20 results about "Customer retention" patented technology

The present disclosure provides a system for predicting customer churn and generating retention strategies. The system includes a data ingestion module that collects and normalizes customer interaction data and network telemetry data comprising latency, bandwidth, and packet loss metrics. A generative artificial intelligence (GenAI) labeling agent applies chain-of-thought reasoning to categorize this data based on contextual features and temporal patterns. A machine learning module executes time-series regression models to predict customer churn probabilities using the labeled datasets. Finally, a prescriptive GenAI agent generates actionable customer retention recommendations based on these predictions, which are delivered through an automated engagement system. The system integrates real-time data processing with artificial intelligence (AI)-driven analysis to identify at-risk customers and develop targeted retention strategies.

The present disclosure provides a text translation method and device, electronic equipment and medium, relates to the technical field of computers, in particular to the technical field of cloud mobile phones, cloud computing and cloud services. The specific implementation scheme is: rendering video stream data obtained from a cloud device to generate a current display interface; obtaining text information to be translated included in the current display interface according to a translation instruction for the current display interface, and translating the text information to be translated. The present disclosure realizes the effect of translating the rendered display interface of the cloud device, so that the user can still read smoothly when facing the multi-language content in the cloud device, improves the use experience of the cloud device, and improves the customer retention rate of the cloud device.

The application discloses a customer intention recognition method and device, an electronic device, and a storage medium. The method comprises the following steps: obtaining a data processing result of a chat record, and inputting the data processing result into a pre-configured agent group; obtaining an analysis result and a user portrait through a first agent and a second agent in the agent group, wherein the first agent is used for planning and summarizing customer emotions, and the second agent is used for extracting a user portrait; and obtaining a customer intention recognition result according to the analysis result and the user portrait. Through the application, not only basic data or simple labels can be output, but also targeted optimization suggestions can be provided for follow-up actions of customer managers based on large model agent technology, and planning schemes for subsequent customer development and customer retention are generated in combination with customer insight results, so that the follow-up direction of employees is clear, customer insight is converted into an actual business growth strategy, and customer conversion and retention efficiency is improved.

The invention discloses an incentive strategy determination method and device for issuing business and electronic equipment, and relates to the field of artificial intelligence or other related fields, and the incentive strategy determination method comprises the steps: collecting issuing business data of a target enterprise under the condition of obtaining the authorization of the target enterprise, and determining the issuing business of the target enterprise based on the issuing business data; and determining cycle characteristics corresponding to a plurality of cycles by adopting multi-scale sliding window convolution, determining an enterprise loss score of the target enterprise and a personal loss score of the personal customer based on the dispatched business data and the cycle characteristics corresponding to different cycles, and determining a target incentive strategy by adopting a preset learning model based on the enterprise loss score and the personal loss score. The technical problem that the customer retention rate is low due to the fact that an effective incentive strategy cannot be determined in the prior art is solved.

The invention provides a customer personalized retention strategy determination method and device, equipment and a storage medium, and the method comprises the steps: collecting multi-modal data of a target customer; constructing and updating a dynamic customer portrait of the target customer based on the multi-modal data; extracting customer features from the dynamic customer portrait, and inputting the customer features into a dual-channel insurance renewal prediction model to obtain an insurance policy insurance renewal probability; performing interpretability analysis on the insurance renewal probability of the insurance policy, and determining key features influencing the insurance renewal probability; and generating a personalized retention strategy of the target customer based on the key features. By adopting the scheme, the scientificity and effectiveness of insurance policy renewal prediction and customer retention work can be comprehensively improved.

The invention belongs to the technical field of life insurance management, and discloses an intelligent group life insurance management system based on group dynamic portraits, which comprises the following modules: a multi-dimensional data fusion module, a group health risk dynamic modeling module, a self-adaptive group pricing and automatic underwriting engine and a claim settlement anti-fraud collaborative network. Enterprise HR data, financial data, employee health data and industry macroscopic data are integrated through a multi-dimensional data fusion module to construct a group dynamic portrait, and comprehensive and dynamic data support is provided for risk assessment, so that group health risk dynamic modeling based on the portrait can accurately capture health risk changes of an insured group; according to the method, a rate adjustment model is established in combination with dynamic factors such as employee flow rate and enterprise financial stability, high matching of insurance premium and group real risks can be realized, more competitive prices are provided for low-risk enterprises to improve the customer retention rate, and accurate fee adding can be performed on high-risk enterprises to effectively cover risk openness.

