84 results about "Customer classification" patented technology

The invention relates to the technical field of marketing strategies, in particular to an intelligent marketing service platform and method for financial services, and the platform comprises a user behavior analysis module, a customer classification module, a content adjustment module, a marketing strategy optimization module and a market trend prediction module. According to the method, the behaviors and preferences of the users can be accurately predicted by monitoring the behaviors of the users and identifying key interaction events of the users, customer groups can be more finely divided by classifying the customer data, more customized services are provided for different customer groups, the personalized experience of the customers is enhanced, and the user experience is improved. The marketing strategy is adjusted in real time to respond to the change of the market and consumer behaviors, so that the enterprise can keep leading in the market with intense competition, and the perspectiveness of the marketing strategy is enhanced through the dynamic prediction of the market trend, so that the enterprise can predict and adapt to the market change and keep the competitive advantage.

The invention discloses a hotel check-in information management method and system based on a database, and belongs to the technical field of information management. Nodes and edges are defined based on the extracted data, node embedding is generated through a random walk method, a customer classification model is constructed, and classification labels of customers are generated; generating a room distribution result by constructing a linear programming model based on the classified customers in combination with the real-time room related information of the hotel; through short-time Fourier transform and BERT model extraction emotion features and text embedding, constructing an emotion analysis model and a demand recognition model, and generating an emotion score and a demand recognition result; and based on the customer classification, the room allocation result, the emotion analysis result and the demand identification result, using a logic rule to generate a personalized service recommendation list. An intelligent management mode from efficient data extraction, accurate customer classification and dynamic room allocation to real-time service response and recommendation is realized.

The invention provides an AI voice interaction method and system. The method comprises the following steps: acquiring customer type information and customer car purchase preference records from a historical sales interaction database of a car main engine plant; generating a customer type identifier by using a preset customer classification rule; analyzing the customer car purchase preference record, generating a preference feature vector, and generating a car sales verbal skill text containing a car function keyword in combination with the customer type identifier and a preset car sales verbal skill template; processing the acoustic physical parameters to obtain acoustic physical parameters representing the rhythm characteristics of the voice; generating a synthetic speech associated with the vehicle function keyword by using the parameter; in the sales process, the synthesized voice is played, and automobile function three-dimensional display corresponding to the automobile sales verbal skill text is synchronously triggered; according to the technical scheme provided by the invention, the personalized automobile sales voice is automatically generated according to the customer type and the automobile purchase preference, and the intelligent and personalized level and the user experience of the automobile sales process are improved.

The invention discloses an AI customer service robot automatic reply system based on a knowledge base, and relates to the technical field of intelligent customer service. Personal information of a user and a new question text sent by a user side are obtained, and user portrait analysis is carried out based on the personal information of the user; the emotion fluctuation state of the user is analyzed according to the historical dialogue record of the user, client classification is carried out, client classification results comprise emotion stable clients, pacifying demand clients and efficiency demand clients, and question feature words and emotion feature words are extracted based on the question text; querying a corresponding answer text in the knowledge base management unit according to the question feature words, judging the emotion of the user according to a customer classification result and the emotion feature words, correspondingly generating an emotion safety response text, and training based on a historical training text to obtain a text generation language model; and integrating the answer text and the emotion demand word into an answer text through a text generation language model, and outputting the answer text to a user side.

The invention relates to a tobacco enterprise customer classification and grading method and system based on sentiment analysis, and the method comprises the steps: obtaining the session data inputted by a customer in a tobacco customer service scene, and judging the intention of the customer; inputting the session data into a pre-trained multi-mode sentiment analysis model, and outputting a sentiment analysis result of the current customer dialogue data; acquiring target customer information initiating the current session, and inputting the target customer information into a pre-trained customer classification and grading model for processing; and generating a recommended customer strategy based on a Q-Learning algorithm, optimizing a service strategy by combining a preset generative AI model with the customer intention of the current customer, the sentiment analysis result and the recommended customer strategy, and generating a response mode and response content based on the optimized service strategy. According to the scheme, a tobacco industry dynamic knowledge base is constructed, and personalized service strategies are formulated in combination with emotional appeals and behavioral habits of customers, so that the answer accuracy and satisfaction of intelligent customer service staff are improved.

