A Member Health Assessment Method Based on Member Churn Prediction

Abstract

The present invention relates to the fields of member management and data analysis, and discloses a member health assessment method based on member churn prediction, comprising the following steps: multi-sensor data acquisition and fusion: using multiple sensors to collect member behavior data, environmental data, social data, and device data, and fusing the data to ensure data accuracy and comprehensiveness; behavior pattern modeling: based on the collected member behavior data, a nonlinear dynamic system modeling method is used to model member behavior to capture long-term trends and short-term fluctuations in member behavior; member churn prediction: based on member behavior data and a churn prediction model, the risk of future member churn is predicted and dynamically adjusted. A nonlinear dynamic system model and a fractal analysis model are used to model member behavior patterns, and the ability to capture behavioral complexity is improved through phase space reconstruction and fractal dimension analysis.

Description

Technical Field

[0001] This invention relates to the fields of membership management and data analysis, specifically to a method for assessing member health based on member churn prediction. Background Technology

[0002] With the rapid development of internet and big data technologies, more and more merchants and businesses are relying on membership management systems to maintain relationships with customers and improve user experience and customer loyalty. In this process, member health assessment has become a crucial step. By analyzing member behavior, merchants can effectively identify potential churned customers and take intervention measures to improve customer retention and loyalty.

[0003] Existing health assessment methods typically rely on a single data source, such as purchase records or login frequency. These methods fail to fully utilize data from diverse sources (e.g., social behavior, device information, external environmental factors). This results in insufficient accuracy and comprehensiveness in health assessments, failing to fully reflect the behavioral characteristics of members.

[0004] Many traditional membership health assessment methods rely on historical data and static scoring, lacking the ability to dynamically respond to changes in member behavior. Member behavior is dynamic, especially when faced with environmental factors such as seasonal promotions, holidays, and weather changes, where member activity and loyalty often fluctuate significantly. Existing technologies mostly depend on static models, which cannot reflect changes in member behavior in a timely manner, leading to lags in health scoring and churn prediction.

[0005] To address the shortcomings of existing technologies, this invention provides a method for assessing member health based on member churn prediction. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this 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 existing member health assessment methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing member health based on member churn prediction, comprising the following steps:

[0008] Multi-sensor data acquisition and fusion: Collect members' behavioral data, environmental data, social data, and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data;

[0009] Behavioral pattern modeling: Based on the collected member behavior data, a nonlinear dynamic system modeling method is used to model member behavior and capture the long-term trends and short-term fluctuations of member behavior.

[0010] Member churn prediction: Based on member behavior data and churn prediction models, predict the risk of future member churn and make dynamic adjustments;

[0011] Personalized health assessment: Based on members' historical behavioral data, a personalized health assessment model is used to generate a health score for each member.

[0012] Furthermore, multi-sensor data acquisition and fusion involves collecting member behavior data, environmental data, social data, and device data from various sensors to ensure the comprehensiveness and accuracy of the data. Specifically, behavioral data includes member purchase records, clickstreams, browsing history, and social interactions; environmental data includes promotional activities, weather conditions, and external factors such as holidays; social data covers member interactions on social platforms; and device data includes the types of devices used by members and their stability. This heterogeneous data is effectively fused using advanced data fusion technology to ensure data consistency and effectiveness across different sensors. Secondly, behavioral pattern modeling is performed. Based on the collected member behavior data, a nonlinear dynamic system modeling method is used to model member behavior, capturing long-term trends and short-term fluctuations. By constructing a phase space model and introducing fractal theory and nonlinear dynamics models, potential behavioral patterns can be extracted from complex time-series data, and behavioral fluctuations can be accurately analyzed to reveal the inherent laws of member behavior. Finally, member churn prediction is performed. Based on member behavior data and the constructed churn prediction model, member churn risk is predicted, and the prediction results are updated in real time using a dynamic adjustment mechanism. The churn prediction model uses Kalman filtering and particle swarm optimization algorithms to provide real-time feedback on changes in member behavior, enabling timely identification of potential churn risks and providing merchants with effective risk warnings. Next, a personalized health assessment is performed. Based on each member's historical behavioral data, a personalized health assessment model generates a health score for each member. This model combines multi-dimensional data on member consumption behavior, interaction frequency, and social behavior, using Bayesian inference and fuzzy comprehensive evaluation methods to comprehensively consider the weights and influencing factors of each behavioral dimension, thereby generating an accurate health score for each member. This helps merchants implement differentiated member management and marketing strategies. Finally, based on the health assessment results, merchants can develop more targeted member retention and incentive measures, effectively improving member loyalty and reducing churn risk.

[0013] Preferably, the method further includes: health score feedback and dynamic adjustment: by monitoring members' behavior in real time, the health score is dynamically adjusted to ensure the timeliness and accuracy of the assessment results;

[0014] Assessment Reports and Strategy Support: Generate member profiles based on health scores, and provide merchants with decision support for member management and marketing strategies based on these profiles.

