Customer relationship intelligent management and analysis system based on CRM

Abstract

The invention discloses a customer relationship intelligent management and analysis system based on CRM, and relates to the technical field of intelligent analysis, a business logic layer comprises a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit, the customer information management unit is used for extracting basic attributes, consumption habits and preference characteristics of customers from customer data by using a data analysis technology so as to construct a comprehensive and three-dimensional customer portrait; and the customer information management unit further divides the constructed customer portraits into subdivided groups with common demands, preferences and behavior patterns through in-depth market research. According to the method, the wide customer groups are divided into the subdivided groups with common requirements, preferences and behavior patterns, so that an enterprise can more accurately understand each user group, a more targeted marketing strategy is formulated, resource allocation can be optimized to a certain extent, and the market response speed is increased.

Description

Technical Field

[0001] The present invention relates to the field of intelligent analysis technology, and in particular to a customer relationship intelligent management and analysis system based on CRM. Background Art

[0002] In the field of customer relationship management, the application of intelligent management is increasingly highlighting its importance. Through advanced intelligent technology, enterprises can more deeply explore and analyze customer data, thereby gaining insights into market trends, customer needs and potential business opportunities. However, the existing customer relationship intelligent management system cannot divide customer groups into segments with common needs, preferences and behavior patterns based on customer portraits when in use, and has certain defects.

[0003] The defects of the existing customer relationship intelligent management system are: 1. In patent document CN115689128B, the accuracy of customer data analysis is improved mainly through the construction of customer profiles of potential customers and customer group management based on target return rates, without considering that the existing customer relationship intelligent management system cannot divide customer groups into sub-groups with common needs, preferences and behavior patterns based on customer profiles when in use; 2. In the patent document CN117892004B, the main consideration is how to establish a project cooperation relationship based on the customers corresponding to the target project to improve the compatibility between the target users and the amusement projects, but it does not take into account that the existing customer relationship intelligent management system has certain shortcomings in understanding and meeting customer needs; 3. In patent document CN118313837B, the customer profile recognition unit mainly adopts automated model building and training, as well as image processing and text recognition technology to improve the processing efficiency and accuracy of customer profiles, reduce the time and error rate of manual entry, and realize accurate product recommendations and marketing strategies by identifying potential cross-selling opportunities and customer conversion points, thereby improving sales conversion rate and revenue growth. However, it does not take into account that the existing customer relationship intelligent management system cannot understand the customer's true feelings and evaluation of products or services; 4. In patent document CN113822727B, customers are mainly profiled and updated in real time based on static information, and dynamically profiled and updated in real time based on dynamic information. Customers are profiled and analyzed from different aspects, which improves the diversity and comprehensiveness of customer profiles so as to better classify and manage customer relationships. However, it does not take into account that the existing customer relationship intelligent management system cannot predict customers' future purchasing behavior and demand trends when used. Summary of the invention

[0004] The purpose of the present invention is to provide a customer relationship intelligent management and analysis system based on CRM to solve the problems raised in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a customer relationship intelligent management and analysis system based on CRM, comprising a front-end interaction layer, a business logic layer, a data service layer, an intelligent analysis layer and an infrastructure layer, wherein the front-end interaction layer is used to provide an intuitive and easy-to-use interface so that users can obtain the required customer data and business information anytime and anywhere, the business logic layer is responsible for processing various requests from the front end, the data service layer is responsible for data storage, retrieval and management and interacts with the business logic layer, the intelligent analysis layer uses advanced data mining and machine learning technologies to conduct in-depth analysis of massive customer data, and the infrastructure layer is used to provide a stable and reliable operating environment; The business logic layer includes a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit. The customer information management unit uses data analysis technology to extract the basic attributes, consumption habits and preference characteristics of customers from customer data to build a comprehensive and three-dimensional customer portrait. The customer information management unit also divides the constructed customer portrait into segmented groups with common needs, preferences and behavior patterns through in-depth market research; The sales opportunity tracking unit is responsible for tracking and managing every opportunity in the sales process, including the identification of potential customers, tracking of sales progress, and feedback on sales results. The marketing activity management unit formulates targeted marketing strategies based on customer portraits and segmented group results, including target audience selection, marketing channel planning, and activity content design. The marketing activity management unit also uses a central processor and the cloud to collect relevant data brought by the marketing strategy and uses data processing technology to perform data evaluation.

