54 results about "Customer attrition" patented technology

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 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 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 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 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 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 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 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.

The invention belongs to the technical field of data management, and discloses a bank informatization intelligent management method and system based on big data, and the method comprises the steps: obtaining multi-source data of a bank customer, carrying out the preprocessing and labeling of the multi-source data, and obtaining a multi-source data set and a corresponding label; constructing a user portrait based on the multi-source data set and the corresponding label; generating a customer time sequence data set based on the user portrait, and performing multiple sampling processing on the customer time sequence data set to obtain a plurality of sub-data sets; inputting the plurality of sub-data sets into a loss prediction model for loss prediction to obtain a customer loss probability; wherein the loss prediction model is constructed based on an ensemble learning algorithm, the loss prediction model is provided with a plurality of base learners, the number of the base learners is in one-to-one correspondence with the number of the sub-data sets, and each sub-data set is input into the corresponding base learner; and generating a customer intervention scheme based on the customer loss probability. The method has high prediction stability and accuracy.

The invention provides an intelligent lease operation system based on multi-dimensional data analysis, and the system comprises a multi-dimensional data collection module which is used for carrying out the three-dimensional modeling of a lease space, and collecting the multi-dimensional data; the data analysis processing module is used for inputting the multi-dimensional data into a pre-constructed dynamic pricing, contract risk, cash flow prediction and customer loss early warning model, and outputting an optimal listing price, a risk score, cash flow probability distribution and a loss probability curve; the intelligent decision execution module is used for automatically generating a strategy packet, and issuing the strategy packet to a corresponding execution unit after approval; and the visual interaction module is used for displaying the comprehensive operation state of each space unit in the three-dimensional rental model in real time, generating a differentiated data view, receiving user input and feeding back the user input to the intelligent decision execution module in real time. Through data driving and automatic intelligent decision making, experience-driven to data-driven transformation is realized for lease operation, so that balance between asset value maximization and operation risk minimization is achieved.

The invention relates to the field of logistics, and discloses a cross-domain customer loss early warning method, device and equipment and a storage medium, and the method is used for predicting and early warning customer loss conditions. The method comprises the steps of obtaining source domain local customer data and target domain local customer data, and performing feature alignment and preprocessing to obtain source domain preprocessing features and target domain preprocessing features; identifying and obtaining key causal features of a source domain and key causal features of a target domain by using a causal inference method, carrying out domain self-adaption by using a federated transfer learning technology, transferring knowledge of the source domain to the target domain, and generating domain-invariant shared feature representation; based on the domain-invariant shared feature representation and the local customer loss label, updating a pre-trained local customer loss classifier, updating a pre-trained global customer loss early warning model, and obtaining a cross-domain customer loss early warning model; and inputting the new customer data of the target domain into the cross-domain customer loss early warning model to obtain a predicted loss probability, and generating an early warning signal.

The invention provides a processing method and device for coping with customer loss, equipment, a medium and a program product, and relates to the technical field of artificial intelligence. The method comprises the steps that in response to a received loss early warning event of a target customer, data of the target customer is inquired from multiple internal systems to obtain a comprehensive data snapshot of the target customer, and different systems in the multiple internal systems are used for recording data of different dimensions of the customer; generating a first prompt word based on the comprehensive data snapshot of the target customer; and using the first prompt word to indicate the fine-tuned large language model to generate a risk abstract of the target customer. In this way, when customer loss early warning needs to be analyzed, the trouble of manually switching data checking, data arrangement and analysis among a plurality of systems can be avoided, and the analysis efficiency of customer loss is improved.

The invention discloses a customer service dialogue evaluation method and device, equipment, a storage medium and a product, and is applied to the technical field of data processing, and the method comprises the steps: obtaining multi-modal dialogue data generated in a customer service process, carrying out the preprocessing of the multi-modal dialogue data, and generating structured dialogue data; based on the structured dialogue data, basic dialogue evaluation indexes are calculated, and the basic dialogue evaluation indexes comprise a service efficiency index, a service quality index, a business capability index, a compliance index and a customer stickiness index; based on the structured dialogue data and the basic dialogue evaluation index, respectively generating a customer satisfaction index, a customer loss early warning index and a potential service demand identification index through a machine learning model; and inputting the basic dialogue evaluation index, the customer satisfaction index, the customer loss early warning index and the potential service demand identification index into the service dialogue evaluation model to obtain a service dialogue evaluation result, so that the actual service quality of the dialogue can be comprehensively and accurately reflected.

The invention belongs to the technical field of intelligent research and judgment of insurance data, and particularly relates to an insurance customer loss early warning method and system fusing causal inference large model learning. The method comprises the steps of collecting multi-source data such as customer behavior data, insurance policy information and claim settlement records and performing standardization processing; constructing a causal map based on the structure equation model, and extracting a high causal degree path set; constructing a multi-layer recurrent neural network model for supervised training by taking the path structure as prior; analyzing and optimizing a model structure through path stability; and finally, in combination with the prediction probability and a path backtracking result, outputting a customer risk level and a reversible intervention node. The method realizes causal interpretable prediction and operable path intervention of customer loss, and is suitable for intelligent customer management and personalized operation scenes in the insurance industry.

