134 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.

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 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.

One object of the present invention is to provide a method for determining the current value and future value of customers who purchase specific merchandise, and the resources thereof, in order to provide data by which to select effective sales-promotion investments suitable for such customers, and for predicting sales according to the target and conditions of the investments. A sales-predicting method that classifies customers are into customer-value cells that determine the magnitude of current and future customer value and resources for future customer value, based on purchase data for specific merchandise and using three axes, including a purchase-amount index (first axis), a user-type index (second axis), and a customer-purchase-relevance index (third axis), and that measures changes in the customer-asset cells over time and changes due to sales-promotion investments, and that simulates sales by quantifying causal relationships between sales-promotion investments and sales.

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 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 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 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 invention provides a client transaction behavior analyzing method which comprises the following steps: acquiring the client transaction data and sorting the transaction data according to time; extracting the attributive characters representing the purchasing time, the transaction frequency and the transaction amount from the transaction data, and performing normalization processing on the purchasing time, the transaction frequency and the transaction amount; digging the hidden attributes of the attributive characters, acquiring the score quantity of the attributive characters on the hiddenattributes, and determining the cluster number according to the score quantity; processing with a genetic algorithm based on the score quantity; and determining the final clustering result to obtainthe client class. Through the method, multiple attributes of client transaction can be accurately analyzed, the accuracy of client classification can be effectively improved, and accurate data supportis provided for the production marketing of an enterprise.