[0006] Preferably, the customer information management unit also includes customer lifecycle management, from the identification and acquisition of potential customers to the conversion and retention of customers to the enhancement of customer value and the cultivation of loyalty.

[0007] Preferably, the intelligent analysis layer includes an intelligent analysis and prediction unit, which includes data analysis, predictive modeling and sentiment analysis. Data analysis uses data analysis tools to conduct in-depth analysis of customer data, identify trends, patterns and anomalies, predictive modeling builds a predictive model based on historical data, and the predictive model is used to predict customer behavior and market trends. Sentiment analysis uses big data processing technology, sentiment dictionaries and machine learning algorithms to perform sentiment analysis on social media and customer feedback data.

[0008] Preferably, the data analysis includes statistical analysis, mining analysis, predictive analysis and visual analysis. Statistical analysis grasps the basic characteristics of the customer group through descriptive statistics, mining analysis uses data mining technology to reveal the deep information in the customer data, predictive analysis uses time series analysis technology based on historical transaction data and business rules to predict sales trends in the future, and visual analysis converts complex data into intuitive and easy-to-understand charts and graphs through data visualization technology.

[0009] Preferably, the process of predictive modeling is as follows: S1. Build a prediction model using logistic regression, random forest and deep learning algorithms based on the results of data analysis; S2. Use historical data to train the model and continuously adjust the model parameters; S3. Evaluate the trained model using test data. S4. Apply the optimized model to actual business scenarios.

[0010] Preferably, the front-end interaction layer includes obtaining required customer data and business information through the Web and mobile application login interfaces.

[0011] Preferably, the data service layer includes data integration and consolidation, which includes collecting customer data from various sources. The data integration and consolidation also includes cleaning, deduplication and standardization of the collected data and integrating it into a unified CRM database.

[0012] Preferably, the basic characteristics include the number of customers, geographical distribution and consumption level, and the deep information mines the hidden patterns in customer purchasing behavior based on association rules to reveal which products or services are often purchased together. The predictive analysis also includes using regression analysis to explore the influence of various market factors on customer behavior.

[0013] Preferably, the various sources include one or more of social media, email, website, call center, and mobile application.

[0014] Preferably, the infrastructure layer includes servers, networks and storage.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention divides a wide range of customer groups into segmented groups with common needs, preferences and behavior patterns, so that enterprises can understand each user group more accurately and formulate more targeted marketing strategies, thereby optimizing resource allocation and improving market response speed to a certain extent.

[0016] 2. The present invention uses comprehensive customer life cycle management, starting from the identification and acquisition of potential customers, through customer conversion and retention, and ultimately achieving the improvement of customer value and the cultivation of loyalty, which helps companies better understand and meet customer needs, and can also effectively enhance the company's market competitiveness and profitability.

[0017] 3. The present invention uses advanced data analysis tools and technologies to conduct a comprehensive and in-depth analysis of customers' basic information, transaction records, interaction history, social media activities, etc. Through data analysis, enterprises can identify trends, patterns and anomalies in customer behavior, and predict customers' future behavior and market trends through the constructed prediction model, providing a scientific basis for enterprises to formulate marketing strategies and optimize resource allocation, and performing sentiment analysis on text data such as customer comments and feedback on social media, so that enterprises can understand customers' true feelings and evaluations of products or services, and promptly discover potential problems and improvement points. Finally, based on the results of sentiment analysis, targeted improvement measures and optimization plans are formulated to enhance customer satisfaction and loyalty.

