AI-based customer behavior analysis and prediction system and method

Abstract

The invention discloses an AI-based customer behavior analysis and prediction system and method, and the system comprises a data collection module which is used for automatically collecting customer behavior data from a plurality of data sources; the data processing module is used for cleaning and standardizing the collected data so as to facilitate analysis; the AI model training module is used for training a machine learning model by utilizing the processed data; the prediction analysis module is used for predicting future behaviors of the customer by using the trained AI model; the user interface module is used for displaying a prediction result and allowing a user to adjust prediction parameters; the report generation module is used for automatically generating a report containing prediction information according to the selection of the user; the customer behavior data can be automatically collected from multiple data sources, the customer behavior mode can be learned and recognized through advanced data processing and the AI model training module, the prediction accuracy can be improved, the user experience can be improved, and enterprises can better adapt to market changes and customer requirements.

Description

Technical Field

[0001] The present invention belongs to the field of analysis and prediction, and more specifically, it particularly relates to an AI-based customer behavior analysis and prediction system. At the same time, the present invention also relates to an AI-based customer behavior analysis and prediction method. Background Art

[0002] In the current fast-evolving business environment, enterprises are facing fierce market competition and rapid changes in customer needs. In order to maintain a competitive advantage, enterprises must have a deep understanding of customer behavior and make quick and accurate decisions based on it. Traditional customer behavior analysis methods rely on manual data collection and analysis, which is not only time-consuming and labor-intensive, but also difficult to process massive amounts of data. With the advancement of artificial intelligence (AI) technology, AI-based customer behavior analysis and prediction systems have emerged, providing enterprises with an efficient and automated solution.

[0003] Although existing AI analysis systems can collect data from multiple data sources and use machine learning models for analysis and prediction, these systems often lack user interaction and customized reports. Users may find it difficult to understand complex data analysis results and cannot adjust prediction parameters or customize report content according to personal needs, which limits the system's usability and user experience.

[0004] In addition, as the amount of data continues to increase and customer behavior continues to change, the system needs to continuously update and optimize the model to maintain the accuracy of the prediction. Existing systems also face challenges in automatically updating models and adapting to new customer behavior data. Therefore, we propose an AI-based customer behavior analysis and prediction system and method to solve the above problems. Summary of the invention

[0005] The purpose of the present invention is to address the shortcomings of the prior art, and the proposed AI-based customer behavior analysis and prediction system and method can not only automatically collect customer behavior data from multiple data sources, but also learn and identify customer behavior patterns through advanced data processing and AI model training modules, which can improve the accuracy of predictions, enhance user experience, and enable enterprises to better adapt to market changes and customer needs.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] AI-based customer behavior analysis and prediction system, including:

[0008] A data collection module for automatically collecting customer behavior data from multiple data sources;

[0009] Data processing module, used to clean and standardize the collected data for analysis;

[0010] The AI ​​model training module uses the processed data to train a machine learning model that can learn and identify customer behavior patterns;

[0011] The AI ​​model training module includes:

[0012] The feature selection submodule is used to identify and select the most influential features from the data;

[0013] Model optimization submodule, used to optimize model performance through algorithm selection and parameter tuning;

[0014] The validation submodule is used to evaluate the generalization ability of the model through the cross-validation method;

[0015] Predictive analysis module, which uses trained AI models to predict customers’ future behavior;

[0016] The prediction analysis module includes:

[0017] Real-time analysis submodule, used to monitor and analyze customers' current behavior data in real time;

[0018] Trend analysis submodule, used to identify long-term customer behavior trends;

[0019] Anomaly detection submodule, used to detect abnormal behaviors that are significantly different from normal behavior patterns;

[0020] The user interface module provides a graphical interface for user interaction, displays the prediction results and allows the user to adjust the prediction parameters;

[0021] The user interface module comprises:

[0022] Customizable report unit, allowing users to select the content and format of the report according to their needs;

[0023] The chart display unit displays the forecast results and historical data comparison in an intuitive way;

[0024] Feedback unit, where users can provide feedback to help improve the accuracy of the system and the user experience;

[0025] A report generation module that generates reports containing forecast information based on user selection or automatically; the system can automatically update the model to improve the accuracy of the forecast and can adapt to new customer behavior data;

[0026] The report generation module comprises:

[0027] Automatic summary submodule, which is used to extract key information and generate concise report summaries;

[0028] Customizable output submodule, allowing users to customize the detail and focus of reports according to their needs;

[0029] The distribution submodule is used to distribute the generated reports to relevant stakeholders through email or other electronic means.

