258 results about "Customer relationship management" patented technology

The present disclosure relates to systems and methods for automatic generation of summaries for record objects using one or more large language models. The system can identify a plurality of electronic activities matched to a record object of a customer relationship management system. The system can generate one or more text strings from the plurality of electronic activities using the large language models. The system can generate a first set of topics by inputting a first set of text strings into the large language models. The system can generate a second set of topics by inputting a first subset of text strings of the first set of text strings into the large language models. The system can transmit one or more topics to a computing device for presentation.

The invention provides a supply chain sales anomaly detection and root cause analysis system and method fused with a knowledge graph, and the system comprises a demand collection and preprocessing module which is used for connecting an order system, a supply chain system, a customer relationship management system and an external data source, and completing the data cleaning, entity analysis and feature extraction; the supply chain knowledge graph construction module is used for defining an entity type and a relationship type; the real-time anomaly detection module is used for accessing a sales index data stream, performing anomaly detection in combination with lightweight filtering and a graph neural network model, and calculating node and global anomaly scores; and the visual report generation module is used for automatically generating a visual report. According to the method, the dynamic supply chain knowledge graph is constructed, the graph neural network is applied, multi-source heterogeneous data is deeply fused, the complex dependency relationship between entities is effectively captured, the accuracy and timeliness of sales anomaly detection are remarkably improved, automatic positioning of abnormal root causes and evidence chain tracing are achieved, and the analysis efficiency is greatly improved.

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The present disclosure relates to systems and methods for automatic generation of summaries for record objects using one or more large language models. The system can identify a plurality of electronic activities matched to a record object of a customer relationship management system. The system can generate one or more text strings from the plurality of electronic activities using the large language models. The system can generate a first set of topics by inputting a first set of text strings into the large language models. The system can generate a second set of topics by inputting a first subset of text strings of the first set of text strings into the large language models. The system can transmit one or more topics to a computing device for presentation.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The present invention relates to the field of data analysis technology, and specifically to a customer relationship management data analysis method and system based on artificial intelligence, comprising the following steps: step one, collecting customer data; step two, storing the data; step three, analyzing and mining the data; step four, building a customer portrait; step five, providing intelligent decision support for customer relationship management based on the customer portrait and data analysis results; the customer relationship management data analysis method and system based on artificial intelligence of the present invention can significantly improve the customer relationship management level of an enterprise. Through artificial intelligence deep learning, the system can automatically analyze a customer's massive conversation records, social media interactions, and transaction data, quickly identify customer needs and preferences, and then generate personalized customer portraits and behavior analysis reports, to help enterprises accurately locate potential customer groups, optimize service strategies and marketing plans, and thus improve customer satisfaction and loyalty.

A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.

The invention relates to the technical field of customer relationship management, in particular to an enterprise digital customer relationship management method and system. The method comprises the following steps: acquiring attribute data and behavior data of a target user; analyzing the attribute data and the behavior data, and generating a plurality of independent portrait modules; combining the independent portrait modules through a genetic algorithm, and generating an initial user portrait containing a multi-branch candidate label set; and real-time interaction data of the target user is obtained, pruning optimization is performed on the multi-branch candidate label set in the initial user portrait according to the real-time interaction data, and an accurate user portrait is obtained to perform customer relationship management. And more comprehensive analysis of user demands is facilitated, and personalized service and marketing strategies are formulated. By acquiring and analyzing the real-time interaction data, the initial user portrait is updated in time, no relevant labels are removed, the label most relevant to the latest behavior of the user is reserved, and the accuracy and timeliness of the accurate user portrait are improved.

Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.

The invention relates to the technical field of enterprise cloud management, and discloses an ERP enterprise cloud management system, which comprises a system main body deployed based on a cloud computing architecture, the system main body is integrated with a financial management module, an inventory control module, a purchase management module, a sales management module, a production plan module, a human resource management module and a customer relationship management module, and data interaction interfaces and cooperative work mechanisms are arranged among the modules; data are stored by the cloud storage unit, and a data processing task is executed by the cloud operation unit. The ERP enterprise cloud management system breaks data barriers among departments of an enterprise, integrates core modules of financial management, inventory control, purchase management, sales management, production planning, human resource management, customer relationship management and the like, ensures the accuracy and consistency of data, and realizes the cloud management of the enterprise through a data sharing function. All the modules can obtain needed data and information in real time, and seamless transmission and cooperative work of the information are achieved.