[0015] Furthermore, by monitoring member behavior in real time, the system can continuously track and analyze changes in member behavior and adjust member health scores accordingly. Specifically, when a member's behavior changes significantly, the system automatically identifies these changes and adjusts the weights of the scoring model based on these changes, ensuring that the health assessment always reflects the member's latest behavioral status. For example, if a member frequently engages in high-value transactions within a short period, the system will automatically increase their health score; conversely, if a member's interaction frequency drops sharply or they do not participate in platform activities for an extended period, the system will automatically decrease their score. In addition, the system can dynamically adjust based on member churn risk. When an increased churn risk is detected, the health score will automatically adjust accordingly to allow for timely and effective intervention. Ultimately, this dynamic adjustment mechanism ensures the real-time nature, accuracy, and adaptability of the health assessment results. Assessment reports and strategy support are provided by generating detailed member profiles based on the member's health score. These profiles include not only basic personal information but also multi-dimensional characteristics such as member consumption habits, social behavior, interaction frequency, and purchasing preferences. Through comprehensive analysis of member profiles, merchants can gain a complete understanding of member needs and behavioral trends and provide personalized member management and marketing strategies accordingly. Merchants can develop precise membership retention plans based on health scores, such as offering exclusive discounts and rewards to members with high health scores, while designing recovery strategies for members with low health scores, such as targeted promotions and personalized recommendations. Furthermore, merchants can optimize the effectiveness of marketing campaigns through health score reports, designing different marketing strategies for different member groups with varying health scores, thereby improving marketing results and customer retention rates.

[0016] Preferably, the multi-sensor data acquisition and fusion step further includes:

[0017] Collect members' purchase records, clickstream, browsing history, and social media interaction data from behavioral sensors;

[0018] Collect environmental data on promotional activities, weather conditions, and holidays from environmental sensors;

[0019] Collect data on the type and stability of devices used by members from device sensors;

[0020] The principle of maximizing mutual information is used to fuse data collected by different sensors, ensuring maximum sharing and minimum redundancy among the data.

[0021] Furthermore, behavioral sensors collect data on members' purchase records, clickstreams, browsing history, and social media interactions. This data reflects crucial information about members' activity levels, purchasing preferences, and interaction habits on the platform. Specifically, purchase records provide key information such as purchase frequency, single purchase amount, and purchase type; clickstreams and browsing history reveal members' interest in different products or services; and social media interaction data reflects members' social behavior and online influence, such as comments, shares, and likes. By integrating this behavioral data, a comprehensive understanding of member activity and loyalty can be achieved, further providing data support for health assessment. Secondly, environmental sensors collect data on promotional activities, weather conditions, and holidays. Environmental data can influence members' consumption behavior and platform activity. For example, the timing, type, and intensity of promotional activities often directly promote members' purchasing behavior, while weather conditions and holidays also significantly impact members' online activities and consumption habits. By collecting and analyzing this environmental data, the system can more accurately identify which factors have a significant impact on member behavior and adjust the model parameters for health assessment accordingly. Then, data on the type and stability of devices used by members are collected from device sensors. This data provides information about member device usage, such as device brand, operating system, and device failure rate. Device type and stability directly impact member user experience and platform accessibility, thus influencing member behavior patterns and churn probability. For example, members using lower-configuration or unstable devices may experience more operational interruptions or login problems on the platform, increasing the risk of churn. Finally, the principle of maximizing mutual information is used to fuse data collected from different sensors, ensuring 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 is minimized during the fusion process, thereby improving the effectiveness of data fusion, eliminating noise, and enhancing the accuracy and reliability of member health assessment. This method effectively integrates information from multiple data sources, providing more comprehensive and accurate data support, laying a solid foundation for subsequent member behavior analysis, churn prediction, and health assessment.

[0022] Preferably, the behavior pattern modeling step further includes:

[0023] 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 trend and short-term fluctuations of behavior.

[0024] Fractal theory is used to calculate the fractal dimension of member behavior, assess the complexity of the behavior, and identify potential abnormal behaviors.

[0025] Furthermore, Takens' theorem is used to reconstruct the phase space of members' time-series data, transforming the data into a high-dimensional phase space to extract potential patterns in member behavior. Specifically, time-series data, as crucial information reflecting the dynamic changes in member behavior, is embedded using Takens' theorem to reconstruct a multi-dimensional phase space, thus transforming the time-series data into high-dimensional vectors. These high-dimensional vectors can reflect the long-term trends and short-term fluctuations of member behavior. The phase space reconstruction method can capture the nonlinear characteristics in behavioral patterns, thereby revealing the regularity and trends of member behavior. For example, after reconstructing a member's consumption frequency and purchase categories over multiple time periods, the data can be used to analyze the stability and trend changes of their behavior, providing a data foundation for subsequent churn prediction and health assessment. Fractal theory is used to calculate the fractal dimension of member behavior, quantifying its complexity. Fractal dimension can reveal the self-similarity and complexity in member behavior, especially when faced with large amounts of dynamic behavioral data, enabling more effective identification of behavioral patterns at different levels. A higher fractal dimension indicates more complex member behavior, with greater randomness and irregularity, and vice versa. This method can help identify potential abnormal behavioral patterns, such as sudden consumption spikes, frequent device switching, and drastic fluctuations in behavioral patterns. These abnormal behaviors are often precursors to member churn. Therefore, calculating the fractal dimension not only helps understand the complexity of member behavior but also helps identify potential abnormal changes in member behavior, providing strong support for subsequent health assessments. By combining relevant spatial reconstruction and fractal analysis, member behavioral patterns can be modeled more accurately, providing a scientific basis for churn prediction, health scoring, and personalized management.

