Vehicle insurance channel whole-process marketing management method and system

Abstract

The invention relates to the technical field of vehicle insurance marketing management, in particular to a vehicle insurance channel whole-process marketing management method and system, and the method comprises the steps: collecting and integrating various data of clients from multiple data sources to construct a resource library; carrying out customer segmentation and accurate portraying by utilizing algorithms such as clustering analysis and the like; predicting demands based on the portraits, historical behaviors and seasonal factors; personalized pricing is made in combination with characteristics such as risk levels; predicting customer loss by means of logistic regression and the like; implementing a corresponding customer management strategy according to the loss prediction; carrying out personalized recommendation by adopting a deep learning recommendation algorithm; advertisement putting is optimized in combination with a dobby machine and reinforcement learning; evaluating the risk by using multiple algorithms; fraud is prevented and controlled through anomaly detection; optimizing a claim settlement process based on the claim settlement data; and the marketing and advertising effects are evaluated through A / B testing and the like. According to the method, the precision marketing level can be improved, pricing and risk control are optimized, efficient operation and decision support are realized, and the market competitiveness and sustainable development capability of companies are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of auto insurance marketing management, and in particular to a method and system for full-process marketing management of auto insurance channels. Background Art

[0002] In today's highly competitive auto insurance market, the traditional auto insurance marketing and management model faces many challenges and can no longer meet the requirements of industry development and changes in customer demand. With the rapid development of information technology, consumers have increasingly broad channels for obtaining information, and their understanding and expectations of auto insurance products are constantly increasing. Traditional marketing models often lack in-depth exploration of individual customer characteristics and behavior patterns, and are unable to accurately locate customer needs, resulting in weak targeting of marketing activities and low customer attention and response rates to marketing information. At the same time, a unified pricing strategy is difficult to fully consider the risk differences of customers, which can easily lead to the loss of high-quality customers or over-insurance of high-risk customers, affecting the company's profit and risk balance.

[0003] In addition, the traditional claims process is cumbersome, relying on manual operations and experience judgment, with low processing efficiency and prone to errors. Customers often face problems such as long waiting times and complicated procedures during the claims process, which seriously affects customer experience and satisfaction. In terms of risk assessment, traditional methods are mainly based on simple statistical analysis and empirical rules, which make insufficient use of complex customer data, making it difficult to accurately identify potential risks, and easily leading to uncontrolled claims risks.

[0004] Furthermore, the market environment is changing rapidly and competition is becoming increasingly fierce. Insurance companies need to adjust their marketing strategies and product services in a timely manner to adapt to market changes. However, the traditional decision support system lacks real-time and accurate data basis, and the decision-making process is lagging and not scientific enough, making it difficult to maintain a competitive advantage in a rapidly changing market.

[0005] Therefore, in response to the above problems, a method and system for full-process marketing management of auto insurance channels is proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a method and system for full-process marketing management of auto insurance channels to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for full-process marketing management of auto insurance channels, comprising the following steps:

[0009] Step S1, customer data collection and integration step: obtaining customer personal information, driving behavior data, historical claims records and vehicle type data from multiple data sources, and performing data cleaning, sorting and storage to build a complete customer data resource library;

[0010] Step S2, customer segmentation and precise profiling: Use cluster analysis algorithms to process data in the customer data resource library, divide customer groups, and use decision tree and support vector machine algorithms to build customer profiles to determine the demand characteristics and purchasing tendencies of different customer groups. Cluster analysis algorithms include K-Means algorithm and DBSCAN algorithm;

[0011] Step S3, customer demand prediction: based on customer portraits, historical purchase behavior data and seasonal variation factors, the demand for auto insurance products is predicted using time series analysis methods, including ARIMA model and LSTM neural network;

[0012] Step S4, pricing strategy formulation: combining the customer risk level and driving habit characteristics, determining a personalized pricing strategy through a machine learning algorithm, the machine learning algorithm includes an XGBoost algorithm and a regression analysis algorithm;

[0013] Step S5, churn prediction: using customer behavior data, using logistic regression and random forest algorithms to build a churn prediction model to identify potential churn customers;

[0014] Step S6, customer lifecycle management: based on the churn prediction results, implement targeted marketing, preferential strategies or customer care measures for customers with different churn risk levels;

[0015] Step S7: Personalized customer recommendation: using a deep learning recommendation algorithm to recommend auto insurance products that meet the needs and preferences of customers. The deep learning recommendation algorithm includes a neural network and a collaborative filtering algorithm.

[0016] Step S8, advertising delivery optimization: combining the multi-armed bandit algorithm with reinforcement learning, dynamically adjusting the advertising delivery strategy based on customer feedback;

[0017] Step S9, risk assessment: Based on the customer's historical claims data and driving behavior information, use support vector machine, decision tree and deep learning algorithm to conduct risk assessment and determine the customer's risk level;

[0018] Step S10, fraud prevention and control: using anomaly detection algorithms to identify auto insurance fraud and prevent and control compensation risks;

[0019] Step S11, claims process optimization: based on the customer's claims history data, use data mining and machine learning technology to automatically classify and review claims cases;

[0020] Step S12: Sales and market effectiveness evaluation: Use A / B testing and reinforcement learning methods to evaluate the effectiveness of different marketing strategies and advertising, adjust marketing activities in real time based on market feedback, and optimize resource allocation.

[0021] As a preferred solution, in the customer segmentation and precise profiling steps, the K-Means algorithm maximizes the similarity of customer data within the same cluster and minimizes the similarity between different clusters through iterative calculation. The calculation formula is as follows: ;

[0022] in, Represents the total sum of squared errors, which is used to measure the effect of clustering; is the number of clusters; Indicates clusters; Belong to cluster Data points in ; For the The center point of each cluster; Represents data points To the cluster center The distance metric is Euclidean distance, that is, ,in, is the feature dimension of the data point, and Respectively represent the data points and cluster centers in the The value of the feature; by continuously updating the cluster center point , so that The value is minimized to achieve clustering of customer data;

[0023] The DBSCAN algorithm is used to process non-spherical data clusters to ensure the comprehensiveness and accuracy of customer segmentation. Its core concept involves two parameters: is the neighborhood radius, is the minimum number of points; for any point in the data set , if the number of points contained in its neighborhood is greater than or equal to , then it is called point is the core point; if point At the core of In the neighborhood, it is called Subordinate to ; Through this density-based approach, data points are divided into core points, boundary points, and noise points, and different clusters are determined.

[0024] As a preferred solution, in customer demand forecasting, the ARIMA model predicts future demand trends by analyzing the autocorrelation and partial autocorrelation of historical data. The ARIMA model is expressed as , the formula is as follows: ;

[0025] in, Indicates that the time series is The value of the moment; is the autoregressive order, is the autoregression coefficient, indicating With the past The linear relationship between the values ​​at each moment; is the difference order, Represents the time series Order difference operation, is the lag operator, that is , used to transform non-stationary time series into stationary series; is the moving average order, is the moving average coefficient, which represents the random error at the current moment With the past The linear relationship between the random errors at each moment; is a constant term; is a white noise sequence, indicating that Random disturbances that are unpredictable at all times, Represents the time series Order hysteresis operation, It also means that the time series Order hysteresis operation;

[0026] The LSTM neural network improves the accuracy of demand forecasting by capturing long-term dependencies in the data. The core calculation formula of the LSTM unit is as follows: ; ; ; ; ; ;

[0027] in, Represent the activation vectors of the input gate, forget gate, and output gate respectively; Represents the candidate memory unit state vector; Represents the memory unit state vector at the current moment; Represents the hidden state vector at the current moment; Represents the input vector at the current moment; represents the hidden state vector at the previous moment, Indicates the last moment The memory unit state vector of They are the weight matrices of the input gate, forget gate, output gate, and candidate memory cell states; are the corresponding bias vectors respectively; Represents an element-wise multiplication operation: is the sigmoid activation function, and tanh is the hyperbolic tangent activation function. Through the collaborative work of the input gate, forget gate, output gate, and candidate memory units, the LSTM neural network learns the long-term dependencies in time series data and performs demand forecasting.

[0028] As a preferred solution, in the pricing strategy formulation step, the XGBoost algorithm deeply mines customer risk characteristics by integrating multiple weak learners. The objective function of the XGBoost algorithm is: ;

[0029] in, is the sample size; For the The true labels of samples; For the The predicted value of samples; is the loss function, which is used to measure the difference between the predicted value and the true value; is the number of weak learners; Indicates A weak learner; is a regular term used to prevent overfitting, where For the The number of leaf nodes of weak learners, For the The weight of a leaf node, and is the regularization coefficient, is a regular term used to penalize the complexity of the model;

[0030] The regression analysis algorithm establishes a mathematical relationship model between customer characteristics and pricing to determine the precise premium price. The regression analysis algorithm is multivariate linear regression, and the model formula of multivariate linear regression is: ;

[0031] in, Indicates the premium price; Indicates the customer's characteristic variables; is the regression coefficient, which indicates the influence of each characteristic variable on the premium price; is a random error term, which indicates the part that cannot be explained by the model; the regression coefficient is estimated by the least squares method and other methods , thereby determining the premium price based on customer characteristics.