[0018] 4. Through descriptive statistics, the present invention enables enterprises to clearly grasp the basic characteristics of customer groups, and through inferential statistics, further help enterprises explore the regularity behind customer groups, help enterprises to do inventory management and sales planning in advance, and convert complex data into intuitive and easy-to-understand charts and graphs, so that business personnel can quickly grasp the key information behind the data. Through comprehensive analysis of historical data and real-time data, the system can predict customers' future purchasing behavior and demand trends. This predictive ability provides strong data support for the formulation of marketing strategies, enabling enterprises to seize the initiative in fierce market competition and achieve more accurate marketing activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a system diagram of the present invention; Figure 2 A flowchart of customer lifecycle management of the present invention; Figure 3 This is a diagram of the structure of the intelligent analysis and prediction unit of the present invention; Figure 4 A diagram showing the structure of the data analysis of the present invention; Figure 5 The flowchart of constructing the prediction model of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 The present invention provides an embodiment: a customer relationship intelligent management and analysis system based on CRM, including a front-end interaction layer, a business logic layer, a data service layer, an intelligent analysis layer and an infrastructure layer. The front-end interaction layer is used to provide an intuitive and easy-to-use interface so that users can obtain the required customer data and business information anytime and anywhere. The business logic layer is responsible for processing various requests from the front end. The data service layer is responsible for data storage, retrieval and management and interacts with the business logic layer. The intelligent analysis layer uses advanced data mining and machine learning technologies to conduct in-depth analysis of massive customer data. The infrastructure layer is used to provide a stable and reliable operating environment. The business logic layer includes a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit. The customer information management unit includes using data analysis technology to extract the basic attributes, consumption habits and preferences of customers from customer data. The customer information management unit also includes dividing the constructed customer portrait into segments with common needs, preferences and behavior patterns through in-depth market research. The sales opportunity tracking unit is responsible for tracking and managing every opportunity in the sales process, including the identification of potential customers, the tracking of sales progress, and the feedback of sales results. The marketing activity management unit formulates targeted marketing strategies based on the results of customer portraits and segmented groups, including target audience selection, marketing channel planning and activity content design. The marketing activity management unit also uses the central processing unit and the cloud to collect relevant data brought by the marketing strategy and uses data processing technology to evaluate the data. The front-end interaction layer includes obtaining the required customer data and business information through the Web and mobile application login interfaces. The infrastructure layer includes servers, networks and storage.

[0022] Furthermore, in the process of customer segmentation, key factors such as customer value, purchasing behavior, consumption preferences and geographical distribution need to be considered comprehensively. Customer value is not only reflected in the transaction amount, but also includes implicit values ​​such as customer loyalty and word-of-mouth communication. Purchasing behavior analysis reveals customers' consumption habits and decision-making paths through data such as customer purchase frequency and purchase channels. At the same time, in-depth exploration of consumption preferences can help companies understand customers' unique needs and aesthetic orientation for products. Geographical distribution reflects the customer characteristics and consumption potential of different regional markets. Precise Positioning Through in-depth analysis of segmentation results, we can clearly depict the profile of each customer group and build a segmentation model, including their age level, occupation distribution, income level and lifestyle. These detailed customer portraits provide strong support for us to formulate differentiated service strategies. For example, for high-value customers, we can provide a more personalized and high-end service experience to enhance customer stickiness and satisfaction. Regularly review and evaluate existing customer segmentation models. By collecting the latest market data and customer feedback, you can promptly identify deficiencies and obsolescence in the segmentation models and make necessary adjustments and optimizations accordingly. This way, customer segmentation and positioning strategies can always keep pace with the market pulse, thereby maintaining the timeliness and accuracy of marketing strategies.

[0023] By integrating advanced data analysis functions, marketers can comprehensively review the effectiveness of marketing activities from multiple key indicators such as exposure, click-through rate, conversion rate and even return on investment. This multi-dimensional evaluation not only reveals the performance of the activities in each link, but also helps the team to deeply understand user behavior and preferences, so as to accurately identify the strengths and weaknesses of the activities; Marketers can quickly identify hot and unpopular activities based on real-time updated data, and adjust resource input and strategic direction in a timely manner. The system can automatically analyze the successful factors and potential improvement points of each activity, and provide scientific optimization suggestions for subsequent marketing activities. It not only improves the work efficiency of the marketing team, but also improves the flexibility and targeting of marketing activities.

[0024] See also Figure 2 The present invention provides an embodiment: a customer relationship intelligent management and analysis system based on CRM, wherein the customer information management unit also includes customer life cycle management, from the identification and acquisition of potential customers to the conversion and retention of customers to the improvement of customer value and the cultivation of loyalty.

[0025] Furthermore, the system helps companies identify customer groups with potential purchasing intentions or needs through multi-channel data collection and analysis. These data sources include social media, search engines, marketing campaign feedback, market research, etc. The operation process is as follows: 1. Data collection: Collect basic information and behavioral data of potential customers through various channels; 2. Data analysis: Use data analysis tools to process the collected data and identify the characteristics and purchasing intentions of potential customers; 3. Customer portrait: Based on the analysis results, build a potential customer portrait to provide accurate positioning for subsequent marketing activities.