[0026] Preferably, the member churn prediction step further includes:

[0027] Based on the collected member data, Kalman filtering is used to smooth the churn prediction results and adjust the prediction values ​​in real time.

[0028] The parameters of the churn prediction model are adjusted using the particle swarm optimization algorithm to optimize the model accuracy.

[0029] The maximum entropy principle is used to optimize the churn prediction process, maximizing the amount of information in churn prediction and reducing the uncertainty in the prediction.

[0030] Furthermore, based on the collected member data, Kalman filtering is used to smooth the churn prediction results, adjusting the predicted values ​​in real time to ensure stability and accuracy during the prediction process. Kalman filtering is a recursive estimation method that dynamically adjusts the prediction results based on the difference between observed values ​​and model predictions, reducing the impact of noise on the prediction. In churn prediction, Kalman filtering can smooth the churn trend, preventing short-term abnormal fluctuations from excessively interfering with the prediction results and improving prediction accuracy. By updating the state estimate of the churn prediction in real time, Kalman filtering can reflect the latest changes in member behavior and adjust the predicted values ​​in a timely manner to address changes in member churn patterns. The parameters of the churn prediction model are adjusted using the Particle Swarm Optimization (PSO) algorithm to optimize model accuracy. PSO is an optimization algorithm that simulates the behavior of groups in nature, optimizing the parameters of the prediction model by searching for the optimal solution. PSO improves the model's prediction accuracy by iteratively updating the parameters in the churn prediction model, gradually approaching the optimal solution. In churn prediction, PSO (Profiled Sort) adaptively adjusts the model's weights and parameters, ensuring consistent efficiency and accuracy under varying environmental and data conditions, thus avoiding overfitting or underfitting. The maximum entropy principle optimizes the churn prediction process by maximizing information entropy. This principle maximizes the amount of information the system can obtain even without sufficient prior information, resulting in a more objective and unbiased prediction process. Introducing the maximum entropy principle into churn prediction effectively reduces data uncertainty, avoids unnecessary assumptions, and guarantees the model's generalization ability and robustness.

[0031] Preferably, the personalized health assessment step further includes:

[0032] Based on members' historical behavior data, a Bayesian inference method is used to generate the posterior probability distribution of each member's health score parameters; a fuzzy comprehensive evaluation method combined with a weighted average model is used to comprehensively score the members' health score.

[0033] Preferably, the health score feedback and dynamic adjustment steps further include:

[0034] The health score of members is dynamically adjusted based on real-time changes in member behavior.

[0035] When abnormal member behavior is detected, the weighting of the health score calculation is automatically adjusted to enhance the flexibility and accuracy of the assessment.

[0036] Furthermore, based on members' historical behavioral data, a Bayesian inference method is used to generate the posterior probability distribution of each member's health parameters. This method dynamically updates each member's health assessment parameters by combining prior information with current observation data, thus generating an accurate health prediction for each member. By establishing a Bayesian network model, the posterior probability distribution of health parameters can be calculated based on members' historical behavioral data (such as purchase records, browsing history, and social interactions), reflecting members' health status under different conditions. This method not only makes full use of existing data but also allows for reasonable inference and prediction even when data is incomplete or highly variable, thereby improving the accuracy and flexibility of health assessment. A comprehensive health score for members is then generated using a fuzzy comprehensive evaluation method combined with a weighted average model. This method addresses the fuzziness and uncertainty in member behavior, making the health score more consistent with reality. By assigning appropriate weights to each dimension (such as activity level, consumption frequency, and social interaction) and combining this with a weighted average model to comprehensively evaluate each behavioral indicator, a comprehensive health score is generated for each member. This helps merchants identify potentially high-risk churned members or highly loyal members, enabling them to implement personalized management strategies.

[0037] Based on real-time changes in member behavior, the system dynamically adjusts member health scores. By monitoring member behavior data in real time, the system can dynamically adjust member health scores. For example, if a member has frequent purchases or interactions in a short period, the system will increase the member's health score in real time; conversely, if a member's activity level decreases, the system will automatically decrease their health score. This process ensures that the health score reflects changes in member behavior in real time, improving the timeliness and accuracy of the assessment results. When abnormal member behavior occurs, the system automatically adjusts the calculation weights of the health score, enhancing the flexibility and accuracy of the assessment. When abnormal fluctuations in member behavior are detected (such as frequent device switching, sudden large purchases, or prolonged inactivity), the system will automatically adjust the weight parameters in the scoring model to give appropriate attention to abnormal behavior and avoid bias 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 assessment remains accurate and robust when facing complex and volatile data, thereby providing timely member management decision support for merchants.

[0038] Preferably, the assessment report and strategy support steps further include:

[0039] Personalized member profiles are generated based on members' health scores, and members are categorized accordingly.

