CN117370412B - Enterprise data management control method, system and medium through service success rate - Google Patents
Enterprise data management control method, system and medium through service success rate Download PDFInfo
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Abstract
The invention belongs to the technical field of data processing, and discloses an enterprise data management control method, system and medium through service success rate, which utilize big data technology to excavate and analyze mass data, the method is used for finding out potential clients or users, realizing quick and efficient contact by adopting a high-concurrency and multi-channel contact mode, finally counting out success rate, and feeding back to a system to continuously treat the existing data. A system, comprising: the device comprises a data processing module, a success rate statistics module and a result feedback module. The accurate high-concurrency delivery system based on the service success rate is an efficient, accurate and high-concurrency mode for contacting the clients or the users, achieves the purposes of marketing or notification, and tracks the final transaction achievement of the clients or the users. The invention can find accurate target users by utilizing new technology and data analysis tools and continuously and deeply knowing the user requirements and market trends.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an enterprise data management control method, system and medium through service success rate.
Background
Currently, due to the fragmented and rapidly changing market environment of information, it becomes more and more difficult to find a core target user, and the main reasons for the difficulty of finding the target user are as follows: 1. the market competition is strong: as the market evolves and competition increases, the market for many products and services has become saturated and competitors are numerous, resulting in more difficulty in finding the target users. 2. Fragmentation of information: the current information fragmentation is serious, and coherent information clues are difficult to find, so that the requirements and interests of target users are difficult to accurately know. 3. Market change is rapid: the current market changes rapidly, as well as the target and demand, making it difficult to accurately grasp the demands and trends of the target users. 4. Restriction of geographic location: some products or services have geographical limitations that make it difficult to find the target user. 5. Data lack of accuracy: some of the data lacks accuracy, resulting in difficulty in determining the exact characteristics and needs of the target user. 6. The touch wind control breaks through difficultly: at present, after the marketing telephone is marked many times, accurate users are difficult to reach with high probability. 7. The casting cost is very high: the current market is very high in casting cost, the tremble sound casting threshold amount is high, and small enterprises are difficult to bear the high cost.
Through the above analysis, the problems and defects existing in the prior art are as follows:
Data accuracy and integrity: existing data suffer from lack of accuracy and integrity, which makes it difficult to accurately determine the characteristics and needs of a target user. Solving this problem requires improving the data collection and sorting method, ensuring the accuracy and integrity of the data.
Target user portrayal creation: the lack of efficient methods and tools to build accurate target user portraits limits the deep understanding of target user needs and interests. More accurate target user portrait establishment methods need to be developed, and comprehensive analysis is performed by combining multidimensional data so as to better understand characteristics and behaviors of target users.
Data analysis and mining capabilities: the prior art has the defects in large-scale data analysis and mining, valuable information cannot be quickly and effectively extracted from mass data, and accurate positioning and demand prediction of target users are limited. There is a need to develop more powerful data analysis and mining algorithms to improve understanding and insight into the target user.
Geographic location limitation overcomes: some products or services have geographic location restrictions that make it difficult to reach the target user. There is a need to find innovative ways to overcome the limitations of geographic location, such as locating and reaching target users through channels such as online platforms or social media.
Efficiency and cost issues: the existing marketing methods are inefficient and costly, especially for small businesses. The market promotion method with higher efficiency and low cost needs to be searched, and the technical means such as digital marketing, accurate advertisement and the like are combined so as to improve the touch effect of target users and reduce the input cost.
In summary, the prior art has drawbacks in terms of data accuracy, target user image creation, data analysis and mining capabilities, geographic location limitation overcoming, efficiency and cost, etc., in terms of finding core target users. Solving the problems needs to combine technical means such as data quality improvement, target user portrait modeling, data analysis algorithm improvement, innovative touch mode, high-efficiency low-cost marketing method and the like.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides an enterprise data management control method, system and medium through service success rate.
The invention is realized in such a way, and the enterprise data management control method through the service success rate is characterized in that the enterprise data management control method through the service success rate contacts the client or the user based on the service success rate, realizes marketing or notification, and tracks the final transaction achievement of the client or the user.
Further, the enterprise data management control method through the service success rate comprises the following steps:
Firstly, excavating and analyzing mass data by utilizing big data to find potential clients or users;
secondly, adopting a high concurrency and multi-channel touch mode to realize touch and count out success rate;
Thirdly, feeding back to the system to continuously treat the existing data;
Furthermore, the enterprise data management control method through the service success rate utilizes big data to mine and analyze massive fragment data information, obtains age, gender, region, occupation, income level and consumption habit information of potential clients or users, and realizes marketing or notification.