[0032] As a preferred solution, in the churn prediction step, when the churn prediction model is constructed with the help of logistic regression and random forest algorithm, the formula of the churn prediction model is: ; ;

[0033] in, Indicates that given customer characteristics The probability of customer churn under the condition of For the loss label; is the logistic regression coefficient; is a natural constant; is a linear combination term, representing the weighted sum of customer characteristics, and the logistic regression coefficient is estimated through training data , predict the probability of churn based on customer characteristics;

[0034] The random forest algorithm improves the accuracy and stability of churn prediction by constructing multiple decision trees to comprehensively predict the results. Composition, for a sample , the prediction results are: , that is, a majority vote is performed on the prediction results of each decision tree; among them, Indicates A decision tree for a sample The prediction results; is the final prediction result of the random forest. When each decision tree is constructed, samples are randomly selected with replacement from the training data set, and when each node is split, a part of the features is randomly selected from all the features to calculate the splitting conditions.

[0035] As a preferred solution, in the personalized recommendation step, the neural network mines the customer's potential needs and preferences through deep learning. The neural network is a multi-layer feedforward neural network. The forward propagation calculation formula of the multi-layer feedforward neural network is as follows: ;

[0036] in, Indicates the number of layers of the neural network; Indicates The activation vector of the layer; For the The weight matrix of the layer; For the The bias vector of the layer; is the activation function; by taking customer data as input and calculating through a multi-layer neural network, the final recommendation output is obtained;

[0037] Collaborative filtering algorithms recommend products based on similarities among customer groups. , calculate its and other users Similarity between , using cosine similarity calculation, that is, ,in, Indicates user and users A collection of items that have been reviewed together. and Respectively represent users and users A collection of reviewed items. and Respectively represent users and users For items The evaluation scores of the target user are then selected based on the similarity. users, based on the evaluations of these similar users on the items, predict the target user’s evaluation of the unrated items Rating : ,in, Represents the target user Most similar A collection of users, Indicates user For items The evaluation scores are calculated, and the items are sorted according to the predicted scores. Items with scores higher than the threshold are selected and recommended to the target users.

[0038] As a preferred solution, in the risk assessment step, when the support vector machine constructs a risk classification model, for the binary classification problem, its decision function is: ,in, is the customer data vector to be evaluated, is the index set of the support vector, is the Lagrange multiplier, For the The class labels of the training samples, Indicates The data vector of training samples; As the kernel function, the Gaussian kernel function is used ,in, is the kernel parameter, is the bias term, according to the decision function The output value of determines the risk category of the customer; the decision tree constructs a decision tree model by recursively dividing the data features, and selects an optimal feature for division at each node, so that the data purity of the divided sub-nodes is the highest, and the information entropy is used. As a measure of purity, For the data set, is a collection of categories, For the data set belonging to the category The proportion of, through the decision-making process from the root node to the leaf node, determines the key characteristic factors affecting the risk.

[0039] A full-process marketing management system for an auto insurance channel, wherein the management system is equipped with a full-process marketing management method for an auto insurance channel.

[0040] It can be seen from the technical solutions provided by the present invention that the method and system for full-process marketing management of auto insurance channels provided by the present invention have the following beneficial effects:

[0041] Comprehensive data collection and integration covers a wide range of information, and the customer portraits constructed are detailed and complete, helping insurance companies gain in-depth insights into customer needs, preferences and risk characteristics, and achieve accurate market segmentation. Based on this, the company can formulate precise marketing strategies based on the characteristics of different customer groups, push matching products, enhance customer attention to recommended products, improve the response rate and conversion rate of marketing activities, and create more sales opportunities.

[0042] Personalized recommendations and targeted marketing strategies are based on customer portraits, which strengthen the interactive connection between customers and the company. Customers feel that the company pays attention to their needs, which increases their satisfaction and loyalty. Loyal customers are more likely to renew their policies and recommend the company's products, which helps expand the customer base, increase market share, shape a good brand image, and enhance market competitiveness.

[0043] Based on the risk level and driving habits of customers, we use advanced algorithms to formulate personalized pricing strategies to ensure that premiums are accurately matched to risk levels. This makes pricing more fair and reasonable, enhances the company's market competitiveness, optimizes the profit structure, and avoids customer loss or risk imbalance caused by unreasonable pricing.

[0044] Risk assessment based on customer claims history and driving behavior can identify high-risk customers and potential fraud in advance; timely adjustment of underwriting conditions and strengthening of risk monitoring can effectively reduce claims risks; risk control measures ensure the company's stable operation, reduce financial losses, and improve profitability and sustainable development capabilities;

[0045] The marketing management driven by full-process data realizes the digitization and intelligence of each link. Data sharing and collaboration break down departmental barriers and improve the overall efficiency of business processes. For example, the claims process optimization module uses technical means to automatically handle claims cases, shorten the claims cycle, and improve processing speed and accuracy. Efficient claims services enhance customer trust, optimize customer experience, and improve satisfaction and loyalty.

[0046] The sales and market effectiveness evaluation module uses scientific methods to monitor and evaluate marketing strategies and advertising effectiveness in real time. Based on data feedback, the company can promptly adjust marketing activities and resource allocation, optimize marketing channel combinations, and improve resource utilization efficiency. Data-driven decision support runs through the business process, helping management make scientific decisions, respond quickly to market changes, seize opportunities, meet challenges, and promote sustainable and healthy business development. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flow chart of a method for full-process marketing management of auto insurance channels. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0049] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] like Figure 1 As shown, an embodiment of the present invention provides a method for full-process marketing management of auto insurance channels, including the following steps:

[0051] Step S1, customer data collection and integration step: obtaining customer personal information, driving behavior data, historical claims records and vehicle type data from multiple data sources, and performing data cleaning, sorting and storage to build a complete customer data resource library;

[0052] Step S1-1: Acquisition of data from multiple data sources

[0053] Determine the data source:

[0054] Connect with internal systems related to auto insurance business, including the customer relationship management system (CRM) to obtain customer personal information, the underwriting system to obtain vehicle type data, and the claims system to obtain historical claims records; at the same time, establish a data sharing mechanism with external partners or third-party data providers to obtain customer driving behavior data;

[0055] Develop corresponding data extraction interfaces or transmission protocols for different data sources to ensure that data is accurately and timely transmitted to the data processing center; extract structured data in an appropriate manner, and develop parsing programs to convert the format of unstructured data; use encryption technology to ensure security during transmission, establish a monitoring mechanism to ensure integrity and accuracy, and issue timely alarms and recover in case of abnormalities;

[0056] Step S1-2: Data cleaning, organization and storage

[0057] Data cleaning:

[0058] Data denoising: Identify and remove erroneous or abnormal data points, check data rationality, and correct or delete unreasonable values ​​based on distribution and business logic;

[0059] Data missing processing: Count the missing rate of each field, use appropriate filling methods for fields with low missing rates, consider deleting fields with high missing rates and small impact, otherwise evaluate the impact and try to supplement or specially process them;

[0060] Deduplication: Identify and delete duplicate customer records through duplicate checking algorithms to ensure data uniqueness;

[0061] Data consistency check and processing: Check the data consistency between different data sources and adjust the inconsistencies according to business rules and credibility;

[0062] Data sorting and conversion:

[0063] Data format unification: Convert data in different formats into a format suitable for analysis and processing, unify the data formats of dates, values, etc., and avoid calculation errors and analysis deviations;

[0064] Data standardization: standardize some data to make them comparable, such as normalizing numerical data and converting categorical data formats;

[0065] Feature engineering: Extract or construct new features based on business needs and goals, calculate risk scores, safe driving indexes, etc., and extract key vehicle attributes;

[0066] Data Storage:

[0067] Choose a suitable database management system (DBMS): Choose according to the data size, structure, and read and write requirements. Relational databases are used for frequent read and write of large amounts of structured data, while non-relational databases are used for massive amounts of unstructured or semi-structured data.

[0068] Design a reasonable data storage structure: Design the database table structure according to data characteristics and business logic, establish associations, and perform partitioning and index design to improve query retrieval performance;

[0069] Data backup and recovery strategy: formulate regular backup plans, combine full and incremental backups, back up to offsite or cloud storage, establish recovery mechanisms and test them regularly to ensure data security and business continuity;

[0070] Step S2, customer segmentation and precise profiling: Use cluster analysis algorithms to process data in the customer data resource library, divide customer groups, and use decision tree and support vector machine algorithms to build customer profiles to determine the demand characteristics and purchasing tendencies of different customer groups. Cluster analysis algorithms include K-Means algorithm and DBSCAN algorithm;

[0071] Step S2-1: Data preparation and clustering algorithm selection

[0072] Data selection and preprocessing:

[0073] Select key data related to customer profile construction from the customer data resource library, such as personal information (age, gender, occupation, etc.), vehicle information (brand, model, age, etc.), driving behavior data (mileage, driving time, average speed, number of sudden acceleration and braking, etc.) and historical claims data (number of claims, claim amount, claim reason, etc.);

[0074] Clean the selected data and process missing values ​​and outliers. For missing values, choose appropriate filling methods according to data distribution, such as mean filling, median filling, or predictive filling using machine learning algorithms. For outliers, set reasonable thresholds or use statistical methods (such as outlier detection based on standard deviation) to identify, correct, or delete them.