[0040] Based on member profiles and churn prediction results, we provide merchants with personalized marketing strategies and member management solutions.

[0041] Furthermore, personalized member profiles are generated based on members' health scores, and members are categorized. By combining members' health scores with other behavioral data (such as purchase frequency, purchase preferences, and interaction patterns), the system can generate detailed personalized profiles for each member. Member profiles 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, and browsing preferences. Through comprehensive analysis of member profiles, the system can divide members into different groups, such as high-health members, medium-health members, and low-health members. This categorization not only helps merchants identify currently highly loyal members but also enables them to promptly discover potential churned members and high-risk groups, allowing for targeted management.

[0042] Preferably, the loss prediction model employs a nonlinear dynamic system model and a fractal analysis model, and further includes:

[0043] In churn prediction, the parameters of the prediction model are adjusted in real time by combining short-term and long-term changes in member behavior.

[0044] By reconstructing phase space and performing fractal dimension analysis, we can improve our ability to capture behavioral complexity.

[0045] Preferably, the health scoring method, which combines multi-sensor data and personalized behavioral pattern analysis, further includes:

[0046] Multidimensional data, including environmental factors, social behavior, and device information, are used as inputs to the model to dynamically adjust the weights of the health score. Fuzzy comprehensive evaluation is used to weight the data from different dimensions to ensure the objectivity and accuracy of the assessment results.

[0047] Furthermore, in churn prediction, the parameters of the prediction model are adjusted in real time by combining short-term and long-term change patterns of member behavior. By monitoring and analyzing member behavior data in real time, 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 stable member behavior patterns. Through this combination of short-term and long-term change patterns, the prediction model can more accurately reflect the dynamic changes in member churn and provide early warnings of impending churn. By using 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 perform high-dimensional transformation of member behavior data to reveal nonlinear characteristics in behavior, and fractal dimension analysis is used to quantify the complexity of behavior.

[0048] By using multidimensional data such as environmental factors, social behaviors, and device information as model inputs, the system dynamically adjusts the weights of health scores. It comprehensively considers environmental factors (such as promotional activities and weather changes), social behaviors (such as comments, sharing, and interaction frequency), and device information (such as device type and usage stability) to conduct a multidimensional weighted analysis of member health scores. Through this diversified data input, the model can dynamically adjust the weights of different factors in the health score, ensuring that the assessment results accurately reflect the true behavioral characteristics of members and the risk of churn. For example, during promotional activities, promotional behaviors may increase the health scores of some members, while device instability may negatively impact the health score.

[0049] This invention provides a method for assessing member health based on member churn prediction. It has the following beneficial effects:

[0050] 1. This invention employs a nonlinear dynamic system model and a fractal analysis model to model member behavior patterns. Through phase space reconstruction and fractal dimension analysis, it enhances the ability to capture behavioral complexity, achieving the technical effect of accurately identifying member behavior patterns and churn risk. Compared to existing technologies that only use linear models and traditional statistical methods, this invention can deeply explore the nonlinear characteristics and complex patterns in member behavior, promptly identifying potential abnormal behaviors and churn risks. It solves the limitations and insufficient accuracy of traditional methods when processing complex behavioral data, thereby significantly improving the accuracy of churn prediction and health assessment.

[0051] 2. This invention employs multi-sensor data acquisition and fusion technology. By collecting member behavior data, environmental data, social data, and device information, and utilizing the principle of maximizing mutual information to fuse different data sources, it achieves the technical effect of improving data accuracy and comprehensiveness. Compared to existing technologies that rely solely on a single data source for behavioral analysis and health assessment, this invention comprehensively considers data from multiple dimensions, ensuring the comprehensiveness and accuracy of member behavior assessment. It solves the problems of single data sources and lack of multi-dimensional information support in existing technologies, making health assessment results more reliable and providing more accurate member churn prediction and behavioral analysis.

[0052] 3. This invention employs a personalized health assessment method combining Bayesian inference and fuzzy comprehensive evaluation, achieving the technical effect of dynamically generating health scores based on members' historical behavioral data and automatically adjusting score weights. Compared to existing technologies that rely on static models and single behavioral dimensions for health scoring, this invention can dynamically adjust health scores based on real-time changes in members' behavior, flexibly responding to fluctuations in member behavior. It solves the problems of lack of real-time and personalization in existing health assessments, ensuring the accuracy and flexibility of assessment results, and helping merchants develop more precise and effective member management strategies. Attached Figure Description

[0053] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see the appendix Figure 1 This invention provides a method for assessing member health based on member churn prediction, comprising the following steps:

[0056] Multi-sensor data acquisition and fusion: Collect members' behavioral data, environmental data, social data, and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data;

[0057] Behavioral pattern modeling: Based on the collected member behavior data, a nonlinear dynamic system modeling method is used to model member behavior and capture the long-term trends and short-term fluctuations of member behavior.

[0058] Member churn prediction: Based on member behavior data and churn prediction models, predict the risk of future member churn and make dynamic adjustments;

[0059] Personalized health assessment: Based on members' historical behavioral data, a personalized health assessment model is used to generate a health score for each member.

