection module, uses event-driven architecture to execute channel configuration instructions, collects channel feedback data after instruction execution through real-time data stream processing technology, and establishes data feedback loop to transmit monitoring results back to the data collection module in real time, forming a closed-loop control structure.

[0029] The channel investment cost data obtained by the data collection module includes advertisement expense detail data extracted from the financial management system, channel management personnel cost data obtained from the human resource system, and commission ratio setting data extracted from the sales system; Channel output effect data includes customer conversion rate indicators obtained from e-commerce platform application programming interfaces, sales time series data collected from point-of-sale systems, and customer lifetime value calculation data obtained from customer analytics systems; Market environment data includes market competition intensity indices collected from public market reports through web crawler technology and macroeconomic indicator data obtained from data interfaces; All data collection processes use configurable data extraction frequency settings, support data collection scheduling by hour, day, or week, and use data encryption transmission protocols to ensure data transmission security.

[0030] The data processing module also includes a data verification unit that uses a rule engine-based verification framework, configured with effective range check rules for numerical data 0 to 1,000,000, enumeration data predefined value range check rules, data format consistency check rules, and business logic reasonableness check rules; A multi-level verification strategy is used in the verification process, first performing single-field basic verification, then multi-field association verification, and finally cross-data source consistency verification; for data records that fail verification, the system uses an exception data processing flow, including automatic correction, manual review marking, and data source feedback mechanisms; all verification rules are managed through rule templates, supporting dynamic addition and modification of verification conditions without modifying program code.

[0031] The pre-set optimization model in the optimization analysis module is defined using mathematical programming modeling language, with the objective function constructed as a mathematical expression that minimizes total channel input cost or maximizes total channel output effect, where input cost includes direct financial investment and indirect operating cost, and output effect is measured using a weighted composite index; The constraint system includes upper limit constraints on channel investment based on historical data statistical analysis, minimum channel coverage constraints based on business needs, total channel resource constraints based on resource availability, and dynamic adjustment constraints based on market environment changes; Model parameters are initialized through the parameter estimation module, using regression analysis techniques and time series prediction techniques based on historical data to determine model coefficients; the solving process uses branch and bound algorithm to handle integer constraints, simplex method to handle linear programming problems, and supports multi-objective optimization solving.

[0032] The optimization analysis module also includes a model update unit that uses an incremental learning mechanism to update model parameters based on channel feedback data returned by the execution monitoring module using recursive least squares method; the update process uses a sliding time window strategy, using only data from a specific recent time period for model adjustment; Where E j ^new=λ*(Aj / B j )+(1-λ)*E j ^old; E j ^new: The efficiency coefficient of the j-th channel after the update.

[0033] E j ^old: The efficiency coefficient of the j-th channel before the update.

[0034] A j : The actual performance and revenue generated by the j-th channel during the latest monitoring period (which can be quantified based on actual business, such as actual profit, actual number of converted customers, etc.).

[0035] B j The actual resource budget allocated to the j-th channel during the latest monitoring period.

[0036] λ: Learning rate or smoothing coefficient, a preset parameter between 0 and 1, used to control the degree of influence of new observations on model parameters.

[0037] This formula is the core of incremental learning in the model update unit, used to dynamically adjust and optimize the efficiency coefficient E of each channel in the model. j It smoothly updates model parameters based on real feedback data generated in the latest execution cycle, enabling the model to continuously approximate the changing market reality, thereby improving the accuracy of subsequent optimization results.

[0038] The model structure update adopts a hypothesis testing-based approach. When the deviation between the feedback data and the model predictions continues to exceed a predetermined range, the model structure reconstruction process is initiated, including variable selection, constraint adjustment, and objective function reconstruction. All update operations are managed through a version control system, which supports model version rollback and performance comparison analysis.

[0039] The channel allocation module also includes an instruction verification unit, which adopts a rule engine-based verification framework and is configured with a business rule base, including channel resource allocation logic consistency rules, channel management system operation constraint rules, and execution timing dependency rules; the verification process adopts a multi-level verification mechanism, including syntax verification, semantic verification, and execution feasibility verification. For allocation schemes that fail verification, the system uses an automatic correction algorithm to attempt to generate feasible solutions. The correction algorithm includes constraint relaxation and priority adjustment methods. All verification rules are managed through configurable rule templates, allowing business personnel to modify verification rules through a graphical interface.

[0040] The execution monitoring module also includes an alarm unit that uses a monitoring framework based on complex event processing technology to analyze channel feedback data streams in real time, identify abnormal situations by setting threshold rules and pattern detection rules; the alarm generation adopts a multi-level early warning mechanism, and is divided into three levels of prompt, warning and serious according to the deviation degree; The alarm distribution adopts a push mechanism based on a subscription mode, supports sending alarm information through multiple ways such as email, instant message and mobile application; all alarm events are recorded in the event log, supporting post-query analysis and alarm effect evaluation.