[0030] The AI-based customer behavior analysis and prediction method includes the following steps:

[0031] S1. Automatically collect customer behavior data from multiple data sources, clean and standardize the collected data for easy analysis;

[0032] S2. Using the processed data to train a machine learning model, the model can learn and identify customer behavior patterns; the training of the machine learning model includes:

[0033] S21, feature selection, used to identify and select the most influential features from the data;

[0034] S22, model optimization, used to optimize model performance through algorithm selection and parameter tuning;

[0035] S23, validation, is used to evaluate the generalization ability of the model through the cross-validation method;

[0036] S3. Use the trained AI model to perform predictive analysis on the customer's future behavior, including:

[0037] S31, real-time analysis, used to monitor and analyze customers’ current behavior data in real time;

[0038] S32, Trend analysis, used to identify long-term customer behavior trends;

[0039] S33, anomaly detection, used to detect abnormal behaviors that are significantly different from normal behavior patterns;

[0040] S4. Providing a graphical user interface for user interaction, displaying the prediction results and allowing the user to adjust the prediction parameters, wherein the user interface user interaction includes:

[0041] S41, custom report unit, allows users to select the content and format of the report according to their needs;

[0042] S42, a chart display unit, which displays the forecast results and historical data comparison in an intuitive way;

[0043] S43, feedback unit, users can provide feedback to help improve the accuracy of the system and user experience;

[0044] S5. Generate a report containing forecast information according to the user's selection or automatically, wherein the report generation includes:

[0045] S51, automatic summarization, used to extract key information and generate concise report summaries;

[0046] S52, customized output, allowing users to customize the level of detail and focus of the report according to their needs;

[0047] S53, Distribution, is used to distribute the generated report to relevant stakeholders via email or other electronic means.

[0048] Preferably, step S1 specifically comprises:

[0049] S11. Identify data sources for collecting customer behavior data, including website click streams, transaction records, social media interactions, and customer service logs;

[0050] S12. Design data collection methods based on the API or database interface of each data source and perform periodic data extraction;

[0051] S13, import the data collected from different sources into a central database or database, and implement data cleaning to identify and handle missing values, outliers, and duplicate data;

[0052] S14. Build features for machine learning, including data aggregation, categorical variable encoding, store data, and optimize data indexes to improve the efficiency of subsequent queries.

[0053] Preferably, in step S21, it is necessary to evaluate the linear relationship and importance relationship between the customer behavior data features and the target variable respectively. Feature selection is used to identify the linear relationship between the collected customer behavior data features and the target variable. The expression is:

[0054] ;

[0055] Where cov(X,Y) is the covariance of X and Y, σ X and σ Y are the standard deviations of X and Y respectively; this expression calculates the correlation coefficient between the feature and the target variable. The closer the coefficient is to 1 or -1, the stronger the linear relationship between the two is. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and a value close to 0 indicates no linear relationship.

[0056] The feature importance evaluation expression is:

[0057] ;

[0058] In the formula, Importance j represents the importance score of the jth feature, n represents the number of samples, x i,j represents the value of the i-th sample on the j-th feature, x i+1,j : represents the value of the i+1th sample on the jth feature, (xi,j −x i+1,j ) 2 Represents the square of the difference between the values ​​of adjacent samples on the jth feature;

[0059] Step S21 helps researchers or developers identify and select the most influential features by quantifying the contribution of each feature to the model's prediction performance, thereby improving the performance and interpretability of the model.

[0060] Preferably, in step S22, the model optimization is performed by combining hyperparameter tuning with ensemble learning, and the hyperparameter tuning is specifically as follows:

[0061] ;

[0062] Where θ is the model parameter set, f(x i ;θ) is the given parameter θ and input x i The predicted output of the model at time y i is the actual observed value of the ith data point, (y i −f(x i ;θ)) 2 is the square of the prediction error, that is, the square of the difference between the actual observation and the model prediction, is the sum of squared prediction errors of all data points, also known as the squared loss function;

[0063] Preferably, in step S22, the ensemble learning expression is:

[0064] ;

[0065] In the formula, e i is the error of the ith basis model, N is the number of basis models, is the sum of all basis model errors, It means taking the average, that is, dividing the total error by the number of base models to get the average error.

[0066] Preferably, in step S3, the real-time analysis expression is:

[0067] ;

[0068] Where x[t−i] is the data point in the time window. Specifically, x[t−i] represents the i-th data point before time t, and n is the window size, that is, the number of data points used to calculate the average. It means to sum the current time t and the n-1 data points before it. It is the calculation of the average value of n data points, which ensures the correctness of the average value;

[0069] The trend analysis is achieved by linear trend line fitting combined with exponential smoothing, and the linear trend line fitting expression is:

[0070] ;

[0071] In the formula, m is the slope, which describes the strength and direction of the linear relationship between variables x and y. A positive slope indicates a positive correlation, and a negative slope indicates a negative correlation. The size of the slope determines the strength of the correlation. b is the intercept, which represents the intercept point of the straight line on the y-axis and is used to understand the expected value of the dependent variable y when the independent variable x does not exist.

[0072] Preferably, the step S41 is specifically as follows:

[0073] Dynamic report generation: users select templates and parameters to generate customized reports based on their personal needs. The system provides a variety of preset templates, including sales analysis and customer churn prediction, and allows users to customize the content of the report, including selecting data dimensions, time ranges, and statistical indicators.