The invention relates to the technical field of semantic label generation management, in particular to an enterprise-level customer data dynamic label management system which comprises an enterprise customer data storage unit, a knowledge fusion unit, a semantic analysis unit and a label generation application unit. The enterprise customer data storage unit obtains enterprise customer data and product related data and broadens data sources, the knowledge fusion unit carries out data standardization processing, entities and relationships are extracted by utilizing an advanced technology and are stored in a knowledge graph, and the semantic analysis unit selects samples by adopting stratified sampling and active learning, so that the data source is expanded. A plurality of technical means are combined to judge and process semantic drift, a deep learning model is optimized, a label generation application unit generates dynamic labels according to an optimization model, the dynamic labels are updated to an enterprise customer relationship management center in real time after clustering, screening and matching operation, real intentions of customers can be accurately obtained, accurate semantic labels can be generated, and the real intentions of the customers can be accurately analyzed. Enterprises are assisted to accurately serve customers, and the market competitiveness is improved.

The invention discloses a customer relationship management method and system based on big data, and the method comprises the following steps: S1, data collection and integration, S2, data preprocessing, S3, customer portrait construction, S4, customer value evaluation, S5, personalized marketing strategy making, and S6, customer relationship dynamic monitoring and optimization. The customer relationship management system based on the big data comprises a data acquisition interface, a data preprocessing unit, an analysis and processing engine, a strategy execution module and a user interaction interface. Multi-channel, massive and rich data can be integrated through customer relationship management of big data, a detailed and comprehensive customer portrait is constructed through an advanced algorithm, customer values are scientifically evaluated, behavior characteristics, preferences and potential demands of different customer groups are deeply informed, and the customer experience is improved. And personalized marketing strategies and service experiences are customized for all levels of customers.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention relates to the technical field of customer relationship management, and discloses an intelligent customer life cycle management SCRM system, which comprises a multi-source data acquisition and fusion engine; a dynamic customer portrait modeling engine; a customer life cycle state evaluation engine; a predictive intervention engine; an intelligent strategy decision engine; a task distribution and execution engine; and a feedback closed loop and learning engine. According to the intelligent customer life cycle management SCRM system and method, by setting a multi-modal decision engine, taking a customer state as input, learning an optimal strategy of maximizing a long term value (LTV) through a DQN / PPO algorithm, when a customer is in a high loss risk state, triggering an exclusive retention scheme for the customer, and generating a strategy according to a comprehensive conversion rate, cost and satisfaction target, so that the customer life cycle management is realized. Moreover, the personalized content template can be automatically generated, the optimal information channel is accurately sent to different clients, personalized matching is carried out for the clients, and the loss rate of the clients is reduced.

Computerized methods and systems analyze, using one or more natural language processing algorithms, at least one source of data that is representative of an interaction between a first party and a second party to extract from the at least one source of data, information that is descriptive of at least part of the interaction. The computerized methods and systems upload information derived from the extracted information to a data management system that manages data associated with the first party.

The invention relates to the technical field of customer data analysis, in particular to a customer relationship management system in an SaaS mode, which comprises a customer behavior acquisition module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module, a customer behavior analysis module and a customer behavior analysis module, wherein the customer behavior analysis module counts the customer purchase frequency, the purchase amount and the service request times according to time periods on the basis of customer purchase records and service request data; and extracting behavior characteristics of each time period, and generating a customer time sequence behavior characteristic analysis result. According to the method, time period statistics is performed on the customer purchase record and the service request data, and the behavior characteristics are extracted, so that the behavior change of the customer can be dynamically captured. Meanwhile, through cross matching of customer behavior data and channel activity performance, the behavior change of the customer before and after the activity can be accurately judged, so that the marketing strategy is optimized. In combination with behavior prediction of the customer at multiple time nodes, future purchase demands and behavior trends of the customer can be insighted in advance, and opportunity grasp in customer relationship management is optimized.

The invention relates to the field of customer relationship management (CRM), in particular to a business recommendation method and system based on a CRM customer portrait, and the method comprises the steps: inputting the key information and behavior data of a customer into a pre-established CRM customer portrait for matching when a server side receives an access request from a client, and obtaining the grouping information of the customer; and performing upper-layer application management on the customer grouping information, formulating a targeted strategy according to characteristics of different customer grouping information, pushing corresponding target customers to related sales personnel, determining demands and value degrees of the target customers, and formulating a personalized business recommendation scheme. Therefore, the problem that content cannot be screened and recommended according to user requirements in the prior art is solved, accurate grouping of users is achieved by deeply mining user portraits in a CRM system, deep association with the system is established on the basis, the response rate and satisfaction of clients are improved through an accurate pushing and follow-up mechanism of a foreground, and the user experience is improved. And the continuous growth of businesses is promoted.

