78 results about "Customer attrition" patented technology

The invention relates to the technical field of stock customer loss prediction, and discloses a stock customer loss prediction system based on machine learning. The system comprises a data preprocessing module, a feature extraction module, an anomaly detection module, a time sequence prediction model training module, a real-time risk assessment module, a probability correction module, an anomaly source analysis module, a retrieval measure generation module and a retrieval effect assessment module. According to the system, the scheme execution effect is retrieved through real-time monitoring, loss risk assessment is continuously updated, high-loss-risk customers can be recognized in time, accurate intervention is achieved, the customer loss rate is effectively reduced, and customer retention and value are improved.

The invention relates to the technical field of power marketing, in particular to an AI large model-based power marketing data analysis method, which comprises the following steps of: acquiring structured data and unstructured data in a power marketing system, and generating unified coded data after space-time alignment and pre-training word embedding model processing; inputting the unified coding data into a pre-trained power field large model, and extracting static, dynamic and semantic features through multi-modal fusion, feature decoupling and semantic anchoring; constructing entity link feature pairs in combination with a power knowledge graph, and enhancing fusion feature expression through a graph attention mechanism; and finally, inputting a dynamic weight gating network, outputting an abnormal user identification tag, a demand response strategy and a customer loss early warning probability, and executing strategy optimization under specific conditions. The method can be widely applied to risk identification, strategy making and user behavior prediction tasks in power marketing.

The invention relates to the technical field of financial risks, in particular to a tourism company financial risk evaluation system, which comprises a data acquisition module for acquiring and integrating internal and external multi-source data and supporting dynamic risk evaluation and early warning; the mixed algorithm evaluation module is fused with multiple models to dynamically evaluate the risk level and quantify the financial risk probability; the dynamic early warning response module identifies risk signals in real time, triggers multi-stage early warning and visualizes a conduction path; the intelligent decision support module is matched with the risk disposal scheme library and provides corresponding decisions; and the self-learning optimization module is used for evaluating parameters through case iteration optimization. According to the method, information collection and standardization are more comprehensive, a self-learning optimization module is added, a tourism company can conveniently balance the cost and the long-term influence of customer loss to make corresponding strategy adjustment by constructing a digital twin sandbox and simulating the financial influence of a rehearsal decision, and strategy hedging risks are added through measures such as customer reservation excitation.

The invention relates to the technical field of artificial intelligence, and discloses a customer loss management method and device, equipment and a storage medium, and the method comprises the steps: obtaining multi-dimensional target data of a target customer; inputting the multi-dimensional target data into a pre-trained customer loss prediction model to obtain a target prediction value output by the customer loss prediction model; constructing a customer portrait of the target customer based on the multi-dimensional target data; determining a loss risk level and a target loss type of the target customer based on the target predicted value; based on the loss risk level, the target loss type and the customer portrait, determining a target retention strategy corresponding to the target loss type; according to the scheme, the customer loss value can be automatically predicted, the loss tendency of the customer can be determined, the accuracy and efficiency of customer loss prediction can be improved, and the customer can be better retained by searching the retention strategy corresponding to the loss type.

The invention relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. An information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening out target users with loss risks according to the user original information data; s2, extracting customer loss risk features from the original information data of the target users, importing the customer loss risk features into the trained risk assessment calculation model, and outputting results to indicate loss risk levels of the target users; and S3, generating a reservation scheme according to the loss risk level of the target user and the corresponding original information data, and outputting the reservation scheme to the corresponding target user. According to the invention, grading intervention is carried out on the target users based on different risk grades, a uniform retention strategy is prevented from being adopted for all users, and the retention rate of clients is improved.

