154 results about "Customer relationship management" patented technology

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 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.

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.

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.

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 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.

Example solutions for reducing the likelihood of hallucinations by large language models (LLMs) and improving the relevance of generated text are disclosed. A set of multiple of machine learning (ML) models, each custom-trained to identify a different specific topic within text, dynamically generate multiple topic-specific prompts from a received email in an email thread. A user, responding to the prompts, produces material that is used in conjunction with customer relations management (CRM) data and enterprise suite data (e.g., calendar / schedule information) to dynamically generate an LLM prompt. An LLM, using the prompt, generates output text suitable for a business correspondence, such as a reply email, to the sender of the email. The combination of the responses to the multiple topic-specific prompts and the CRM and enterprise suite data in the LLM prompt both ensures relevance of the reply correspondence and minimizes the risk of hallucinations.

The invention discloses a control method and device of a customer relationship management system and a storage medium, and belongs to the technical field of management systems. The method comprises the steps of generating an association problem set according to service scene information of at least one predefined service scene, constructing graph structure data based on the association problem set, forming a domain knowledge graph, distributing priority weights for conflict problems in the domain knowledge graph, and integrating the domain knowledge graph into an intention recognition module, and in response to a target service scene triggered by a user, outputting at least one guide problem matched with the target service scene through an intention recognition module on the basis of the domain knowledge graph and the priority weight. According to weight distribution and a context association mechanism of the knowledge graph, the accuracy of a recommendation system in a complex service scene is improved.

An artificial intelligence-based business management system for real-time decision optimization, consisting of: a data acquisition unit configured to ingest structured, semi-structured and unstructured data streams from enterprise resource planning (ERP) systems, customer relationship management (CRM) platforms, Internet of Things (IoT) devices, external market feeds and financial transaction systems, encrypting, timestamping and verifying the data prior to further processing; a graph processing unit that is communicatively connected to the said acquisition layer, wherein the unit is configured to encode heterogeneous data into dynamic graph structures comprising nodes representing business entities and edges representing transaction or relationship dependencies, wherein the unit is further configured to perform deduplication, metadata tagging and real-time data synchronization; a decision optimization unit operationally linked to the graph processing unit, wherein the decision optimization unit includes modules for reinforcement learning, modules for Bayesian optimization and multi-objective solvers configured to simulate multiple alternative decision paths and select an optimal path based on performance indicators such as cost efficiency, resource utilization, customer satisfaction and risk minimization; a real-time inference control unit with specialized hardware cores, including at least one graphics processing unit (GPU), a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC), wherein the accelerator performs inference tasks of the decision optimization unit with a latency in the millisecond range; a diagnostic processing unit configured to generate causal diagrams, feature mapping maps, and interpretable result summaries according to the optimization outputs; and a control interface unit configured to transmit optimized decisions to process controls within the enterprise, robot actuators, planning systems or interactive dashboards, with the interface supporting bidirectional communication for higher-level interventions, error feedback and triggers for re-optimization.

The invention discloses a product recommendation method and device, computer equipment and a storage medium, belongs to the technical field of artificial intelligence, and is applied to recommendation of insurance products or medical products. According to the method, the natural language processing technology and the pre-training language model are utilized, key intentions such as user input, recognition budget, guarantee object and insurance amount are accurately analyzed, and user requirements in multiple rounds of conversations are dynamically updated. And on the basis of the updated intention set, candidate products are screened by adopting a collaborative filtering algorithm, weighted sorting is performed in combination with service rules and user preferences, and accurate product priority recommendation is realized. And finally, through double filtering of budget and guarantee demands, it is ensured that the recommended insurance product not only meets the economic ability of the user, but also meets the risk guarantee demand, so that the recommendation individuation and accuracy are remarkably improved, the user experience is optimized, and insurance sales and customer relationship management are promoted.

A system may include a data classification module configured to classify data into classified data based on predefined sensitivity levels and regulatory compliance requirements. A system may include an access control module configured to manage permissions for different user roles within an enterprise, granting access to the classified data in accordance with the sensitivity levels and regulatory compliance requirements. A system may include a data formatting module configured to format classified data into formatted data with customized presentations for various enterprise departments. A system may include an integration module configured to interface with at least one of an Enterprise Resource Planning (ERP) system or a Customer Relationship Management (CRM) system to retrieve and classify the data. A system may include a user interface module configured to present the formatted data within the host application, providing a seamless user experience for accessing the embedded marketplace.