[0075] Standardize the cleaned data to make different features comparable and avoid the impact of feature dimension differences on clustering results. Common standardization methods include normalization (mapping data to a specific interval, such as [0,1]) and standardization (making data follow a normal distribution with a mean of 0 and a variance of 1).

[0076] K-Means algorithm operation process:

[0077] Determine the K value (number of clusters): Preliminarily set the K value based on business experience and data analysis goals; test different K values ​​using evaluation indicators such as the elbow method and silhouette coefficient method, observe the clustering effect, and select the K value that optimizes the evaluation indicators;

[0078] Initialize cluster centers: randomly select K data points in the data set as the initial cluster centers;

[0079] Assign data points to clusters: Calculate the distance between each data point and the K cluster centers (usually using the Euclidean distance formula), and assign the data point to the cluster to which the nearest cluster center belongs;

[0080] Update cluster centers: For each cluster, calculate the mean of all data points within it and use the mean as the new cluster center;

[0081] Repeated allocation and update: Repeat the steps of data point allocation and cluster center update until the convergence condition is met, such as the cluster center no longer changes significantly (the change amplitude is less than the preset threshold) or the maximum number of iterations is reached;

[0082] DBSCAN algorithm operation process (if selected):

[0083] Determine parameters: According to the data distribution density and business understanding, set the two key parameters of neighborhood radius (epsilon) and minimum number of points (MinPts); the appropriate parameter values ​​can be determined through visual analysis of the data or parameter tuning experiments;

[0084] Determination of core points, boundary points and noise points: traverse each point in the data set and calculate the number of points in the epsilon neighborhood; if the number of points is greater than or equal to MinPts, the point is a core point; if the number of points is less than MinPts but in the neighborhood of a core point, it is a boundary point; points that are neither core points nor boundary points are noise points;

[0085] Cluster generation: Starting from the core point, all points in its neighborhood (including core points and boundary points) are classified into the same cluster; clustering is continuously expanded until all core points are processed, and finally different clusters are formed; if there are noise points in the data set, it can be decided whether to process them separately or ignore them according to business needs;

[0086] Step S2-2: Use decision trees and support vector machines to build customer profiles

[0087] Feature engineering and data set partitioning:

[0088] Based on the clustering results, extract representative features of each customer group and other relevant features of the customer as input features for building decision trees and support vector machine models; these features may include cluster labels (indicating the group to which the customer belongs), personal information features, vehicle features, driving behavior features, and historical claims features;

[0089] Encode and transform the extracted features, such as one-hot encoding or label encoding of categorical features, so that the algorithm can process them; discretize continuous features according to business needs, such as dividing mileage into different intervals to better reflect the inherent laws of the data;

[0090] Divide the data set into a training set and a test set. Usually, 70%-80% of the data is used as the training set, and the rest is used as the test set. Ensure that the training set and the test set have similar data distribution to ensure the generalization ability of the model.

[0091] Decision tree algorithm builds a portrait:

[0092] Model training: Use the training set data to train the decision tree model; during the training process, select appropriate information gain, Gini index and other indicators as the basis for feature selection, and recursively build a decision tree; starting from the root node, select the optimal feature for splitting according to the selected indicator, divide the data set into different subsets, and repeat this process until the stopping condition is met, such as the tree reaches the preset depth, the number of node samples is less than the threshold, or all samples belong to the same category;

[0093] Model evaluation: Use the test set data to evaluate the trained decision tree model and calculate evaluation indicators such as accuracy, recall, and F1 value to understand the performance of the model. If the model performance is not ideal, the accuracy and stability of the model can be improved by adjusting the parameters of the decision tree (such as tree depth, pruning strategy, etc.) or using ensemble learning methods (such as random forest).

[0094] Portrait interpretation: Analyze the structure and node splitting conditions of the decision tree model to understand the characteristics and behavior patterns of different customer groups. For example, based on the branching path of the decision tree, it can be inferred which characteristics play a key role in the division of customer groups, thereby explaining the demand characteristics and purchasing tendencies of different customer groups.

[0095] Support vector machine algorithm to build a profile (optional):

[0096] Data preprocessing: normalize or standardize the input feature data to ensure that the feature values ​​are within a similar range to avoid affecting the model training effect due to feature scale differences;

[0097] Model training: Use the training set data to train the support vector machine model, select the appropriate kernel function (such as linear kernel, Gaussian kernel, etc.) and regularization parameters; determine the support vector and decision boundary by solving the optimization problem, so that the model can effectively classify different customer groups;

[0098] Model evaluation and adjustment: Use test set data to evaluate the performance of the support vector machine model, and adjust kernel function parameters, regularization parameters, etc. based on the evaluation results to improve the classification accuracy and generalization ability of the model;

[0099] Portrait analysis: Based on the classification results of the support vector machine model and the distribution of the support vector, we analyze the distribution patterns of different customer groups in the feature space, further understand the differences between customer groups, and thus determine the demand characteristics and purchasing tendencies of different customer groups. For example, by observing the relative position relationship between the support vector and the decision boundary, we can understand which features play a key role in customer classification, and then infer the behavior patterns and demand preferences of different customer groups.

[0100] Step S2-3: Determine customer group demand characteristics and purchasing tendencies

[0101] Analyze demand characteristics based on model results:

[0102] Combine the analysis results of decision trees and support vector machines to conduct in-depth research on the distribution of different customer groups in terms of various characteristics; compare the differences between different groups in terms of personal information, vehicle information, driving behavior, and historical claims, and determine the significant characteristics of each group;

[0103] The demand information behind the customer behavior pattern is mined from the branch rules of the decision tree and the classification boundaries of the support vector machine. For example, a branch in the decision tree indicates that a customer group with a specific occupation and aggressive driving behavior may have higher requirements for the coverage and claim settlement speed of auto insurance because they face relatively higher risks.

[0104] Summarize purchasing tendency:

[0105] Based on the analysis of demand characteristics, summarize the purchasing tendencies of different customer groups for auto insurance products; including the preference for different insurance types (such as compulsory traffic insurance, vehicle damage insurance in commercial insurance, third-party liability insurance, theft insurance, etc.), sensitivity to insurance limits and premium prices, and demand for value-added services (such as roadside assistance, designated driver services, vehicle maintenance, etc.);

[0106] For example, young office workers with high-value vehicles may prefer to purchase higher-insurance vehicle damage insurance and third-party liability insurance, and are more interested in car insurance packages that provide free roadside assistance and vehicle maintenance services; while self-employed customers who frequently use their vehicles may pay more attention to preferential premiums and convenient claims, and have relatively low demand for commercial insurance products such as theft and robbery insurance;

[0107] Generate portrait reports and visualizations (optional):

[0108] The customer segmentation results, demand characteristics and purchase tendency analysis are organized into a detailed customer portrait report; the report content includes the basis for the division of customer groups, the characteristics of each group, demand analysis, purchase tendency summary and marketing strategy suggestions for different groups, etc., providing a comprehensive reference for subsequent marketing decisions;

[0109] Visualize customer portraits, such as using bar charts, pie charts, radar charts, etc. to display the distribution ratios and differences of different customer groups in various characteristics, and use decision tree visualization tools to display the decision tree model structure, so as to more intuitively understand the characteristics and behavior patterns of customer groups, and assist marketing personnel and management to quickly grasp the key information of customer portraits;

[0110] Through the above detailed steps S2-1, S2-2 and S2-3, the cluster analysis algorithm can be used to effectively process customer data, divide customer groups, and use decision tree and support vector machine algorithms to build accurate customer portraits, accurately determine the demand characteristics and purchase tendencies of different customer groups, and provide strong support for precision marketing, product design and customer service in auto insurance marketing management; in actual operation, the algorithm and parameters can be flexibly selected and adjusted according to data characteristics and business needs to achieve the best effect;

[0111] Step S3, customer demand prediction: based on customer portraits, historical purchase behavior data and seasonal variation factors, the demand for auto insurance products is predicted using time series analysis methods, including ARIMA model and LSTM neural network;

[0112] Step S3-1: Data preparation

[0113] Accurately obtain customer portrait data from the customer data resource library, covering multi-dimensional information such as customer group classification, personal attributes, vehicle-related information, and driving habits, to ensure the accuracy and completeness of the data and properly handle missing or erroneous data;

[0114] Carefully extract historical purchase behavior data, including key information such as the time, type, premium amount, and purchase channel of auto insurance products, and systematically organize and classify them according to customer IDs to present them in a time series format to meet subsequent analysis needs;

[0115] Extensively collect data related to seasonal factors, such as fluctuations in the frequency of traffic accidents in different seasons, the law of industry sales peak and off-peak seasons, and seasonal changes in vehicle usage frequency. Data sources may include internal statistics of insurance companies, authoritative industry reports, and statistical data from traffic management departments, etc., to ensure the reliability and relevance of the data;