[0026] After identifying potential customers, the system needs to support converting these potential customers into actual customers and retain them through effective management and service strategies. The conversion process involves follow-up by the sales team, implementation of marketing activities, and satisfaction of customer needs. The specific operation process is as follows: 1. Sales follow-up: Sales personnel enter the system interface to track potential customers' purchasing intentions and develop personalized sales strategies and follow-up plans; 2. Marketing activities: Promote products or services to potential customers through various means such as email marketing, social media marketing and online advertising; 3. Customer Service: Provide high-quality after-sales service and continuous support after the customer purchases to ensure customer satisfaction; 4. Customer Care: Strengthen emotional connections with customers through regular customer care activities such as birthday wishes and holiday greetings.

[0027] On the basis of customer retention, the system also needs to support the enhancement of customer value and the cultivation of customer loyalty. This includes increasing customers' purchase frequency and amount through personalized services, precision marketing and membership systems, while enhancing customers' loyalty and stickiness to the company. Personalized services provide personalized recommendations and services based on customers' historical purchase records and behavioral data. Precision marketing uses data analysis to identify high-value customers and customers at risk of churn and develop targeted marketing strategies. The membership system establishes a membership system to provide members with exclusive discounts and privileged services to enhance members' loyalty and sense of belonging. Continuous optimization continuously optimizes products and services based on market changes and changes in customer needs to enhance customer experience and value.

[0028] See also Figure 3 An embodiment of the present invention is: a customer relationship intelligent management and analysis system based on CRM, the intelligent analysis layer includes an intelligent analysis and prediction unit, the intelligent analysis and prediction unit includes data analysis, predictive modeling and sentiment analysis, the data analysis uses data analysis tools to conduct in-depth analysis of customer data, identify trends, patterns and anomalies, the predictive modeling builds a predictive model based on historical data, the predictive model is used to predict customer behavior and market trends, and the sentiment analysis uses big data processing technology, sentiment dictionary and machine learning algorithm to perform sentiment analysis on social media and customer feedback data.

[0029] Furthermore, through in-depth analysis of customer data, we can accurately understand customer preferences, needs and purchasing habits. Enterprises can launch products and services in a targeted manner to improve customer satisfaction and loyalty. We can also identify the characteristics of different customer groups through different data analysis methods to provide strong support for personalized marketing. We can also monitor abnormalities in the data and quickly discover potential problems, such as product quality problems and service dissatisfaction. Enterprises can take timely measures to solve them and avoid the expansion of problems. At the same time, identifying trends and patterns can also help enterprises discover new market opportunities, plan ahead, and seize opportunities. Prediction models built based on historical data can predict customer behavior and market trends in advance, giving companies enough time to prepare. For example, if they predict the growth of customer demand, companies can increase inventory and adjust production plans in advance, helping companies to reasonably allocate resources and improve operational efficiency. By predicting market trends, companies can reduce the risk of blind investment and decision-making errors, be more cautious in new product launches and market expansion, and reduce the possibility of failure. Predictions of customer behavior can also allow companies to take measures in advance to prevent customer churn and improve customer retention rates, so that companies can respond quickly to market changes and have more advantages than competitors. They can grasp market dynamics in advance, adjust strategies, and meet customer needs, thereby enhancing their market competitiveness. By conducting sentiment analysis on social media and customer feedback data, we can understand customers' emotions and attitudes in a timely manner. Companies can strengthen and promote positive feedback and respond and resolve negative feedback quickly, thereby improving customer satisfaction, establishing good customer relationships, and enhancing customers' trust and loyalty to the company. By analyzing customers' emotional tendencies, companies can understand the strengths and weaknesses of products and services, make improvements based on customer needs and pain points, improve product quality and service levels, continuously optimize products and services, meet customer expectations, and improve the company's reputation.

[0030] See also Figure 4 The present invention provides an embodiment: a customer relationship intelligent management and analysis system based on CRM, data analysis includes statistical analysis, mining analysis, predictive analysis and visual analysis, statistical analysis grasps the basic characteristics of customer groups through descriptive statistics, mining analysis uses data mining technology to reveal deep information in customer data, predictive analysis uses time series analysis technology based on historical transaction data and business rules to predict sales trends in the future, and visual analysis converts complex data into intuitive and easy-to-understand charts and graphs through data visualization technology. Basic characteristics include customer quantity, geographical distribution and consumption level. Deep information mines hidden patterns in customer purchase behavior based on association rules to reveal which products or services are often purchased together. Predictive analysis also includes using regression analysis to explore the impact of various market factors on customer behavior.