Automatic analyses of customer-agent interactions provide valuable, actionable feedback for managers, agents, and customers. To provide such analyses, methods for recording and analysis of customer-agent interactions using a customer relationship management (CRM) system are disclosed. A recorder application records the customer-agent interaction, and sensitive information may be identified. Sensitive portions of the recording may then be redacted and removed from the recording. The redacted recording is then analyzed to generate useful summary and analytics information.

A social networking system is provided. The system encompasses a robust processing subsystem hosted on a server, comprises various modules including a registration module linking social media accounts, a blockchain-backed bio page module storing audience metrics, a payment gateway with a reward system, and a CRM module offering dashboards for influencers, brands, agencies, and followers. An analysis module integrates natural language processing to assess content tone, engagement, and sentiment. A button module generates emoticon categorization and auto-suggestions based on metadata, leveraging machine learning for predictions. A compliance analysis module ensures adherence to regulations in paid social media content, and a royalty distribution module calculates and allocates royalties based on engagement, location, compliance, and fan type via smart contracts, This comprehensive subsystem integrates functionalities to elevate user interaction, content analysis, and monetization opportunities within the social media platforms.

A method for configuring the operation of the software of a data as a service (DAAS) system during run time is described. The configuring includes receiving a match query from a customer relationship management system that transmitted the match query responsive to a user using an interface to trigger an update of records in the customer relationship management system that were previously imported from the DAAS system, querying for records in the dataset that match records in the customer relationship management system previously imported from the DAAS system, the querying configured at run time according to metadata that identifies, for records in the dataset, a field to match on and a match threshold, and producing a match query result that includes records in the dataset to be imported to update records that were previously imported from the DAAS system.

The invention relates to the technical field of customer relationship management, and discloses an information data analysis method and system based on CRM. An information data analysis method based on CRM comprises the following steps: S1, collecting user original information data based on CRM, and screening out target users with loss risks according to the user original information data; s2, extracting customer loss risk features from the original information data of the target users, importing the customer loss risk features into the trained risk assessment calculation model, and outputting results to indicate loss risk levels of the target users; and S3, generating a reservation scheme according to the loss risk level of the target user and the corresponding original information data, and outputting the reservation scheme to the corresponding target user. According to the invention, grading intervention is carried out on the target users based on different risk grades, a uniform retention strategy is prevented from being adopted for all users, and the retention rate of clients is improved.

The invention discloses a customer information updating and management method and system based on big data, and relates to the technical field of big data and customer relationship management. Comprising the following steps: firstly, collecting multi-source customer data and constructing a multi-dimensional time sequence characteristic panel; then, through sharing an integrated model of a bottom layer, synchronously predicting a dynamic life cycle value and a continuous loss risk probability of the customer; the network influence weight of the customer is quantified by using an influence penetration model and Monte Carlo simulation; further, fusing the three dimensions, and generating a resource allocation coefficient through a multi-objective optimization function based on a customer type dynamic configuration weight; and finally, automatically updating the customer label based on the coefficient, and combining atomic operations through a strategy arrangement engine to form personalized strategy workflow execution and closed-loop optimization. According to the method, the problems of static isolation and strategy lag of traditional customer information management are solved, and accurate insight of customer values and dynamic intelligent distribution of management resources are realized.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing cloud system offers a variety of industrial applications to end customers, including but not limited to MES, ERP, quality management, supply chain management, and customer relationship management (CRM). The system includes extensibility tools that allows industrial customers to customize databases, data collection templates, reporting fields, and other features of their consumed services, eliminating the need for these features to be customized by an administrator of the cloud system. Some embodiments of the manufacturing cloud system can also leverage generative artificial intelligence (AI) in connection with executing its supported services.