[0116] Accurately associate and integrate customer profiles, historical purchase behaviors, and seasonal change data through customer IDs to build a complete and logically clear data set, providing a solid data foundation for subsequent in-depth analysis;

[0117] Step S3-2: ARIMA model prediction

[0118] Stationarity test and difference operation:

[0119] For the target variables in the historical purchase behavior data, professional stationarity test methods (such as ADF test, KPSS test, etc.) are used to conduct strict tests; if the test results show that the data is not stationary, the difference order is determined based on the test indicators, and the data is subjected to difference processing until the data reaches a stable state; in the difference process, the autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs can be used to assist in judging the rationality of the difference order, ensuring that the ACF and PACF graphs after difference show characteristics that meet the requirements of stationarity;

[0120] Model order determination:

[0121] In-depth analysis of the ACF and PACF diagrams of the stable time series data, preliminary estimation of the moving average order q based on the lag order that is significantly non-zero in the ACF diagram, and preliminary estimation of the autoregressive order p based on the lag order that is significantly non-zero in the PACF diagram; at the same time, calculation of the information criterion values ​​(such as AIC, BIC, etc.) under different order combinations (p, d, q), and by comparing the information criterion values, accurately select the order combination that minimizes the information criterion as the optimal order of the ARIMA model, thereby determining the preliminary architecture of the model;

[0122] Model training and performance evaluation:

[0123] Use the ARIMA model with a determined order to perform parameter estimation and model training on the carefully divided training set data (usually the first 80% of the historical data) to ensure that the model can fully learn the patterns in the data;

[0124] Use the trained model to predict the test set data (the last 20% of the historical data) and calculate the prediction error indicators (such as mean square error MSE, mean absolute error MAE, etc.) to comprehensively evaluate the prediction performance of the model; if the prediction error indicator exceeds the acceptable range, it indicates that the model prediction effect is not ideal. At this time, it is necessary to review the model order or check the data processing flow, and perform multiple iterations of training and evaluation until the model performance meets the expected requirements;

[0125] Demand Forecast Execution:

[0126] Apply a mature and well-performing ARIMA model to a complete data set (including training and test sets), and fully combine customer profile data and seasonal variation factors (if they can be incorporated into the model) to accurately predict the demand for auto insurance products in a specific time period in the future (such as the next year, broken down by quarter or month). The prediction results are presented in the form of intuitive indicators such as purchase volume or sales volume, providing strong data support for subsequent decision-making;

[0127] Step S3-3: LSTM neural network prediction

[0128] Data preprocessing and sequence construction:

[0129] Divide the data set into a training set and a test set according to a scientific and reasonable ratio (such as 70%-80%) to ensure that the two parts of data have a high degree of similarity in distribution characteristics to ensure the generalization ability of the model;

[0130] Perform comprehensive preprocessing on the data and convert it into a three-dimensional tensor format (number of samples, time steps, number of features) that meets the input requirements of the LSTM neural network. The time step should be carefully determined based on the actual business needs and data cycle characteristics, and the number of features is the sum of the dimensions of the encoded and processed customer profile, historical purchase behavior, seasonal changes, and other features.

[0131] Perform normalization on the target variable (auto insurance product demand) to ensure that the data is within the appropriate numerical range for model training; at the same time, strictly record the parameters used in the normalization process so that the original data scale can be accurately restored when the prediction results are restored;

[0132] Model building and training optimization:

[0133] Carefully construct the LSTM neural network architecture, specify the number of network layers (usually between 2-3 layers), the number of neurons in each layer (reasonably set according to the complexity of the data and the computing resources), the activation function (such as tanh or ReLU, etc.), the optimizer (such as Adam), the loss function (such as mean square error MSE), and other key hyperparameters (such as learning rate, number of iterations, batch size, etc.);

[0134] The LSTM model is rigorously trained using the training set data. During the training process, the model continuously calculates the prediction error based on the loss function, and continuously updates the network weights and biases through the optimizer back-propagation algorithm, dynamically adjusting the model parameters to minimize the loss function. During the training process, the model performance is evaluated on the validation set (reasonably divided from the training set) at a certain number of iterations (such as 10 times), and the trend of the loss function value and evaluation indicators (such as accuracy, recall, etc., if applicable) is closely monitored. If the model performance no longer improves or there are signs of overfitting, stop training in time to avoid wasting resources.

[0135] Model evaluation and fine-tuning:

[0136] Use the test set data to comprehensively evaluate the performance of the trained LSTM model, calculate various prediction error indicators (such as MSE, MAE, RMSE, etc.), and conduct in-depth comparative analysis with the prediction results of the ARIMA model;

[0137] If the performance of the LSTM model does not meet expectations, optimize it by adjusting the hyperparameters; for example, appropriately increase the number of network layers and observe performance changes, fine-tune the learning rate to balance the model convergence speed and stability, and reasonably use regularization techniques (such as L1 or L2 regularization, Dropout layer) to effectively prevent overfitting; after each hyperparameter adjustment, retrain and evaluate the model, and repeat the cycle until the model performance reaches a satisfactory level;

[0138] Demand forecast and results in-depth analysis:

[0139] By using the LSTM model optimized to the best state, closely combining customer profiles and seasonal change factors, the demand for future auto insurance products is predicted. The prediction results are expected to capture more complex nonlinear relationships in the data and provide richer information for decision-making;

[0140] In-depth analysis of the prediction results of the LSTM model, using advanced visualization techniques (such as drawing a comparison chart of the predicted value and the actual value, observing the hidden layer output or the changes in the activation state of neurons, etc.), to fully understand the model's response mechanism and learning mode to different input features, thereby providing a scientific basis for the precise formulation of marketing strategies;

[0141] Step S3-4: Results integration and decision-oriented application

[0142] Prediction result fusion:

[0143] Use scientific and reasonable methods (such as simple averaging or weighted averaging, assigning weights based on model performance) to effectively integrate the prediction results of ARIMA and LSTM models to form a comprehensive prediction result, thereby improving the accuracy and reliability of the prediction;

[0144] Uncertainty analysis (optional):

[0145] Use professional statistical methods (such as Bootstrap method or other appropriate techniques) to conduct uncertainty analysis, calculate prediction intervals, provide risk assessment references for decision-making, and help decision-makers fully understand the reliability and potential risks of prediction results;

[0146] Decision support and strategy generation:

[0147] Provide the integrated forecast results and uncertainty analysis results (if any) to sales, production, marketing and other related departments in a timely manner; the sales department formulates personalized and precise sales strategies based on the demand forecasts of different customer groups to improve sales efficiency and customer satisfaction; the production department reasonably arranges production plans and inventory management based on the forecast results to ensure optimal resource allocation; the marketing department adjusts promotion activities and advertising strategies in a timely manner based on demand trends to enhance market competitiveness; through the collaboration of various departments, ensure that the company's operational decisions are closely aligned with market demand and maximize overall operational efficiency and benefits;

[0148] Step S4, pricing strategy formulation: Combine the customer risk level and driving habit characteristics, and determine the personalized pricing strategy through machine learning algorithms, including XGBoost algorithms and regression analysis algorithms; the specific operations are:

[0149] Step S4-1: Data preparation and feature engineering

[0150] Data collection and integration:

[0151] Extract customer risk level-related data from the customer data resource library, including historical claims data (number of claims, claim amount, claim reason, etc.), vehicle information (brand, model, age, vehicle value, etc.) and credit records obtained from external data sources (if any), etc., as basic data for assessing customer risk;

[0152] Collect data on customers' driving habits, such as mileage, driving time, average speed, number of sudden accelerations and braking, proportion of nighttime driving, number of fatigue driving warnings (if relevant equipment records), etc. These data can be obtained from in-vehicle equipment or through data sharing with relevant partners;

[0153] Integrate customer risk level data and driving habit data through customer ID to ensure that each customer's risk information and driving behavior data accurately correspond to each other and build a complete pricing analysis data set.