[0031] Furthermore, through descriptive statistics, companies can clearly grasp the basic characteristics of customer groups, such as the number of customers, geographical distribution, and consumption levels, and through inferential statistics, they can further help companies explore the regularity behind customer groups, such as seasonal changes in customer purchasing behavior or cyclical fluctuations in market trends; Data mining technology is based on association rules to discover hidden patterns in customer purchasing behavior and reveal which products or services are often purchased together. For example, in the field of e-commerce, by analyzing user purchase records, it may be found that there is a strong association between one category of goods and another category of goods. The discovery of such associations is of great guiding value for product recommendations and promotion package design. Predictive analysis is based on historical transaction data and business rules. It uses time series analysis technology to predict sales trends in the future, helping companies to manage inventory and plan sales in advance. Predictive analysis also uses regression analysis to explore the impact of various market factors on customer behavior, thereby providing data support for companies to formulate more accurate marketing strategies. Visual analysis converts complex data into intuitive and easy-to-understand charts and graphs, allowing business personnel to quickly grasp the key information behind the data. Through data visualization, companies can more intuitively display the characteristics of customer groups, market distribution, sales trends, etc., which can not only improve internal decision-making efficiency, but also enhance communication between companies and external partners.

[0032] See also Figure 5 , an embodiment provided by the present invention: a customer relationship intelligent management and analysis system based on CRM, the process of predictive modeling is as follows: S1. Build a prediction model using logistic regression, random forest and deep learning algorithms based on the results of data analysis; S2. Use historical data to train the model and continuously adjust the model parameters; S3. Evaluate the trained model using test data. S4. Apply the optimized model to actual business scenarios.

[0033] Furthermore, before building a model, you first need to conduct an in-depth analysis of the collected data to understand the data distribution, missing values, outliers, correlations, etc. to ensure the quality and applicability of the data. Then, based on the results of data analysis and business needs, select a suitable prediction algorithm. Use the results of data processing and feature engineering to build an initial prediction model. Then divide the historical data into training set, validation set and test set. The training set is used to train the model, the validation set is used to adjust the model parameters during the training process, and the test set is used to finally evaluate the model performance. Then use the training set data to train the model, that is, let the model learn the rules or patterns in the data, and evaluate the model through the validation set. Estimate the performance of the model and adjust the model parameters accordingly to optimize the performance of the model. After the model training is completed, use the test set data that has not been involved in the previous training to evaluate the model, analyze the performance of the model on the test set, identify possible problems and room for improvement, and finally deploy the trained and optimized model to the actual business environment for real-time prediction or batch prediction. Continuously monitor the performance of the model in actual applications, regularly re-evaluate the model performance, and adjust the model according to business changes or data drift. The whole process is an iterative cycle, and the model needs to be continuously adjusted and optimized according to business needs and data changes to ensure the accuracy and practicality of the model.

[0034] See also Figure 1 An embodiment of the present invention is: a customer relationship intelligent management and analysis system based on CRM, wherein the data service layer includes data integration and consolidation, and the data integration and consolidation include collecting customer data from various sources, and the data integration and consolidation also include cleaning, deduplication and standardization of the collected data and integrating it into a unified CRM database, and the various sources include one or more of social media, email, website, call center and mobile application.

[0035] Furthermore, data integration refers to the process of integrating data from different sources into a unified data storage and management system. Data integration can be achieved through a variety of methods, including ETL tools, API integration, and data synchronization technology. ETL tools can automatically extract, transform, and load data, while API integration allows real-time data interaction between different systems. On the basis of data integration, the collected data needs to be integrated, including merging data from different sources into one or more new data sets for unified management and analysis. The compatibility and consistency of data need to be considered during the integration process. Data from different sources may have differences in format, unit, and precision, and need to be converted and standardized accordingly, which can eliminate the differences between data from different sources and improve the comparability and analyzability of data. Data cleaning is an important part of data processing. By removing noise, filling missing values, correcting errors and removing duplicate records, the accuracy and completeness of the data can be ensured. Specifically, it includes missing value processing, noise processing, error processing and removing duplicate records in the data set. The data after cleaning, deduplication and standardization will be integrated into a unified CRM database. In the subsequent use process, enterprises can use this data to conduct in-depth customer analysis, formulate marketing strategies and optimize customer experience.

