Cross-industry adaptive big data marketing system
By using a cross-industry adaptable big data marketing system, we have achieved rapid adaptation of data from multiple industries and generation of personalized marketing strategies. This has solved the problem of the lack of universality in existing systems, reduced development costs, and improved marketing effectiveness.
Patent Information
- Application Number
- CN202511424341.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-30
- Publication Date
- 2026-01-13
AI Technical Summary
Existing big data marketing systems are typically designed for specific industries, lacking versatility and flexibility, making them difficult to apply across industries, resulting in low system reusability and high development costs.
A cross-industry adaptable big data marketing system was designed, including a data acquisition and access module, a data storage and management module, an industry-adaptive configuration module, a core processing engine module, a marketing strategy generation module, a strategy execution and feedback module, and a system management and monitoring module. It supports multi-source data access, distributed storage, graphical configuration, machine learning, and multi-channel marketing, and realizes dynamic adaptation and personalized strategy generation.
It significantly reduces system development and maintenance costs, improves marketing effectiveness and resource utilization, and can quickly adapt to changes in data structures and business needs across different industries, generating high-precision user profiles and personalized marketing strategies.
Smart Images
Figure CN121326291A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of marketing system technology, specifically to a cross-industry adaptable big data marketing system. Background Technology
[0002] With the rapid development of big data technology, enterprises are increasingly relying on data analysis for precision marketing. However, existing big data marketing systems are typically designed for specific industries or business scenarios, lacking versatility and flexibility. For example, user behavior analysis models in the e-commerce industry are difficult to directly apply to the financial or education industries, resulting in low system reusability and high development costs. Furthermore, traditional systems often employ fixed data processing workflows, making it difficult to adapt to changes in data structures and business needs across different industries, thus limiting their cross-industry application potential. Summary of the Invention
[0003] Therefore, this application provides a cross-industry adaptable big data marketing system to solve the problem that existing big data marketing systems are usually designed for specific industries or business scenarios and lack versatility and flexibility.
[0004] To achieve the above objectives, this application provides the following technical solution:
[0005] A cross-industry adaptable big data marketing system includes the following modules:
[0006] The data acquisition and access module is used to collect raw data from different industries through multi-source heterogeneous data interfaces, and to standardize and clean the data; the data storage and management module uses a distributed storage architecture to store the processed data and to build a multi-dimensional data index based on data tags and industry attributes.
[0007] The industry-adaptive configuration module provides a graphical configuration interface, allowing users to dynamically configure data mapping rules, feature extraction strategies, and marketing processes according to the business needs of the target industry.
[0008] The core processing engine module includes a user profile generation submodule, a behavior analysis submodule, and a prediction model submodule, which are used for data calculation and model training based on configuration rules;
[0009] The marketing strategy generation module automatically generates personalized marketing strategy solutions suitable for different industries based on the output of the core processing engine.
[0010] The strategy execution and feedback module executes strategies through multi-channel marketing tools and collects user feedback data to optimize strategy effectiveness in real time.
[0011] The system management and monitoring module is used to monitor the system's operating status, resource scheduling, and performance indicators, and provides access control and log auditing functions.
[0012] The data acquisition and access module supports API interfaces, log files, database synchronization, and real-time streaming data access methods, and has built-in data quality verification rules.
[0013] The data storage and management module adopts columnar storage and data partitioning technology, supporting the separation of hot and cold data and dynamic expansion.
[0014] The industry-adaptive configuration module includes a pre-built industry template library, allowing users to quickly configure industry-specific data processes by dragging and dropping components.
[0015] The core processing engine module integrates a machine learning framework, supports supervised learning, unsupervised learning and deep learning algorithms, and provides model version management functionality.
[0016] The user profile generation submodule dynamically constructs a user tag system based on multi-source data fusion technology and supports dynamic adjustment of tag weights.
[0017] The marketing strategy generation module supports A / B testing, allowing multiple strategy options to be tested in parallel and automatically selecting the optimal strategy based on performance data.