[0154] Data cleaning and preprocessing:

[0155] Check whether there are missing values, outliers, or erroneous data in the data set; for missing values, select appropriate processing methods based on data characteristics and distribution, such as mean filling (applicable to numerical data with relatively uniform distribution), median filling (applicable to data with a small number of extreme values), mode filling (applicable to categorical data), or use machine learning algorithms for prediction filling (such as using other relevant features to build a regression model to predict missing values);

[0156] Identify and handle outliers. You can identify abnormal data points by setting reasonable thresholds (based on the statistical characteristics of the data, such as mean and standard deviation) or using density-based outlier detection methods (such as local outlier factor algorithm); For outliers, correct them according to business logic and data conditions (such as correcting obviously wrong values ​​to a reasonable range) or delete them (if the number of outliers is small and has a greater impact on the overall data distribution);

[0157] Encoding categorical data so that it can be recognized and processed by machine learning algorithms; common encoding methods include One-Hot Encoding, which converts each categorical variable into multiple binary variables, each representing a category; Label Encoding, which assigns a unique numerical label to each category;

[0158] Standardize or normalize numerical data to eliminate the impact of different dimensions between features and improve model training efficiency and accuracy; for example, use Z-score normalization to convert data into a distribution with a mean of 0 and a standard deviation of 1, or use minimum-maximum normalization to map data to a specific interval (such as [0,1]);

[0159] Feature Engineering and Feature Selection:

[0160] Extract or construct meaningful features from raw data based on business knowledge and data analysis experience; for example, calculate new features such as the average claim interval of customers and the ratio of claim amount to vehicle value based on historical claims data; calculate driving behavior stability indicators (such as the ratio of sudden acceleration and braking times to driving time), risky driving time ratio (such as the ratio of night driving time to total driving time) based on driving habit data;

[0161] Use feature selection methods to screen out features that have a significant impact on pricing strategies, reduce data dimensions, avoid overfitting, and improve the interpretability and generalization ability of the model. Common feature selection methods include:

[0162] Correlation analysis: Calculate the correlation coefficient (such as Pearson correlation coefficient or Spearman correlation coefficient) between each feature and the target variable (such as premium price or risk level), and select the features with higher correlation;

[0163] Chi-square test: Applicable to the correlation test between categorical features and target variables. It evaluates the independence of features and target variables by calculating the chi-square statistic and selects features with lower independence (i.e., higher correlation).

[0164] Model-based feature selection: Use the feature importance evaluation function of machine learning models (such as decision trees, random forests, etc.) to select features that contribute more to model predictions. For example, in a random forest model, the importance of each feature is evaluated by calculating the information gain or Gini index reduction during the decision tree splitting process, and features with higher importance are retained.

[0165] Step S4-2: XGBoost algorithm pricing model construction and training:

[0166] Model parameter settings:

[0167] Determine the key parameters of the XGBoost model, which directly affect the performance and generalization ability of the model; the main parameters include: n_estimators (number of weak learners): controls the number of decision trees in the ensemble model. Generally, a suitable value is selected through cross-validation. The value range can be from dozens to hundreds. It can be initially set to 100 and then adjusted according to the model performance. max_depth (maximum depth of the tree): limits the growth depth of the decision tree to prevent overfitting; the value is usually between 3-10. You can set it to 5 first, observe the performance of the model on the training set and validation set, and then optimize it; learning_rate: controls the update step size of the model weights in each iteration. A smaller learning rate can make the model converge more stably, but may require more iterations. The value is generally between 0.01-0.3, initially set to 0.1, and can be adjusted according to the model convergence. Subsample (subsample ratio): used to control the ratio of data samples used in each training to prevent overfitting. The value range is between 0.5-1. The default value is 1. You can try setting it to 0.8 for experimentation. colsample_bytree (column sampling ratio): the ratio of randomly selected features each time a decision tree is built. It is also used to prevent overfitting. The value range is between 0.5 and 1. The default value is 1. You can set it to 0.8 for tuning. Gamma (minimum loss reduction required for node splitting): used to control the complexity of the decision tree. The larger the value, the more conservative the model is to prevent overfitting. It can be set to 0 first and then adjusted according to the model performance; lambda and alpha (regularization parameters): used for L2 and L1 regularization respectively, to control model complexity and prevent overfitting. The default values ​​are 1 and 0 respectively, which can be adjusted according to actual conditions;

[0168] Model training and cross validation:

[0169] Divide the data set into training set and validation set according to a certain ratio (e.g. 70%-80% for training set and 20%-30% for validation set) to ensure the consistency of data distribution;

[0170] The XGBoost model is trained using the training set data. During the training process, the model is iteratively optimized according to the set objective function (usually including loss function and regularization term) and parameters. The loss function is used to measure the difference between the model prediction value and the true value. Common loss functions include square loss function (for regression problems) and logarithmic loss function (for classification problems). Regularization term is used to penalize model complexity and prevent overfitting.

[0171] Use cross-validation methods (such as k-fold cross-validation, k is usually 5 or 10) to evaluate the performance of the model on different data subsets and select the model parameter combination with the best average performance; in each round of cross-validation, the training set is further divided into k subsets, of which k-1 subsets are used to train the model and 1 subset is used to verify the model. This is repeated k times to ensure that each subset has the opportunity to be used as a verification set. Finally, the average performance indicator is calculated (such as mean square error (MSE), mean absolute error (MAE), accuracy, etc., select the appropriate indicator according to the problem type);

[0172] Model evaluation and tuning:

[0173] Use the validation set data to evaluate the performance of the trained XGBoost model and calculate relevant evaluation indicators (such as MSE, MAE, etc. for regression problems, accuracy, recall, F1 value, etc. for classification problems) to determine whether the model meets business requirements; if the model performance is not ideal, analyze the reasons based on the evaluation indicators, which may be that the model is underfitting (both the training set and validation set performance are poor) or overfitting (the training set performance is good but the validation set performance is poor);

[0174] For underfitting problems, you can try to increase the number of weak learners (n_estimators), reduce regularization parameters (lambda and alpha), adjust the learning rate (learning_rate), or increase feature engineering to introduce more valuable features; for overfitting problems, you can reduce the number of weak learners, increase regularization strength (increase lambda and alpha), reduce the maximum depth of the tree (max_depth), reduce the subsample ratio (subsample) and the column sampling ratio (colsample_bytree) to optimize the model;

[0175] Repeat the model training, cross-validation, and evaluation and tuning process until satisfactory model performance is obtained; at the same time, record the model performance indicators after each parameter adjustment to facilitate comparative analysis and selection of the optimal model;

[0176] Step S4-3: Regression analysis algorithm pricing model construction and training

[0177] Model selection and hypothesis testing:

[0178] Select an appropriate regression analysis model based on data features and business needs; common regression models include linear regression (applicable to situations where data is linearly related), polynomial regression (used to handle nonlinear relationships by adding high-order terms of features), ridge regression (solves multicollinearity problems in linear regression by adding L2 regularization terms), and Lasso regression (uses L1 regularization terms, which can be used for feature selection and sparse model construction), etc.

[0179] Before building a regression model, perform hypothesis testing on the data to ensure that the data meets the basic assumptions of regression analysis, mainly including:

[0180] Linear relationship assumption: By drawing a scatter plot between the feature and the target variable or using correlation analysis methods, it is preliminarily determined whether there is a linear relationship between the variables; if a nonlinear relationship is found, consider using appropriate transformations (such as logarithmic transformation, polynomial transformation, etc.) or selecting a nonlinear regression model;

[0181] Independence assumption: Check whether the data points are independent of each other. Usually, the independence of samples is ensured during the data collection process. If there is time series or spatially correlated data, special processing methods (such as difference, autoregressive model, etc.) may be required to meet the independence assumption.

[0182] Homoscedasticity assumption: Verify whether the variance of the error term remains constant at different predicted value levels; this can be checked by drawing residual graphs (such as scatter plots of standardized residuals and predicted values). If heteroscedasticity is found, it can be corrected by weighted least squares or data transformation.

[0183] Normality assumption: To test whether the error term follows a normal distribution, you can use a normal probability plot (QQ plot) or perform a normality test (such as the Shapiro-Wilk test, Kolmogorov-Smirnov test, etc.); if the error term does not follow a normal distribution, you may need to transform the data or choose other regression methods that do not rely on the normality assumption;

[0184] Model construction and parameter estimation:

[0185] Taking linear regression as an example, assuming the model form is ;in, Indicates the premium price; Indicates the customer's characteristic variables; is the regression coefficient, which indicates the influence of each characteristic variable on the premium price; is the random error term;

[0186] Use the least squares method (OLS) or other appropriate parameter estimation methods to estimate the regression coefficients; the goal of the least squares method is to minimize the residual sum of squares (RSS), that is, in, is the sample size, is the true value, is the model prediction value; by solving the normal equation is the design matrix, is the regression coefficient vector, is the target variable vector) or use optimization methods such as gradient descent algorithm to obtain the estimated value of the regression coefficient ;

[0187] For polynomial regression, determine the degree of the polynomial based on the characteristics of the data ,Then the original features are transformed into the corresponding number of powers to construct the polynomial feature matrix , and then use the least squares method to estimate the regression coefficients , the model form is ; Ridge regression and Lasso regression add regularization terms on the basis of least squares method. The objective function of ridge regression is , the objective function of Lasso regression is in, is the regularization parameter, and a suitable The value is used to balance the model between fitting the data and controlling the complexity.

[0188] Model evaluation and diagnosis:

[0189] Use evaluation metrics such as mean squared error Root mean square error , coefficient of determination etc.) to evaluate the performance of the regression model. It is used to measure the degree of fit of the model to the data. The value range is between 0 and 1. The closer it is to 1, the better the model fit is. The calculation formula is: , in, is the total sum of squares of deviations;

[0190] Perform model diagnosis to check whether there are any problems with the model, mainly including:

[0191] Multicollinearity diagnosis: Check whether there is a high correlation between independent variables, which can be determined by calculating the variance inflation factor (VIF); if the VIF value is large (it is generally believed that VIF>5 or 10 indicates serious multicollinearity), measures may need to be taken (such as deleting related variables, principal component analysis, etc.) to resolve it;

[0192] Residual analysis: Draw a residual graph to check whether the residuals meet the assumptions of normality, independence, and homoscedasticity; if the residuals are non-normally distributed, or have autocorrelation or heteroscedasticity, further analysis of the causes is required and appropriate correction measures should be taken, such as data transformation, use of generalized least squares method, etc.