The invention provides a financial customer digital archive management method and system based on a block chain, and a medium, and belongs to the field of archive management. The problem of low efficiency of financial customer archive management is solved; the method comprises the following steps: acquiring identity information and financial information of a customer; designing an identity label according to the identity information; constructing a Merkle tree to store financial information, and deploying the financial information on a block chain; clients are divided into high-net-value clients and common clients; the credit score of the high-net-value customer is calculated, and the loan interest rate of the high-net-value customer is drawn up; the transaction time, the transaction object and the transaction amount of the common customers are analyzed, the frequently-taken shop of each common customer is determined, then the common customers are classified, and a customer consumption behavior report is obtained; customer consumption behavior reports are summarized and fed back; synchronously updating financial information on the block chain; the financial information of the customer is digitally processed and stored, so that the safety of the financial information is improved, and a financial institution can know the consumption change of the customer in real time.

The invention relates to the technical field of data processing, in particular to an enterprise management data governance method and system based on big data analysis, and the method comprises the following steps: building a relation network between clients based on the interaction record data of the existing clients and potential clients of an enterprise, calculating the interaction strength in a client group through the connection relation between nodes, and obtaining the enterprise management data governance result. According to the method and the system, through analyzing the relation connection among the clients, the closely interactive client groups are extracted, and the behavior pattern and the consumption tendency, especially the participation degree and the economic influence in the multi-region market, are identified. According to the client behavior data and the abnormal change of the payment mode, the behavior deviation of the abnormal client and the influence of the abnormal client on the market fluctuation can be further analyzed. Meanwhile, the social influence of the clients in the group is analyzed, the interaction strength and the network status of the clients are integrated, the potential negative influence of the clients is judged, and enterprises are helped to classify the clients and optimize resource allocation.

The invention relates to the technical field of financial credit investigation and credit risk management, and particularly provides a credit risk assessment and dynamic limit adjustment method and system, and the method comprises the following steps: S1, data collection and analysis; s2, risk assessment and quota allocation; s3, customer classification and adjustment; and S4, dynamic monitoring and emergency. Compared with the prior art, the real-time data can be combined with the dynamic model, so that the hysteresis of the static scoring model is reduced, and the timeliness of risk assessment is enhanced. Manual intervention is reduced, and the default rate and the collection cost are reduced through an automatic strategy.

The invention provides a customer classification management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining customer contact data of target customers in different databases, and carrying out the data preprocessing of the customer contact data, and obtaining target customer contact data; performing data analysis on the target customer contact data, judging a target transaction stage of the target customer, and determining a customer classification corresponding to the target transaction stage; and based on the target customer contact data, generating display information of the target customer under the customer classification, and sending the display information to the target terminal for display. Compared with the prior art, the embodiment of the invention has the advantages that the customer contact data of the target customer is acquired from the plurality of databases, and data analysis is performed on the target customer contact data, so that the transaction stage of the target customer can be accurately judged; according to the analysis result of the target customer contact data, the customers can be automatically classified and managed, so that an enterprise can formulate personalized marketing strategies for different types of customers, and the marketing efficiency and the conversion rate are improved.

The invention discloses a customer classification method, business processing method and system based on causal inference and interpretability, and the method comprises the steps: obtaining a customer classification model through pre-training: collecting historical data to obtain a training data set, the historical data comprising customer features corresponding to customers, business processing variables and result variables; inputting the training data set into a basic model based on a causal forest model and interpretability for training until the model converges to obtain a customer classification model; based on the causal forest model, determining an individual processing effect value of a customer according to customer characteristics, business processing variables and result variables; and calculating contribution values of the customer features to individual processing effects of the customers based on the interpretability, classifying the customers according to the individual processing effect values, the contribution values and the customer features, collecting the customer features to be classified, and inputting the customer features to a customer classification model to output a customer classification result. The accuracy and rationality of the customer classification result can be improved.

Embodiments of the present application provide a data analysis method, apparatus, computer device, and storage medium, which are applied to the field of fintech. The method includes: obtaining in-bank data, external data, and public data of an offshore customer in a target bank; updating an equity penetration map by using the in-bank data, a first risk list included in the external data, and a second risk list included in the public data to obtain an updated equity penetration map; when determining that the offshore customer is a first type of customer according to the updated equity penetration map, obtaining a target transaction behavior feature set of the offshore customer and the holding data of the offshore customer; and invoking an offshore customer classification model to classify the offshore customer according to the target transaction behavior feature set, the holding data, and the registered business information of the offshore customer included in the public data. By adopting the present application, the accuracy of identifying the category of offshore customers can be improved. The present application relates to blockchain technology, and the data can be obtained through a blockchain network.