[0018] The strategy execution and feedback module integrates SMS, email, social media and mobile push channels, and tracks user clicks, conversions and retention metrics in real time.
[0019] The system management and monitoring module provides a visual dashboard that displays system throughput, response latency, and resource utilization metrics in real time.
[0020] The system also includes a data security and compliance module, which is used to implement data encryption, anonymization, and compliance auditing functions.
[0021] Compared with the prior art, this application has at least the following beneficial effects:
[0022] This system, through modular design and dynamic configuration mechanisms, enables rapid adaptation to data structures and business needs across different industries, significantly reducing system development and maintenance costs. Simultaneously, based on multi-source data fusion and an intelligent algorithm engine, the system can generate high-precision user profiles and marketing strategies, improving marketing effectiveness and resource utilization. Furthermore, the system's scalability and real-time processing capabilities allow it to adapt to future growth in data scale and business complexity. Attached Figure Description
[0023] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).
[0024] Figure 1 This is a module diagram of a cross-industry adaptable big data marketing system according to this application. Detailed Implementation
[0025] The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0026] like Figure 1 As shown, this application discloses a cross-industry adaptable big data marketing system, including the following modules:
[0027] The data acquisition and access module is used to collect raw data from different industries through multi-source heterogeneous data interfaces, and to standardize and clean the data.
[0028] The data storage and management module uses a distributed storage architecture to store the processed data and establishes a multi-dimensional data index based on data tags and industry attributes.
[0029] The industry-adaptive configuration module provides a graphical configuration interface, allowing users to dynamically configure data mapping rules, feature extraction strategies, and marketing processes according to the business needs of the target industry.
[0030] The core processing engine module includes a user profile generation submodule, a behavior analysis submodule, and a prediction model submodule, which are used for data calculation and model training based on configuration rules;
[0031] The marketing strategy generation module automatically generates personalized marketing strategy solutions suitable for different industries based on the output of the core processing engine.
[0032] The strategy execution and feedback module executes strategies through multi-channel marketing tools and collects user feedback data to optimize strategy effectiveness in real time.
[0033] The system management and monitoring module is used to monitor the system's operating status, resource scheduling, and performance indicators, and provides access control and log auditing functions.
[0034] In this invention, the data acquisition and access module obtains raw, heterogeneous data from different industries through multi-source interfaces (such as APIs, logs, and streaming data) and performs standardized cleaning to ensure data quality. The processed data is then sent to the data storage and management module, which utilizes a distributed architecture and multi-dimensional indexing to efficiently organize and store the data. The industry adaptation configuration module is the control core of the entire system. Users can dynamically define how data is mapped, how features are extracted, and how marketing processes are executed through its graphical interface, thereby achieving flexible adaptation to the needs of different industries. The configured rules are called by the core processing engine module, which performs complex calculations and machine learning model training through its user profiling, behavior analysis, and prediction model sub-modules to deeply mine the value of the data. Based on the analysis results, the marketing strategy generation module automatically combines and generates personalized cross-industry marketing plans. The strategy execution and feedback module then delivers these strategies to users through various channels (such as email and SMS) and collects feedback data in real time to form a closed-loop optimization. The entire process is uniformly scheduled, monitored, and guaranteed by the system management and monitoring module, ensuring the efficient, stable, and secure operation of the system.
[0035] As one implementation of this system, it has the following features:
[0036] In this system's application within the education sector, the data acquisition and access module is specifically designed to collect multi-source heterogeneous data from the education field, including but not limited to: student login duration, video viewing completion rates, and assignment submission records obtained through API interfaces from online learning platforms (such as Moodle and Blackboard); student personal information, course selection information, and historical grades obtained through database synchronization from the Student Information Management System (SIS); real-time streaming data collected from course websites, such as clickstreams, page dwell time, forum posts, and interaction behavior; and assessment results and feedback data accessed from third-party educational applications. This module will standardize and clean this raw data (e.g., standardize the format of "course numbers" and handle missing exam score values) and perform data quality verification.
