Member health degree evaluation method based on member loss prediction

Abstract

The invention relates to the field of member management and data analysis, and discloses a member health degree evaluation method based on member loss prediction, which comprises the following steps: multi-sensor data acquisition and fusion: collecting behavior data, environment data, social data and equipment data of members through multiple sensors, and performing fusion processing on the data to obtain a fusion result; the accuracy and comprehensiveness of the data are ensured; behavior pattern modeling: based on the collected member behavior data, modeling member behaviors by adopting a nonlinear dynamic system modeling method, and capturing long-term trends and short-term fluctuations of the member behaviors; and member loss prediction: predicting the risk of member loss in the future according to the member behavior data and the loss prediction model, and performing dynamic adjustment. A nonlinear dynamic system model and a fractal analysis model are adopted to model a behavior pattern of a member, and the capability of capturing behavior complexity is improved through phase-space reconstruction and fractal dimension analysis.

Description

Technical Field

[0001] The present invention relates to the field of member management and data analysis, and in particular to a member health assessment method based on member churn prediction. Background Art

[0002] With the rapid development of the Internet and big data technology, more and more merchants and enterprises have begun to rely on membership management systems to maintain relationships with customers and improve user experience and customer stickiness. In this process, member health assessment has become a key link. By analyzing member behavior, merchants can effectively identify potential lost customers and take intervention measures to improve customer retention and loyalty.

[0003] Existing health assessment methods usually rely on a single data source, such as purchase records, login frequency and other behavioral data. These methods fail to fully utilize data from different channels (such as social behavior, device information, external environmental factors, etc.). This leads to insufficient accuracy and comprehensiveness of health assessments, and is unable to fully reflect the behavioral characteristics of members.

[0004] Many traditional member health assessment methods are based on historical data and static scoring, lacking the ability to dynamically respond to changes in member behavior. Member behavior changes dynamically, especially when faced with environmental factors such as seasonal promotions, holidays, and weather changes, member activity and loyalty tend to fluctuate greatly. However, most existing technologies rely on static models and cannot reflect member behavior changes in a timely manner, resulting in a lag in health scoring and churn prediction.

[0005] In view of the deficiencies of the prior art, the present invention provides a member health assessment method based on member churn prediction. Summary of the invention

[0006] In view of the deficiencies of the prior art, the present invention provides a member health assessment method based on member churn prediction, which solves the problems of single data source, lack of dynamic adjustment and personalized assessment in the existing member health assessment methods.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a member health assessment method based on member churn prediction, comprising the following steps: Multi-sensor data collection and fusion: collect members' behavior data, environmental data, social data and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data; Behavior pattern modeling: Based on the collected member behavior data, nonlinear dynamic system modeling methods are used to model member behavior and capture the long-term trend and short-term fluctuations of member behavior; Member churn prediction: Based on member behavior data and churn prediction models, predict the risk of future member churn and make dynamic adjustments; Personalized health assessment: Based on the members' historical behavior data, a health score is generated for each member through a personalized health assessment model.

[0008] Furthermore, multi-sensor data collection and fusion collects members' behavior data, environmental data, social data and device data through multiple sensors to ensure the comprehensiveness and accuracy of the data. Specifically, behavior data includes members' purchase records, click streams, browsing history, and social interactions; environmental data includes promotional activities, weather conditions, and holiday external factors; social data covers members' interactive information on social platforms; and device data includes the types of devices used by members and their stability. These heterogeneous data are effectively fused and processed using advanced data fusion technology to ensure data consistency and validity between different sensors. Secondly, behavior pattern modeling, based on the collected member behavior data, uses nonlinear dynamic system modeling methods to model member behavior and capture long-term trends and short-term fluctuations in member behavior. By constructing a phase space model, introducing fractal theory and nonlinear dynamics models, it is possible to extract potential behavior patterns from complex time series data, and accurately analyze the fluctuations in behavior to reveal the inherent laws of member behavior. Then, member churn prediction, based on member behavior data and the constructed churn prediction model, predicts member churn risk, and combines dynamic adjustment mechanisms to update the prediction results in real time. The churn prediction model uses Kalman filtering and particle swarm optimization algorithms to provide real-time feedback on changes in member behavior, and can promptly identify potential churn risks and provide merchants with effective risk warnings. Next, personalized health assessment uses a personalized health assessment model to generate a health score for each member based on each member's historical behavior data. The personalized health assessment model combines multi-dimensional data on members' consumption behavior, interaction frequency, and social behavior. Based on Bayesian inference and fuzzy comprehensive evaluation methods, it comprehensively considers the weights and influencing factors of each behavioral dimension, thereby generating an accurate health score for each member, helping merchants implement differentiated member management and marketing strategies. Finally, based on the results of the health assessment, merchants can formulate more targeted member retention and incentive measures to effectively improve member loyalty and reduce churn risks.

[0009] Preferably, the method further comprises: health score feedback and dynamic adjustment: by real-time monitoring of member behavior, the health score is dynamically adjusted to ensure the timeliness and accuracy of the evaluation results; Evaluation reports and strategy support: Generate member portraits based on health scores, and provide merchants with decision support for member management and marketing strategies based on this.