The invention discloses a customer group classification method and device based on machine learning and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the dimension reduction processing of an original data set through employing a kernel principal component analysis algorithm, obtaining a data point set after the dimension reduction, calculating the outlier factor of each data point through employing a local density factor algorithm, and obtaining an outlier classification result; removing abnormal data values by using outlier factors, calculating connection strength between nodes according to a relationship between data points, establishing a linear equation set, solving the linear equation set, obtaining an initial clustering center point, performing clustering analysis based on the initial clustering center point, classifying each data point in a data point set after dimension reduction into a cluster to which the closest centroid belongs, and performing clustering analysis based on the clustering center point. And obtaining a financial management customer group classification result until a clustering convergence condition is met. The technical problems that the real-time updating demand of large-scale customer data cannot be adapted during financial management customer classification in the related technology, the recommended product does not meet the demand, and the customer satisfaction is reduced are solved.

The invention discloses a historical data-based case intelligent money return prediction method and system, and belongs to the technical field of intelligent money return prediction, and the method comprises the steps: firstly obtaining historical case money return data, and generating a historical case money return feature field; secondly, performing customer classification on the historical repayment customers to form at least two customer categories; then, performing classification model training on each customer category to construct a plurality of intelligent money return prediction models; acquiring real-time case money return data, and generating a real-time money return prediction result by using the corresponding intelligent money return prediction model so as to generate a money return prediction report; and finally, based on the real-time money return prediction result, forming a behavior prediction report after money return, and visually displaying the money return prediction report and the behavior prediction report after money return. According to the method, the classification model is constructed, corresponding money return prediction is carried out according to the characteristics of various customers, more accurate money return prediction is obtained, and analysis of subsequent behaviors of the user is realized by introducing behavior prediction after money return.

The application discloses a passenger group classification method and device based on a clustering and factor analysis model, and relates to the field of data analysis and processing.The method comprises the following steps: connecting a big data platform to a T-BOX to receive Internet of Vehicles data uploaded by the T-BOX, and performing cleaning processing on the received Internet of Vehicles data; dividing each trip of a vehicle based on the Internet of Vehicles data, extracting a resident feature, and obtaining a plurality of preliminary feature indexes for describing driving habits; performing a sufficiency test on the preliminary feature indexes, and performing dimension reduction processing on the preliminary feature indexes after the test is passed, so as to retain a preset number of preliminary feature indexes; fusing the retained preliminary feature indexes with driving-related main factors, performing K-Means clustering processing on the fused driving-related main factors, and forming passenger group labels.The application can accurately realize passenger group classification and avoid classification deviation.

The invention discloses a customer value classification method and system based on an RFAT model, and relates to the technical field of customer classification, and the method comprises the steps: analyzing the transaction proximity, the customer frequency, the average transaction amount and the transaction trend of each customer in each gas station, and then classifying each customer in each gas station, the development trend of customers of each category is analyzed, then the category with the good development trend is selected for activity promotion, and the resource delivery proportion of the target category in each gas station in the future period is predicted, so that the defect of customer value classification based on an RFM model in the prior art is overcome, the diversity of customer classification results is improved, and the customer value classification efficiency is improved by using a "two-eight" criterion. According to the method, the flexibility of evaluation standards is improved, accurate data is provided for subsequent enterprise activity promotion and resource release, activity promotion is formulated for customer types with good development trends, the complexity and cost of enterprise activity promotion are greatly reduced, the satisfaction of customers is improved, and sustainable increase of services is realized.

The invention relates to the technical field of big data processing, in particular to a method and system for improving the accuracy of a customer experience analysis result, and the method is characterized in that the method comprises the following steps: designing customer data and a data source, dividing the source data into network data and customer data, and enabling the network data to reflect the network quality of an operator; the method has the beneficial effects that the process of analyzing the customer experience is clearly determined, different analysis models can be used through customer classification, the customer experience can be conveniently and continuously analyzed through the analysis process template, and the accuracy and timeliness of analyzing the customer experience are improved.

This invention provides a blockchain-based method, system, and medium for managing digital financial customer files, belonging to the field of file management. It solves the problem of low efficiency in financial customer file management. Specifically, it involves: acquiring customer identity and financial information; designing identity identifiers based on the identity information; constructing a Merkle tree to store financial information and deploying it on the blockchain; classifying customers into high-net-worth and ordinary customers; calculating credit scores for high-net-worth customers and determining loan interest rates for them; analyzing the transaction time, transaction partners, and transaction amounts of ordinary customers to determine their frequently visited shops, further classifying them, and obtaining customer consumption behavior reports; summarizing customer consumption behavior reports and providing feedback; and synchronously updating financial information on the blockchain. This invention improves the security of financial information and enables financial institutions to understand changes in customer consumption in real time by digitizing and storing customer financial information.