Further, the enterprise data management control method through the service success rate achieves access by analyzing behavior, interest and preference information of clients or users.
Further, the enterprise data governance control method through the service success rate adopts a user to construct, configure and adjust a visual interface of marketing or notification tasks.
Further, the enterprise data management control method through the service success rate supports multiple touch modes, namely a short message, a mail, a Push, a telephone and an AI robot, and different touch modes are selected according to different scenes;
the enterprise data management control method through the service success rate provides an intelligent management function and supports real-time monitoring, adjustment and optimization of the touch tasks;
the enterprise data management control method through the service success rate tracks the effect of the service success rate and is incorporated into a learning model.
It is a further object of the present invention to provide a computer apparatus comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the enterprise data governance control method of pass-through service success rate.
It is another object of the present invention to provide a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the enterprise data governance control method of pass-through service success rate.
Another object of the present invention is to provide an information data processing terminal for implementing the enterprise data governance control method for success rate of passing service.
Another object of the present invention is to provide a service success rate enterprise data governance control system based on the service success rate enterprise data governance control method, the service success rate enterprise data governance control system comprising:
the data processing module is used for utilizing big data to mine and analyze the mass data and finding out potential clients or users;
the success rate statistics module is used for realizing contact by adopting a high-concurrency and multi-channel contact mode and counting the success rate;
and the result feedback module is used for feeding back to the system to continuously treat the existing data.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
First, the present invention still finds accurate target users by utilizing new technology and data analysis tools, as well as by continually understanding user needs and market trends. The accurate high-concurrency delivery system based on the service success rate is an efficient, accurate and high-concurrency mode for contacting the clients or the users, achieves the purposes of marketing or notification, and tracks the final transaction achievement of the clients or the users. The system utilizes a big data technology to mine and analyze mass data so as to find potential customers or users, adopts a high-concurrence and multi-channel touch mode to realize quick and efficient touch, finally calculates the success rate, and feeds back to the system to continuously treat the existing data.
Second, the present invention is based on big data: the system utilizes big data technology to excavate and analyze massive fragment data information, obtains information such as age, gender, region, occupation, income level, consumption habit and the like of potential clients or users, and realizes accurate marketing or notification. Accurate touch: the system realizes accurate touch through analyzing information such as behaviors, interests, preferences and the like of clients or users, and improves the conversion rate. High concurrency: the system supports high concurrency, can simultaneously contact a large number of clients or users, and improves the touch efficiency. Visual interface: the system provides a visual interface, which is convenient for users to quickly construct, configure and adjust marketing or notification tasks. A plurality of touch modes: the system supports multiple touch modes, such as short messages, mails, push, telephones, AI robots and the like, and can select different touch modes according to different scenes. And (3) intelligent management: the system provides an intelligent management function, and supports real-time monitoring, adjustment and optimization of the touch task so as to improve the touch effect. Self-learning: through effect tracking of service success rate, the effect tracking is incorporated into a learning model. The high availability and accurate calculation of the model are continuously improved. In general, the accurate high-speed and high-speed delivery system based on the service success rate is a very effective marketing or notification tool, and can help enterprises or institutions to achieve accurate, efficient and high-conversion delivery and improve the service effect.
Third, the business data management control method based on the service success rate can provide an effective marketing and notification mode for the business, and can effectively evaluate and improve marketing strategies by tracking final transaction achievement of clients or users.
The first step: utilizing big data to mine and analyze mass data to find potential clients or users
Big data technology is introduced, and potential clients or users can be found out from mass data through advanced data analysis and data mining technology. This approach is more efficient than traditional data analysis methods because it can process larger-scale data and extract valuable information therefrom.
And a second step of: the contact is realized by adopting a high concurrency and multi-channel contact mode, and the success rate is counted
By adopting a high concurrency and multi-channel mode, more clients or users can be contacted simultaneously, and the success rate of the service can be counted more rapidly. This approach is more efficient than traditional single channel, low concurrency approaches because it can reach more customers or users in a shorter time.
And a third step of: feedback system continues to govern existing data
Feedback information of the service success rate is input into the data management system in real time, so that the system can continuously optimize and improve the existing data according to the feedback information. The method is more intelligent than the traditional data management method, because the method can realize automation and intellectualization of data management.
