136 results about "Customer classification" patented technology

Methods and apparatus for classification-based dynamic allocation of computing resources are described. A method comprises determining usage data sources corresponding to one or more clients of a computing infrastructure, and assigning values to client classification categories associated with a particular client based on metrics obtained from the particular client's usage data sources. The method includes generating a recommendation mapping between values of the client classification categories, and one or more resources of the infrastructure, based at least in part on resource classification information. The method further includes allocating at least a portion of the one or more resources to the particular client based at least in part on the recommendation mapping.

Internet-based software and associated database provide group analysis overlay to monitor client-server web traffic and provide direct marketing to client group. Client car, patient, office or school sensor and interface provides overlay attribute for database comparison to classify usage pattern, location, timing, or family for targeted messaging for enhanced service from server source. Database group registry tracks client classification and provides adaptive context mapping according to set attribute relative to targeted on-line transaction.

A method and system for targeted in-store advertising content is disclosed. In order to improve the success of advertising content on a group of customers, the present invention monitors and records items as they are purchased from the store. These records generate data that is used to categorize customers into defined segments. Segments are matched to pre-defined profiles, which describe certain demographic characteristics common to customers in a particular segment. Advertisers can create advertising campaigns directed to these profiles, and when a certain number of matching segments appear within a certain amount of time within the store, the present invention will display the matching media content. The present invention tracks segment frequency, and applies a weighted T-distribution analysis to determine the likelihood of certain frequencies of segments appearing at a certain time. The present invention also tracks the number of times certain media content has been displayed within a certain amount of time to prevent content overexposure. Use of the present invention's data will help advertisers by introducing a pay for performance model where advertisers pay to deliver content to their target demographic, and by providing information on whether or not an advertising campaign was successful.

The present invention relates to systems (100) and methods used in marketing goods and services. The system includes a call router (124) which determines how a dialler (116) routes outbound calls to sales consultants, based on the output of a consultants skills model (122). The controller (118) also includes a lead sorting component (126) which performs propensity based sorting of sales leads based on the output of a sales propensity model (120). The output of the propensity sorting component is provided to the dialler (116) to control the ordering of the dialling of customers.

A method is provided that identifies and categorizes the reason a customer contacts a customer service center, i.e. the customer's goal or problem. In addition, the method allows the customer to be categorized into a set of behavior-based groupings. This integration of goal and customer categorization establishes the foundation to optimally direct the customer's request through an appropriate center. This method can collect information regarding the customer's behavior and can use that behavioral information to determine the optimal manner for handling the customer request.

The invention provides a potential customer mining and recommending method, which comprises the following steps of obtaining personal information and social activity information of a user from a social platform, fusing the personal information and the social activity information with locally stored user shopping records, and carrying out data cleaning and screening to obtain data for training andtesting a potential customer classification model; constructing a user portrait according to the personal information, the social record and the shopping record of the user, processing the social record and the shopping record of the user into a feature vector form which can be used by a model, then training a user interest prediction model, and dividing the users into potential customers and passers-by; and finally, identifying and providing more targeted commodity pages for the potential customers according to the interests of the potential customers. According to the method, the interest ofthe user can be judged while the user is accurately classified; corresponding products are displayed or precise advertisement putting is implemented according to the interest judgement of the users,so that conversion of potential customers is realized; targeted recommendation can also be provided for old customers, and customer stickiness is improved.

The invention provides a business method for remote preparation of tax return services comprising of: (a) establishing a secure website offering tax return preparation services (b) registering the customer for said service via a service channel (c) collecting basic customer information including income, deduction and preference of service channel (d) classification of customer based on predetermined criteria (e) billing the customer as per customer classification (f) request for authorization and predetermined information from the customer (g) receipt of predetermined information from the customer (h) storage of customer data into the remote database (i) preparation of tax return by a first professional tax preparer (j) review by a second professional tax preparer (k) transmitting the prepared tax return to the customer for acceptance, optionally (l) filing of the prepared tax return upon receipt of customer acceptance (m) providing notification and a copy of filed return to the customer.

The embodiment of the invention discloses a financial product recommendation method and device , a computer device and a storage medium. The method comprises the following steps of determining that acustomer belongs to a customer category with the greatest similarity by comparing customer information with various customer characteristics, calculating a matching degree between a financial productand various customers, and recommending the financial product to the customer when the matching degree is greater than a preset threshold value. Among them, the matching degree between financial products and various customers is calculated by a BP neural network model trained by historical investment records. After developing a new financial product, an investment institution can quickly launch amatching customer base. When a customer invests and manages money, the investment institution can also recommend the appropriate financial products to the customer pertinently. Customer segmentation and matching between financial products and customers are based on the historical data analysis, making this recommendation more accurate and intelligent.