The invention relates to the technical field of advertisement marketing, in particular to an advertisement marketing system for after-sales data statistics. The system comprises a data acquisition module, an intelligent analysis module, a precision marketing module and an evaluation feedback module. The customer basic information, purchase information, after-sales behavior data and customer feedback data are acquired through the data acquisition module, the intelligent analysis module establishes a recurrent neural network model for predicting the customer loss probability, and the precision marketing module performs precision marketing by taking the customer loss probability predicted by the intelligent analysis module as the horizontal axis and the customer value as the longitudinal axis. The method comprises the following steps: establishing a probability-divided two-dimensional matrix, formulating a corresponding advertisement marketing strategy, adopting different advertisement marketing strategies for different types of customers, improving pertinence, redeeming the customers to the greatest extent, reducing enterprise loss, establishing an evaluation system by an evaluation feedback module, evaluating the effect of advertisement marketing activities at regular intervals, and improving the efficiency of advertisement marketing. And the advertisement marketing strategy is continuously optimized.

The invention provides a churn identification model training method and apparatus, an electronic device and a storage medium, and compared with the related art, the embodiment of the invention can comprehensively reflect customer behaviors and attributes by obtaining feature data of different data sources of a target customer, can more accurately judge customer churn risks, avoids limitation of single data, and improves the user experience. The prediction precision is improved; by preprocessing the collected data, the data quality can be improved, the model training is enabled to be based on more reliable data, error information interference is reduced, and the accuracy of a prediction result is guaranteed; a customer loss function is used as a mapping function, neural network parameters and loss factor vectors are integrated, and the customer loss possibility is calculated; a function is continuously optimized through training, and the relation between customer loss and each factor is accurately reflected; constructing a training data set based on the preprocessed data to comprehensively represent customer features; the model learns various data features and modes in the training process, adapts to different customer conditions, and improves the generalization ability.

The invention discloses an efficient image analysis and question answering system for intelligent customer service, and aims to solve the commercialized problems of slow response and high cost when an existing AI customer service processes user picture questions. In order to improve customer service interaction experience, the system greatly reduces the amount of visual data input into a core language agent through an innovative visual information compression engine, for example, the amount of the visual data is compressed to one fourth of the original amount. By means of the key optimization, the intelligent customer service staff can respond to picture inquiry of customers in real time, and customer loss caused by waiting is avoided. Meanwhile, the system ensures high-precision identification of commodity details through a unique training strategy, provides reliable and accurate answers for customers, and successfully solves the problem that speed and precision are difficult to consider in customer service application in the industry. Finally, a practical solution with instant response and high reliability is provided for the fields such as e-commerce intelligent customer service and the like needing real-time interaction, and commercialized popularization of the advanced AI technology is powerfully promoted.

The invention discloses a customer loss prediction method based on oversampling classification, and the method comprises the steps: obtaining a customer loss data set, carrying out the preprocessing, carrying out the data clustering, selecting and marking a cluster with a dominant minority of samples, calculating a sampling weight value, calculating the membership degree of the samples in the cluster with the sampling weight value larger than 0, and carrying out the space division, and selecting a sample according to a region division result, executing linear interpolation oversampling to generate a balanced data set, training a classifier, preferentially constructing a final customer loss prediction model, and outputting a customer loss prediction category and probability. According to the method, the customer loss data set is balanced through an oversampling method of clustering and membership region division, the safest samples are selected for synthesis, the risk of introducing noise and fuzzy boundaries is effectively reduced, data category distribution is balanced by generating high-quality minority-class samples, and the accuracy of data classification is improved. And therefore, the recognition performance of minority class samples can be improved, and the accuracy of customer loss prediction can be improved.

The invention discloses a CRM system constructed based on a front-end low-code platform and a business process. The CRM system comprises a low-code platform module, a business process design module, a data model construction module, an intelligent recommendation engine, a system integration module and a response type front-end generation module, the invention discloses a method based on a front-end low-code platform and business process construction. The method comprises the following steps: constructing a low-code platform; the business process is automatic; the development process is simplified; through combination of automatic approval and SLA monitoring, process node timeout automatic alarm and work order upgrading are realized, and customer complaint processing efficiency is improved; flexible business process support is realized; service requirements are quickly responded; the intelligent recommendation engine generates customer value scores and loss early warning in real time based on an RFM model and machine learning, the loss early warning model is accurate in prediction, and customer loss is prevented; in combination with behavior trajectory analysis, a customer interaction thermodynamic diagram is generated, and a marketing strategy reaching opportunity is optimized; and automatic flow and load balancing.