By introducing artificial intelligence and machine learning techniques, the system can better understand and process data, thereby more accurately finding potential customers or users, more effectively counting service success rates, and more intelligently conducting data governance. The method can greatly improve the efficiency and accuracy of data management, thereby improving the marketing effect and user satisfaction of enterprises.
Drawings
FIG. 1 is a flow chart of an enterprise data governance control method through service success rate provided by an embodiment of the present invention;
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
To further enhance the effectiveness of enterprise data governance control methods through service success rates, intelligent techniques may be introduced to improve the processing of the individual steps. The following is an example of an intelligent improvement scheme:
first, intelligent data mining and analysis:
And introducing a machine learning and data mining algorithm to intelligently mine and analyze mass data.
Automated model training and feature extraction techniques are used to help discover more accurate features and patterns of potential customers or users.
And identifying a target user group with high conversion potential by using an intelligent algorithm and a model.
The detailed steps for realizing intelligent data mining and analysis comprise the following steps:
Data preprocessing:
Most of the data needs to be preprocessed before it can be put to practical use. This includes cleaning the data (e.g., processing missing and outliers), normalizing the data (e.g., converting all features to the same scale), and feature engineering the data (e.g., extracting new features or converting existing features).
Model training:
An appropriate machine learning algorithm or model is selected for training. This includes decision trees, random forests, gradient augmentation, support vector machines, logistic regression, neural networks, and the like. The training process typically requires the use of a portion of data (referred to as a training set) to adjust the parameters of the model.
Feature extraction:
automated feature extraction techniques, such as Principal Component Analysis (PCA), linear Discriminant Analysis (LDA), and the like, are used to help discover more accurate features and patterns of potential customers or users.
Model evaluation and optimization:
and evaluating the performance of the model by using cross verification and other technologies, and performing parameter tuning when necessary. This step is repeated a number of times until the performance of the model reaches a satisfactory level.
Prediction and application model:
Once the model is trained and optimized, it can be used to predict new data. In this case, the model will be used to identify a target user population with high conversion potential.
In carrying out these steps, various machine learning and data science tools may be used, such as the Scikit-learn, pandas and NumPy libraries of Python, or the Caret and dplyr packages of R language. Meanwhile, for large-scale data processing, a large data processing framework such as APACHE SPARK can be used.
Secondly, intelligent touch and success rate statistics:
based on the intelligent user behavior analysis and prediction model, the optimal touch mode and time are determined, and marketing or notification information is sent in a personalized manner.
The touch content is optimized by using natural language processing and emotion analysis technology, so that the touch content is attractive and personalized.
And monitoring response and behavior feedback of the user in real time, calculating the success rate in real time by using an intelligent algorithm, and performing real-time adjustment and optimization according to feedback data.
Thirdly, intelligent data management feedback and optimization:
And an automatic data management and analysis tool is introduced to automatically process and analyze the feedback data, so that key information and trends are rapidly identified.
Using machine learning and data mining algorithms, impact factors and patterns related to success rate are automatically discovered and intelligent advice and optimization strategies are provided.
The automatic decision support system is combined to realize real-time intelligent treatment of the existing data, including data cleaning, deduplication, fusion and other operations.
The detailed signal and data processing procedures of the intelligent scheme will vary according to specific services and requirements. In general, it relates to the following aspects:
data preprocessing: including data cleaning, denoising, filling in missing values, etc., to ensure the quality and integrity of the data.
Feature extraction and selection: significant features are extracted from the raw data using machine learning and statistical methods, and feature selection is performed to reduce dimensionality and improve model effectiveness.
Model training and optimizing: and constructing a prediction model by using a machine learning algorithm and a deep learning algorithm, and optimizing the model by using cross-validation, super-parameter tuning and other technologies.
User behavior analysis: by analyzing the behavior data of clicking, browsing, purchasing and the like of the user, the preference, interest and purchasing intent of the user are known.
Real-time monitoring and feedback: and monitoring and feeding back the user behavior in real time by utilizing a real-time data stream processing technology so as to adjust the marketing strategy and the touch mode in time.
An intelligent scheme for customizing according to specific scenes and data characteristics is needed to improve service success rate and data treatment effect to the greatest extent.