[0074] Export and share functions: the generated reports can be exported in different formats and shared via email or instant messaging platforms, facilitating team collaboration and decision-making;

[0075] The step S42 is specifically as follows:

[0076] Real-time data visualization, the interface contains real-time updated charts, showing the latest data analysis results and trends, including the use of line charts to show the trend of sales volume, and bar charts to show the sales volume comparison of different products or services;

[0077] Compare the cross-cloud graph with historical data. Users can adjust the time range to view data comparisons for different time periods, and gain in-depth insights into the reasons and relationships behind the data through interactive charts.

[0078] The step S43 is specifically as follows:

[0079] User feedback collection: The interface provides an easily accessible feedback button, allowing users to submit their opinions and suggestions directly on the interface. The feedback form supports multiple input types, including text, ratings, and multiple selections, to collect specific and useful feedback;

[0080] Feedback processing process: All user feedback is recorded and classified, and is regularly reviewed and processed by the review team. Common problems and important feedback will be adjusted and optimized in a timely manner, and the update log will record and notify users of the details of the improvements.

[0081] Preferably, step S51 is specifically as follows:

[0082] Key indicator extraction, the system automatically identifies the most important data and trends in the report, including sales growth, customer churn rate, market trends, and summarizes this information into a concise summary;

[0083] Intelligent text generation, using natural language processing technology to automatically generate text descriptions of report summaries, directly explaining the meaning of data in a readable form, reducing the need for users to interpret complex charts;

[0084] Step S52 is specifically as follows:

[0085] Dynamic report parameter adjustment: users can adjust report parameters through the interface, select different data sets, apply different analysis models, and adjust time ranges to customize the content and focus of the report;

[0086] Report style customization, providing a variety of preset report templates and style options, allowing users to customize the appearance of reports according to personal or company brand style, including color schemes, font selection, and chart types;

[0087] Step S53 is specifically as follows:

[0088] Automatic distribution system, users can set the automatic distribution time of reports and send the latest reports to team members, management or external partners on a daily, weekly or monthly basis;

[0089] Multi-channel distribution options: Reports are distributed through a variety of channels, including instant messaging platforms, corporate social networks, and print mail, in addition to email, to ensure that recipients can obtain reports in a timely manner on any device or platform.

[0090] Technical effects and advantages of the present invention: Compared with the prior art, the AI-based customer behavior analysis and prediction system and method provided by the present invention have the following effects:

[0091] Improve decision-making efficiency. Automated data collection and processing reduce manual intervention and speed up data analysis. Real-time analysis and trend forecasting enable enterprises to respond quickly to market changes and improve the timeliness of decision-making.

[0092] Enhance prediction accuracy. Through feature selection and model optimization, key influencing factors can be identified and high-performance prediction models can be built. Hyperparameter tuning and ensemble learning further improve the prediction accuracy of the model.

[0093] Personalized user experience, graphical user interface and customized reporting functions meet the personalized needs of different users; users can adjust forecast parameters according to their personal preferences and generate reports that meet their needs;

[0094] Facilitate customer insights, capable of processing and analyzing massive amounts of customer behavior data to reveal hidden patterns and trends; anomaly detection capabilities help identify potential risks or market opportunities;

[0095] Improve report practicality. Automatic summary and intelligent text generation simplify the report reading process, making it easy for non-professionals to understand complex data. Customized output allows users to adjust the content and format of the report as needed.

[0096] Optimize collaboration and communication. Automatic distribution of reports and multi-channel distribution options ensure timely delivery of information and facilitate collaboration and communication between team members, management or external partners.

[0097] Continuous improvement and adaptability: the user feedback unit enables the system to continuously collect user opinions and make timely optimization and adjustments; the automatic model update function ensures that the system can adapt to new customer behavior data and maintain long-term effectiveness;

[0098] Enhanced security and privacy protection: the system uses advanced data security technology and privacy protection measures to ensure the security and compliance of customer data;

[0099] Reduce operating costs. Automated and intelligent processes reduce dependence on professionals and reduce labor costs. At the same time, efficient data processing and accurate predictions reduce resource waste.