A system for analyzing sports science data with integrated financial management, consisting of: a data acquisition module configured to ingest heterogeneous data streams from multiple sources, including digital advertising platforms, customer relationship management systems, ticket databases, sponsorship activation protocols, broadcast audience statistics, and point-of-sale systems for goods; a telemetry harmonization processing unit configured to encode temporal, categorical, and numerical marketing and engagement signals into structured tensors, correcting inconsistencies in sampling frequency and missing values; an attribution modeling processor comprising a neural sequence encoder selected from a bidirectional long-term-short-term memory network or a transformer-based attention model, coupled with a causal inference submodule that generates attribution scores by unraveling overlapping influences of concurrent marketing campaigns; an explainability controller configured to generate interpretable attribution outputs by applying Shapley value decomposition, local surrogate model explanations, counterfactual simulation, and temporal sensitivity analysis, with the outputs visualized for end users in real time; a financial simulation engine that is operationally coupled with the attribution modeling processor and configured to translate incremental marketing contribution signals into structured financial reports, including profit and loss statements, balance sheets, and cash flow forecasts, using stochastic financial models to predict key indicators under variable marketing scenarios; a secure audit logging module that includes a blockchain anchoring layer configured to record allocation decisions, explainability results, and financial simulation results in tamper-proof records to ensure traceability and compliance; and a machine interface device consisting of a processing unit, a storage unit, a visualization subsystem and an interaction console, displaying mapping, explainability and financial simulation results on interactive dashboards.

The invention provides an intelligent customer relationship management system for a bid invitation agency. According to the invention, a customer scoring function of automatic customer scoring according with business conditions is provided. Through docking with a third-party platform, client cooperation data is obtained, a client scoring method conforming to a bid invitation agency is formulated in combination with business conditions, and a client value score is generated. And the bid invitation agency is helped to better understand and manage the customer relationship. The invention provides a comprehensive and efficient customer relationship management solution to adapt to unique requirements of the bid invitation agent industry. According to the method, client information, client business cooperation conditions, client risk check data and a client comprehensive scoring method are integrated, so that a bid invitation agency is helped to better manage the client information, the client management efficiency of the bid invitation agency can be remarkably improved, various data of clients can be comprehensively, efficiently and truly mastered, and the user experience is improved. And the customer service efficiency and quality are improved.

Concept 1:A computer-implemented method, computer program product and computing system for: defining a dealership hierarchical structure within a CRM overlay that is configured to interface with a generic customer relationship management platform, wherein the dealership hierarchical structure identifies a plurality of dealership entities associated with a dealership group; receiving a new sales lead from a potential customer for a specific dealership entity within the dealership group; determining if the potential customer has previously-submitted any sales leads to the specific dealership entity within the dealership group; and if the potential customer has previously-submitted any sales leads for the specific dealership entity within the dealership group: selectively reconciling the new sales lead from the potential customer for the specific dealership entity with any previously-submitted sales leads from the potential customer for the specific dealership entity; and maintaining the autonomy of the new sales lead from the potential customer for the specific dealership entity with respect to any previously-submitted sales leads from the potential customer for any other dealership entities within the dealership group.

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.

The invention provides an event processing method, device and equipment for an Internet of Things card, and relates to the technical field of event-driven architectures, and the method comprises the steps: obtaining a message event of the Internet of Things card; according to a preset triggering rule, performing triggering judgment on the message event to determine whether a screening operation is triggered or not; if it is determined that the screening operation is triggered, screening judgment is conducted on the message event according to a preset screening rule to determine whether the message event is executed or not; if the message event is determined to be executed, generating an operation work order and / or a follow-up operation work order according to the message event; writing the operation work order and / or the follow-up operation work order into a message queue of a customer relationship management universal platform to trigger the customer relationship management universal platform to execute the operation work order and / or the follow-up operation work order; acquiring an execution log of the operation work order and / or the follow-up operation work order; the method provided by the invention is used for realizing the technical effect of improving the real-time performance and the processing efficiency of event processing of the Internet of Things card.