As shown in fig. 1, the method for controlling enterprise data management through service success rate provided by the embodiment of the invention comprises the following steps:
s101: utilizing big data to mine and analyze mass data to find out potential clients or users;
S102: the contact is realized by adopting a high concurrency and multi-channel contact mode, and the success rate is counted;
S103: the feedback system continues to treat the existing data;
In an embodiment of the invention, the invention is based on big data: the system utilizes big data technology to excavate and analyze massive fragment data information, obtains information such as age, gender, region, occupation, income level, consumption habit and the like of potential clients or users, and realizes accurate marketing or notification.
In the embodiment of the invention, the invention accurately touches: the system realizes accurate touch through analyzing information such as behaviors, interests, preferences and the like of clients or users, and improves the conversion rate.
In embodiments of the present invention, the present invention is highly concurrent: the system supports high concurrency, can simultaneously contact a large number of clients or users, and improves the touch efficiency.
In an embodiment of the present invention, the present visual interface: the system provides a visual interface, which is convenient for users to quickly construct, configure and adjust marketing or notification tasks.
In the embodiment of the invention, the invention has multiple touch modes: the system supports multiple touch modes, such as short messages, mails, push, telephones, AI robots and the like, and can select different touch modes according to different scenes.
In the embodiment of the invention, the invention intelligently manages: the system provides an intelligent management function, and supports real-time monitoring, adjustment and optimization of the touch task so as to improve the touch effect.
In an embodiment of the invention, the invention learns itself: through effect tracking of service success rate, the effect tracking is incorporated into a learning model. The high availability and accurate calculation of the model are continuously improved.
The enterprise data management control system through service success rate provided by the embodiment of the invention comprises the following components:
the data processing module is used for utilizing big data to mine and analyze the mass data and finding out potential clients or users;
the success rate statistics module is used for realizing contact by adopting a high-concurrency and multi-channel contact mode and counting the success rate;
and the result feedback module is used for feeding back to the system to continuously treat the existing data.
Embodiment one:
Assume that an appliance sub-commerce platform desires to increase the purchase conversion of its users. They can achieve this goal using enterprise data governance control methods that pass service success rates. The following are specific embodiments:
First, data mining and analysis:
The platform can utilize big data technology to mine and analyze massive user data so as to find out potential purchase intention users.
By analyzing data such as browsing history, purchase records, search keywords and the like of the user, user portraits and purchase intention models can be established.
Secondly, multi-channel reaching and success rate statistics:
The platform can reach potential users in a high-concurrency and multi-channel mode, such as a mode of push notification, email, short message marketing and the like.
After the user is touched, the response and the behavior of the user are recorded, and the proportion of the user which is successfully touched is counted, namely the service success rate.
Successful touchdown may be defined as the user clicking on a push notification or email and completing the purchase within a period of time.
Thirdly, data governance feedback and optimization:
the platform treats and optimizes the existing data according to the success rate statistics result.
For a successful user, the information such as transaction behavior, purchasing preference and the like can be further tracked for personalized recommendation and accurate marketing.
For users who have not successfully reached, the cause of this can be analyzed and improvements can be made, such as optimizing push content, adjusting reach time, etc.
Embodiment two:
Suppose an insurance company wishes to increase the rate of orders for its sales agents. They can achieve this goal using enterprise data governance control methods that pass service success rates. The following are specific embodiments:
First, data mining and analysis:
Insurance companies can mine and analyze large amounts of sales data using big data techniques to find potential high conversion sales agents.
By analyzing information such as historical sales data, customer interaction records and the like of sales agents, a performance evaluation model of the agents can be established.
Secondly, multi-channel reaching and success rate statistics:
Companies may contact potential customers through a variety of channels, such as telemarketing, network marketing, and the like.
After contacting the customer, recording the contact times of the agent, the response and feedback information of the customer, and counting the success rate of the agent.
Success rate may be defined as the proportion of the agent that makes effective contact with the customer and ultimately reaches the order.
Thirdly, data governance feedback and optimization:
The company will manage and optimize the existing data according to the success rate statistics.
For sales agents with high success rates, key factors for their success, such as communication skills, product knowledge, etc., can be further analyzed and experience shared with other agents.
For sales agents with low success rates, the problem can be analyzed and training and support can be provided to help the sales agents to increase sales capacity.
The above are two embodiments and specific implementations of enterprise data governance control methods through service success rates. The specific embodiments may be adapted and optimized according to the needs of different enterprises and industries.
It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principles of the present invention will be apparent to those skilled in the art within the scope of the present invention.