The invention discloses an electric power economic risk and customer value comprehensive evaluation system, which relates to the technical field of electric power system operation and comprises a multi-source data acquisition and preprocessing module, a multi-source data fusion module, a two-dimensional coupling evaluation modeling module, a dynamic weight adaptive adjustment module and an intelligent decision-making and visual output module. According to the method, the risk and the value are regarded as probability measurement, coupling is carried out by adopting optimal transmission methods such as Wasserstein gravity center and the like, and then quantiles, tail features and high-order moments are extracted from coupling distribution to serve as coupling evaluation vectors. The evaluation result of the distribution level fusion not only provides'single-point 'risk / value judgment, but also provides complete distribution form information. In combination with approximate Shapley-based interaction attribution, the system can reveal nonlinear interaction and marginal contribution between customer values and market / risk factors, thereby providing quantifiable and explainable decision basis for regionalized pricing, differentiated services, target customer retention strategies and risk slow release measures.

To provide an advertisement distribution system in a coin laundry store capable of not only efficiently operating and managing advertisement information but also greatly contributing to customer analysis and customer retention.SOLUTION: The display 12 is disposed in the vicinity of the laundry apparatus 11 in the real store 10, the customer terminal 3 reads the identification code displayed on the display 12, the first server 6 to which the real store ID and the settlement availability information of the use of the laundry apparatus 11 are transmitted through the network is provided, the first server 6 lists, registers, and stores the customer information and the advertisement information, and the second server 7 that transmits the operation permission signal through the network to set the laundry apparatus 11 to the operation state when the first server 6 determines that the settlement is permitted is provided. The operation permission signal transmitted from the second server 7 is received, and the first server 6 selects and outputs the advertisement information corresponding to the attribute information of the customer.SELECTED DRAWING: Figure 1

The present invention discloses a dynamic subscription management system and method utilizing artificial intelligence. The system comprises a computing device configured to preauthorize a customer's payment instrument, forecast potential chargebacks using machine learning, estimate future cash flow, detect fraudulent activity using AI models, and dynamically adjust subscription terms based on customer characteristics. The computing device generates user interfaces displaying analytics and recommendations, and can automatically implement optimizations. The method includes acquiring real-time transaction data, analyzing attributes using machine learning to predict optimal payment processors, routing transactions accordingly, and dynamically adjusting subscriptions based on AI analysis of customer behavior. The system and method aim to prevent fraud, reduce chargebacks, optimize payment routing, and improve customer retention.

Systems and methods are presented herein for post-sale and no-sale feature advertisements that improve customer satisfaction and customer retention. At least one feature advertisement is identified that corresponds to one or more product features of a new model of a product purchased by the purchaser. At least one teaser advertisement is identified that corresponds to one or more other product features of the new model. A data structure corresponding to an associated viewer, based on a relationship characterized by communication between the purchaser and the associated viewer, is identified using one or more of an advertisement server or an eCommerce platform. The at least one feature advertisement based on a first product feature of the new model is presented to the purchaser. The at least one teaser advertisement based on a second product feature is displayed via a device of the associated user.

The embodiment of the invention provides a complaint order processing method and system and a computer readable medium, and can solve the technical problem of poor flexibility of work order processing in related technologies. The method comprises the steps of obtaining key information of a to-be-processed work order based on natural language processing; inputting the question type and the emergency degree result into a case supervised learning model to obtain a complaint processing result; based on the scene parameters, the key information and the complaint processing result, obtaining a resource configuration strategy according to a reinforcement learning algorithm; and obtaining a target processing result according to the state space variable, the action definition set, the reward function and the resource configuration strategy. The ever-increasing requirements for quick response and high-precision work order processing are met, and the limitation of related technologies in dealing with large-scale data sets and diversified problem types is solved, so that the flexibility of work order processing is improved. And the processing speed, the accuracy, the first solution rate and the like are remarkably improved. And moreover, the labor cost is saved, and the customer retention rate is improved.

This invention provides a time-aware incremental learning method and system for multimodal structured communication data. It involves accessing and preprocessing multimodal structured incremental data, organizing the incremental data into message queues, parsing the message queues based on replay technology to identify the set of affected nodes, assigning higher sampling priority to affected nodes when constructing the computational subgraph using a time-aware neighbor sampling strategy, employing a lightweight gradient masking mechanism during the model update phase to incrementally update parameters related to affected nodes only within a local subgraph, and combining this with an asynchronous parameter update mechanism to achieve overlap between computation and communication. Finally, the incrementally updated embedding results are synchronously fed back to a user profile system, providing real-time updated feature representations for services such as precision marketing, customer retention, and risk warning.