A computer-implemented method of categorizing a customer includes steps of selecting a measure on which the status of the customer is to be analyzed, selecting a calculation period and selecting a comparison method. A lifecycle factor may then be computed, the lifecycle factor being related to both the size and the growth of the customer according to the selected measure. Both the size and growth are determined over two selected calculation periods using the selected comparison method. Selected customers may then be categorized into one of a plurality of lifecycle stages by evaluating a plurality of categorization criteria, at least one of the plurality of categorization criteria including the computed lifecycle factor. For example, the lifecycle stages may include the stages of New, Growing, Stable, Declining, Defected and / or Insignificant. The lifecycle factor may be computed by determining the absolute value of a difference of the selected measure and by multiplying the determined absolute value with a percentage measure growth of the selected measure, both the difference and the percentage measure growth being determined over two selected periods according to the selected comparison method.

The embodiment of the invention provides a customer classification method and device based on an improved particle swarm optimization algorithm, and the method comprises the steps: initializing a particle speed and a particle position according to a classification number and a feature dimension, and setting an initial value, so as to build an initial population of a particle swarm; performing iterative updating operation on the inertia weight, the particle speed and the particle position of the population according to a preset fitness function including the customer characteristic data until apreset iteration frequency is reached; after the number of iterations is preset, respectively carrying out selection operation, crossover operation and mutation operation on the particle swarm according to a genetic algorithm after each update for next iteration update until the iteration update reaches the total number of iterations or meets a convergence condition; and obtaining a clustering center according to the particle swarm reaching the total number of iterations or meeting the convergence condition, and classifying the customers. According to the method, through organic fusion of thegenetic algorithm, falling into a local optimal solution can be avoided, the later convergence speed is increased, and the search precision is improved.

The invention discloses a property insurance claim settlement realization method and a realization system thereof. The method comprises the following steps of according to case report information or claim settlement receipt information, carrying out case filing and acquiring case filing information; according to the case filing information and a preset risk control model, calculating and acquiring a case report risk model, and determining whether the case report risk model is overflown; when the case report risk model is not overflown, carrying out surveying and acquiring surveying information; according to the surveying information and the risk control model, calculating and acquiring a surveying risk model, and determining whether the surveying risk model is overflown; and when the surveying risk model is not overflown, carrying out duty assessment, loss assessment and adjustment successively, and according to adjustment information, carrying out payment. In the invention, an intelligent risk control model, surveying guidance, duty assessment guidance, loss assessment guidance, and car finance professional sharing are programmed and designed, and compensation fragmentation, risk control integration and analysis automation data excavation functions are formed so that client classification is accurate and claim settlement can be performed in a high-efficiency mode.

The invention relates to a queueing time estimation method for estimating the queueing time of clients in a service system. The queueing time estimation method includes the steps of client classification and providing queueing time probability distribution functions of various clients; when more than five clients wait, the attributes of the clients being served and the clients to be served are respectively checked, and the corresponding queueing time probability distribution function is generated; the number of the left clients is estimated according to the function, and the queueing time is estimated according to the number of the left clients and is provided for the clients; and when less than five clients wait, the queueing time of maximum probability of the clients to be served is added to obtain the queueing time of the clients directly according to the queueing time probability distribution function. The number of the left clients is estimated through the client classification and the probability distribution functions, and the estimated service queueing time can be more accurately provided for the clients compared with the prior art; and based on the estimated service queueing time provided for the clients, the utilization rate of the queueing time can be improved by the clients, and the trust, the satisfaction and the loyalty for the service quality of service improvers can be improved by the clients.

The invention belongs to the technical field of data mining, and provides a customer classification method and a system thereof based on transaction data. The method comprises steps: transaction data are acquired from a retail enterprise transaction database; the acquired transaction data are pre-processed, and the transaction data after pre-processing are coded according to layers; according to layer information after coding, a customer purchase tree is built for each customer; all built customer purchase trees are clustered until a clustering center does not change or the change is smaller than a preset threshold, and a customer classification result is acquired. The technical scheme provided by the invention can efficiently and quickly process large amounts of data, and the problem that the traditional method is over high in computational complexity and can not process the current large data can be solved.

The embodiment of the invention provides a strategy pushing method based on big data, and the method comprises the steps: obtaining a target data set of a target customer, and enabling the target dataset to comprise a plurality of target insurance features corresponding to a plurality of target insurance feature items; inputting the plurality of target insurance features into a pre-configured decision tree model, and obtaining a feature combination corresponding to the target customer through the decision tree; inputting the feature combination into a pre-configured customer classification model, and obtaining a target group category corresponding to the target customer through the customer classification model; determining one or more transaction strategies according to the target groupportrait corresponding to the target group category, wherein each transaction strategy comprises a corresponding verbal skill template and a product category; and pushing the one or more transaction policies to the client. According to the embodiment of the invention, the transaction strategy can be intelligently pushed, the pushing efficiency and the data pushing accuracy are maintained at a relatively high level, and the method has relatively good applicability.