[0100] Enhance competitiveness. Through in-depth customer demand analysis and accurate market forecasting, companies can formulate more precise market strategies to gain advantages in a highly competitive market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] Figure 1 This is a structural diagram of the AI-based customer behavior analysis and prediction system of the present invention;

[0102] Figure 2 This is a flow chart of the AI-based customer behavior analysis and prediction method of the present invention;

[0103] Figure 3 This is a flow chart of automatically collecting customer behavior data from multiple data sources in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0105] The present invention provides an AI-based customer behavior analysis and prediction system and method, which can not only automatically collect customer behavior data from multiple data sources, but also learn and identify customer behavior patterns through advanced data processing and AI model training modules. The prediction analysis module is used to predict the future behavior of customers and interact with users through a graphical user interface to display prediction results and allow users to adjust prediction parameters. In addition, the system and method can also automatically generate reports containing prediction information based on user selections and distribute them to relevant stakeholders via email or other electronic means;

[0106] like Figure 1 As shown in the figure, the AI-based customer behavior analysis and prediction system specifically includes:

[0107] A data collection module for automatically collecting customer behavior data from multiple data sources;

[0108] Data processing module, used to clean and standardize the collected data for analysis;

[0109] The AI ​​model training module uses the processed data to train a machine learning model that can learn and identify customer behavior patterns;

[0110] AI model training modules include:

[0111] The feature selection submodule is used to identify and select the most influential features from the data;

[0112] Model optimization submodule, used to optimize model performance through algorithm selection and parameter tuning;

[0113] The validation submodule is used to evaluate the generalization ability of the model through the cross-validation method;

[0114] Predictive analysis module, which uses trained AI models to predict customers’ future behavior;

[0115] The predictive analysis module includes:

[0116] Real-time analysis submodule, used to monitor and analyze customers' current behavior data in real time;

[0117] Trend analysis submodule, used to identify long-term customer behavior trends;

[0118] Anomaly detection submodule, used to detect abnormal behaviors that are significantly different from normal behavior patterns;

[0119] The user interface module provides a graphical interface for user interaction, displays the prediction results and allows the user to adjust the prediction parameters;

[0120] The user interface module includes:

[0121] Customizable report unit, allowing users to select the content and format of the report according to their needs;

[0122] The chart display unit displays the forecast results and historical data comparison in an intuitive way;

[0123] Feedback unit, where users can provide feedback to help improve the accuracy of the system and the user experience;

[0124] A report generation module that generates reports containing forecast information based on user selection or automatically; the system can automatically update the model to improve the accuracy of the forecast and can adapt to new customer behavior data;

[0125] The report generation module includes:

[0126] Automatic summary submodule, which is used to extract key information and generate concise report summaries;

[0127] Customizable output submodule, allowing users to customize the report detail and focus according to their needs;

[0128] The distribution submodule is used to distribute the generated reports to relevant stakeholders through email or other electronic means.

[0129] Based on the above, the data collection module, as the front end of the system, is responsible for monitoring and collecting customer behavior data from multiple channels; the data includes but is not limited to website visit records, shopping habits, service feedback, and social media interactions; the key point of this module is that it can obtain data in real time and ensure the accuracy and timeliness of the data, laying a solid foundation for subsequent analysis;

[0130] The data processing module is responsible for cleaning and standardizing raw data. Since raw data often contains noise and irrelevant information, this module ensures data quality and consistency through a series of preprocessing steps, including removing outliers, filling missing data, and standardizing data formats. This not only improves the efficiency of data use, but also ensures the reliability of analysis results.

[0131] The AI ​​model training module is the core part of the system, which consists of three sub-modules: feature selection, model optimization, and verification. The feature selection sub-module uses advanced algorithms to identify the most influential data features to improve model accuracy and reduce invalid calculations. The model optimization sub-module uses parameter tuning and algorithm comparison to find the most suitable model architecture and hyperparameter combination to improve model performance. Finally, the verification sub-module uses cross-validation technology to test the generalization ability of the model to ensure that the model can perform well when facing new data.

[0132] The predictive analysis module uses trained AI models to predict customers' future behavior. It includes three sub-modules: real-time analysis, trend analysis, and anomaly detection. The real-time analysis sub-module can instantly capture changes in customer behavior and help companies respond quickly. The trend analysis sub-module reveals long-term trends through historical data analysis to provide support for strategic decision-making. The anomaly detection sub-module focuses on identifying abnormal behaviors, which is of great value in preventing fraud and reducing losses.

[0133] The user interface module provides users with a graphical interface for interacting with the system; through the customized report unit, users can select the content and format of the report according to their needs; the chart display unit presents the analysis results in an intuitive way, enhancing the user experience; the feedback unit allows users to make improvement suggestions to continuously optimize system performance;

[0134] The report generation module is responsible for automatically generating reports based on user settings or system default settings; the automatic summary submodule extracts key information and generates concise and clear report summaries; the customized output submodule allows users to customize report content according to specific needs; the distribution submodule sends reports to relevant stakeholders in a timely manner via email to ensure efficient information delivery;

[0135] The system integrates data collection, processing, AI model training, predictive analysis and user interaction to form a powerful and complete analysis tool. The system can not only automatically update the model to improve prediction accuracy, but also adapt to new customer behavior data and continuously optimize analysis results. For enterprises, it can more accurately grasp customer needs, predict market trends, optimize products and services, and thus improve customer satisfaction and loyalty. At the same time, by timely detecting abnormal behavior, enterprises can also effectively prevent risks and reduce potential losses.