[0041] An optimization method applied to a marketing channel, the specific implementation steps of the method include: S1, acquiring marketing channel related raw data from heterogeneous data sources through multiple data interfaces of the data acquisition module concurrently, and using an incremental data extraction strategy to reduce data transmission volume; S2, pipeline processing of raw data through a distributed processing framework of the data processing module, including data cleaning phase using rule matching algorithm to identify and correct data errors, data integration phase using entity resolution technology to unify the same entity from different sources, and data formatting phase using pattern mapping technology to convert data into a unified format; S3, performing optimization calculation through a mathematical programming solver of the optimization analysis module, including four stages of model initialization, parameter setting, solution execution and result verification; S4, converting numerical optimization results into executable instructions through an instruction generation engine of the channel allocation module, including instruction template filling, parameter binding and instruction sequence optimization; S5, executing channel configuration instructions and monitoring execution effect through a real-time control loop of the execution monitoring module, using data stream processing technology to calculate key performance indicators in real time, and forming a closed-loop optimization control through a feedback mechanism.

[0042] The cleaning, integration and formatting processing of the raw data includes: in the data cleaning phase, an automatic cleaning process based on a rule engine is used, including missing value processing using interpolation method or default value filling strategy, outlier detection using statistical outlier detection algorithm, and data deduplication using record matching method based on hash value; In the data integration phase, entity resolution technology is used to establish a unified enterprise data view by defining a master data model, and a fuzzy matching algorithm is used to identify the same entity in different data sources; in the data formatting phase, a data conversion rule engine is used to define conversion rules including data type conversion, unit of measurement unification and coding standardization, all conversion operations are managed through an extensible conversion rule library, and user-defined conversion rules are supported.

[0043] The channel resource allocation scheme is converted into executable channel configuration instructions, including: analyzing the optimization result data structure, extracting the key decision variable value; mapping the decision variable to the specific channel operation parameter, establishing the conversion rule from mathematical optimization result to business operation; considering the operation dependency relationship and execution timing constraint when generating the instruction sequence; For online advertising channels, operation instruction sets containing application programming interface call parameters are generated, including advertising platform authentication tokens, budget adjustment parameters, delivery time parameters, and target audience parameters; For offline agent channels, structured data files that can be recognized by the agent management system are generated, containing agent identification, commission structure adjustment parameters, and performance target parameters; after all instructions are generated, syntax verification and semantic verification are performed to ensure the executability and business rationality of the instructions.

[0044] The workflow of the present application starts from the data acquisition module, which automatically obtains original data from a plurality of internal systems (such as enterprise resource planning systems, customer relationship management systems) and external data sources (such as third-party advertising platforms, public market data interfaces) through configured application programming interfaces, web crawlers and database connectors. These data include but are not limited to daily advertising costs, personnel costs, commission expenses and other input cost data of each channel, as well as real-time updated click-through rate, conversion rate, sales, customer lifetime value and other output effect data, as well as market competition intensity index and macroeconomic indicators and other market environment data; The collected original data is sent to the data processing module after preliminary caching and encrypted transmission. The module first starts the data cleaning sub-process, uses a matching algorithm based on a rule engine to remove duplicate records, uses an interpolation method or a pre-set default value to fill in missing values, and uses a statistical outlier detection algorithm to identify and mark abnormal data for subsequent review; The cleaned data enters the integration and formatting sub-process, which unifies the same entity (such as different names of the same channel agent) from different sources through entity parsing technology, and converts all data to a unified standardized format using pre-defined pattern mapping rules, such as converting all currency units to RMB and all percentages to decimals between 0 and 1. Finally, a standardized channel data set with rigorous structure and reliable quality is generated.

[0045] Subsequently, the standardized channel data is received by the high-bandwidth data interface of the optimization analysis module and loaded into the in-memory computing engine. The module calls pre-installed mathematical programming models (such as linear programming models with total input cost minimization or total output effect maximization as the objective function), sets constraints (such as total budget upper limit, single channel minimum input requirement) in combination with current market environment parameters, and uses the simplex method or branch and bound algorithm for solution calculation to generate a set of schemes containing optimal channel resource allocation ratios; The scheme is transmitted to the channel allocation module, and the business rule engine of the module parses and converts the mathematical allocation scheme into specific instructions executable by downstream systems, such as generating a package of instructions for calling a specific advertising platform application programming interface to adjust the advertising budget, or generating a structured data file for modifying the proportion parameter in the agent commission management system, and the instructions are subjected to logical consistency and executability verification before being issued.