The invention discloses a power utilization mode consistency analysis method, system and device based on load time-frequency domain feature fusion and a medium, and the method comprises the steps: obtaining power utilization load mode data of a client, extracting time domain features through dimension reduction, carrying out the Fourier transformation of the power utilization load mode data, calculating the amplitude of current harmonic waves, and obtaining the consistency of the current harmonic waves; selecting a preset number of significant harmonics to construct a frequency domain load feature vector; inputting the load characteristic template library and the transient current waveform template library into a clustering algorithm for time domain dominant clustering to obtain an initial clustering result, performing quality analysis, and triggering frequency domain clustering correction if the initial clustering result does not meet the standard; and the initial clustering result is combined with the frequency domain load feature vector to be input into an improved following leader algorithm to be corrected, so that a final consistency result is obtained. According to the method, time domain and frequency domain collaborative optimization is combined with the dynamic cluster number to adapt to a complex scene to ensure quality analysis reliability, accurate customer classification is achieved, and the method is suitable for personalized services of the electricity market.

The invention relates to an insurance customer-oriented insurance amount information analysis method and system, and the method comprises the steps: collecting customer information through a user interface, and carrying out the scanning through an OCR technology, and obtaining to-be-processed customer data; performing text extraction to obtain entities, relationships and keyword information, and performing grammar and semantic analysis; identifying insurance policy entities and relationships by using a machine learning technology, and organizing the insurance policy entities and relationships into structured data; according to the insurance information category, establishing a plurality of customer information models based on the structured data; establishing customer feature tags for each customer information model by using a machine learning algorithm, performing customer classification, and predicting behaviors and potential risks of customers; and quantizing the insurance amount gap by adopting a gap deduction formula of a corresponding insurance type, providing a corresponding insurance guarantee suggestion, and presenting the insurance guarantee suggestion in a visual report form. Compared with the prior art, the insurance amount analysis is more intelligent and accurate, and more personalized and specialized insurance guarantee can be provided for customers.

The present invention discloses a customer classification evaluation method based on RFM model, which relates to the technical field of data analysis and classification; specifically includes the following steps: S1: customer data collection, collecting customer information through big data or past transaction records; S2: primary classification, using RFM model combined with customer information for primary classification to form multiple category sets; S3: secondary classification, combining customer information, extracting features for customers of each category set in step S2, and then performing secondary classification according to the extraction results; S4: customer stability evaluation, regularly updating steps S1-S3, and extracting customers whose classification labels have changed. After the present invention performs primary classification of customer potential value based on the RFM model, it then forms a degree of preference based on the range of goods sold by the enterprise and the multi-dimensional action frequency of the customer, and then performs secondary classification based on the degree of preference, so as to be more targeted.

The application discloses a bank fixed deposit target customer classification method based on a quantum neural network, relates to the technical field of classification based on quantum computing, and comprises the following steps: preprocessing a bank fixed deposit data set, obtaining feature information of bank fixed deposit customers, and loading the feature information into a quantum state; processing the quantum state by using a quantum neural network to obtain a prediction result of the feature information of the bank fixed deposit customers, constructing a loss function according to the prediction result of the feature information of the bank fixed deposit customers and a real label of the bank fixed deposit customers, updating parameters of the quantum neural network according to the loss function until a termination condition is met; and classifying feature information of a bank fixed deposit customer of a preset user according to the latest obtained quantum neural network. The quantum neural network is superior to a classical neural network in extraction of global features of bank deposit customer information and convergence speed, and requires less data for training.

The application provides a government and enterprise customer large customer classification method and device, electronic equipment and computer readable storage medium, and relates to the technical field of data processing. The method comprises the following steps: obtaining a government and enterprise customer name set and a large customer name set; respectively splitting a first government and enterprise customer name and a first large customer name to obtain a first government and enterprise customer name segmentation result and a first large customer name segmentation result, wherein the first large customer name segmentation result comprises N-level distance weight words; calculating the sum of the edit distances between the first government and enterprise customer name segmentation result and the N-level distance weight words of the first large customer name; and in response to the sum of the edit distances being less than a preset classification threshold, classifying the first government and enterprise customer name to the first large customer name. The application at least solves the problem of low accuracy of government and enterprise customer classification in the related art. The application is suitable for data analysis, customer marketing and the like.