[0037] The data storage and management module uses distributed columnar storage (such as HBase) to efficiently store massive amounts of student behavior data. It establishes multi-dimensional data indexes based on the characteristics of educational data, such as partitioning by attributes like "college," "major," "grade," and "course," and separates frequently accessed real-time behavior data (such as current online quiz clickstreams) from less accessed cold data (such as historical student records) for efficient querying and management.
[0038] The industry-adaptive configuration module provides pre-built templates for the education industry. Administrators or analysts in educational institutions can dynamically configure rules specific to their educational scenarios by dragging and dropping components through the module's graphical interface, without writing any code. For example, they can configure data mapping rules to map the "assessment_score" field in the source data to the system's standard "grade" feature; define feature extraction strategies, such as calculating "weekly learning activity" as a weighted sum of "login days" and "total video viewing time"; and configure marketing processes, such as "automatically triggering a tutoring resource recommendation strategy for students with a predicted failure risk higher than 80%."
[0039] These customized configuration rules for the education industry are invoked by the core processing engine module. Its user profile generation submodule integrates multi-source data such as learning behavior, grades, and interactions to dynamically label students with educational tags such as "active learner," "at risk of dropping out," "prefers video learning," and "weak in math." The behavior analysis submodule analyzes students' learning path patterns to identify effective and ineffective learning behaviors. The prediction model submodule runs machine learning algorithms (such as logistic regression and random forest) to train and generate prediction models based on historical data, used to accurately predict students' final exam scores, risk of failing courses, or likelihood of dropping out.
[0040] The marketing strategy generation module automatically generates personalized education marketing strategies. For example, for students identified as having a "high risk of failing" and "preferring video learning," a strategy is generated to "automatically push links to the course's essential video explanations and schedule one-on-one tutoring sessions with teaching assistants"; for "active learners," a strategy is generated to "push invitations to advanced course certificate programs."
[0041] The data acquisition and access module supports API interfaces, log files, database synchronization, and real-time streaming data access methods, and has built-in data quality verification rules.
[0042] This system supports multiple access methods, including API, log files, database synchronization, and real-time streaming data. It also has built-in data quality verification rules, which ensures that the data obtained from the source is reliable, complete, and in a uniform format, laying a solid foundation for subsequent processing.
[0043] The data storage and management module adopts columnar storage and data partitioning technology, supports the separation of hot and cold data and dynamic expansion, thereby significantly improving the efficiency of massive data query and analysis, while making the storage architecture flexible and able to expand flexibly according to business growth, reducing costs.
[0044] The industry adaptation configuration module includes a pre-built industry template library. Users can quickly configure industry-specific data processes by dragging and dropping components, thereby reducing the technical threshold and enabling business personnel to quickly build and deploy marketing processes that meet specific industry needs, thus improving the adaptation speed.
[0045] The core processing engine module integrates a machine learning framework, supports supervised learning, unsupervised learning, and deep learning algorithms, and provides model version management functions. This enables the system to cope with complex analysis scenarios and ensures that the model iteration process is orderly and traceable, thereby improving the accuracy and scientific nature of the analysis.
[0046] The user profile generation submodule dynamically constructs a user tag system based on multi-source data fusion technology and supports dynamic adjustment of tag weights, making the generated user profile more comprehensive, three-dimensional and able to dynamically evolve with changes in user behavior, thus ensuring the timeliness and accuracy of the profile.
[0047] The marketing strategy generation module supports A / B testing, allowing for parallel testing of multiple strategy options and automatic selection of the optimal strategy based on performance data. This enables a data-driven decision-making mechanism to continuously optimize marketing effectiveness and improve ROI.
[0048] The strategy execution and feedback module integrates SMS, email, social media and mobile push channels, and tracks user clicks, conversions and retention metrics in real time.