[0010] Furthermore, by monitoring members' behaviors in real time, the system can continuously track and analyze changes in members' behaviors and adjust members' health scores in real time. Specifically, when members' behaviors change significantly, the system will automatically identify these changes and adjust the weights of the scoring model according to the changes to ensure that the health assessment always reflects the latest behavior status of the members. For example, if a member frequently conducts high-value transactions in a short period of time, the system will automatically increase his health score; if a member's interaction frequency drops sharply or he does not participate in platform activities for a long time, the system will automatically reduce his score. In addition, the system can also dynamically adjust according to the risk of member churn. When an increase in churn risk is detected, the health score will automatically adjust accordingly so that effective intervention measures can be taken in time. Ultimately, this dynamic adjustment mechanism ensures the real-time, accuracy and adaptability of the health assessment results. Assessment reports and strategy support generate detailed member portraits based on members' health scores. Member portraits include not only basic personal information, but also multi-dimensional characteristics of members' consumption habits, social behaviors, interaction frequency, and purchase preferences. Through comprehensive analysis of member portraits, merchants can fully understand members' needs and behavior trends, and provide personalized member management and marketing strategies based on this. Merchants can formulate precise membership retention plans based on health scores, such as providing exclusive discounts and reward plans for high-health members, and designing recovery strategies for low-health members, such as pushing targeted promotions and personalized recommendations. In addition, merchants can also optimize the effectiveness of marketing activities through health score reports, design different marketing strategies for different health groups of members, thereby improving marketing effectiveness and customer retention rate.

[0011] Preferably, the multi-sensor data collection and fusion step further comprises: Collect members’ purchase records, click streams, browsing history, and social platform interaction data from behavioral sensors; Collect promotional activities, weather conditions, and holiday environment data from environmental sensors; Collect data on the type and stability of devices used by members from device sensors; The data collected by different sensors are fused using the mutual information maximization principle to ensure maximum sharing and minimum redundancy among data.

[0012] Furthermore, the purchase records, clickstreams, browsing histories, and social platform interaction data of members are collected from behavioral sensors. These data can reflect important information about members' activity level, purchase preferences, and interaction habits on the platform. Specifically, purchase records can provide key information about members' consumption frequency, single consumption amount, and purchase types. Clickstreams and browsing histories can reveal members' interest in different products or services, while social platform interaction data can reflect members' social behavior and network influence, such as commenting, sharing, and liking behaviors. By integrating these behavioral data, we can fully understand the activity and loyalty of members, and further provide data support for health assessment. Secondly, we collect promotional activities, weather conditions, and holiday environmental data from environmental sensors. Environmental data can affect members' consumption behavior and platform activity. For example, the time, type, and intensity of promotional activities usually directly promote members' purchasing behavior, while weather conditions and holiday external factors can also have an important impact on members' online activities and consumption habits. By collecting and analyzing these environmental data, the system can more accurately identify which factors have a significant impact on member behavior and adjust the model parameters of health assessment accordingly. Then, the device type and stability data used by members are collected from device sensors. These device data can provide information about the usage of member devices, such as device brand, operating system, and device failure rate. Device type and stability have a direct impact on the member's experience and the accessibility of the platform, which in turn affects the member's behavior pattern and churn probability. For example, members using low-configuration or unstable devices may experience more operation interruptions or login problems on the platform, resulting in an increased risk of churn. Finally, the data collected by different sensors are fused using the principle of mutual information maximization to ensure that data from different sources can share information to the maximum extent and minimize redundancy. By calculating the mutual information between different data sources, the information overlap of multidimensional data in the fusion process is minimized, thereby improving the effect of data fusion, eliminating noise, and improving the accuracy and reliability of member health assessment. This method can effectively integrate information from multiple data sources, provide more comprehensive and accurate data support, and lay a solid foundation for subsequent member behavior analysis, churn prediction, and health assessment.

[0013] Preferably, the behavior pattern modeling step further comprises: Takens theorem is used to reconstruct the phase space of members’ time series data, transforming the data into a high-dimensional phase space to capture the long-term trends and short-term fluctuations of behavior; Use fractal theory to calculate the fractal dimension of member behavior, evaluate the complexity of behavior, and identify potential abnormal behavior.

[0014] Furthermore, the Takens theorem is used to reconstruct the phase space of the members' time series data, and the data is converted into a high-dimensional phase space so as to extract the potential patterns of members' behavior. Specifically, as important information reflecting the dynamic changes of members' behavior, time series data is embedded through Takens theorem to reconstruct a multidimensional phase space, thereby converting time series data into high-dimensional vectors, which can reflect the long-term trend and short-term fluctuations of members' behavior. The phase space reconstruction method can capture the nonlinear characteristics in the behavior pattern, and then reveal the law and change trend of members' behavior. For example, the consumption frequency and purchase category data of a member in multiple time periods can be reconstructed through the relevant space, which can be used to analyze the stability and trend changes of their behavior, thereby providing a data basis for subsequent churn prediction and health assessment. The fractal dimension of member behavior is calculated using fractal theory, and this method is used to quantify the complexity of member behavior. The fractal dimension can reveal the self-similarity and complexity in member behavior, especially when faced with a large amount of dynamic behavior data, it can more effectively identify different levels of behavior patterns. The larger the fractal dimension, the more complex the member behavior, the more randomness and irregularity there are, and vice versa. This method can help identify potential abnormal behavior patterns, such as sudden consumption peaks, frequent device switching, and drastic fluctuations in behavior patterns. These abnormal behaviors are often precursors to member churn. Therefore, calculating the fractal dimension can not only help understand the complexity of member behavior, but also help identify potential abnormal changes in member behavior, providing strong support for subsequent health assessments. Through the combination of correlation space reconstruction and fractal analysis, member behavior patterns can be modeled more accurately, providing a scientific basis for churn prediction, health scoring, and personalized management.