[0136] In actual applications, companies use the insights generated by the system to guide marketing activities and achieve precision marketing; optimize inventory management and reduce inventory costs; and even provide directional guidance for the company's new product development through an in-depth understanding of customer behavior; in addition, the user feedback unit provided by the system also provides a way to continuously improve products and services, enabling companies to constantly adjust their strategies to adapt to market changes.

[0137] In this embodiment, a customer behavior analysis and prediction method based on AI is also proposed. The method adopts the above system, such as Figure 2 As shown, the following steps are included:

[0138] S1. Automatically collect customer behavior data from multiple data sources, clean and standardize the collected data for easy analysis; e.g. Figure 3 As shown, step S1 is specifically as follows:

[0139] S11. Identify data sources for collecting customer behavior data, including website clickstreams, transaction records, social media interactions, and customer service logs;

[0140] S12. Design data collection methods based on the API or database interface of each data source and perform periodic data extraction;

[0141] S13, import the data collected from different sources into a central database or database, and implement data cleaning to identify and handle missing values, outliers, and duplicate data;

[0142] S14. Build features for machine learning, including data aggregation, categorical variable encoding, store data, and optimize data indexes to improve the efficiency of subsequent queries.

[0143] S2. Using the processed data to train a machine learning model that can learn and identify customer behavior patterns; training the machine learning model includes:

[0144] S21, feature selection, used to identify and select the most influential features from the data; in step S21, it is necessary to evaluate the linear relationship and importance relationship between the customer behavior data features and the target variable respectively. Feature selection is used to identify the linear relationship between the collected customer behavior data features and the target variable. The expression is:

[0145] ;

[0146] Where cov(X,Y) is the covariance of X and Y, σ X and σ Y are the standard deviations of X and Y respectively; this expression calculates the correlation coefficient between the feature and the target variable. The closer the coefficient is to 1 or -1, the stronger the linear relationship between the two is. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and a value close to 0 indicates no linear relationship.

[0147] Feature importance evaluation, the expression is:

[0148] ;

[0149] In the formula, Importance j represents the importance score of the jth feature, n represents the number of samples, x i,j represents the value of the i-th sample on the j-th feature, x i+1,j : represents the value of the i+1th sample on the jth feature, (x i,j −x i+1,j ) 2 Represents the square of the difference between the values ​​of adjacent samples on the jth feature;

[0150] Step S21 helps researchers or developers identify and select the most influential features by quantifying the contribution of each feature to the model prediction performance, thereby improving the performance and interpretability of the model;

[0151] S22, model optimization, used to optimize model performance through algorithm selection and parameter tuning; in step S22, model optimization is optimized by hyperparameter tuning combined with ensemble learning, and hyperparameter tuning is specifically as follows:

[0152] ;

[0153] Where θ is the model parameter set, f(x i ;θ) is the given parameter θ and input x i The predicted output of the model at time y i is the actual observed value of the ith data point, (y i −f(x i ;θ)) 2 is the square of the prediction error, that is, the square of the difference between the actual observation and the model prediction, is the sum of squared prediction errors of all data points, also known as the squared loss function;

[0154] In step S22, the ensemble learning expression is:

[0155] ;

[0156] In the formula, e i is the error of the ith basis model, N is the number of basis models, is the sum of all basis model errors, It means taking the average, that is, dividing the total error by the number of base models to get the average error;

[0157] S23, validation, is used to evaluate the generalization ability of the model through a cross-validation method; wherein, the validation is performed by combining cross-validation with performance measurement, and the cross-validation expression is:

[0158] ;

[0159] Among them, E iis the test error of the i-th fold, and k is the total number of folds. When performing k-fold cross-validation, first determine the number of folds k, and then divide the data set into k equal parts. For each part, use it as a test set, and combine the rest as a training set to train and test the model, and record the test error of that time. Repeat this process k times, and finally average all test errors to get the cross-validation error estimate. K-fold cross-validation can effectively utilize all data, both for training and testing, and reduce resource waste. Through multiple iterations, the model's dependence on data is reduced, and the model's generalization ability is enhanced. In addition, this method provides a more stable and reliable estimate of model performance.

[0160] The performance measurement expression is:

[0161] ;

[0162] Among them, TP, FP and FN are the number of true positives, false positives and false negatives respectively;

[0163] As mentioned above, during the k-fold cross validation process, a confusion matrix is ​​calculated for each fold, and then the precision, recall and F1 score of the fold are obtained, and then the indicators are averaged to obtain the performance evaluation of the overall model;

[0164] Exemplarily, the above method is used to train a customer behavior analysis prediction model, which predicts future behavior trends, including purchase tendencies and possible churn, by analyzing customers' historical behavior data. Accurate predictions can help companies optimize resource allocation and improve customer satisfaction and loyalty.

[0165] First, k-fold cross validation was used to train and test different model algorithms.