This disclosure relates to a travel mode recommendation method, apparatus, storage medium, and computer program product. The method includes, in response to receiving a user's selection of a first travel mode, sending to a server the user's target travel route under the first travel mode and the user's waiting station on the target travel route; receiving and displaying travel recommendation information sent by the server; the travel recommendation information is used to prompt the user to use a second travel mode at the waiting station, and the travel recommendation information is sent to the client by the server after determining that the user's waiting behavior at the waiting station is a travel risk event based on the route status information of the waiting station on the target travel route. This method can provide users with multiple travel options, improve the user's travel success rate, and can also improve the application's customer retention rate to a certain extent.

Anomalies are detected corresponding to user equipment (UE) performance degradation. A neural network is trained using historical network key performance indicators (KPIs), customer retention metrics, and network KPIs. As a result, the trained neural network is configured to output a plurality of customer impact zones, including a threshold for each of the plurality of customer impact zones. The trained neural network may further identify an anomaly that has caused the UE's performance degradation to exceed a threshold and identify an actionable field task that may be implemented by a field team and that may lower the UE's performance degradation below a threshold.

The invention relates to the field of edge intelligent control, and discloses an intelligent central control system based on ARM gateway equipment, which comprises a plurality of cameras, acquisition entrance, hall and counter camera data and a plurality of pieces of ARM gateway equipment. The entrance ARM gateway device generates entrance face information, and the hall ARM gateway device generates hall face information. The counter ARM gateway equipment receives and matches the face information, and generates a preprocessed information sequence; determining a customer retention probability corresponding to each piece of preprocessing information in the sequence, and generating a priority processing sequence; generating target customer information containing a target employee identifier and a predicted business category according to the priority processing sequence, the scheduling information and customer historical business information; and obtaining a corresponding actual service category according to the target customer identifier, comparing the actual service category with the predicted service category, generating target number calling information, and pushing the information to a number calling system. Therefore, the customer service satisfaction is improved, and the customer retention probability is improved.

The invention provides a credit card integration method which can be applied to the fields of big data and artificial intelligence. The credit card integration method comprises the steps of obtaining business feature data of a credit card to be integrated, and determining an initial probability that the type of the credit card to be integrated is a target type by adopting a fitting model based on the business feature data, the fitting model being obtained by training based on an evolutionary algorithm; client retention rate data generated after the previous credit card is integrated to the target credit card is obtained, the initial probability is corrected by adopting a weighting mode based on the client retention rate data, the final probability that the type of the credit card to be integrated is the target type is obtained, and the previous credit card is integrated based on a fitting model; and when the final probability is greater than a preset threshold, determining the type of the to-be-integrated credit card as a target type, and integrating the to-be-integrated credit card to the target credit card. The invention further provides a credit card integration device, credit card integration equipment, a medium and a program product.

[Problem] To provide an advertisement distribution system in a self-service laundry establishment, said advertisement distribution system being capable not only of efficiently managing the application of advertisement information, but also of greatly contributing to customer analysis and customer retention. [Solution] A display 12 is positioned in the vicinity of a laundry machine 11 in a brick-and-mortar establishment 10. There is a first server 6 to which a brick-and-mortar establishment ID and information about the possibility of payment for the use of the laundry machine 11 are transmitted via a network due to a customer terminal 3 reading an identification code that is displayed on the display 12. The first server 6 creates a list of customer information and advertisement information and registers and stores the result. There is a second server 7 that, when the first server 6 determines that payment is permitted, transmits an operation permission signal via the network and causes the laundry machine 11 to be in an operating state. The operation permission signal that was transmitted from the second server 7 is received, and the first server 6 selects and outputs advertisement information corresponding to attribute information for the customer.

Systems and methods for retaining users of a subscribed service are disclosed. A user interface is installed in a terminal device, and is used to interact with end user, receive prompt from and display information to end user. An artificial intelligence agent is trained to communicate with end users in natural language or video streaming about cancellation of the subscribed service via the user interface. The artificial intelligence agent is trained with end user specific information, such as subscription, prior usage of the subscribed service. Such communication may further lead to human-to-human interaction between end users with customer support, service providers, etc.

A computer-implemented method that receives a plurality of customer content and calculates a sentiment score and a weightage score for at least a subset of the plurality of customer content. The method may further include generating a customer score for one or more factors of a set of factors affecting customer satisfaction based on the sentiment score and the weightage score for at least the subset of the plurality of customer content. In embodiments, the method further determines an attrition risk score and a first ranked set of recommended action items for the one or more factors of the set of factors affecting customer satisfaction and notifies user of the attrition risk score and the first ranked set of recommended action items to be executed. The method may further perform a first action of the first ranked set of recommended action items.