A method for classifying customers via probabilistic scores from a predictive model, where each probabilistic score represents the probability that a customer will achieve a predicted outcome. The method includes: recording previous probabilistic scores and grouping them within a dataset; sorting the dataset by increment; deriving a reference quantile set based on the sorted previous probabilistic scores in the dataset; determining an action threshold that limits a target quantile range within the reference quantile set; receiving a probabilistic score from the predictive model for a first customer in the current interaction; comparing the first customer's probabilistic score with the probabilistic scores in the reference quantile set to determine a matching quantile; and determining whether the matching quantile is within the target quantile range and selectively classifying the first customer into a target audience based on this.

The present invention relates to the field of artificial intelligence technology, and discloses a method, system and medium for recommending customer marketing strategies based on artificial intelligence. The method includes: obtaining a customer data set, calculating customer correlation factors between each customer in the customer data set, supplementing missing items in the customer data set according to the customer correlation factors to obtain a target data set; performing customer project score prediction on the target data set to obtain project prediction scores; constructing a customer interest matrix according to the project prediction scores, classifying customers according to the customer interest matrix to obtain a set of customer categories; calculating project association scores between customers according to the project prediction scores, and calculating path association scores according to the target data set; calculating a recommendation list through the project association scores and the path association scores, and generating a customer marketing strategy based on the recommendation list. The present invention can improve the accuracy of customer marketing recommendations.

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 invention relates to the technical field of data analysis, can be applied to business system platforms of financial science and technology, medical treatment and health and the like, and discloses a customer classification method, device, equipment and medium, and the method comprises the steps: obtaining multi-dimensional data of a customer, and carrying out the effective feature extraction of the multi-dimensional data according to a preset effective customer standard, and obtaining a customer validity feature; creating derivative features according to the client validity features, and combining the client validity features and the derivative features into a feature data set; constructing a customer classification model according to the feature data set, and adjusting and optimizing the customer classification model to obtain an optimized customer classification model; performing key feature screening on the feature data set according to the optimized customer classification model to obtain key features; and performing customer classification on preset customer data according to the key features and the optimized customer classification model, and outputting a customer classification result. By acquiring the multi-dimensional data and creating the derivative features, the depiction ability of the customer features is improved, and the accuracy of customer classification is improved.

The invention discloses a customer group classification method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: retrieving target customer group data from a financial database, and carrying out the preprocessing of the target customer group data, the target customer group data comprises M pieces of target customer data, and M is a positive integer; performing feature extraction on each piece of target customer data based on predetermined feature items to obtain a customer group intention feature set containing M customer intention features; performing iterative clustering processing on the customer group intention feature set based on a preset clustering algorithm, and ending iterative clustering to obtain K classification clusters under the condition of meeting a preset convergence requirement, K being a positive integer; and generating a classification result based on the K classification clusters, and displaying the classification result. According to the method and the device, the technical problem of relatively low accuracy of processing large-scale customer data by a traditional customer group classification method in related technologies is solved.

We provide the system. [Solution] Means for collecting customer data, A means of analyzing collected customer data and classifying customers into segments, A means of generating optimal pricing plans and sales strategies for each customer based on analysis, A means of delivering generated pricing plans and sales strategies to customers in real time, A means to monitor the effectiveness of each measure and reflect the results in the next data analysis, A system that includes this.

To realize payment settlement with high accuracy and low workload even when the source of payment for receivables changes each time. [Solution] According to this embodiment, the deposit reconciliation device comprises: a deposit input means for creating deposit data including the deposit date, specific information, deposit amount, reconciled deposit amount, and customer classification obtained from the billing master using the specific information as a key; and a reconciliation means for aggregating the scheduled collection amounts of the collection schedule data by scheduled collection date in the receivables master, in order of customer granularity specified in the customer classification of the deposit data to customer granularity decreasing, excluding records other than the customer classification of the receivables origination reason master linked to the reason for the receivable of the collection schedule data, creating reconciliation candidate pattern list data, and comparing the scheduled collection amounts and deposit amounts in the reconciliation candidate pattern list data in order of customer granularity decreasing, and performing automatic reconciliation.

The embodiment of the application provides a customer classification method and device, relates to the field of data analysis, and can also be applied to the financial field. The method comprises the following steps: dividing a customer set into an online consumption customer set and an offline consumption customer set, and respectively determining consumption intervals, consumption frequencies and consumption amounts; respectively performing clustering, and determining the dividing points of the consumption intervals, the consumption frequencies and the consumption amounts of the online consumption customer set and the offline consumption customer set according to the clustering results; and respectively determining the value classification results of each customer in the online consumption customer set and the offline consumption customer set according to the dividing points and a value classification rule. The embodiment of the application considers the differences in customer consumption behaviors in multiple scenarios, adopts a differentiated RFM value calculation mode, constructs different models and uses a segmentation mode, and finally obtains individualized classification results.