Claims (8)
1. The enterprise data management control method through the service success rate is characterized in that the enterprise data management control method through the service success rate contacts a client or a user based on the service success rate, realizes marketing or notification, and tracks the final transaction achievement of the client or the user;
The method specifically comprises the following steps:
first, intelligent data mining and analysis:
Introducing a machine learning and data mining algorithm to intelligently mine and analyze mass data;
Using automated model training and feature extraction techniques, help discover more accurate features and patterns of potential customers or users;
Identifying a target user group with high conversion potential by using an intelligent algorithm and a model;
secondly, intelligent touch and success rate statistics:
based on the intelligent user behavior analysis and prediction model, determining the optimal touch mode and opportunity, and personalized sending marketing or notification information;
Optimizing touch content by using natural language processing and emotion analysis technology, so that the touch content is attractive and personalized;
Monitoring response and behavior feedback of a user in real time, calculating success rate in real time by using an intelligent algorithm, and performing real-time adjustment and optimization according to feedback data;
Thirdly, intelligent data management feedback and optimization:
Introducing an automatic data management and analysis tool, automatically processing and analyzing feedback data, and rapidly identifying key information and trend;
Automatically discovering influence factors and modes related to success rate by utilizing machine learning and data mining algorithms, and providing intelligent suggestion and optimization strategies;
The method combines an automatic decision support system to realize real-time intelligent treatment of the existing data, and comprises the operations of data cleaning, de-duplication and fusion;
The data blowing method comprises the following steps:
Data preprocessing: the method comprises the operations of data cleaning, denoising and missing value filling so as to ensure the quality and the integrity of data;
Feature extraction and selection: extracting meaningful features from the original data by using a machine learning and statistical method, and performing feature selection to reduce dimensionality and improve model effect;
Model training and optimizing: constructing a prediction model by using a machine learning and deep learning algorithm, and optimizing the model by using a cross-validation and super-parameter tuning technology;
User behavior analysis: analyzing clicking, browsing and purchasing behavior data of the user to know the preference, interest and purchasing intention of the user;
real-time monitoring and feedback: and monitoring and feeding back the user behavior in real time by utilizing a real-time data stream processing technology so as to adjust the marketing strategy and the touch mode in time.
2. The method for controlling management of enterprise data by service success rate as claimed in claim 1, wherein said method for controlling management of enterprise data by service success rate comprises the steps of:
Firstly, excavating and analyzing mass data by utilizing big data to find potential clients or users;
secondly, adopting a high concurrency and multi-channel touch mode to realize touch and count out success rate;
And thirdly, feeding back to the system to continuously treat the existing data.
3. The method for controlling business data management of through-service success rate according to claim 2, wherein the method for controlling business data management of through-service success rate uses big data to mine and analyze massive fragment data information, obtain age, sex, region, occupation, income level and consumption habit information of potential clients or users, and realize marketing or notification.
4. The business data governance control method of through-service success rate according to claim 2, wherein said business data governance control method of through-service success rate achieves touchdown by analyzing behavior, interest, preference information of a client or user; the enterprise data management control method through the service success rate adopts a user to construct, configure and adjust a visual interface of marketing or notification tasks;
the enterprise data management control method through the service success rate supports multiple touch modes, short messages, mails, push, telephones and AI robots, and different touch modes are selected according to different scenes;
the enterprise data management control method through the service success rate provides an intelligent management function and supports real-time monitoring, adjustment and optimization of the touch tasks;
the enterprise data management control method through the service success rate tracks the effect of the service success rate and is incorporated into a learning model.
5. A computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the enterprise data governance control method of pass-through service success rate as claimed in any one of claims 1 to 4.
6. A computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to execute the enterprise data management control method for passing service success rate according to any one of claims 1 to 4.
7. An information data processing terminal, characterized in that the information data processing terminal is used for realizing the enterprise data management control method through service success rate according to any one of claims 1 to 4.
8. A service success rate enterprise data governance control system based on the service success rate enterprise data governance control method as described in any one of claims 1 to 4, wherein said service success rate enterprise data governance control system comprises:
the data processing module is used for utilizing big data to mine and analyze the mass data and finding out potential clients or users;
the success rate statistics module is used for realizing contact by adopting a high-concurrency and multi-channel contact mode and counting the success rate;
and the result feedback module is used for feeding back to the system to continuously treat the existing data.
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