The invention discloses a customer loss risk assessment method and device based on a neural network model, and relates to the field of artificial intelligence or other related technical fields, and the method comprises the steps: obtaining the assessment data of a target customer, the assessment data at least comprises a user portrait, a journey map and a behavior data set, the journey map is used for describing the experience feeling of the target customer in the processes of purchasing financial products and using financial services; performing feature extraction on the user portrait and the journey map of the target customer to obtain a user portrait feature vector and a journey map feature vector; drawing a behavior graph based on the behavior data set of the target customer; and inputting the user portrait feature vector, the journey map feature vector and the behavior graph into a loss risk assessment model, and outputting a loss risk value of the target customer. According to the method and the device, the technical problem of relatively low accuracy of an assessment result caused by relatively single assessment data in a mode of performing loss risk assessment based on text mining or user portraits in related technologies is solved.

The present invention provides a customer churn warning method and device, particularly relating to the field of artificial intelligence. The method comprises: obtaining element type weights based on a random forest, and determining a historical customer target vector based on the weights and historical customer feature vectors; constructing a fitness function based on the historical customer target vectors; performing genetic iteration based on the fitness function to determine a final power coefficient and a final multiplication coefficient of the historical customer target vectors; determining whether a current customer is about to churn based on the classification value of the random forest and the current customer feature vector; and if so, determining the churn type based on the current customer feature vector, the final power coefficient, and the final multiplication coefficient, and issuing a warning based on the churn type. The present invention can improve the speed and accuracy of customer churn warnings, thereby improving the efficiency of customer churn warnings, and further facilitating the retention of current customers of different churn types, thereby increasing bank revenue.

The present application relates to the field of customer churn prediction, and specifically discloses a customer churn prediction system and method based on machine learning. The system first obtains basic data of an enterprise's target customers collected from a database and behavioral data of the enterprise's target customers collected from a database, then uses deep learning technology to perform feature extraction and correlation analysis on the two, and finally uses a classifier to determine whether the target customers have a tendency to churn, so as to detect signs of customer churn in advance and take targeted retention measures to reduce the customer churn rate.

The present invention discloses an intelligent return visit method based on reinforcement learning, comprising: obtaining a customer portrait, wherein the customer portrait includes a plurality of characteristic tags for identifying customers; determining a target customer to be returned based on the selected characteristic tags of the customer portrait; reading relevant information of the target customer and establishing a call connection with the target customer; inputting relevant information of the target customer into a preset return visit model; generating text conversation content for a conversation with the target customer through the return visit model, and outputting the text conversation content after speech synthesis. The present invention also discloses an intelligent return visit device, equipment, and computer-readable storage medium based on reinforcement learning. The present invention realizes dialogue communication with the returned customers, and the answers are accurate and timely, effectively improving the return visit effect, and also reducing the customer churn rate.

The invention belongs to the technical field of big data customer information matching, and discloses an intelligent customer information matching method based on big data, which comprises the following steps of: firstly, establishing a scene customer behavior semantic dictionary according to three groups of new customers, high-value customers and loss early-warning customers, and determining behavior label mapping rules of different customer types; for example, the browsing duration of a new customer exceeds 3 minutes, the consultation frequency exceeds 2, the behavior is defined as a high-intention behavior, the semantic dictionary is dynamically updated every 24 hours based on cross-platform semantic conflict feedback, and the cross-platform data semantic standard is unified; secondly, screening weak association features by adopting a mutual information and customer value double-weight algorithm, setting weights according to customer value differences, and preferentially retaining features with higher association degree with customer demands to generate structured feature vectors; and finally, deploying a preprocessing effect verification module at an edge computing node, performing field secondary labeling, null interpolation complementation and time format unification operation on the data, and improving the data standardization level.