[0046] Subsequently, the execution monitoring module distributes the configuration instructions to the execution terminals (such as advertising servers and agent management systems) of the target marketing channels through the enterprise service bus and supervises the execution, and the module starts real-time data stream monitoring to continuously collect channel feedback data (such as new click rates and updated sales data) after the instructions are executed; The feedback data is immediately returned to the data collection module, forming a closed-loop feedback loop, so that the system can dynamically update its data pool based on the latest market effect and provide input for the next round (such as the next day or week) of optimization calculation, thereby realizing a continuous iteration and self-adjusting channel resource optimization process.

[0047] Finally, it should be noted that in the description of the present application, unless otherwise specified and limited, the terms "installation", "connection", "connection" should be broadly understood, which can be mechanical connection or electrical connection, or the communication between two elements, or direct connection, "up", "down", "left", "right" and the like are only used to indicate relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may change; Secondly: the drawings of the disclosed embodiments of the present application only involve the structures related to the disclosed embodiments, other structures can be referred to the usual design, and in the case of no conflict, the same embodiment and different embodiments of the present application can be combined with each other; Finally: the above only describes the preferred embodiments of the present application, and is not used to limit the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An optimization system applied to a marketing channel, characterized by, The system comprises: a data acquisition module configured to acquire original data related to marketing channels from a plurality of data sources, the original data including channel input cost data, channel output effect data, and market environment data; a data processing module connected to the data acquisition module and configured to clean, integrate, and format the original data to generate standardized channel data, the data processing module including a data deduplication unit and a data conversion unit; an optimization analysis module connected to the data processing module and configured to receive the standardized channel data and perform calculations based on a pre-set optimization model to generate an optimization result including a channel resource allocation scheme, the optimization analysis module including a model operation unit configured to perform linear programming or integer programming calculations; a channel allocation module connected to the optimization analysis module and configured to receive the optimization result and convert the channel resource allocation scheme into executable channel configuration instructions; an execution monitoring module connected to the channel allocation module and the data acquisition module and configured to execute the channel configuration instructions on predetermined marketing channels and collect channel feedback data after execution of the instructions in real time, and transmit the channel feedback data back to the data acquisition module.

2. The system for optimizing marketing channels according to claim 1, wherein, The channel input cost data acquired by the data acquisition module includes at least one of advertising costs, personnel costs, and commission rates, the channel output effect data includes at least one of customer conversion rates, sales, and customer lifetime values, and the market environment data includes at least one of market competition intensity indexes and macroeconomic indicators.

3. The system for optimizing marketing channels according to claim 1, wherein, The data processing module further includes a data verification unit configured to perform compliance verification on the cleaned data based on pre-defined rule ranges, the rule ranges including a valid numerical value range of 0 to 1,000,000 for numerical data and a pre-defined set of selectable values for enumerated data.

4. The system for optimizing marketing channels according to claim 1, wherein, The pre-set optimization model in the optimization analysis module has a target function of minimizing total channel input costs or maximizing total channel output effects, and at least one of a channel input budget constraint, a single channel minimum input constraint, and a total channel resource constraint as a constraint condition.

5. The system for optimizing marketing channels according to claim 4, wherein, The optimization analysis module further includes a model updating unit configured to adjust the target function coefficients or constraint condition parameters of the pre-set optimization model in a periodic manner based on the channel feedback data transmitted back by the execution monitoring module.

6. The system for optimizing marketing channels of claim 1, wherein, The channel allocation module further includes an instruction verification unit configured to verify the logical consistency and executability of the channel resource allocation scheme before converting the scheme, and to accept, reject, or modify the scheme based on the verification result.

7. The system for optimizing marketing channels of claim 1, wherein, The execution monitoring module further includes an alarm unit configured to generate and send alarm information when the deviation of the channel feedback data from expected effect data exceeds a pre-set threshold range of 10% to 30%.

8. A method for optimizing a marketing channel, characterized by, The method is applied to the system of any one of claims 1 to 7, and the implementation steps include: S1, acquiring original data related to marketing channels from a plurality of data sources through the data acquisition module; S2, cleaning, integrating and formatting the original data through the data processing module to generate standardized channel data; S3, receiving the standardized channel data through the optimization analysis module and calculating based on a preset optimization model to generate an optimization result containing a channel resource allocation scheme; S4, receiving the optimization result through the channel allocation module and converting the channel resource allocation scheme into executable channel configuration instructions; S5, executing the channel configuration instructions to the predetermined marketing channel through the execution monitoring module, and collecting channel feedback data after the execution of the instructions in real time, and returning the channel feedback data to the data collection module for subsequent data processing and optimization analysis.

9. The method for optimizing a marketing channel according to claim 8, wherein, The cleaning, integrating and formatting of the original data include: adopting rule-based data deduplication processing to delete or merge records that are repeatedly entered; and uniformly converting the data format, converting the cost data into RMB amount representation, and converting the percentage data into decimal representation between 0 and 1.

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