The invention relates to a method for advertising through a cash register paper. According to the method, a cash system is added with analysis functions of customer classification and demand prediction analysis as well as management functions of advertisement classification, editing, counting and the like, and a three-print head structure combining thermal printing and ink jet printing and printing duplex printing technique are adopted. The method allows advertisement information to be provided accurately on a basis of anticipating customer wants, and achieves any effect of temperature sensing, single color and multiple colors. The method has characteristics of simple advertising flow, convenient management, clear charge, accurate advertisement object and high timeliness, and reduction in waste advertisements.

The invention discloses a limit adjustment method and device based on customer group screening, an electronic device and a computer readable medium. The method comprises the steps of constructing userportraits according to user information data, and dividing the users into different customer groups through the user portraits; constructing a user income model based on behavior scores, incomes andrisk data of the different customer group users, and further dividing the different customer group users into different customer group groups; constructing a limit adjustment rule; and screening the customer group by using the limit adjustment rule and adjusting the limit of the users of the customer group. By using the method provided by the invention, the customer classification can be more accurately divided, and the coverage rate, the balance scale, the risk and the income of the overall combination are improved, so that multiple risk control screening strategies based on different business objectives are formulated, the overall risk level is finally optimized, and the business income converted after the actuation is expanded.

The invention discloses a vehicle insurance assessment system which is characterized by comprising an information inputting module, an information processing module, a credit reporting module, a control module and an information interactive exhibition module. The information inputting module is connected with the information processing module. The control module is connected with the information processing module, the credit reporting module and the information interactive exhibition module. The invention evaluates the vehicle insurance in a more scientific and reasonable manner, and provides different products and service strategies based on the system score, which can greatly enhance the user's product experience. The use of machine learning algorithms for data processing is more objective to evaluate customer classification and key factors.

The invention discloses a context-aware system based on user power data. The context-aware system based on the user power data comprises a data acquisition unit, a context-aware model establishing and inference engine unit and a data storage unit, wherein the data acquisition unit is provided with a data acquisition module, a data cleaning module, a data preprocessing module, a consumption behavior data clustering module and a clustering evaluation feedback module; the context-aware model establishing and inference engine unit is provided with a data processing module and a model establishing module; the data acquisition module is provided with a sensor; the data cleaning module is used for modifying or deleting error data; the power data are obtained through the sensor, and then the data are processed and correspondingly subjected to partition clustering, then data modeling is carried out on context-awareness to understand consumption behaviors of a user and extract the power mode of the user so as to form a multidimensional user-customer classification system to provide effective reference data for working of power supply bureau personnel.

The present disclosure relates to a method and apparatus for behavior-based vehicle purchase recommendations. A system includes a processor configured to wirelessly receive data indicating vehicle-feature usage for an individual vehicle. The processor is also configured to aggregate received data to form a feature-usage customer profile defining feature preferences. The processor is further configured to select vehicles associated with a customer-classification, including predefined feature-usage characteristics, the customer-classification determined based on a correlation between the predefined feature-usage characteristics and the aggregated data in the feature-usage profile defining feature preferences. The processor is also configured to compare the aggregated data to the selected vehicles to determine a vehicle having features preferred by a customer as indicated by the aggregated data in the feature-usage profile and recommend the determined vehicle to the customer.

The invention discloses a product recommendation method and system for realizing customer classification based on integrated feature selection. The method comprises the steps of obtaining historical product purchase data of a to-be-recommended customer; preprocessing the historical product purchase data of the to-be-recommended customer; performing feature selection on the preprocessed data basedon set indexes, and selecting a plurality of features; inputting the selected features into a pre-trained first neural network model, and outputting the category of the to-be-recommended customer; andinputting the category of the to-be-recommended customer into a pre-trained second neural network model, and outputting a recommended product of the to-be-recommended customer.