The invention provides a customer management method and system based on intelligent marketing and a medium, and belongs to the technical field of artificial intelligence and big data. The method comprises the steps of obtaining customer basic information, customer social information and customer behavior preference information, constructing a customer portrait, and extracting customer value information and customer loss risk information; client value evaluation data can be obtained according to the client value information; and obtaining customer loss probability data according to the customer loss risk information. And obtaining a market trend influence coefficient according to the market trend dynamic information. And processing according to the customer value evaluation data and the customer loss probability data in combination with the market trend influence coefficient, obtaining and correcting a customer priority index, obtaining a customer priority correction index, querying through a preset marketing database, and obtaining a customer management strategy scheme. According to the method and the system, a comprehensive customer portrait is constructed, clear guidance is provided for marketing and customer service of enterprises, and the method and the system have very high practicability.

The invention relates to the technical field of data processing, and discloses an insurance policy renewal strategy generation method and device based on dynamic health analysis and a medium, and aims at converting health data of a target user into health factor parameters through a dynamic configuration mapping rule to form a health factor parameter table so as to accurately evaluate the health condition of the user. According to the method, a corresponding insurance premium weighting algorithm is matched for each health factor parameter through an algorithm configuration rule, differential influence of different health factors on insurance premium is fully considered, an insurance premium floating coefficient is obtained through weighting calculation of each health factor parameter, and an insurance policy renewal strategy is generated according to the coefficient and original insurance policy data. The renewal strategy can be accurately matched with the current health condition and risk level of the user, so that the accuracy of the insurance policy renewal strategy is effectively improved, risk mismatching and customer loss risks caused by health changes are reduced, and the method has remarkable beneficial effects on accurate renewal management of insurance services in the financial and medical field.

The present invention relates to the field of Internet financial technology, and discloses a loan information prompt method, device, computer equipment and storage medium. The method includes: generating prompt information based on a prompt information template; sending the prompt information to the terminal device corresponding to the terminal mobile phone number according to a preset method: a combination of 5G messages and flash messages; entering the loan application page when detecting that the target customer clicks the application button; performing approval and generating a loan approval result when detecting that the target customer submits a loan application; sending the loan approval result to the terminal device according to a preset method; when determining to purchase a loan product based on the loan approval result, disbursing the loan funds to the loan account, and sending the account arrival information to the terminal device according to a preset method. Based on the technical solution of the present invention, the target customer can directly enter the loan application page by clicking the button in the prompt information, which simplifies the pre-steps of the application process, reduces customer churn, and improves the information reach rate.

The invention provides a task flow switching control method and a related device, and is applied to a controller of a target service robot, and the method comprises the steps: detecting a call request of a discrete user for the target service robot when a target task flow is executed, and outputting a topic interruption verbal skill to a target client to interrupt the execution of the target task flow; and determining a temporary task flow according to call requests of discrete users. Thus, even in the process that the target service robot provides service for the target customer, the controller of the target service robot can timely detect the call request of the discrete user and timely output the topic interruption verbal skill to interrupt execution of the target task flow, and then temporary service is provided for the discrete user according to the call request of the discrete user. The number of received customers can be increased, customer loss caused by no idle service robot in a service scene is avoided, and the flexibility and intelligence of the service robot are improved.

The invention provides a customer loss early warning method, device and equipment, a medium and a program product, relates to the technical field of artificial intelligence, and can be applied to the field of financial science and technology. The method comprises the following steps: under the condition that authorization of a target customer to use related data thereof is obtained, obtaining data of the target customer in each of W continuous time periods ending the current moment to obtain a target sequence; standardizing the target sequence to obtain an original standard sequence; the original standard sequence is input into a target time sequence behavior anomaly detection model, a reconstruction sequence output by the target time sequence behavior anomaly detection model is obtained, and the target time sequence behavior anomaly detection model is a time sequence behavior anomaly detection model independently constructed for a target customer; comparing the original standard sequence with the reconstructed sequence to obtain a deviation degree; and when the deviation degree meets an abnormal alarm condition, triggering loss early warning of the target customer.