[0015] Preferably, the member churn prediction step further comprises: Based on the collected member data, Kalman filtering is used to smooth the churn prediction results and adjust the prediction value in real time; The parameters of the churn prediction model are adjusted through the particle swarm optimization algorithm to optimize the model accuracy; The maximum entropy principle is used to optimize the churn prediction process, maximize the amount of information in churn prediction, and reduce the uncertainty in prediction.

[0016] Furthermore, based on the collected member data, Kalman filtering is used to smooth the churn prediction results and adjust the prediction values ​​in real time to ensure stability and accuracy during the prediction process. Kalman filtering is a recursive estimation method that can dynamically adjust the prediction results according to the difference between the observed value and the model prediction value to reduce the impact of noise on the prediction. In churn prediction, Kalman filtering can smooth the churn trend so that abnormal fluctuations in the short term will not cause excessive interference to the prediction results, thereby improving the accuracy of the prediction. By updating the state estimation of churn prediction in real time, Kalman filtering can reflect the latest changes in member behavior and adjust the prediction value in time to cope with changes in member churn patterns. The parameters of the churn prediction model are adjusted by the particle swarm optimization algorithm to optimize the model accuracy. The particle swarm optimization algorithm (PSO) is an optimization algorithm that simulates the behavior of groups in nature and optimizes the parameters of the prediction model by searching for the optimal solution. Particle swarm optimization repeatedly iterates and updates the parameters in the churn prediction model to gradually approach the optimal solution, thereby improving the prediction accuracy of the model. In member churn prediction, PSO can adaptively adjust the weights and parameters of the model, so that the model always remains efficient and accurate under different environments and data conditions, avoiding overfitting or underfitting. Use the maximum entropy principle to optimize the churn prediction process, and optimize the churn prediction model by maximizing information entropy. The maximum entropy principle can maximize the amount of information that the system can obtain without sufficient prior information, making the prediction process more objective and unbiased. By introducing the maximum entropy principle in churn prediction, the uncertainty in the data can be effectively reduced, unnecessary assumptions can be avoided, and the generalization ability and robustness of the model can be guaranteed.

[0017] Preferably, the personalized health assessment step further includes: Based on the historical behavior data of members, the Bayesian inference method is used to generate the posterior probability distribution of each member's health parameters; the fuzzy comprehensive evaluation method is combined with the weighted average model to give a comprehensive score to the members' health.

[0018] Preferably, the health score feedback and dynamic adjustment step further includes: Dynamically adjust members’ health scores based on real-time changes in member behavior; When member behavior is abnormal, the calculation weight of the health score is automatically adjusted to enhance the flexibility and accuracy of the assessment.

[0019] Furthermore, based on the historical behavior data of members, the Bayesian inference method is used to generate the posterior probability distribution of the health parameters of each member. The Bayesian inference method dynamically updates the health assessment parameters of each member by combining prior information with current observation data, thereby generating accurate health predictions for each member. By establishing a Bayesian network model, the posterior probability distribution of health parameters can be calculated based on the historical behavior data of members (such as purchase records, browsing history, and social interactions), reflecting the health status of members under different conditions. This method can not only make full use of existing data, but also make reasonable inferences and predictions when the data is incomplete or changes greatly, thereby improving the accuracy and flexibility of health assessment. The fuzzy comprehensive evaluation method is combined with the weighted average model to comprehensively score the health of members. The fuzzy comprehensive evaluation method can handle the ambiguity and uncertainty in member behavior, making the health score more in line with the actual situation. By assigning appropriate weights to each dimension (such as activity, consumption frequency, and social interaction), and combining the weighted average model to comprehensively evaluate each behavioral indicator, a comprehensive health score is generated for each member, helping merchants identify potential high-risk loss members or high-loyalty members so as to adopt personalized management strategies.

[0020] Based on the real-time changes in member behavior, the health score of members is dynamically adjusted. By real-time monitoring of member behavior data, the system can dynamically adjust the health score of members. For example, if a member has frequent purchases or interactions in a short period of time, the system will increase the health score of the member in real time; conversely, if the activity of a member decreases, the system will automatically reduce its health score. This process ensures that the health score can reflect the changes in member behavior in real time and improve the timeliness and accuracy of the evaluation results. When abnormal member behavior occurs, the calculation weight of the health score is automatically adjusted to enhance the flexibility and accuracy of the evaluation. When abnormal fluctuations in member behavior are detected (such as frequent device switching, sudden large-scale consumption, or long-term inactivity), the system will automatically adjust the weight parameters in the scoring model to give appropriate attention to abnormal behavior and avoid deviations caused by over-reliance on a single behavioral dimension. Through this mechanism, the system can not only flexibly respond to dynamic changes in member behavior, but also ensure that the health evaluation remains accurate and robust in the face of complex and changing data, thereby providing merchants with timely member management decision support.

[0021] Preferably, the evaluation report and strategy support step further includes: Generate personalized member portraits based on members' health scores and classify members; Based on member portraits and churn prediction results, we provide merchants with personalized marketing strategies and member management solutions.