[0193] Outlier and influence point detection: By calculating statistics such as studentized residuals, leverage values, and Cook's distance, outliers in the data set and points that have a greater impact on the model can be identified; for outliers and influence points, it is necessary to carefully check whether the data is accurate. If the data is correct, consider deleting it or using robust regression methods to reduce its impact;

[0194] Model improvement and optimization:

[0195] Improve and optimize the regression model based on the model evaluation and diagnosis results; if the model has multicollinearity problems, use stepwise regression, ridge regression, Lasso regression and other methods to deal with it; if the residuals do not meet the assumptions, choose appropriate data transformation methods (such as logarithmic transformation, Box-Cox transformation, etc.) or use more complex regression models (such as generalized linear models, nonlinear regression models, etc.) according to the specific problems; if outliers or influencing points are found to have a significant impact on the model, consider deleting them or performing robust processing while ensuring data accuracy;

[0196] Repeat the model building, parameter estimation, evaluation diagnosis and improvement optimization process until the model performance reaches a satisfactory level, while ensuring that the model meets the basic assumptions of regression analysis and has good interpretability and generalization capabilities;

[0197] Step S4-4: Personalized pricing strategy generation and application

[0198] Pricing strategy development:

[0199] Based on the well-trained and high-performance XGBoost model and regression analysis model, a personalized pricing strategy is formulated in combination with business rules and market conditions. For each customer, their risk level and driving habit characteristic data are input into the model to obtain the premium price predicted by the model.

[0200] Adjust the model-predicted price based on the company's pricing objectives (such as profit maximization, market share expansion, risk control, etc.) and market competition conditions; for example, if the company hopes to increase its market share in a highly competitive market, it can give low-risk customers certain price discounts (such as multiplying the model-predicted price by a discount factor less than 1); for high-risk customers, appropriately increase the premium price, but ensure that the price is within a reasonable range to avoid customer loss;

[0201] Consider the impact of other factors on pricing, such as regional differences (risk levels, market demands and cost structures may differ in different regions), sales channel costs (marketing costs and service costs vary across channels), customer loyalty (providing certain price discounts or rewards to old customers or long-term cooperative customers), etc., incorporate these factors into the pricing strategy to form a final personalized pricing plan;

[0202] Strategy Evaluation and Monitoring:

[0203] After implementing the personalized pricing strategy, continue to track and evaluate the effectiveness of the strategy; collect actual sales data and customer feedback, calculate key indicators such as sales, profits, customer satisfaction, customer churn rate, etc., compare and analyze the data before the pricing strategy was implemented, and evaluate the impact of the pricing strategy on the company's business;

[0204] Establish a monitoring mechanism to regularly monitor market dynamics (such as price changes of competitors, adjustments to industry policies, fluctuations in market demand, etc.) and changes in customer behavior (such as changes in purchasing patterns and risk characteristics, etc.) to promptly identify factors that may affect the effectiveness of pricing strategies;

[0205] Dynamically adjust pricing strategies based on strategy evaluation and market monitoring results; if it is found that pricing strategies lead to excessively high customer churn or a decline in market share, analyze the reasons and adjust price levels or pricing rules in a timely manner; if there are major changes in the market environment (such as substantial price cuts by competitors, changes in industry regulatory policies, etc.), respond quickly and optimize pricing strategies to ensure that the company maintains a dominant position in market competition and achieves sustainable development;

[0206] Step S5, churn prediction: using customer behavior data, using logistic regression and random forest algorithms to build a churn prediction model to identify potential churn customers;

[0207] Step S6, customer lifecycle management: based on the churn prediction results, implement targeted marketing, preferential strategies or customer care measures for customers with different churn risk levels;

[0208] Step S7: Personalized customer recommendation: using a deep learning recommendation algorithm to recommend auto insurance products that meet the needs and preferences of customers. The deep learning recommendation algorithm includes a neural network and a collaborative filtering algorithm.

[0209] Step S8, advertising delivery optimization: combining the multi-armed bandit algorithm with reinforcement learning, dynamically adjusting the advertising delivery strategy based on customer feedback;

[0210] Step S9, risk assessment: Based on the customer's historical claims data and driving behavior information, use support vector machine, decision tree and deep learning algorithm to conduct risk assessment and determine the customer's risk level;

[0211] Step S10, fraud prevention and control: using anomaly detection algorithms to identify auto insurance fraud and prevent and control compensation risks;

[0212] Step S11, claims process optimization: based on the customer's claims history data, use data mining and machine learning technology to automatically classify and review claims cases;

[0213] Step S12: Sales and market effectiveness evaluation: Use A / B testing and reinforcement learning methods to evaluate the effectiveness of different marketing strategies and advertising, adjust marketing activities in real time based on market feedback, and optimize resource allocation.

[0214] In this embodiment, in the customer segmentation and precise profiling step, the K-Means algorithm maximizes the similarity of customer data within the same cluster and minimizes the similarity between different clusters through iterative calculation. The calculation formula is as follows: ;

[0215] in, Represents the total sum of squared errors, which is used to measure the effect of clustering; is the number of clusters; Indicates clusters; Belong to cluster Data points in ; For the The center point of each cluster; Represents data points To the cluster center The distance metric is Euclidean distance, that is, ,in, is the feature dimension of the data point, and Respectively represent the data points and cluster centers in the The value of the feature; by continuously updating the cluster center point , so that The value is minimized to achieve clustering of customer data;

[0216] The DBSCAN algorithm is used to process non-spherical data clusters to ensure the comprehensiveness and accuracy of customer segmentation. Its core concept involves two parameters: is the neighborhood radius, is the minimum number of points; for any point in the data set , if the number of points contained in its neighborhood is greater than or equal to , then it is called point is the core point; if point At the core of In the neighborhood, it is called Subordinate to ; Through this density-based approach, data points are divided into core points, boundary points, and noise points, and different clusters are determined.

[0217] In this embodiment, in customer demand forecasting, the ARIMA model predicts future demand trends by analyzing the autocorrelation and partial autocorrelation of historical data. The ARIMA model is expressed as , the formula is as follows: ;

[0218] in, Indicates that the time series is The value of the moment; is the autoregressive order, is the autoregression coefficient, indicating With the past The linear relationship between the values ​​at each moment; is the difference order, Represents the time series Order difference operation, is the lag operator, that is , used to transform non-stationary time series into stationary series; is the moving average order, is the moving average coefficient, which represents the random error at the current moment With the past The linear relationship between the random errors at each moment; is a constant term; is a white noise sequence, indicating that Random disturbances that are unpredictable at all times, Represents the time series Order hysteresis operation, It also means that the time series Order hysteresis operation;

[0219] The LSTM neural network improves the accuracy of demand forecasting by capturing long-term dependencies in the data. The core calculation formula of the LSTM unit is as follows: ; ; ; ; ; ;

[0220] in, Represent the activation vectors of the input gate, forget gate, and output gate respectively; Represents the candidate memory unit state vector; Represents the memory unit state vector at the current moment; Represents the hidden state vector at the current moment; Represents the input vector at the current moment; represents the hidden state vector at the previous moment, Indicates the last moment The memory unit state vector of They are the weight matrices of the input gate, forget gate, output gate, and candidate memory cell states; are the corresponding bias vectors respectively; Represents an element-wise multiplication operation: is the sigmoid activation function, and tanh is the hyperbolic tangent activation function. Through the collaborative work of the input gate, forget gate, output gate, and candidate memory units, the LSTM neural network learns the long-term dependencies in time series data and performs demand forecasting.

[0221] In this embodiment, in the pricing strategy formulation step, the XGBoost algorithm deeply mines customer risk characteristics by integrating multiple weak learners. The objective function of the XGBoost algorithm is: ;

[0222] in, is the sample size; For the The true labels of samples; For the The predicted value of samples; is the loss function, which is used to measure the difference between the predicted value and the true value; is the number of weak learners; Indicates A weak learner; is a regular term used to prevent overfitting, where For the The number of leaf nodes of weak learners, For the The weight of a leaf node, and is the regularization coefficient, is a regular term used to penalize the complexity of the model;

[0223] The regression analysis algorithm establishes a mathematical relationship model between customer characteristics and pricing to determine the precise premium price. The regression analysis algorithm is multivariate linear regression, and the model formula of multivariate linear regression is: ;

[0224] in, Indicates the premium price; Indicates the customer's characteristic variables; is the regression coefficient, which indicates the influence of each characteristic variable on the premium price; is a random error term, which indicates the part that cannot be explained by the model; the regression coefficient is estimated by the least squares method and other methods , thereby determining the premium price based on customer characteristics.