[0166] Then, for each fold result, the confusion matrix is ​​calculated to get the precision, recall, and F1 score for each fold.

[0167] Finally, these indicators are combined to train the model to ensure the stability and accuracy of the model on various data sets, especially for the dynamic and diverse customer behavior data;

[0168] By combining the k-fold cross-validation error estimation and confusion matrix, we can not only evaluate the generalization ability of the model on different data sets, but also gain an in-depth understanding of the model's performance on specific classification tasks. This combined method provides a comprehensive and reliable evaluation method for this application, helping companies make more accurate decisions in a complex and changing market environment.

[0169] S3. Use the trained AI model to predict the customer's future behavior. The predictive analysis includes:

[0170] S31, real-time analysis, used to monitor and analyze the customer's current behavior data in real time; the real-time analysis expression is:

[0171] ;

[0172] Where x[t−i] is the data point in the time window. Specifically, x[t−i] represents the i-th data point before time t, and n is the window size, that is, the number of data points used to calculate the average. It means to sum the current time t and the n-1 data points before it. It is the calculation of the average value of n data points, which ensures the correctness of the average value;

[0173] S32. Trend analysis is used to identify long-term customer behavior trends. Trend analysis is achieved by linear trend line fitting combined with exponential smoothing. The linear trend line fitting expression is:

[0174] ;

[0175] Where m is the slope, which describes the strength and direction of the linear relationship between variables x and y. A positive slope indicates a positive correlation, and a negative slope indicates a negative correlation. The size of the slope determines the strength of the correlation. b is the intercept, which represents the point where the straight line intercepts on the y-axis and is used to understand the expected value of the dependent variable y when there is no independent variable x.

[0176] S33, anomaly detection, used to find abnormal behaviors that are significantly different from normal behavior patterns; anomaly detection is performed using a distance-based method, expressed as:

[0177] ;

[0178] In the formula, x is the data point vector, μ is the mean vector, and S-1 is the inverse of the covariance matrix. In outlier identification, the Mahalanobis distance between each sample point and the mean of the data set is calculated. The larger the distance, the higher the probability of abnormality of the sample point. In practical application, the distance threshold is set according to the actual situation. If the threshold is exceeded, it is judged as data anomaly. This method not only considers the covariance of the data, but also eliminates the interference of the correlation between variables, making anomaly detection more accurate.

[0179] S4. Provide a graphical user interface for user interaction, display the prediction results and allow the user to adjust the prediction parameters. The user interface user interaction includes:

[0180] S41, custom report unit, allowing the user to select the content and format of the report according to the needs; step S41 is specifically as follows:

[0181] Dynamic report generation: users select templates and parameters to generate customized reports based on their personal needs. The system provides a variety of preset templates, including sales analysis and customer churn prediction, and allows users to customize the content of the report, including selecting data dimensions, time ranges, and statistical indicators.

[0182] Export and share functions: the generated reports can be exported in different formats and shared via email or instant messaging platforms, facilitating team collaboration and decision-making;

[0183] S42, a chart display unit, which displays the prediction results and historical data comparison in an intuitive way; step S42 is specifically as follows:

[0184] Real-time data visualization, the interface contains real-time updated charts, showing the latest data analysis results and trends, including the use of line charts to show the trend of sales volume, and bar charts to show the sales volume comparison of different products or services;

[0185] Compare the cross-cloud graph with historical data. Users can adjust the time range to view data comparisons for different time periods, and gain in-depth insights into the reasons and relationships behind the data through interactive charts.

[0186] S43, feedback unit, the user can provide feedback to help improve the accuracy of the system and user experience; step S43 is specifically:

[0187] User feedback collection: The interface provides an easily accessible feedback button, allowing users to submit their opinions and suggestions directly on the interface. The feedback form supports multiple input types, including text, ratings, and multiple selections, to collect specific and useful feedback;

[0188] Feedback processing process: All user feedback is recorded and classified, and is regularly reviewed and processed by the review team. Common problems and important feedback will be adjusted and optimized in a timely manner, and the update log will record and notify users of the details of the improvement;

[0189] S5. Generate a report containing forecast information according to the user's selection or automatically, and the report generation includes:

[0190] S51, automatic summary, used to extract key information and generate a concise report summary; step S51 is specifically as follows:

[0191] Key indicator extraction, the system automatically identifies the most important data and trends in the report, including sales growth, customer churn rate, market trends, and summarizes this information into a concise summary;

[0192] Intelligent text generation, using natural language processing technology to automatically generate text descriptions of report summaries, directly explaining the meaning of data in a readable form, reducing the need for users to interpret complex charts;

[0193] S52, customized output, allowing users to customize the detail and focus of the report according to their needs; step S52 is specifically as follows:

[0194] Dynamic report parameter adjustment: users can adjust report parameters through the interface, select different data sets, apply different analysis models, and adjust time ranges to customize the content and focus of the report;

[0195] Report style customization, providing a variety of preset report templates and style options, allowing users to customize the appearance of reports according to personal or company brand style, including color schemes, font selection, and chart types;

[0196] S53, distribution, for distributing the generated report to relevant stakeholders via email or other electronic means; step S53 specifically comprises:

[0197] Automatic distribution system, users can set the automatic distribution time of reports and send the latest reports to team members, management or external partners on a daily, weekly or monthly basis;

[0198] Multi-channel distribution options: Reports are distributed through a variety of channels, including instant messaging platforms, corporate social networks, and print mail, in addition to email, to ensure that recipients can obtain reports in a timely manner on any device or platform.