[0022] Furthermore, personalized member portraits are generated based on the members' health scores, and members are classified. By combining the members' health scores and other behavioral data (such as consumption frequency, purchase preferences, and interaction), the system can generate detailed personalized member portraits for each member. Member portraits are not only based on basic member information (such as age and gender), but also include their behavioral characteristics on the platform, such as purchasing habits, activity participation, browsing preferences, and multiple dimensions. Through a comprehensive analysis of member portraits, the system can divide members into different groups, such as high-health members, medium-health members, and low-health members. This classification can not only help merchants identify members with higher current loyalty, but also promptly detect potential lost members and high-risk groups, and then carry out targeted management.

[0023] Preferably, the churn prediction model adopts a nonlinear dynamic system model and a fractal analysis model, and further includes: In churn prediction, the parameters of the prediction model are adjusted in real time based on the short-term and long-term change patterns of member behavior; Through phase space reconstruction and fractal dimension analysis, the ability to capture behavioral complexity is improved.

[0024] Preferably, the health scoring method combines multi-sensor data and personalized behavior pattern analysis, and further includes: Multidimensional data of environmental factors, social behavior, and equipment information are used as model inputs to dynamically adjust the weight of the health score; fuzzy comprehensive evaluation method is used to weight data of different dimensions to ensure the objectivity and accuracy of the evaluation results.

[0025] Furthermore, in churn prediction, the parameters of the prediction model are adjusted in real time by combining the short-term and long-term change patterns of member behavior. By real-time monitoring and analysis of member behavior data, the system can identify sudden fluctuations in the short term and long-term trend changes, thereby adjusting the parameters of the churn prediction model in real time. For example, short-term promotional activities or abnormal behaviors may have a significant impact on member activity and churn risk, while long-term trends help reveal members' stable behavior patterns. Through this combination of short-term and long-term change patterns, the prediction model can more accurately reflect the dynamic changes of member churn and provide early warning of impending churn. Through phase space reconstruction and fractal dimension analysis, the ability to capture behavioral complexity is improved. In churn prediction, phase space reconstruction technology is used to transform member behavior data into high dimensions to reveal nonlinear characteristics in behavior, and fractal dimension analysis is used to quantify the complexity of behavior.

[0026] Environmental factors, social behaviors, and device information multidimensional data are used as model inputs to dynamically adjust the weight of the health score. The system conducts a multi-dimensional weighted analysis of the member's health score by comprehensively considering environmental factors (such as promotional activities, weather changes), social behaviors (such as comments, sharing, and interaction frequency), and device information (such as device type and usage stability). Through this diversified data input, the model can dynamically adjust the weight of different factors in the health score to ensure that the evaluation results can accurately reflect the members' true behavioral characteristics and churn risks. For example, during a promotion, promotional behavior may increase the health score of some members, while when the device is unstable, the device information may have a negative impact on the health score.

[0027] The present invention provides a member health assessment method based on member churn prediction. It has the following beneficial effects: 1. The present invention uses a nonlinear dynamic system model and a fractal analysis model to model the behavior patterns of members, and improves the ability to capture the complexity of behavior through phase space reconstruction and fractal dimension analysis, achieving the technical effect of accurately identifying member behavior patterns and churn risks. Compared with the technical solutions that only use linear models and traditional statistical methods in the prior art, the present invention can deeply explore the nonlinear characteristics and complex patterns in member behaviors, timely discover potential abnormal behaviors and churn risks, and solve the limitations and insufficient precision of traditional methods in processing complex behavior data, thereby significantly improving the accuracy of churn prediction and health assessment.

[0028] 2. The present invention adopts multi-sensor data collection and fusion technology, collects members' behavior data, environmental data, social data and device information, and uses the mutual information maximization principle to fuse different data sources, thereby achieving the technical effect of improving data accuracy and comprehensiveness. Compared with the technical solutions in the prior art that only use a single data source for behavior analysis and health assessment, the present invention can comprehensively consider data from multiple dimensions to ensure the comprehensiveness and accuracy of member behavior assessment, solve the problems of single data source and lack of multi-dimensional information support in the prior art, make health assessment results more reliable, and provide more accurate member churn prediction and behavior analysis.

[0029] 3. The present invention adopts a personalized health assessment method that combines Bayesian inference and fuzzy comprehensive evaluation method, achieving the technical effect of dynamically generating health scores based on members' historical behavior data and automatically adjusting the score weights. Compared with the technical solutions in the prior art that rely on static models and single behavioral dimensions for health scoring, the present invention can dynamically adjust the health score according to the real-time behavioral changes of members, flexibly respond to fluctuations in member behavior, and solve the problem of lack of real-time and personalization of health assessment in the prior art, ensuring the accuracy and flexibility of the assessment results, and helping merchants to formulate more accurate and effective member management strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the specification 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.

[0032] Please see attached Figure 1 The embodiment of the present invention provides a member health assessment method based on member churn prediction, comprising the following steps: Multi-sensor data collection and fusion: collect members' behavior data, environmental data, social data and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data; Behavior pattern modeling: Based on the collected member behavior data, nonlinear dynamic system modeling methods are used to model member behavior and capture the long-term trend and short-term fluctuations of member behavior; Member churn prediction: Based on member behavior data and churn prediction models, predict the risk of future member churn and make dynamic adjustments; Personalized health assessment: Based on the members' historical behavior data, a health score is generated for each member through a personalized health assessment model.