[0049] This solution integrates mainstream marketing channels (SMS, email, social media, etc.) and defines real-time tracking of key metrics such as clicks, conversions, and retention. This enables closed-loop management of the entire marketing campaign chain, providing direct evidence for performance evaluation and real-time optimization.
[0050] The system management and monitoring module provides a visual dashboard that displays system throughput, response latency, and resource utilization metrics in real time.
[0051] The management and monitoring module provides a visual dashboard that displays key performance indicators such as throughput and latency in real time. This enables operations and maintenance personnel to clearly understand the system's operating status, quickly locate performance bottlenecks, and ensure the system's stability and availability.
[0052] The system also includes a data security and compliance module, which enables data encryption, anonymization, and compliance auditing, ensuring the system operates securely under legal and compliant conditions and protecting user privacy.
[0053] The technical features of the above embodiments can be combined in any way (as long as there is no contradiction in the combination of these technical features). For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described; these embodiments not explicitly written should also be considered to be within the scope of this specification.
Claims
1. A cross-industry adaptable big data marketing system, characterized in that, Includes the following modules: The data acquisition and access module is used to collect raw data from different industries through multi-source heterogeneous data interfaces, and to standardize and clean the data. The data storage and management module uses a distributed storage architecture to store the processed data and establishes a multi-dimensional data index based on data tags and industry attributes. The industry-adaptive configuration module provides a graphical configuration interface, allowing users to dynamically configure data mapping rules, feature extraction strategies, and marketing processes according to the business needs of the target industry. The core processing engine module includes a user profile generation submodule, a behavior analysis submodule, and a prediction model submodule. The core processing engine module is used for data calculation and model training based on configuration rules. The marketing strategy generation module automatically generates personalized marketing strategy solutions suitable for different industries based on the output of the core processing engine. The strategy execution and feedback module executes strategies through multi-channel marketing tools and collects user feedback data to optimize strategy effectiveness in real time. The system management and monitoring module is used to monitor the system's operating status, resource scheduling, and performance indicators, and provides access control and log auditing functions.
2. The cross-industry adaptable big data marketing system as described in claim 1, characterized in that, The data acquisition and access module supports API interfaces, log files, database synchronization, and real-time streaming data access methods, and has built-in data quality verification rules.
3. The cross-industry adaptable big data marketing system as described in claim 2, characterized in that, The data storage and management module adopts columnar storage and data partitioning technology, supporting the separation of hot and cold data and dynamic expansion.
4. The cross-industry adaptable big data marketing system as described in claim 1, characterized in that, The industry-adaptive configuration module includes a pre-built industry template library, allowing users to quickly configure industry-specific data processes by dragging and dropping components.
5. The cross-industry adaptable big data marketing system as described in claim 4, characterized in that, The core processing engine module integrates a machine learning framework, supports supervised learning, unsupervised learning and deep learning algorithms, and provides model version management functionality.
6. The cross-industry adaptable big data marketing system as described in claim 5, characterized in that, The user profile generation submodule dynamically constructs a user tag system based on multi-source data fusion technology and supports dynamic adjustment of tag weights.
7. The cross-industry adaptable big data marketing system as described in claim 1, characterized in that, The marketing strategy generation module supports A / B testing, allowing multiple strategy options to be tested in parallel and automatically selecting the optimal strategy based on performance data.
8. A cross-industry adaptable big data marketing system as described in claim 7, characterized in that, The strategy execution and feedback module integrates SMS, email, social media and mobile push channels, and tracks user clicks, conversions and retention metrics in real time.
9. A cross-industry adaptable big data marketing system as described in claim 8, characterized in that, The system management and monitoring module provides a visual dashboard that displays system throughput, response latency, and resource utilization metrics in real time.
10. A cross-industry adaptable big data marketing system as described in claim 9, characterized in that, It also includes a data security and compliance module, which enables data encryption, anonymization, and compliance auditing functions.