The embodiment of the invention provides a multi-source heterogeneous data processing method and device, equipment, a medium and a product, and relates to the field of artificial intelligence or the field of financial science and technology. A dynamic knowledge graph is constructed by acquiring multi-source heterogeneous data including customer-related data from different data sources and having different formats or structures, and multi-task processing of customer loss prediction, product recommendation and compliance detection is performed on the dynamic knowledge graph through a graph neural network. According to the method, the dynamic knowledge graph which can be updated in real time is constructed, and the graph neural network is combined to realize multi-task processing, so that the problem that an existing service system depends on a static label system and cannot meet dynamic change of customer requirements is solved, dynamic perception of customer states is realized, and customer experience is improved. The processing precision and efficiency of tasks such as customer loss prediction, product recommendation and compliance detection are effectively improved, and reliable data support is provided for business decision making.

The invention provides a solid state disk fault prediction method and system based on artificial intelligence. The solid state disk fault prediction method comprises the following steps: establishing an association relationship for a target solid state disk; performing running log acquisition on the target solid state disk based on the association relationship to obtain solid state disk log information; analyzing the log information of the solid state disk, and extracting target associated information; respectively performing hardware fault prediction analysis and software fault prediction analysis according to the target associated information by using an artificial intelligence model; and performing fault risk assessment according to the fault prediction analysis data to obtain a fault prediction assessment result. Comprehensive fault prediction of the solid state disk is efficiently realized by means of artificial intelligence, the time delay of fault prediction is reduced, and the situation that when the solid state disk fails, data loss is caused due to the fact that countermeasures cannot be taken in time, normal use of the solid state disk is affected, and customer loss and economic loss are caused due to business failure is avoided. And meanwhile, the fault prediction accuracy is improved.

The invention discloses a call center information pushing system and method based on big data analysis, and belongs to the field of big data analysis. A transaction information abnormity evaluation model is constructed, customer basic information and customer transaction information are imported into the transaction information abnormity evaluation model for transaction abnormity analysis, and a customer abnormity judgment model is constructed; and substituting the obtained transaction abnormity analysis result, the historical sales data and the score data after each transaction of the customer into a customer abnormity judgment model for customer abnormity analysis, comparing the obtained customer abnormity analysis result with a set customer loss threshold value, and performing customer loss level analysis. And the customer loss grade analysis result obtained through judgment is pushed to the customer service terminal, customer loss early warning is carried out, the customer loss grade is estimated according to the sales characteristics of customer loss, and the accuracy of customer loss early warning is improved.

The invention provides a railway freight customer loss prediction method, and belongs to the field of railway transportation. The method comprises the following steps: constructing a railway freight customer portrait system; constructing a customer freight demand prediction model; constructing a customer loss early warning model based on data in the railway freight customer portrait system; and a demand prediction value output by the customer freight demand prediction model is converted into dynamic input features of the customer loss early warning model, loss risk calculation is executed, and railway freight customer loss prediction is completed. According to the invention, problems of low demand prediction precision, lack of an effective loss early warning mechanism and low data utilization efficiency in existing railway freight customer management are solved.

The present application relates to a kind of railway freight service dynamic matching method and system based on customer characteristics, comprising the following steps: obtaining the customer data of each customer in each customer, customer index and customer traffic data;For each customer, according to customer data, determine customer classification, customer classification includes at least one of customer value, customer loyalty, customer potential value, customer transportation characteristics and customer transfer cost;For each customer, according to customer index, determine customer rating;For each customer, according to customer traffic data, determine customer loss trend;For each customer, according to the customer classification, customer rating and customer loss trend corresponding to customer, match the target freight service corresponding to customer from each freight service, each freight service includes transport capacity guarantee, freight product and value-added service, transport price strategy and transport timeliness.Provide differentiated transport capacity tilt and product service for the needs of different customers and the contribution to railway.