[0225] As a preferred solution, in the churn prediction step, when the churn prediction model is constructed with the help of logistic regression and random forest algorithm, the formula of the churn prediction model is: ; ;

[0226] in, Indicates that given customer characteristics The probability of customer churn under the condition of For the loss label; is the logistic regression coefficient; is a natural constant; is a linear combination term, representing the weighted sum of customer characteristics, and the logistic regression coefficient is estimated through training data , predict the probability of churn based on customer characteristics;

[0227] The random forest algorithm improves the accuracy and stability of churn prediction by constructing multiple decision trees to comprehensively predict the results. Composition, for a sample , the prediction results are: , that is, a majority vote is performed on the prediction results of each decision tree; among them, Indicates A decision tree for a sample The prediction results; is the final prediction result of the random forest. When each decision tree is constructed, samples are randomly selected with replacement from the training data set, and when each node is split, a part of the features is randomly selected from all the features to calculate the splitting conditions.

[0228] In this embodiment, in the personalized recommendation step, the neural network mines the customer's potential needs and preferences through deep learning. The neural network is a multi-layer feedforward neural network. The forward propagation calculation formula of the multi-layer feedforward neural network is as follows: ;

[0229] in, Indicates the number of layers of the neural network; Indicates The activation vector of the layer; For the The weight matrix of the layer; For the The bias vector of the layer; is the activation function; by taking customer data as input and calculating through a multi-layer neural network, the final recommendation output is obtained;

[0230] Collaborative filtering algorithms recommend products based on similarities among customer groups. , calculate its and other users Similarity between , using cosine similarity calculation, that is, ,in, Indicates user and users A collection of items that have been reviewed together. and Respectively represent users and users A collection of reviewed items. and Respectively represent users and users For items The evaluation scores of the target user are then selected based on the similarity. users, based on the evaluations of these similar users on the items, predict the target user’s evaluation of the unrated items Rating : ,in, Represents the target user Most similar A collection of users, Indicates user For items The evaluation scores are calculated, and the items are sorted according to the predicted scores. Items with scores higher than the threshold are selected and recommended to the target users.

[0231] In this embodiment, in the risk assessment step, when the support vector machine constructs a risk classification model, for a binary classification problem, its decision function is: ,in, is the customer data vector to be evaluated, is the index set of the support vector, is the Lagrange multiplier, For the The class labels of the training samples, Indicates The data vector of training samples; As the kernel function, the Gaussian kernel function is used ,in, is the kernel parameter, is the bias term, according to the decision function The output value of determines the risk category of the customer; the decision tree constructs a decision tree model by recursively dividing the data features, and selects an optimal feature for division at each node, so that the data purity of the divided sub-nodes is the highest, and the information entropy is used. As a measure of purity, For the data set, is a collection of categories, For the data set belonging to the category The proportion of, through the decision-making process from the root node to the leaf node, determines the key characteristic factors affecting the risk.

[0232] A full-process marketing management system for auto insurance channels, wherein the management system is equipped with a full-process marketing management method for auto insurance channels, specifically including:

[0233] Customer Data Management Module:

[0234] Responsible for collecting customer data from multiple data sources, and integrating, cleaning, preprocessing and storing it; providing data quality monitoring and reporting functions, displaying data integrity, accuracy and consistency indicators in real time, and promptly identifying and handling data problems;

[0235] Supports data query, export and analysis functions, making it easier for business personnel and data analysts to obtain the required data for in-depth analysis. For example, business personnel can query the characteristic data of a specific customer group to formulate marketing strategies; data analysts can obtain historical claims data to optimize risk assessment models.

[0236] Customer segmentation and portrait module:

[0237] Use clustering analysis algorithms to process customer data and divide customer groups; provide multiple clustering algorithm selection and parameter adjustment functions, allowing users to select appropriate algorithms and parameters based on business needs and data characteristics to obtain the best customer segmentation results;

[0238] Use decision trees, support vector machines and other algorithms to build customer portraits and determine the demand characteristics and purchasing tendencies of different customer groups; generate customer portrait reports and visualization charts to display information such as the distribution, characteristic differences and purchasing tendencies of customer groups, providing a basis for precision marketing;

[0239] Demand forecasting module:

[0240] Based on customer profiles, historical purchase behavior data, and seasonal variation factors, time series analysis methods (such as ARIMA and LSTM) are used to predict the demand for auto insurance products. Model training, evaluation, and prediction functions are provided, allowing users to select different prediction models and time ranges, and view prediction results and error analysis.

[0241] Based on the demand forecast results, provide decision support to production, sales and inventory management departments, such as determining the production plan of auto insurance products, adjusting sales strategies and optimizing inventory levels;

[0242] Pricing Strategy Module:

[0243] Develop personalized pricing strategies based on customer risk levels and driving habits through machine learning algorithms (such as XGBoost and regression analysis); provide pricing model training, verification, and adjustment functions to calculate customer premiums in real time based on customer data and market conditions;

[0244] Supports pricing strategy simulation and analysis functions to help business personnel evaluate the impact of different pricing strategies on company profits, market share and customer satisfaction, so as to select the optimal pricing plan;

[0245] Churn prediction and customer lifecycle management module:

[0246] Using customer behavior data, we build a churn prediction model with the help of logistic regression and random forest algorithms to identify potential churn customers; we provide the training, evaluation and update functions of the churn prediction model, and timely adjust the model parameters according to changes in customer behavior to improve prediction accuracy;

[0247] Based on the churn prediction results, targeted marketing, preferential policies or customer care measures are implemented for customers with different churn risk levels; the customer lifecycle management strategy formulation, execution and monitoring functions are provided to track customer responses and behavior changes and evaluate the effectiveness of strategies;

[0248] Personalized recommendation module:

[0249] Use deep learning recommendation algorithms (such as neural networks and collaborative filtering) to recommend auto insurance products that meet customers' needs and preferences; generate personalized recommendation lists in real time based on customers' personal information, purchase history, browsing behavior and other data;

[0250] Provide optimization and adjustment functions for recommendation algorithms, continuously improve recommendation effects through user feedback and behavior data, and increase recommendation accuracy and customer satisfaction; for example, adjust the weights and parameters of the recommendation model based on customer clicks, purchases, and other behaviors on recommended products;

[0251] Advertisement delivery optimization module:

[0252] Combining the multi-armed bandit algorithm with reinforcement learning, dynamically adjust the advertising strategy based on customer feedback; select appropriate advertising channels, delivery time and advertising content based on customer characteristics, behaviors and interests to improve the click-through rate and conversion rate of advertisements;

[0253] Provides monitoring and analysis functions for advertising effectiveness, real-time tracking of advertising exposure, clicks, conversion rates and other indicators, evaluates the effectiveness of advertising strategies, and provides a basis for optimizing advertising;

[0254] Risk Assessment Module:

[0255] Based on the customer's historical claims data and driving behavior information, support vector machines, decision trees and deep learning algorithms are used to conduct risk assessment and determine the customer's risk level; the risk assessment model training, verification and update functions are provided to continuously optimize the accuracy and reliability of the model;

[0256] Based on the risk assessment results, support is provided for underwriting decisions, pricing strategies and claims management. For example, for high-risk customers, stricter review measures can be taken during underwriting, with premiums raised or the scope of insurance liability limited. Investigations and reviews can be strengthened during claims to prevent fraud.

[0257] Fraud prevention module:

[0258] Use anomaly detection algorithms (such as IsolationForest) to identify auto insurance fraud and prevent and control claims risks; monitor claims data and customer behavior in real time, and issue timely warnings when abnormal patterns and behaviors are discovered;

[0259] Provide fraud case analysis and reporting functions to help insurance companies understand the characteristics and trends of fraudulent behavior and formulate corresponding preventive measures and strategies; for example, by analyzing fraud cases, it is found that certain regions or customer groups have higher fraud risks, and the monitoring and prevention of these regions and groups can be strengthened;

[0260] Claims process optimization module:

[0261] Based on the historical claims data of customers, data mining and machine learning technologies are used to automatically classify and review claims cases; by establishing a claims model, the claim amount and claim duration are predicted to improve the efficiency and accuracy of claims;

[0262] Provide monitoring and management functions for the claims process, track the progress of claims cases in real time, promptly identify and resolve problems in the claims process, and improve customer satisfaction. For example, for claims cases that have not been processed within the time limit, automatically issue reminders and warnings to urge claims personnel to speed up the processing.

[0263] Sales and marketing effectiveness evaluation module:

[0264] Use A / B testing and reinforcement learning methods to evaluate different marketing strategies and advertising effects; select the best strategy for promotion by comparing the implementation results of different marketing plans;

[0265] It provides analysis and reporting functions for sales and market data, including analysis of indicators such as sales, market share, customer satisfaction, and customer acquisition costs, to provide data support and decision-making basis for marketing decisions; for example, according to the changing trend of market share, it can adjust marketing strategies and resource allocation to improve the company's market competitiveness.