[0060] Steps: Multi-sensor data acquisition and fusion

[0061] This step collects member behavioral data, environmental data, social data, and device information through multiple sensors, and processes and integrates this data using data fusion technology to ensure the accuracy, comprehensiveness, and reliability of the health assessment. Multi-sensor data acquisition and fusion eliminates biases from single data sources, fully utilizing data from multiple dimensions to improve the accuracy of the health assessment and a comprehensive understanding of member behavior.

[0062] In this embodiment, the first step involves collecting data from various sources using multiple sensors. This data includes, but is not limited to, member behavior data, social media platform data, device information, and environmental data. By integrating data from different data sources, a comprehensive reflection of member behavior habits, social interactions, environmental factors, and device usage can be achieved, thus providing reliable foundational data for subsequent health assessments and churn predictions.

[0063] Specifically, behavioral data sensors are used to record various member behaviors on the platform, including but not limited to purchase records, clickstreams, browsing history, and interaction frequency. Purchase records reflect the frequency and amount of member purchases on the platform; clicks and browsing history reflect member interest 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 member interaction data on social platforms, such as comments, likes, shares, and friend recommendations. This data reflects the member's influence and engagement on the social platform.

[0064] In one possible implementation, environmental data sensors are used to collect information on external factors related to member behavior, such as promotional activities, weather conditions, and holidays. This environmental data can help identify and analyze the impact of external factors on member behavior. For example, a promotional activity may trigger a peak in member purchases, holidays may affect member activity levels, and weather changes may significantly impact participation in offline activities.

[0065] It's important to note 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, and desktop computers. Device stability refers to the smoothness of device operation and the frequency of problems. The device failure rate reflects the probability of device malfunctions, which may affect the member's user experience and behavior. Device information has a significant impact on health assessment because unstable or poor-performing devices may lead to a negative member experience, thereby increasing the risk of churn.

[0066] In some embodiments, after initial acquisition via sensors, all collected data from these different sources are fused using the mutual information maximization principle. Mutual information is an important concept in information theory, used to quantify the dependency between two variables. In this invention, the mutual information maximization principle optimizes the data by calculating the information overlap between data sources, thereby ensuring that data collected by different sensors share information to the greatest extent and reducing redundancy. Specifically, mutual information maximization is performed using the following formula:

[0067] I(X,Y)=H(X)+H(Y)-H(X,Y)

[0068] Here, 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 mutual information, we can ensure that data from different sources share information to the maximum extent during the fusion process, and reduce duplication and redundancy, thereby effectively improving data quality.

[0069] Understandably, through the collection and fusion of multi-sensor data, the system can acquire a comprehensive and all-encompassing model of member behavior data, making subsequent steps such as behavior analysis, health assessment, and churn prediction 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.

[0070] Alternatively, this invention can also employ other advanced data fusion techniques, such as weighted average, principal component analysis (PCA), and deep learning, in this step, depending on the specific application requirements, to improve the effectiveness and accuracy of data fusion. Through these techniques, the system can more effectively identify the key features of each data source and provide more accurate input data for subsequent steps.

[0071] Through data collection and integration in this step, the resulting member behavior data is not only highly accurate and comprehensive but also adaptable to dynamic changes in different scenarios, providing a solid foundation for subsequent member health assessments and churn predictions. This provides merchants with more precise data on member management and marketing strategy development, further enhancing the platform's ability to provide personalized services and improve marketing efficiency.

[0072] Steps: Behavioral Pattern Modeling

[0073] Summary description:

[0074] In this invention, behavioral pattern modeling involves modeling behavioral patterns based on collected member behavior data. This is achieved through nonlinear dynamic system modeling methods, capturing long-term trends and short-term fluctuations in member behavior. Specifically, the goal of this step is to comprehensively analyze and reveal the changing patterns of member behavior using advanced mathematical models, providing effective support for subsequent churn prediction and health assessment. To achieve this goal, this invention utilizes phase space reconstruction and fractal analysis techniques, which can deeply mine the complexity of data and reveal underlying behavioral patterns.

[0075] In this embodiment, to achieve accurate modeling of member behavior patterns, the present invention employs a nonlinear dynamic system modeling method and fractal analysis. The combination of these two methods effectively captures the nonlinear characteristics and complex patterns in behavioral data. The phase space reconstruction method is used to transform time-series data into a high-dimensional phase space to extract the inherent patterns in the behavioral data. Meanwhile, fractal dimension quantifies the complexity of behavior to identify potential abnormal behavior and churn risks. This technical process is described in detail below.

[0076] The principle and implementation of phase space reconstruction

[0077] In this invention, phase space reconstruction is the core step in processing the member's time series data using Takens' theorem. Takens' theorem is a mathematical tool for reconstructing the 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 as X = {x(t1), x(t2) ... x(t...}. N The formula for reconstructing the phase space is:

[0078] S(t)=[x(t),x(t+τ),x(t+2τ)…x(t+(m-1)τ)]

[0079] Where S(t) is the reconstructed phase space vector, m is the embedding dimension, and τ is the time delay. This method maps the original time series data X into a high-dimensional space, becoming a high-dimensional vector describing member behavior. Each element in the vector reflects the member's behavioral state at multiple time steps, revealing long-term trends and short-term fluctuations. The core objective of this step is to transform the original one-dimensional time series data into multi-dimensional data, thereby revealing deep patterns and inherent laws in member behavior. It should be noted that the choice of time delay τ and embedding dimension m has a significant impact on the reconstruction results. Typically, an appropriate time delay τ can be selected using autocorrelation functions or mutual information methods, while the embedding dimension m is usually determined using empirical rules (such as False Nearest Neighbors). Through this high-dimensional space construction, the system can capture complex, non-linear behavioral patterns, providing accurate data support for subsequent analysis.