The application discloses a customer loss early warning method and device, a storage medium, a program product and computer equipment. The method comprises the following steps: obtaining behavior data of a target customer, and determining key behavior characteristics corresponding to the behavior data; determining behavior mode change information of the target customer in a target period based on the key behavior characteristics; in the case that the behavior mode change information meets a preset mode deviation condition, determining risk index information of the target customer based on the behavior mode change information, the key behavior characteristics, reference characteristic information associated with the key behavior characteristics; obtaining an early warning strategy based on the key behavior characteristics, the risk index information and a first risk trend direction corresponding to the risk index information, and historical loss-related data of a customer group to which the target customer belongs; and pushing a retention information message corresponding to the early warning strategy to the target customer. Thus, the loss risk early warning accuracy can be improved, and the pushing accuracy of the retention information message pushed to the customer with a high loss risk can be improved.

The invention discloses a telecommunication enterprise customer loss prediction method, system and device based on a random forest and a medium, belongs to the technical field of telecommunication operation, and aims to solve the technical problem of how to accurately and efficiently predict the telecommunication customer loss tendency, formulate personalized retention measures and improve the customer loss prediction efficiency. According to the technical scheme, the method comprises the following steps: data collection: collecting customer feature information and customer behavior information tags, and constructing a data set; data preprocessing: performing preprocessing operations of missing value processing, classification variable conversion and standardized numerical variable processing on the customer feature data and behavior data, and obtaining feature data of a to-be-predicted customer; feature selection: evaluating the importance of each feature of the customer in a training process by using a random forest algorithm, and selecting human features having significant influence on a prediction result as input variables; constructing a random forest model; training, adjusting and optimizing the model; and predicting customer loss.

The application discloses a financial institution asset management scale growth prediction and optimization method and related equipment, first, customer behavior and market data are acquired; then, based on the market data, a time series analysis model is used to predict future asset return rate, and asset allocation strategies of different risk preference customer groups are optimized accordingly; then, expected maximum drawdown of each group is evaluated according to the optimized strategies, and customer churn rate is predicted in combination with the mapping relationship between the expected maximum drawdown and the customer churn rate; meanwhile, based on the predicted asset return rate, customer behavior data and risk attitude, a behavior decision model based on prospect theory is used to predict customer conversion rate; finally, the overall asset management scale growth prediction value of the financial institution is calculated by comprehensively integrating the above results, and an optimization suggestion report containing risk grouping, customer communication and product recommendation strategies is generated. The purpose of the application is to organically integrate market yield prediction, asset allocation optimization, churn rate prediction based on portfolio risk and conversion rate prediction based on customer behavior and preference, and realize result linkage and comprehensive optimization.

The invention relates to the field of data analysis, in particular to a customer loss early warning feedback method, system, device and product based on customer behavior analysis, and the method comprises the steps: S1, obtaining the historical data of the delivery amount of a customer, and obtaining the time sequence data of the delivery amount; s2, performing seasonal identification on the delivery quantity time sequence data to obtain a main seasonal period; s3, based on the main seasonal period, performing characteristic decomposition on the delivery quantity time sequence data by adopting an STL time sequence decomposition algorithm to obtain a trend item, a seasonal item and a residual item of the delivery quantity time sequence data; s4, based on the obtained trend item, marking the customer as a potential loss customer; s5, early warning information is pushed to a customer manager, customer communication is carried out, and negative labels are marked for the customers based on the customer communication result; and S6, implementing an operation intervention strategy for customer retention for the customers marked with the negative labels. According to the invention, automatic analysis of the customer loss risk can be realized, and accurate and timely customer loss risk reminding is carried out.

The application relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. The information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening target users with loss risks according to the user original information data; S2, extracting customer loss risk features from the original information data of the target users, and inputting the customer loss risk features into a trained risk assessment calculation model to output a result indicating the loss risk grades of the target users; and S3, generating a retention scheme according to the loss risk grades of the target users and the corresponding original information data and outputting the retention scheme to the corresponding target users. The application classifies and intervenes the target users based on different risk grades, avoids adopting a unified retention strategy for all users, and improves the customer retention rate.