The invention provides a store consumption behavior analysis guiding marketing system based on face recognition. The system can comprise a face image acquisition module, an image recognition module, acustomer classification module, a customer grading evaluation module and a payment service module; the face image acquisition module acquires face information and the image recognition module and accurately matches the customer information in the system; the customer classification module automatically stores head portrait pictures of new customers and pushes related customer information to the customer service terminal; if the customer is identified to be a revisited customer according to the face, a linked entrance guard is automatically opened, the customer grading evaluation module formscustomer consumption rating evaluation and credit rating evaluation by utilizing customer information, and service content guidance is performed by utilizing a system recommendation package to promotecustomer service ordering; the payment service module generates an electronic consumption contract, and a client can perform service payment by using payment modes such as face recognition or a two-dimensional code to complete whole service tracking and management. According to the system, shop managers can conveniently carry out comprehensive monitoring and statistical analysis on the operationand sales conditions of shops by utilizing the shop visiting records of the users, and relevant management and marketing means are adjusted.

The invention provides a novel customer information management system, and the system comprises a customer terminal, a security authentication module, an administrator module, an employee module and acustomer module. The customer terminal module facilitates the administrator to log in the management system, and the security authentication module and a client initiate the connection request. The request needs to pass the verification, permission setting and data encryption of the security authentication module, and the user who passes the security authentication can interact with the corresponding module, and respond to the request of the corresponding client. The administrator module achieves the setting of the system and various operations of an enterprise, a customer and an employee. The customer module can check the personal information, modify the password and perform complaining. The customer module comprises a customer classification module, a customer information management module, a business reminding module, a system information module, and a customer positioning module. The beneficial effects of the invention are that the system can be suitable for small and medium-sizedenterprises, is economical, simple and convenient, applicable and easy to maintain, precisely locates the customers, and improves the work efficiency.

The invention provides a method for segmenting metered industry customers on the basis of CRFM (customer recency, frequency and monetary) models. The method includes extracting historical sample dataof the metered customers; preprocessing the extracted sample data; defining and computing index values in the CRFM models and carrying out normalization processing on the index values; clustering themetered inspected customers on the basis of the index values of the CRFM models by the aid of K-Means algorithms; computing average values of various indexes of different clustered customer groups, comparing the average values of the various indexes of the different clustered customer groups to average values of various indexes corresponding to the sample data before the sample data are clustered,creating customer classification matrixes according to change of index values and classifying values of the customer groups. The index values in the CRFM models include recent inspection time R, inspection frequencies F, detection money amounts M and average inspection periods C. The method for segmenting the customers has the advantages that the method is combined with actual business requirements of metered industries, accordingly, the customers can be segmented, bases can be provided for customizing personalized service, and the ultimate purpose of maximizing profits of the metered industries can be achieved.

The invention discloses a customer relationship management method and system. The improved Apriori algorithm is used to classify the customers to obtain the credit rating of each user, the improved Apriori algorithm of the invention does not need to scan the database frequently, It takes only one scan to get there, the method and sytem has higher efficiency; different credit limits are set for customers of different credit grades, different collection schemes are adopted for customers according to customer classification and high-level credit evaluation, and customer service level is improved.The system of the invention actively sends power outage announcement and other information to customers, so as to improve customer satisfaction.

The embodiment of the invention provides a customer classification method and system and electronic equipment. The customer classification method comprises the steps: obtaining a mixed data sample setcomposed of a plurality of mixed data samples, wherein each mixed data sample is composed of a numerical variable sample and a classification variable sample; performing feature extraction on the numerical variable sample to obtain a comprehensive evaluation factor; obtaining a dissimilarity measurement value of the comprehensive evaluation factor and the classification variable sample; and optimizing the K-Means clustering algorithm of the dissimilarity measurement value based on a particle swarm optimization algorithm, constructing a customer classification model, and further obtaining a customer classification result. By combining the K-Means clustering algorithm and the improved particle swarm optimization algorithm, and considering multiple indexes influencing customer consumption, the customer classification method carries out clustering analysis on customer consumption data by adopting an optimized clustering algorithm to obtain customer groups with different characteristics, so that the analysis result is more reasonable and accurate, and different operation and customer service strategies can be conveniently adopted for different groups.

The present invention enables an overlay capability to be invoked on network systems and elements that are designed to support multiple customer bases. Depending on the registered identification of the user, screens and other user interfaces that provide access to functions can be overlaid on the network component and segmented along customer classifications.

The invention discloses a customer classification method and system based on data mining. The method comprises the following steps of 1, determining target customer objects and target data; 2, collecting the data, and preprocessing the customer data by means of data cleaning, data integration, data conversion, attribute reduction and principal component analysis; 3, adopting and combining a K-means algorithm and a Hopfield neural network algorithm to mine the customer data, and obtaining classification results of different customer groups; 4, analyzing and evaluating the classification resultsof the step 3, and if the classification results are optimal, displaying a mining result, otherwise executing the step 3 again. By means of the customer classification method and system based on datamining, the customer data can be effectively and accurately classified so that enterprises can timely and accurately master customer resources and changing tendencies, and the customer resources areeffectively managed.