[0080] Fractal Analysis and Dimension Calculation

[0081] Fractal analysis is an important tool for assessing the complexity of member behavior. In this invention, fractal dimension is used to quantify the complexity of member behavior, thereby revealing irregularities and self-similarity in the behavior. The fractal dimension is calculated using the following formula:

[0082]

[0083] Here, N(∈) is the number of data points covered at scale ∈, and D represents the fractal dimension. When behavioral data exhibits high complexity, its fractal dimension is larger, and vice versa. Fractal analysis can effectively capture subtle changes in member behavior and identify potential abnormal behavioral patterns, especially when facing complex and variable behavioral data, providing 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, thus providing important evidence for churn prediction and health assessment.

[0084] Combining phase space reconstruction and fractal analysis

[0085] The combination of phase space reconstruction and fractal analysis enables a more comprehensive analysis of the complexity in member behavior. In this invention, these two techniques are combined to capture long-term behavioral patterns through phase space reconstruction and to identify short-term fluctuations and complexities through fractal analysis. This combination allows the system to comprehensively assess the stability and anomalies of member behavior, thereby identifying potential churn risks.

[0086] For example, a member's behavior might remain relatively stable for a period, but fluctuate significantly during a promotional event. The system can use fractal dimension calculation and phase space reconstruction to discover the complexity of this behavioral pattern and, combined with a churn prediction model, further determine whether the member has a high risk of churn. Through this multi-dimensional modeling, the system can dynamically and accurately predict members' future behavior.

[0087] Other implementations

[0088] In one possible implementation, in addition to phase space reconstruction and fractal analysis, this 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. These advanced mathematical tools can further enhance the ability to capture the complexity of member behavior, thereby improving the accuracy of churn prediction and the reliability of health assessment.

[0089] Steps: Member Churn Prediction

[0090] Summary description:

[0091] In this invention, member churn prediction involves predicting churn based on member behavioral data. The goal of churn prediction is to predict future churn risk by analyzing members' historical behavioral data and adjust member health scores in real time based on the prediction results. This step is a core component of this invention, effectively predicting which members are likely to churn and reducing the churn rate through corresponding interventions. This step employs techniques such as Kalman filtering, particle swarm optimization, and the maximum entropy principle, enabling accurate and real-time adjustment and optimization of the churn prediction model, improving prediction accuracy and adaptability. The following is a detailed description of the steps.

[0092] In this embodiment, to improve the accuracy and real-time performance of churn prediction, the present invention employs a variety of advanced technologies, including Kalman filtering, particle swarm optimization, and the maximum entropy principle. These technologies work together to dynamically adjust model parameters, smooth prediction results, and minimize uncertainty during the prediction process when analyzing member behavior data. Through these methods, the system can accurately predict churn risk and provide timely feedback while monitoring member behavior in real time.

[0093] Applications of Kalman Filtering

[0094] In this invention, Kalman filtering is used to smooth the churn prediction results. Kalman filtering is a recursive algorithm that estimates the model's state based on the difference between measured and predicted data. In churn prediction, Kalman filtering effectively smooths noise in the original data, ensuring the stability and accuracy of the prediction results.

[0095] Specifically, the core formula of the Kalman filter is as follows:

[0096]

[0097] P k =(IK k H)P k-1

[0098] in, y represents the predicted value at the current moment. k K represents the observed value. k For Kalman gain, P k Let H be the covariance matrix and H be the observation matrix. Kalman gain K k The update formula is 2

[0099]

[0100] Where R is the covariance of the process noise, and P k-1 Here, is the error covariance matrix from the previous time step, and H is the matrix used to transform the system state into the observation space. Through these recursive formulas, the Kalman filter can update the prediction results based on real-time observations, providing smooth, noise-free predictions for churn prediction. This method improves the robustness of the prediction results and ensures real-time adjustment and updating of the churn prediction model.

[0101] Subgroup Optimization (PSO) is used in this invention to optimize the parameters of the churn prediction model. PSO is an optimization algorithm that simulates the behavior of groups in nature, employing a swarm of particles to search for the optimal solution in the solution space. Each particle represents a possible solution, and its movement and velocity are guided by its current position and the globally optimal solution. The globally optimal solution is ultimately obtained through iterative optimization. In churn prediction, PSO optimizes the model's accuracy by adjusting the parameters within the prediction model. For example, a churn prediction model may contain multiple parameters, such as behavioral weights and churn risk thresholds. PSO iteratively optimizes these parameters, enabling the model to make more accurate predictions based on members' historical behavioral data.