A system and method for minimizing customer churn in device service businesses commences with execution of a customer service contract. Ongoing customer data capture is made for each contract. Customer data includes contract events, environmental events, service events, device usage analytics and personnel events. Machine learning is applied to captured customer data, which machine learning is based on a state of customer data at the time of contract determination. Customer data is assigned weights, and aggregate data for each customer is compared to a preselected threshold level. Customers above a threshold are deemed happy and customers below the threshold are deemed to be at risk. Remedial measures relative to at risk customer data generates levels of automated remediation followed by remedial measure suggestions to an administrator when not sufficiently successful.

This article belongs to the field of bank risk control technology, and particularly relates to a risk prediction method, device, and apparatus for a bank's corporate customers, wherein the method comprises: obtaining first account information and first transaction information of the corporate customer; inputting the first account information and first transaction information of the corporate customer into a pre-trained profit risk prediction model to obtain a positive profit probability score for the corporate customer; obtaining second account information and second transaction information of the corporate customer; inputting the second account information, second transaction information, and the positive profit probability score of the corporate customer into a pre-trained churn risk prediction model to obtain a churn probability score for the corporate customer; and when the churn probability score is higher than a first preset threshold, determining that the corporate customer is at risk of churn. This article addresses the problem of how to further improve the accuracy of predicting churn risk for corporate customers.

The embodiments of the present application belong to the field of intelligent decision-making technology, and relate to a method for obtaining intervention measures for customers with high churn risk and related equipment. The method includes: extracting a feature subset related to the activity from the customer attribute features and dividing them into an intervention group and a control group; training a random forest Uplift model for each feature subset; predicting and evaluating the incremental effect of each intervention measure based on the trained random forest Uplift model to obtain the incremental effect; constructing a personalized Uplift scoring matrix based on the comprehensive score of each user; and prioritizing the intervention measures for each customer based on the personalized Uplift scoring matrix to determine the optimal intervention measure. The present application can more accurately identify groups with high churn risk and take targeted intervention measures to retain these customers. This can not only improve customer satisfaction and loyalty, but also reduce customer churn rate and enhance the overall competitiveness of the enterprise.

The application relates to the technical field of loss prediction, and discloses a marketing intervention method and system based on customer loss prediction, which comprises the following steps: performing abnormal fluctuation detection on the interactive behavior data of customers to obtain loss sign data; performing state evolution analysis on the loss sign data to obtain active decline characteristics; performing time sequence trajectory fitting on the loss sign data to obtain an active decline trajectory graph; matching the active decline trajectory graph with the life cycle of the customers, and fusing the static attribute data of the customers to obtain a loss risk multidimensional portrait; performing decision factor analysis on the loss risk driving factors of the customers, comprehensively judging the root cause characteristics after the analysis, and obtaining a loss risk grade; mapping the root cause characteristics and the loss risk grade to a preset strategy intervention library, combining the static attribute data, and generating an individualized intervention strategy; and the application can improve the efficiency of marketing intervention based on customer loss prediction.

The invention relates to the technical field of loss prediction, and discloses a marketing intervention method and system based on customer loss prediction, and the method comprises the steps: carrying out the abnormal fluctuation detection of the interaction behavior data of a customer, and obtaining loss symptom data; carrying out state evolution analysis on the loss symptom data to obtain active decline features; performing time sequence trajectory fitting on the loss symptom data to obtain an activity decline trajectory diagram; performing stage matching on the activity decline trajectory diagram and the life cycle of the customer, and fusing static attribute data of the customer to obtain a loss risk multi-dimensional portrait; performing decision factor analysis on the loss risk driving factor of the customer, and performing risk comprehensive research and judgment on the analyzed root cause feature to obtain a loss risk level; mapping the root cause feature and the loss risk level to a preset strategy intervention library, and generating a personalized intervention strategy in combination with the static attribute data; according to the invention, the efficiency of marketing intervention based on customer loss prediction can be improved.

The invention relates to the technical field of animal epidemic disease epidemiological survey data analysis and processing systems, and discloses an animal epidemic disease epidemiological survey data analysis and processing system. The exploration data warehouse is used for reading survey data of animal epidemic situation epidemiology, the model library is used for storing various calculation models and algorithm formulas, and the component library is used for storing customer division data, customer loss data and customer product data of different industries. The mining algorithm library is used for performing mining calculation on data according to information of the model library, the component library and the exploration data warehouse and importing a calculation result into the survey data client, the knowledge storage end is used for receiving and storing the data imported by the data mining algorithm library, and the survey data client is used for reading the data of the knowledge storage end for query. The problems that a traditional data analysis and processing system is complex in architecture design structure and low in efficiency are solved.