[0033] Step: Multi-sensor data acquisition and fusion This step collects members' behavioral data, environmental data, social data, and device information through multiple sensors, and processes and integrates these data through data fusion technology to ensure the accuracy, comprehensiveness, and reliability of health assessment. Multi-sensor data collection and fusion can eliminate the bias caused by a single data source and make full use of data sources from multiple dimensions, thereby improving the accuracy of health assessment and a comprehensive understanding of member behavior.

[0034] In this embodiment, in this step, it is first necessary to collect data from different sources through multiple sensors, including but not limited to member behavior data, social platform data, device information, and environmental data. By integrating data from different data sources, it is possible to fully reflect the member's behavior habits, social interactions, environmental factors, and device usage, thereby providing reliable basic data for subsequent health assessment and churn prediction.

[0035] Specifically, behavioral data sensors are used to record various behaviors of members on the platform, including but not limited to purchase records, click streams, browsing history, and interaction frequency. Purchase records reflect the frequency and amount of consumption of members on the platform, clicks and browsing history reflect the interest of members in different products or services and their level of activity, and interaction frequency reflects the interaction between members and the platform. Social platform data sensors are used to collect members' interaction data on social platforms, such as comments, likes, shares, friend recommendations, etc. These data can reflect the influence and participation of members on social platforms.

[0036] In one possible implementation, environmental data sensors are used to collect external factors related to member behavior, such as promotions, weather conditions, holidays, etc. These environmental data can help identify and analyze the impact of external factors on member behavior. For example, a promotion may trigger a peak in member purchases, holidays may affect member activity, and weather changes may have a significant impact on participation in offline activities.

[0037] It should be noted that the device information sensor is used to collect information such as the type of device used by members, device stability, and device failure rate. Device types include mobile phones, tablets, desktop computers, etc. Device stability refers to the smoothness of device operation and the frequency of problems. The device failure rate reflects the probability of device problems, which may affect members' usage experience and behavioral performance. Device information has an important impact on health assessment, because unstable or poorly performing devices may lead to a bad experience for members, thereby increasing the risk of churn.

[0038] In some embodiments, after all the collected data are initially collected by sensors, the data from these different sources will be fused using the mutual information maximization principle. Mutual information is an important concept in information theory, which is used to quantify the dependency between two variables. In the present invention, the mutual information maximization principle is optimized by calculating the information overlap between data sources, thereby ensuring that the data collected by different sensors share information to the greatest extent and reduce redundancy. Specifically, mutual information maximization is processed by the following formula: I(X,Y)=H(X)+H(Y)-H(X,Y) Among them, I(X,Y) represents the mutual information between data X and Y, H(X) and H(Y) are the entropies of data X and Y respectively, and H(X,Y) is the joint entropy of X and Y. By maximizing the mutual information, it can ensure that data from different sources can maximize information sharing during the fusion process, reduce duplication and redundancy, and effectively improve data quality.

[0039] It is understandable that through the collection and fusion of this multi-sensor data, the system can obtain a comprehensive and all-round member behavior data model, making subsequent behavior analysis, health assessment and churn prediction steps more accurate and reliable. The fused data not only includes direct records of member behavior, but also captures information such as external environmental factors and social interactions, thereby providing a deeper understanding of member behavior.

[0040] As an option, the present invention can also use other advanced data fusion technologies in this step according to actual application needs, such as weighted average method, principal component analysis (PCA), deep learning and other methods to improve the effect and accuracy of data fusion. Through these technologies, the system can more effectively identify the key features of each data source and provide more accurate input data for subsequent steps.

[0041] Through the data collection and integration in this step, the member behavior data finally generated is not only highly accurate and comprehensive, but also able to adapt to dynamic changes in different scenarios, providing a solid foundation for subsequent member health assessment and churn prediction. This provides merchants with a more accurate basis for member management and marketing strategy formulation, and further improves the platform's personalized service capabilities and marketing efficiency for members.

[0042] Step: Behavioral modeling Summary: In the present invention, behavior pattern modeling involves modeling behavior patterns based on the collected member behavior data, modeling member behaviors through nonlinear dynamic system modeling methods, and capturing long-term trends and short-term fluctuations in member behaviors. Specifically, the goal of this step is to comprehensively analyze and reveal the changing patterns of member behaviors through advanced mathematical models, and provide effective support for subsequent churn prediction and health assessment. To achieve this goal, the present invention uses phase space reconstruction and fractal analysis techniques, which can deeply explore the complexity in the data and reveal potential behavioral laws.

[0043] In this embodiment, in order to achieve accurate modeling of member behavior patterns, the present invention adopts nonlinear dynamic system modeling methods and fractal analysis, the combination of which can effectively capture nonlinear characteristics and complex patterns in behavioral data. The phase space reconstruction method is used to convert time series data into high-dimensional phase space to extract intrinsic patterns in behavioral data. The fractal dimension identifies potential abnormal behaviors and churn risks by quantifying the complexity of the behavior. This technical process is described in detail below.