[0266] 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 method for full-process marketing management of auto insurance channels, characterized by: The following steps are involved: Step S1, customer data collection and integration step: obtaining customer personal information, driving behavior data, historical claims records and vehicle type data from multiple data sources, and performing data cleaning, sorting and storage to build a complete customer data resource library; Step S2, customer segmentation and precise profiling: using cluster analysis algorithms to process data in the customer data resource library, dividing customer groups, and using decision tree and support vector machine algorithms to build customer profiles, and determine the demand characteristics and purchase tendencies of different customer groups. The cluster analysis algorithms include K-Means algorithm and DBSCAN algorithm; Step S3, customer demand prediction: based on customer portraits, historical purchase behavior data and seasonal variation factors, a time series analysis method is used to predict the demand for auto insurance products, wherein the time series analysis method includes an ARIMA model and an LSTM neural network; Step S4, pricing strategy formulation: combining the customer risk level and driving habit characteristics, determining a personalized pricing strategy through a machine learning algorithm, wherein the machine learning algorithm includes an XGBoost algorithm and a regression analysis algorithm; Step S5, churn prediction: using customer behavior data, using logistic regression and random forest algorithms to build a churn prediction model to identify potential churn customers; Step S6, customer lifecycle management: based on the churn prediction results, implement targeted marketing, preferential strategies or customer care measures for customers with different churn risk levels; Step S7: Personalized customer recommendation: using a deep learning recommendation algorithm to recommend auto insurance products that meet the needs and preferences of customers, wherein the deep learning recommendation algorithm includes a neural network and a collaborative filtering algorithm; Step S8, advertising delivery optimization: combining the multi-armed bandit algorithm with reinforcement learning, dynamically adjusting the advertising delivery strategy based on customer feedback; Step S9, risk assessment: Based on the customer's historical claims data and driving behavior information, use support vector machine, decision tree and deep learning algorithm to conduct risk assessment and determine the customer's risk level; Step S10, fraud prevention and control: using anomaly detection algorithms to identify auto insurance fraud and prevent and control compensation risks; Step S11, claims process optimization: based on the customer's claims history data, use data mining and machine learning technology to automatically classify and review claims cases; Step S12: Sales and market effectiveness evaluation: Use A / B testing and reinforcement learning methods to evaluate the effectiveness of different marketing strategies and advertising, adjust marketing activities in real time based on market feedback, and optimize resource allocation.

2. The method for full-process marketing management of auto insurance channels according to claim 1, characterized in that: In the customer segmentation and precise profiling steps, the K-Means algorithm maximizes the similarity of customer data within the same cluster and minimizes the similarity between different clusters through iterative calculation. The calculation formula is as follows: ； in, Represents the total sum of squared errors, which is used to measure the effect of clustering; is the number of clusters; Indicates clusters; Belong to cluster Data points in ; For the The center point of each cluster; Represents data points To the cluster center The distance metric is Euclidean distance, that is, ,in, is the feature dimension of the data point, and Respectively represent the data points and cluster centers in the The value of the feature; by continuously updating the cluster center , so that The value is minimized to achieve clustering of customer data; The DBSCAN algorithm is used to process non-spherical data clusters to ensure the comprehensiveness and accuracy of customer segmentation. Its core concept involves two parameters: is the neighborhood radius, is the minimum number of points; for any point in the data set , if the number of points contained in its neighborhood is greater than or equal to , then it is called point is the core point; if point At the core of In the neighborhood, it is called Subordinate to ; Through this density-based approach, data points are divided into core points, boundary points, and noise points, and different clusters are determined.

3. The method for full-process marketing management of auto insurance channels according to claim 2 is characterized by: In the customer demand forecast, the ARIMA model predicts future demand trends by analyzing the autocorrelation and partial autocorrelation of historical data. The ARIMA model is expressed as , the formula is as follows: ； in, Indicates that the time series is The value of the moment; is the autoregressive order, is the autoregression coefficient, indicating With the past The linear relationship between the values ​​at each moment; is the difference order, Represents the time series Order difference operation, is the lag operator, that is , used to transform non-stationary time series into stationary series; is the moving average order, is the moving average coefficient, which represents the random error at the current moment With the past The linear relationship between the random errors at each moment; is a constant term; is a white noise sequence, indicating that Random disturbances that are unpredictable at all times, Represents the time series Order hysteresis operation, It also means that the time series Order hysteresis operation; The LSTM neural network improves the accuracy of demand forecasting by capturing long-term dependencies in the data. The core calculation formula of the LSTM unit is as follows: ； ； ； ； ； ； in, Represent the activation vectors of the input gate, forget gate, and output gate respectively; Represents the candidate memory unit state vector; Represents the memory unit state vector at the current moment; Represents the hidden state vector at the current moment; Represents the input vector at the current moment; represents the hidden state vector at the previous moment, Indicates the last moment The memory unit state vector of They are the weight matrices of the input gate, forget gate, output gate, and candidate memory cell states; are the corresponding bias vectors respectively; Represents an element-wise multiplication operation: is the sigmoid activation function, and tanh is the hyperbolic tangent activation function. Through the collaborative work of the input gate, forget gate, output gate, and candidate memory units, the LSTM neural network learns the long-term dependencies in time series data and performs demand forecasting.

4. The method for full-process marketing management of auto insurance channels according to claim 3 is characterized by: In the pricing strategy formulation step, the XGBoost algorithm deeply mines customer risk characteristics by integrating multiple weak learners. The objective function of the XGBoost algorithm is: ； in, is the sample size; For the The true labels of samples; For the The predicted value of samples; is the loss function, which is used to measure the difference between the predicted value and the true value; is the number of weak learners; Indicates A weak learner; is a regular term used to prevent overfitting, where For the The number of leaf nodes of weak learners, For the The weight of a leaf node, and is the regularization coefficient, is a regular term used to penalize the complexity of the model; The regression analysis algorithm establishes a mathematical relationship model between customer characteristics and pricing to determine the precise premium price. The regression analysis algorithm is a multiple linear regression, and the model formula of the multiple linear regression is: ； in, Indicates the premium price; Indicates the customer's characteristic variables; is the regression coefficient, which indicates the influence of each characteristic variable on the premium price; is a random error term, which indicates the part that cannot be explained by the model; the regression coefficient is estimated by the least squares method , thereby determining the premium price based on customer characteristics.

5. The method for full-process marketing management of auto insurance channels according to claim 4 is characterized by: In the churn prediction step, when the churn prediction model is constructed by means of logistic regression and random forest algorithm, the formula of the churn prediction model is: ； ； in, Indicates that given customer characteristics The probability of customer churn under the condition of For the churn label; is the logistic regression coefficient; is a natural constant; is a linear combination term, representing the weighted sum of customer characteristics, and the logistic regression coefficient is estimated through training data , predict the probability of churn based on customer characteristics; The random forest algorithm improves the accuracy and stability of churn prediction by constructing multiple decision trees to comprehensively predict the results. Composition, for a sample , the prediction results are: , that is, a majority vote is performed on the prediction results of each decision tree; among them, Indicates A decision tree for a sample The prediction results; is the final prediction result of the random forest. When each decision tree is constructed, samples are randomly selected with replacement from the training data set, and when each node is split, a part of the features is randomly selected from all the features to calculate the splitting conditions.

6. The method for full-process marketing management of auto insurance channels according to claim 5 is characterized by: In the personalized recommendation step, the neural network mines the potential needs and preferences of customers through deep learning. The neural network is a multi-layer feedforward neural network. The forward propagation calculation formula of the multi-layer feedforward neural network is as follows: ； in, Indicates the number of layers of the neural network; Indicates The activation vector of the layer; For the The weight matrix of the layer; For the The bias vector of the layer; is the activation function; by taking customer data as input and calculating through a multi-layer neural network, the final recommendation output is obtained; The collaborative filtering algorithm recommends products based on the similarity of customer groups. , calculate its The similarity between , using cosine similarity calculation, that is, ,in, Indicates user and users A collection of items that have been reviewed together. and Respectively represent users and users A collection of reviewed items. and Respectively represent users and users For items The evaluation scores of the target user are then selected based on the similarity. users, based on the evaluations of these similar users on the items, predict the target user’s evaluation of the unrated items Rating : ,in, Represents the target user Most similar A collection of users, Indicates user For items The evaluation scores are calculated, and the items are sorted according to the predicted scores. Items with scores higher than the threshold are selected and recommended to the target users.

7. The method for full-process marketing management of auto insurance channels according to claim 6 is characterized by: In the risk assessment step, when the support vector machine constructs a risk classification model, for a binary classification problem, its decision function is: ,in, is the customer data vector to be evaluated, is the index set of the support vector, is the Lagrange multiplier, For the The class labels of the training samples, Indicates The data vector of training samples; As the kernel function, the Gaussian kernel function is used ,in, is the kernel parameter, is the bias term, according to the decision function The output value of determines the risk category of the customer; the decision tree constructs a decision tree model by recursively dividing the data features, selects an optimal feature for division at each node, so that the data purity of the divided sub-nodes is the highest, and uses information entropy As a measure of purity, For the data set, is a collection of categories, For the data set belonging to the category The proportion of, through the decision-making process from the root node to the leaf node, determines the key characteristic factors affecting the risk.

8. A full-process marketing management system for auto insurance channels, characterized by: The management system is equipped with the method for full-process marketing management of auto insurance channels as described in any one of claims 1-7.

Images