[0036] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A customer relationship intelligent management and analysis system based on CRM, characterized by: It includes the front-end interaction layer, business logic layer, data service layer, intelligent analysis layer and infrastructure layer. The front-end interaction layer is used to provide an intuitive and easy-to-use interface so that users can obtain the required customer data and business information anytime and anywhere. The business logic layer is responsible for processing various requests from the front end. The data service layer is responsible for data storage, retrieval and management and interacts with the business logic layer. The intelligent analysis layer uses advanced data mining and machine learning technologies to conduct in-depth analysis of massive customer data. The infrastructure layer is used to provide a stable and reliable operating environment. The business logic layer includes a customer information management unit, a sales opportunity tracking unit and a marketing activity management unit. The customer information management unit uses data analysis technology to extract the basic attributes, consumption habits and preference characteristics of customers from customer data to build a comprehensive and three-dimensional customer portrait. The customer information management unit also divides the constructed customer portrait into segmented groups with common needs, preferences and behavior patterns through in-depth market research; The sales opportunity tracking unit is responsible for tracking and managing every opportunity in the sales process, including the identification of potential customers, tracking of sales progress, and feedback on sales results. The marketing activity management unit formulates targeted marketing strategies based on customer portraits and segmented group results, including target audience selection, marketing channel planning, and activity content design. The marketing activity management unit also uses a central processor and the cloud to collect relevant data brought by the marketing strategy and uses data processing technology to perform data evaluation.

2. The CRM-based customer relationship intelligent management and analysis system according to claim 1, characterized in that: The customer information management unit also includes customer life cycle management, from the identification and acquisition of potential customers to the conversion and retention of customers to the improvement of customer value and the cultivation of loyalty.

3. The CRM-based customer relationship intelligent management and analysis system according to claim 1, characterized in that: The intelligent analysis layer includes an intelligent analysis and prediction unit, which includes data analysis, predictive modeling and sentiment analysis. Data analysis uses data analysis tools to conduct in-depth analysis of customer data and identify trends, patterns and anomalies. Predictive modeling builds a prediction model based on historical data. The prediction model is used to predict customer behavior and market trends. Sentiment analysis uses big data processing technology, sentiment dictionaries and machine learning algorithms to perform sentiment analysis on social media and customer feedback data.

4. The CRM-based customer relationship intelligent management and analysis system according to claim 3 is characterized by: The data analysis includes statistical analysis, mining analysis, predictive analysis and visual analysis. Statistical analysis grasps the basic characteristics of customer groups through descriptive statistics. Mining analysis uses data mining technology to reveal deep information in customer data. Predictive analysis uses time series analysis technology based on historical transaction data and business rules to predict sales trends in the future. Visual analysis converts complex data into intuitive and easy-to-understand charts and graphs through data visualization technology.

5. The CRM-based customer relationship intelligent management and analysis system according to claim 3 is characterized by: The process of predictive modeling is as follows: S1. Build a prediction model using logistic regression, random forest and deep learning algorithms based on the results of data analysis; S2. Use historical data to train the model and continuously adjust the model parameters; S3. Evaluate the trained model using test data. S4. Apply the optimized model to actual business scenarios.

6. The CRM-based customer relationship intelligent management and analysis system according to claim 1, characterized in that: The front-end interaction layer includes obtaining the required customer data and business information through the Web and mobile application login interfaces.

7. The CRM-based customer relationship intelligent management and analysis system according to claim 1, characterized in that: The data service layer includes data integration and consolidation, which includes collecting customer data from various sources. Data integration and consolidation also includes cleaning, deduplication and standardization of the collected data and integrating it into a unified CRM database.

8. The CRM-based customer relationship intelligent management and analysis system according to claim 4, characterized in that: The basic characteristics include the number of customers, geographical distribution and consumption levels. The deep information mines the hidden patterns in customer purchasing behavior based on association rules, revealing which products or services are often purchased together. Predictive analysis also includes the use of regression analysis to explore the impact of various market factors on customer behavior.

9. The CRM-based customer relationship intelligent management and analysis system according to claim 7, characterized in that: Various sources include one or more of social media, email, website, call center, and mobile application.

10. The CRM-based customer relationship intelligent management and analysis system according to claim 1, characterized in that: The infrastructure layer includes servers, networks, and storage.

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