[0044] Principle and implementation of phase space reconstruction In the present invention, phase space reconstruction is the core step of processing member's time series data using Takens theorem. Takens theorem is a mathematical tool for reconstructing phase space from time series data. It creates a high-dimensional space by extracting delayed embedded data points from the time series. Specifically, the time series is set to X = {x(t1), x(t2)…x(t N )}, the reconstruction formula of phase space is: S(t)=[x(t),x(t+τ),x(t+2τ)…x(t+(m-1)τ)] Among them, S(t) is the reconstructed phase space vector, m is the embedding dimension, and τ is the time delay. Through this method, the original time series data X is mapped into a high-dimensional space and becomes a high-dimensional vector describing member behavior. The elements in each vector reflect the behavioral state of the member at multiple time steps, and these behavioral states can reveal the long-term trend and short-term fluctuations of member behavior. The core purpose of this step is to transform the original one-dimensional time series data into multi-dimensional data, thereby revealing the deep patterns and internal laws in member behavior. It should be noted that the choice of time delay τ and embedding dimension m has an important influence on the reconstruction results. Usually, the appropriate delay time τ can be selected by the autocorrelation function or mutual information method, and the embedding dimension m is usually determined by empirical rules (such as FalseNearestNeighbors). Through the construction of this high-dimensional space, the system can capture complex and nonlinear behavior patterns and provide accurate data support for subsequent analysis.

[0045] Fractal Analysis and Dimension Calculation Fractal analysis is an important means of evaluating the complexity of member behavior. In the present invention, the fractal dimension is used to quantify the complexity of member behavior, thereby revealing the irregularity and self-similarity in the behavior. The fractal dimension is calculated by the following formula: Where N(∈) is the number of data points covered at scale ∈, and D represents the fractal dimension. When the behavioral data presents high complexity, its fractal dimension will be larger, otherwise it will be smaller. Fractal analysis can effectively capture subtle changes in member behavior and identify potential abnormal behavior patterns, especially when faced with complex and changeable behavioral data, it can provide effective anomaly detection capabilities. For example, if a member exhibits frequent and unpredictable behavioral fluctuations over a period of time, the fractal dimension can help identify this irregularity, thereby providing an important basis for churn prediction and health assessment.

[0046] Combining phase space reconstruction with fractal analysis The combination of phase space reconstruction and fractal analysis can more comprehensively analyze the complexity of member behavior. In the present invention, these two technologies are combined to capture the long-term change pattern of behavior through phase space reconstruction and identify short-term fluctuations and complexity in behavior through fractal analysis. Through this combination, the system can comprehensively evaluate the stability and abnormality of member behavior, and then identify potential loss risks.

[0047] For example, if a member's behavior is relatively stable over a period of time, but during a promotional event, his behavior fluctuates greatly, the system can discover the complexity of this behavior pattern through fractal dimension calculation and phase space reconstruction, and further determine whether the member has a high risk of churn by combining the churn prediction model. Through this multi-dimensional modeling, the system can dynamically and accurately predict the future behavior of members.

[0048] Other Implementations In a possible implementation, in addition to phase space reconstruction and fractal analysis, the present invention can also introduce other nonlinear dynamic analysis methods, such as Lyapunov exponents or chaos theory, to further analyze and quantify the sensitivity and chaos of member behavior. Through these advanced mathematical tools, the ability to capture the complexity of member behavior can be further improved, thereby improving the accuracy of churn prediction and the reliability of health assessment.

[0049] Step 1: Member churn prediction Summary: In the present invention, member churn prediction involves churn prediction based on member behavior data. The goal of churn prediction is to predict the future churn risk of members by analyzing their historical behavior data, and to adjust the member health score in real time according to the prediction results. This step is the core component of the present invention, which can effectively predict which members may churn and reduce the churn rate through corresponding intervention measures. This step uses technologies such as Kalman filtering, particle swarm optimization, and maximum entropy principle, which can accurately and real-time adjust and optimize the churn prediction model to improve the accuracy and adaptability of the prediction. The following is a detailed description of the steps.

[0050] In this embodiment, in order to improve the accuracy and real-time performance of churn prediction, the present invention adopts a variety of advanced technical means, including Kalman filtering, particle swarm optimization algorithm and maximum entropy principle. These technologies work together to dynamically adjust model parameters, smooth prediction results, and minimize uncertainty in the prediction process when analyzing member behavior data. Through these methods, the system can accurately predict churn risks and provide timely feedback while monitoring member behavior in real time.

[0051] Applications of Kalman Filter In the present invention, Kalman filtering is used to smooth the member churn prediction results. Kalman filtering is a recursive algorithm that can estimate the state of the model based on the difference between the measured data and the predicted data. In churn prediction, Kalman filtering can effectively smooth the noise in the original data and ensure the stability and accuracy of the prediction results.

[0052] Specifically, the core formula of Kalman filtering is as follows: P k =(IK k H)P k-1 in, Represents the predicted value at the current moment, y k represents the observed value, K k is the Kalman gain, P k is the covariance matrix, H is the observation matrix. Kalman gain K k The update formula is 2 Where R is the covariance of the process noise, P k-1 is the error covariance matrix of the previous moment, and H is the matrix used to transform from system state to observation space. Through these recursive formulas, Kalman filtering can update the prediction results according to real-time observations and provide smooth, noise-free prediction values ​​for churn prediction. In this way, the robustness of the prediction results can be improved and the real-time adjustment and update of the churn prediction model can be ensured.

[0053] The PSO algorithm is used in the present invention to optimize the parameters of the churn prediction model. PSO is an optimization algorithm that simulates group behavior in nature and uses a group of particles to search for the optimal solution in the solution space. Each particle represents a possible solution, and the movement and speed of the particle are guided by its current position and the global optimal solution, and finally the global optimal solution is obtained through iterative optimization. In churn prediction, PSO optimizes the model accuracy by adjusting the parameters in the prediction model. For example, in the churn prediction model, it may contain multiple parameters, such as behavior weights, churn risk thresholds, etc. PSO optimizes these parameters by iterative optimization, so that the model can make more accurate predictions based on the historical behavior data of members.