[0199] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. AI-based customer behavior analysis and prediction system, characterized by: include: A data collection module for automatically collecting customer behavior data from multiple data sources; Data processing module, used to clean and standardize the collected data for analysis; The AI ​​model training module uses the processed data to train a machine learning model that can learn and identify customer behavior patterns; The AI ​​model training module includes: The feature selection submodule is used to identify and select the most influential features from the data; Model optimization submodule, used to optimize model performance through algorithm selection and parameter tuning; The validation submodule is used to evaluate the generalization ability of the model through the cross-validation method; Predictive analysis module, which uses trained AI models to predict customers’ future behavior; The prediction analysis module includes: Real-time analysis submodule, used to monitor and analyze customers’ current behavior data in real time; Trend analysis submodule, used to identify long-term customer behavior trends; Anomaly detection submodule, used to detect abnormal behaviors that are significantly different from normal behavior patterns; The user interface module provides a graphical interface for user interaction, displays the prediction results and allows the user to adjust the prediction parameters; The user interface module comprises: Customizable report unit, allowing users to select the content and format of the report according to their needs; The chart display unit displays the forecast results and historical data comparison in an intuitive way; Feedback unit, where users can provide feedback to help improve the accuracy of the system and the user experience; A report generation module that generates reports containing forecast information based on user selection or automatically; the system can automatically update the model to improve the accuracy of the forecast and can adapt to new customer behavior data; The report generation module comprises: Automatic summary submodule, which is used to extract key information and generate concise report summaries; Customizable output submodule, allowing users to customize the report detail and focus according to their needs; The distribution submodule is used to distribute the generated reports to relevant stakeholders through email or other electronic means.

2. A customer behavior analysis and prediction method based on AI, wherein the method adopts the system of claim 1, characterized in that: The steps include: S1. Automatically collect customer behavior data from multiple data sources, clean and standardize the collected data for easy analysis; S2. Use the processed data to train a machine learning model that can learn and identify customer behavior patterns; The training of the machine learning model comprises: S21, feature selection, used to identify and select the most influential features from the data; S22, model optimization, used to optimize model performance through algorithm selection and parameter tuning; S23, validation, is used to evaluate the generalization ability of the model through the cross-validation method; S3. Use the trained AI model to perform predictive analysis on the customer's future behavior, including: S31, real-time analysis, used to monitor and analyze customers’ current behavior data in real time; S32, Trend analysis, used to identify long-term customer behavior trends; S33, anomaly detection, used to detect abnormal behaviors that are significantly different from normal behavior patterns; S4. Providing a graphical user interface for user interaction, displaying the prediction results and allowing the user to adjust the prediction parameters, wherein the user interface user interaction includes: S41, custom report unit, allows users to select the content and format of the report according to their needs; S42, a chart display unit, which displays the forecast results and historical data comparison in an intuitive way; S43, feedback unit, users can provide feedback to help improve the accuracy of the system and user experience; S5. Generate a report containing forecast information according to the user's selection or automatically, wherein the report generation includes: S51, automatic summarization, used to extract key information and generate concise report summaries; S52, customized output, allowing users to customize the level of detail and focus of the report according to their needs; S53, Distribution, is used to distribute the generated report to relevant stakeholders via email or other electronic means.

3. The AI-based customer behavior analysis and prediction method according to claim 2 is characterized in that: Step S1 is specifically as follows: S11. Identify data sources for collecting customer behavior data, including website click streams, transaction records, social media interactions, and customer service logs; S12. Design data collection methods based on the API or database interface of each data source and perform periodic data extraction; S13, import the data collected from different sources into a central database or database, and implement data cleaning to identify and handle missing values, outliers, and duplicate data; S14. Build features for machine learning, including data aggregation, categorical variable encoding, store data, and optimize data indexes to improve the efficiency of subsequent queries.