[0102] The basic update formula for PSO is as follows:

[0103]

[0104] x i (t+1)=x i (t)+v i (t+1)

[0105] Among them, v i (t) is the velocity of the particle at time t, x i(t) represents the particle's position, w represents the inertial weight, c1 and c2 are the acceleration constants, and r1 and r2 are random numbers. and These represent the individual optimal position and the global optimal position of the particle, respectively. Using these formulas, PSO can optimize the model parameters during churn prediction, further improving prediction accuracy, especially when dealing with complex, multidimensional behavioral data.

[0106] Application of the maximum entropy principle

[0107] The maximum entropy principle is used in churn prediction models to maximize the utilization of information in a system, thereby reducing uncertainty in the data. In churn prediction, the maximum entropy principle optimizes model parameters so that the prediction results reflect the amount of information in the existing data to the greatest extent. 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:

[0108]

[0109] Among them, p i Let represent the probability distribution of each prediction outcome. By maximizing entropy, we can ensure that the churn prediction model fully utilizes all available information without making too many assumptions, thus providing more accurate prediction results. In churn prediction, the maximum entropy principle can effectively avoid overfitting, ensuring the model's generalization ability and stability.

[0110] A comprehensive application of Kalman filtering, particle swarm optimization, and the maximum entropy principle.

[0111] By comprehensively utilizing Kalman filtering, particle swarm optimization, and the maximum entropy principle, this invention significantly improves the accuracy and reliability of churn prediction. Kalman filtering smooths out noise and fluctuations in member behavior data, resulting in more stable prediction results; particle swarm optimization optimizes the parameters of the churn prediction model, improving its adaptability; and the maximum entropy principle further reduces uncertainty in the model by maximizing information content. The combination of these three techniques enables churn prediction to remain efficient and stable in dynamically changing environments, and to promptly reflect changing trends in member behavior.

[0112] Steps: Personalized health assessment

[0113] Summary description:

[0114] In this invention, personalized health assessment involves generating a personalized health score for each member based on their historical behavioral data. Personalized health assessment is based on multi-dimensional data, combining 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 member's specific behavioral characteristics, thereby helping merchants identify high-risk churned members and high-loyalty members, providing precise decision support for subsequent member management and personalized marketing. The technical implementation of this step fully utilizes probabilistic inference methods and fuzzy mathematical models from data science to ensure the accuracy, objectivity, and dynamic adjustment capability of the health score. The following is a detailed description of the steps.

[0115] In this embodiment, to achieve personalized health assessment, the present invention employs a combination of Bayesian inference and fuzzy comprehensive evaluation to dynamically calculate the health score for each member. Bayesian inference provides probability estimates of health parameters based on historical behavioral data, while fuzzy comprehensive evaluation provides a comprehensive and objective health score through weighted fusion of multi-dimensional data. The combination of these methods ensures that member health assessment not only considers a single behavioral dimension but also comprehensively analyzes behavioral patterns and changes, guaranteeing that each member receives a personalized score.

[0116] Bayesian inference methods

[0117] The application of Bayesian inference in this invention is to dynamically calculate the posterior probability distribution of health parameters based on members' historical behavioral data. The Bayesian inference method is based on Bayes' theorem, combining prior knowledge (such as the initial health distribution of members) with observed data (such as recent purchases, browsing history, and interaction records) to derive the posterior distribution of health parameters. The basic form of Bayes' theorem is as follows:

[0118]

[0119] Where θ represents the health score parameter, D is the observed behavioral 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 new member behavioral data, gradually improving the estimation of member health.

[0120] In one possible implementation, the prior distribution P(θ) can be set based on historical member data, typically choosing a Gaussian distribution or other suitable distribution form; the likelihood function P(D|θ) reflects the changing pattern of member health under specific behavioral patterns. For example, a member's purchase frequency, number of times participating in promotional activities, and frequency of social interaction can be used as inputs to the likelihood function, which models the relationship between member behavior and health. It should be noted that, through Bayesian inference, the system can continuously adjust its health estimate based on each member's behavioral history, ensuring that each member's health score reflects their recent behavioral trends, thereby improving the timeliness and accuracy of the assessment.

[0121] Fuzzy comprehensive evaluation method

[0122] Fuzzy comprehensive evaluation is a key method in this invention for integrating multi-dimensional member behavioral data. This method can handle the uncertainty and fuzziness in member behavioral data, especially when there is overlap between behavioral dimensions or when changes in member behavior are difficult to quantify. Fuzzy comprehensive evaluation can effectively fuse data from multiple dimensions to obtain a comprehensive health score.

[0123] The core idea of ​​the fuzzy comprehensive evaluation method is to weight multiple behavioral dimensions of each member (such as activity level, loyalty, purchase frequency, social interaction, etc.) using fuzzy rules, and finally obtain an overall health score. Specifically, each behavioral dimension is fuzzified and transformed into a fuzzy set, and then weighted according to the set weights to obtain the final comprehensive score.

[0124] For example, a member's health assessment may include the following behavioral dimensions:

[0125] Activity level: Based on members' login frequency, number of times they participate in platform activities, etc.