[0054] The basic update formula of PSO is as follows: x i (t+1)=x i (t)+v i (t+1) Among them, v i (t) is the velocity of the particle at time t, x i (t) is the position of the particle, w is the inertia weight, c1, c2 are acceleration constants, r1, r2 are random numbers, and are the personal best position and global best position of the particle, respectively. Through these formulas, PSO can optimize the parameters of the model in the process of churn prediction and further improve the accuracy of prediction, especially when dealing with complex and multi-dimensional behavioral data.

[0055] Application of Maximum Entropy Principle The maximum entropy principle is used in the churn prediction model to maximize the utilization of information in the system, thereby reducing the uncertainty in the data. In churn prediction, the maximum entropy principle can optimize the model parameters so that the prediction results can maximize the amount of information in the existing data. Specifically, the goal of the maximum entropy principle is to maximize the entropy value of the system by adjusting the parameters of the prediction model. The entropy value is defined as: Among them, p i is the probability distribution of each prediction result. By maximizing entropy, we can ensure that the churn prediction model can make full use of all available information without too many assumptions, thereby providing more accurate prediction results. In churn prediction, the maximum entropy principle can effectively avoid overfitting and ensure the generalization ability and stability of the model.

[0056] Comprehensive application of Kalman filtering, particle swarm optimization and maximum entropy principle By using Kalman filtering, particle swarm optimization and maximum entropy principle in combination, the present invention can significantly improve the accuracy and reliability of churn prediction. Kalman filtering smoothes the noise and fluctuations in member behavior data, making the prediction results more stable; the particle swarm optimization algorithm optimizes the parameters of the churn prediction model and improves the adaptability of the model; the maximum entropy principle further reduces the uncertainty in the model by maximizing the amount of information. The combination of the three enables churn prediction to remain efficient and stable in a dynamically changing environment, and to promptly reflect the changing trends of member behavior.

[0057] Step 1: Personalized Health Assessment Summary: In the present invention, personalized health assessment involves generating a personalized health score for each member based on the member's historical behavior data. Personalized health assessment is based on multi-dimensional data, combined with Bayesian inference and fuzzy comprehensive evaluation methods to analyze member behavior. Through this step, the system can calculate an accurate health score based on the specific behavioral characteristics of the members, thereby helping merchants identify high-risk churn members and high-loyalty members, and provide accurate decision-making support for subsequent member management and personalized marketing. The technical implementation of this step makes full use of the probabilistic inference method and fuzzy mathematical model in data science to ensure the accuracy, objectivity and dynamic adjustment ability of the health score. The following is a detailed description of the steps.

[0058] In this embodiment, in order to achieve personalized health assessment, the present invention adopts a combination of Bayesian inference method and fuzzy comprehensive evaluation method to dynamically calculate the health score of each member. Bayesian inference can provide a probabilistic estimate of health parameters based on historical behavior data, while the fuzzy comprehensive evaluation rule provides a comprehensive and objective health score through weighted fusion of multi-dimensional data. After combining these methods, the member health assessment not only considers a single behavioral dimension, but also comprehensively analyzes its behavior patterns and changes, ensuring that each member can get a personalized score.

[0059] Bayesian Inference Methods The application of Bayesian inference in the present invention is to dynamically calculate the posterior probability distribution of health parameters based on the historical behavior data of members. The Bayesian inference method is based on Bayes' theorem, which combines prior knowledge (such as the initial health distribution of members) with observed data (such as recent purchases, browsing, interaction records, etc.) to derive the posterior distribution of health parameters. The basic form of Bayes' theorem is as follows: Among them, θ represents the health parameter, D is the observed behavior data, P(θ|D) is the posterior probability, P(D|θ) is the likelihood function, P(θ) is the prior distribution, and P(D) is the marginal likelihood. Through Bayesian inference, the system can dynamically update the health score based on the new member behavior data and gradually improve the estimation of member health.

[0060] In one possible implementation, the prior distribution P(θ) can be set based on historical member data, and can usually be selected as a Gaussian distribution or other suitable distribution form; the likelihood function P(D|θ) reflects the changing pattern of member health under a specific behavior pattern. For example, a member's purchase frequency, number of times participating in promotional activities, frequency of social interactions, etc. can be used as inputs to the likelihood function, through which the relationship between member behavior and health is modeled. It should be noted that through the Bayesian inference method, the system can continuously adjust its estimate of the health of each member based on his or her behavior history, so that each member's health score can reflect his or her recent behavior trend at any time, thereby improving the timeliness and accuracy of the evaluation.

[0061] Fuzzy comprehensive evaluation method The fuzzy comprehensive evaluation method is a key method used in the present invention to integrate member multi-dimensional behavior data. This method can handle the uncertainty and ambiguity in member behavior data, especially when there is overlap between behavior dimensions or changes in member behavior are difficult to quantify, the fuzzy comprehensive evaluation method can effectively integrate data from multiple dimensions to obtain a comprehensive health score.

[0062] The core idea of ​​the fuzzy comprehensive evaluation method is to weight each member's multiple behavioral dimensions (such as activity, loyalty, purchase frequency, social interaction, etc.) through fuzzy rules, and finally get an overall health score. Specifically, each behavioral dimension is fuzzified and converted into a fuzzy set, and then weighted calculation is performed according to the set weights to finally get a comprehensive score.