The invention relates to the technical field of intelligent sales, and provides an intelligent sales process optimization method and system integrated with customer relationship management, in the aspect of data processing, a multi-source data acquisition and integration module breaks through the limitation of data acquisition, data can be acquired from multiple channels and effectively cleaned and integrated, and compared with a traditional single data source, the data processing efficiency is greatly improved. A more comprehensive and accurate data basis is provided for subsequent analysis; the intelligent sales prediction model construction and optimization module adopts a hybrid prediction model and a real-time update optimization mechanism, is more accurate than a traditional prediction model and can adapt to market changes in time, and the ability of enterprises to grasp sales trends is improved; the sales decision support and feedback module provides a scientific basis for sales decision by means of various optimization algorithms, and is more reasonable than artificial experience decision; and a feedback mechanism can reuse actual sales and promotion feedback for model training to form a closed-loop optimization system, so that the efficiency and benefits of the sales process are continuously improved, and the competitiveness of enterprises in the market is enhanced.

The invention relates to the technical field of data analysis models, and particularly discloses an SCRM-based data analysis model, which is used for acquiring customer data from social media, a customer relationship management system and customer feedback; cleaning and formatting the collected customer data, extracting feature data related to after-sales service, and performing data fusion; based on an output result of the data fusion model, establishing an after-sales service demand prediction model, and predicting a future after-sales service demand change trend of the customer; the distribution of after-sales service resources is optimized according to the after-sales service demand calculated by the after-sales service demand prediction model; and a customer demand trend and a resource allocation scheme are displayed in a graphical mode. According to the method, the multi-source data is integrated, the data island is broken, the resource utilization rate is maximized and the customer waiting time is minimized by optimizing the objective function, and the user satisfaction is improved.

The invention relates to the technical field of customer management, in particular to an intelligent customer relationship management system based on multi-modal data fusion, and the system comprises a multi-modal data acquisition module which is used for extracting a semantic contradiction index, a service request frequency change rate and a historical performance quality score of a text work order of a voice conversation from a customer interaction log; the state vector generation module is used for generating customer relationship state vectors based on health degree scores; the customer relationship network topology construction module is used for constructing a causal association edge of'customer-service defect-relationship attenuation 'based on the customer relationship state vector, outputting a customer relationship network topology structure with an interpretable path, and forming a relationship maintenance knowledge graph; and a self-adaptive relation repair strategy generation module. According to the invention, through identification of the high-weight relation attenuation path, key nodes and paths of customer relation degradation can be positioned.

The invention discloses a multi-platform customer information integration and unified management method and system, and relates to the technical field of customer relationship management, and the method comprises the steps: dynamically accessing multi-platform data through a distributed API gateway, and uniformly converting the multi-platform data into an Avro format data stream through a lightweight container; the method comprises the following steps: processing a data stream by adopting a three-stage cleaning assembly line, extracting multi-modal features in combination with a BERT micro model and a pruned MobileNetV3, and fusing the multi-modal features into 384-dimensional feature vectors according to a weight matrix; dynamically constructing a Transform-XL skeleton network based on the feature flow, and optimizing the model in real time through knowledge distillation and reinforcement learning regulators; constructing a customer portrait version tree by using a dual-channel full-connection layer to realize millisecond-level emergency update; personalized services are generated based on a multi-arm bandit algorithm, feedback is collected, and closed-loop optimization is completed through a preference offset degree driving model scalpel mechanism. According to the method, the problems of real-time fusion of multi-source heterogeneous data, dynamic portrait updating and model adaptive adjustment are solved.

The invention discloses a customer relationship management system and method based on big data analysis, and relates to the technical field of customer relationship management, and the method comprises the steps: collecting data from a plurality of sources, and carrying out the preprocessing; extracting features from the structured and unstructured data by using a data mining technology, and constructing a customer portrait template based on the features; based on the customer portrait template, monitoring new behavior data of the customer in real time, and adjusting the customer portrait template in real time; constructing a prediction model by using historical data, inputting features in the customer portrait template into the prediction model, and identifying customer demands; according to the prediction result and the personal preference in the historical behavior data, calculating the acceptance score of the customer for the solution; the solution is provided to the customer based on the customer's acceptance score of the solution.

The invention discloses a big data cleaning method and system based on artificial intelligence, and particularly relates to the field of big data: efficient and accurate data de-duplication is realized through a systematized big data cleaning process in combination with application of byte-by-byte matching analysis, similarity calculation, clustering analysis and a machine learning model; through similarity evaluation and classification of unmatched target fields, in combination with a clustering algorithm, features are generated, and a machine learning model is trained, the accuracy and reliability of a duplicate removal process are optimized, the risk of mistakenly deleting key customer records is remarkably reduced, the integrity of enterprise customer data is guaranteed, and the enterprise customer data processing efficiency is improved. And the operation efficiency and decision-making quality of customer relationship management are improved.