[0126] Loyalty: Based on members' long-term consumption behavior, renewal status, etc.;

[0127] Social interaction: Members' comments, shares, likes, and other behaviors on social media platforms;

[0128] Purchase frequency: How often a member purchases goods, the amount of each transaction, etc.

[0129] The data for each dimension will be fuzzified, transforming it into a fuzzy set, such as high, medium, and low levels. Then, fuzzy operations are used to weight the health scores of each dimension, resulting in a comprehensive health score. The calculation formula for the fuzzy comprehensive evaluation method is as follows:

[0130]

[0131] Where H represents the member's overall health score, w i For the weights of each dimension, v i This represents the fuzzy score for the corresponding dimension. By weighting the scores from multiple dimensions, a member's health score is ultimately generated.

[0132] Dynamically adjust health score

[0133] In some embodiments, the health score is not merely a statically calculated result, but is adjusted in real time based on 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 predefined rules.

[0134] For example, when a member exhibits abnormal behavior (such as a significant increase in purchase frequency or prolonged inactivity), the system will automatically reassess and adjust the member's health score. To achieve this, this invention employs a weighted adjustment mechanism, dynamically adjusting the weights of each dimension based on changes in the member's behavior when calculating the overall health score. For instance, if a member's purchase frequency increases significantly, the weight of purchase frequency will increase accordingly, thereby improving the member's health score.

[0135] Understandably, this mechanism ensures that a member's health score always reflects their latest behavioral changes, preventing the score from becoming outdated due to historical data. Through this dynamic adjustment, merchants can track members' health status in real time and intervene promptly, thereby improving member retention and loyalty.

[0136] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for assessing member health based on member churn prediction, characterized in that, Includes the following steps: Multi-sensor data acquisition and fusion: Collect members' behavioral data, environmental data, social data, and device data through multiple sensors, and fuse the data to ensure the accuracy and comprehensiveness of the data; Behavioral pattern modeling: Based on the collected member behavior data, a nonlinear dynamic system modeling method is used to model member behavior and capture the long-term trends 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 members' historical behavioral data, a personalized health assessment model is used to generate a health score for each member; The behavior pattern modeling steps further include: 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 trend and short-term fluctuations of behavior. Fractal theory is used to calculate the fractal dimension of member behavior, assess the complexity of the behavior, and identify potential abnormal behaviors. The aforementioned loss prediction model employs 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 by combining short-term and long-term changes in member behavior. By reconstructing phase space and performing fractal dimension analysis, we can improve our ability to capture behavioral complexity.

2. The method for assessing member health based on member churn prediction according to claim 1, characterized in that, The method also includes: health score feedback and dynamic adjustment: by monitoring members' behavior in real time, the health score is dynamically adjusted to ensure the timeliness and accuracy of the assessment results; Assessment Reports and Strategy Support: Generate member profiles based on health scores, and provide merchants with decision support for member management and marketing strategies based on these profiles.

3. The method for assessing member health based on member churn prediction according to claim 1, characterized in that, The multi-sensor data acquisition and fusion steps further include: Collect members' purchase records, clickstream, browsing history, and social media interaction data from behavioral sensors; Collect environmental data on promotional activities, weather conditions, and holidays from environmental sensors; Collect data on the type and stability of devices used by members from device sensors; The principle of maximizing mutual information is used to fuse data collected by different sensors, ensuring maximum sharing and minimum redundancy among the data.

4. The method for assessing member health based on member churn prediction according to claim 1, characterized in that, The aforementioned member churn prediction steps further include: Based on the collected member data, Kalman filtering is used to smooth the churn prediction results and adjust the prediction values ​​in real time. The parameters of the churn prediction model are adjusted using the particle swarm optimization algorithm to optimize the model accuracy. The maximum entropy principle is used to optimize the churn prediction process, maximizing the amount of information in churn prediction and reducing the uncertainty in the prediction.

5. The method for assessing member health based on member churn prediction according to claim 1, characterized in that, The personalized health assessment steps further include: Based on members' historical behavior data, a Bayesian inference method is used to generate the posterior probability distribution of each member's health parameters; The health of members is comprehensively scored by combining the fuzzy comprehensive evaluation method with the weighted average model.

6. The method for assessing member health based on member churn prediction according to claim 2, characterized in that, The health score feedback and dynamic adjustment further include: The health score of members is dynamically adjusted based on real-time changes in member behavior. When abnormal member behavior is detected, the weighting of the health score calculation is automatically adjusted to enhance the flexibility and accuracy of the assessment.

7. The method for assessing member health based on member churn prediction according to claim 2, characterized in that, The assessment report and strategy support further include: Personalized member profiles are generated based on members' health scores, and members are categorized accordingly. Based on member profiles and churn prediction results, we provide merchants with personalized marketing strategies and member management solutions.

8. The method for assessing member health based on member churn prediction according to claim 1, characterized in that, The health assessment method, which combines multi-sensor data and personalized behavioral pattern analysis, further includes: Multidimensional data, including environmental factors, social behavior, and device information, are used as model inputs to dynamically adjust the weights of the health score. The fuzzy comprehensive evaluation method is used to weight data from different dimensions to ensure the objectivity and accuracy of the evaluation results.

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