[0063] Exemplarily, the member's health assessment may include the following behavioral dimensions: Activity: based on the member's login frequency, number of times they participate in platform activities, etc. Loyalty: based on members’ long-term consumption behavior, renewal status, etc. Social interaction: members’ comments, sharing, likes, etc. on social platforms; Purchase frequency: the frequency with which members purchase goods, transaction amount, etc.

[0064] The data of each dimension will be fuzzified and converted into a fuzzy set, such as high, medium and low. Then, the health of each dimension is weighted through fuzzy operation to obtain a comprehensive health score. The calculation formula of the fuzzy comprehensive evaluation method is as follows: Among them, H is the comprehensive health score of the member, w i is the weight of each dimension, v i is the fuzzy score value of the corresponding dimension. By weighting the scores of multiple dimensions, the member's health score is finally generated.

[0065] Dynamically adjust health score In some embodiments, the health score is not only the result of static calculation, but also adjusted in real time according to the member's behavior. This dynamic adjustment mechanism is based on real-time monitoring of member behavior data and adjusts the health score according to set rules.

[0066] For example, when a member exhibits abnormal behavior (such as a significant increase in consumption frequency or long periods of inactivity), the system will automatically re-evaluate and adjust the member's health score. To achieve this, the present invention adopts a weighted adjustment mechanism, that is, when calculating the comprehensive health score, the weights of each dimension are dynamically adjusted according to the changes in member behavior. For example, if a member's purchase frequency increases significantly, the weight of the purchase frequency will increase accordingly, thereby improving the member's health score.

[0067] Understandably, this mechanism can ensure that the member's health score always reflects their latest behavioral changes, and avoid the health score losing timeliness due to the influence of historical data. Through this dynamic adjustment, merchants can track the health status of members in real time and intervene in time, thereby improving member retention and loyalty.

[0068] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A member health assessment method based on member churn prediction, characterized in that: The following steps are involved: Multi-sensor data collection and fusion: collect members' behavior data, environmental data, social data and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data; Behavior pattern modeling: Based on the collected member behavior data, nonlinear dynamic system modeling methods are used to model member behavior and capture the long-term trend and short-term fluctuations of member behavior; Member churn prediction: Based on member behavior data and churn prediction models, predict the risk of future member churn and make dynamic adjustments; Personalized health assessment: Based on the members' historical behavior data, a health score is generated for each member through a personalized health assessment model.

2. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The method also includes: health score feedback and dynamic adjustment: by real-time monitoring of member behavior, the health score is dynamically adjusted to ensure the timeliness and accuracy of the evaluation results; Evaluation reports and strategy support: Generate member portraits based on health scores, and provide merchants with decision support for member management and marketing strategies based on this.

3. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The multi-sensor data collection and fusion step further includes: Collect members’ purchase records, click streams, browsing history, and social platform interaction data from behavioral sensors; Collect promotional activities, weather conditions, and holiday environment data from environmental sensors; Collect data on the type and stability of devices used by members from device sensors; The data collected by different sensors are fused using the principle of maximizing mutual information to ensure maximum sharing and minimum redundancy among data.

4. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The behavior pattern modeling step further includes: Takens theorem is used to reconstruct the phase space of members’ time series data, transforming the data into a high-dimensional phase space to capture the long-term trends and short-term fluctuations of behavior; Use fractal theory to calculate the fractal dimension of member behavior, evaluate the complexity of behavior, and identify potential abnormal behavior.

5. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The member churn prediction step further includes: Based on the collected member data, Kalman filtering is used to smooth the churn prediction results and adjust the prediction value in real time; The parameters of the churn prediction model are adjusted through the particle swarm optimization algorithm to optimize the model accuracy; The maximum entropy principle is used to optimize the churn prediction process, maximize the amount of information in churn prediction, and reduce the uncertainty in prediction.

6. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The personalized health assessment step further includes: Based on the historical behavior data of members, the Bayesian inference method is used to generate the posterior probability distribution of each member's health parameter; The health of members is comprehensively scored through the fuzzy comprehensive evaluation method combined with the weighted average model.

7. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The health score feedback and dynamic adjustment step further includes: Dynamically adjust members’ health scores based on real-time changes in member behavior; When member behavior is abnormal, the calculation weight of the health score is automatically adjusted to enhance the flexibility and accuracy of the assessment.

8. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The evaluation report and strategy support steps further include: Generate personalized member portraits based on members' health scores and classify members; Based on member portraits and churn prediction results, we provide merchants with personalized marketing strategies and member management solutions.

9. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The churn prediction model adopts a nonlinear dynamic system model and a fractal analysis model, and further includes: In churn prediction, the parameters of the prediction model are adjusted in real time based on the short-term and long-term change patterns of member behavior; Through phase space reconstruction and fractal dimension analysis, the ability to capture behavioral complexity is improved.

10. A member health assessment method based on member churn prediction according to claim 1, characterized in that: The health scoring method combines multi-sensor data and personalized behavior pattern analysis, and further includes: Use multi-dimensional data of environmental factors, social behavior, and device information as model input to dynamically adjust the weight of the health score; The fuzzy comprehensive evaluation method is used to weight data of different dimensions to ensure the objectivity and accuracy of the evaluation results.

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