4. The AI-based customer behavior analysis and prediction method according to claim 2 is characterized in that: In step S21, it is necessary to evaluate the linear relationship and importance relationship between the customer behavior data features and the target variable respectively. Feature selection is used to identify the linear relationship between the collected customer behavior data features and the target variable. The expression is: ； Where cov(X,Y) is the covariance of X and Y, σ X and σ Y are the standard deviations of X and Y respectively; this expression calculates the correlation coefficient between the feature and the target variable. The closer the coefficient is to 1 or -1, the stronger the linear relationship between the two is. A positive value indicates a positive correlation, a negative value indicates a negative correlation, and a value close to 0 indicates no linear relationship. The feature importance evaluation expression is: ； In the formula, Importance j represents the importance score of the jth feature, n represents the number of samples, x i,j represents the value of the i-th sample on the j-th feature, x i+1,j : represents the value of the i+1th sample on the jth feature, (x i,j −x i+1,j ) 2 Represents the square of the difference between the values ​​of adjacent samples on the jth feature; Step S21 helps researchers or developers identify and select the most influential features by quantifying the contribution of each feature to the model's prediction performance, thereby improving the performance and interpretability of the model.

5. The AI-based customer behavior analysis and prediction method according to claim 4 is characterized in that: In step S22, the model is optimized by combining hyperparameter tuning with ensemble learning. The hyperparameter tuning is specifically as follows: ； Where θ is the model parameter set, f(x i ;θ) is the given parameter θ and input x i The predicted output of the model at time y i is the actual observed value of the ith data point, (y i −f(x i ;θ)) 2 is the square of the prediction error, that is, the square of the difference between the actual observation and the model prediction, is the sum of squared prediction errors for all data points, also known as the squared loss function.

6. The AI-based customer behavior analysis and prediction method according to claim 5, characterized in that: In step S22, the ensemble learning expression is: ； In the formula, e i is the error of the ith basis model, N is the number of basis models, is the sum of all basis model errors, It means taking the average, that is, dividing the total error by the number of base models to get the average error.

7. The AI-based customer behavior analysis and prediction method according to claim 2, characterized in that: In step S3, the real-time analysis expression is: ； Where x[t−i] is the data point in the time window. Specifically, x[t−i] represents the i-th data point before time t, and n is the window size, that is, the number of data points used to calculate the average. It means to sum the current time t and the n−1 data points before it. It is the calculation of the average value of n data points, which ensures the correctness of the average value; The trend analysis is achieved by linear trend line fitting combined with exponential smoothing, and the linear trend line fitting expression is: ； In the formula, m is the slope, which describes the strength and direction of the linear relationship between variables x and y. A positive slope indicates a positive correlation, and a negative slope indicates a negative correlation. The size of the slope determines the strength of the correlation. b is the intercept, which represents the intercept point of the straight line on the y-axis and is used to understand the expected value of the dependent variable y when the independent variable x does not exist.

8. The AI-based customer behavior analysis and prediction method according to claim 2, characterized in that: The step S41 is specifically as follows: Dynamic report generation: users select templates and parameters to generate customized reports based on their personal needs. The system provides a variety of preset templates, including sales analysis and customer churn prediction, and allows users to customize the content of the report, including selecting data dimensions, time ranges, and statistical indicators. Export and share functions: the generated reports can be exported in different formats and shared via email or instant messaging platforms, facilitating team collaboration and decision-making; The step S42 is specifically as follows: Real-time data visualization, the interface contains real-time updated charts, showing the latest data analysis results and trends, including the use of line charts to show the trend of sales volume, and bar charts to show the sales volume comparison of different products or services; Compare the cross-cloud graph with historical data. Users can adjust the time range to view data comparisons for different time periods, and gain in-depth insights into the reasons and relationships behind the data through interactive charts. The step S43 is specifically as follows: User feedback collection: The interface provides an easily accessible feedback button, allowing users to submit their opinions and suggestions directly on the interface. The feedback form supports multiple input types, including text, ratings, and multiple selections, to collect specific and useful feedback; Feedback processing process: All user feedback is recorded and classified, and is regularly reviewed and processed by the review team. Common problems and important feedback will be adjusted and optimized in a timely manner, and the update log will record and notify users of the details of the improvements.

9. The AI-based customer behavior analysis and prediction method according to claim 2, characterized in that: Step S51 is specifically as follows: Key indicator extraction, the system automatically identifies the most important data and trends in the report, including sales growth, customer churn rate, market trends, and summarizes this information into a concise summary; Intelligent text generation, using natural language processing technology to automatically generate text descriptions of report summaries, directly explaining the meaning of data in a readable form, reducing the need for users to interpret complex charts; Step S52 is specifically as follows: Dynamic report parameter adjustment: users can adjust report parameters through the interface, select different data sets, apply different analysis models, and adjust time ranges to customize the content and focus of the report; Report style customization, providing a variety of preset report templates and style options, allowing users to customize the appearance of reports according to personal or company brand style, including color schemes, font selection, and chart types; Step S53 is specifically as follows: Automatic distribution system, users can set the automatic distribution time of reports and send the latest reports to team members, management or external partners on a daily, weekly or monthly basis; Multi-channel distribution options: Reports are distributed through a variety of channels, including instant messaging platforms, corporate social networks, and print mail, in addition to email, to ensure that recipients can obtain reports in a timely manner on any device or platform.

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