Smart Computing Device For Tracking Customer Relationship Management Data

The invention provides a novel customer relationship management method, and belongs to the technical field of artificial intelligence and application. Establishing a data base which is used for gathering and integrating multi-source data and consists of client omni-channel interaction data and enterprise business data; based on feature engineering and RFM, the FCI model defines customer feature portraits including behavior preferences, management cycle stages and marketing response laws based on a data base. And taking the customer feature portrait as an input parameter of the random forest model, training the random forest model in combination with historical data, and outputting a marketing strategy including a recommendation category, a communication channel, an interaction frequency, a discount threshold value and a docking person level. According to the invention, based on the multi-dimensional marketing strategy output by the random forest model, the execution link can be directly connected, and manual secondary adjustment is not needed; and meanwhile, the model can quickly adapt to different customer group characteristics, so that'homogenization 'and'hysteresis' of traditional manual strategy formulation are avoided, and manual dependence and management cost are reduced.

The invention relates to a visit report generation method and device, computer equipment and a storage medium. The method comprises the following steps: determining a visit strategy for a target object; the visiting strategy is obtained based on the visiting purpose of the target object and the business data and business requirements of the target object in the target business; the business demand is determined and obtained based on a visiting purpose of the target object and business data of the target object in the target business; in a visiting process for the target object based on the visiting strategy, responding to a question operation for the target business, determining a business question, and generating an answer for the business question based on the visiting strategy; under the condition that the record generation triggering condition is met, the visit report for the target object is generated based on the visit record at least comprising the business question and the answer, so that the visit condition can be comprehensively known, and the refinement level of customer relationship management is further improved.

Methods and systems are described for managing business interfaces including web sites, e-commerce platforms, mobile apps, and point-of-sale (POS) systems using databases and user interfaces on a customer relations management (CRM) system. Performance can be improved using two-way synchronization between the CRM database and a second database used by the business interface. Latency and efficiency can be improved by performing the synchronization using a message queue implemented with a single database table that can be replicated using a native replication mechanism. Multiple CRM databases can synchronize to a single second database organized into multiple tenants. Detection of changes in either database can be implemented using polling or change triggers.

A method, a system and a computer program product for lead management and SPOC control are disclosed. The method includes integrating CRM systems and vendor systems to access lead and vendor data into a lead management system. The method also includes assigning a first lead to a first agent via a lead management module. The method further includes assigning a DID number and a masked email address to the first lead, and routing all communication through lead management system to ensure secure, centralized, and compliant interactions. The method includes dynamically tracking first agent's compliance with lead retention conditions. The method also includes automatically reassigning first lead to a second agent if the first agent is non-compliant with the plurality of lead retention conditions. The method further includes providing a color-coded UI that visually displays lead status and agent compliance for monitoring both lead engagement levels and agent performance.

The application discloses a kind of multi-channel interactive customer relationship management systems, it is related to customer relationship management technical field, the application includes dynamic routing decision module, multi-modal data fusion module and intelligent feedback optimization module;Dynamic routing decision module assesses channel load by deep neural network, utilizes reinforcement learning dynamically distributes customer request to optimal node, realizes load balancing and service continuity;Multi-modal data fusion module integrates text, speech, image data, constructs space-time correlation graph, identifies cross-channel behavior mode, and ensures data consistency by multidimensional verification;Intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes service strategy using genetic algorithm, and is synchronized to cross-channel knowledge graph to realize self-adapting iteration;The application effectively solves the problem of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification, and inaccurate service strategy optimization, improves customer service quality and experience.

The application 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: obtaining attribute data and behavior data of a target user; analyzing the attribute data and the behavior data to generate a plurality of independent portrait modules; combining the independent portrait modules through a genetic algorithm to generate an initial user portrait containing a multi-branch candidate label set; obtaining real-time interaction data of the target user, pruning and optimizing the multi-branch candidate label set in the initial user portrait according to the real-time interaction data, and obtaining an accurate user portrait for customer relationship management. The method is helpful for more comprehensively analyzing user demand and formulating personalized service and marketing strategies. Through the acquisition and analysis of real-time interaction data, the initial user portrait can be updated in real time, irrelevant labels can be removed, and the most relevant labels to the latest behavior of the user can be retained, so that the accuracy and timeliness of the accurate user portrait can be improved.

A system may include a communication interface receiving information characterizing a customer of a first database tenant of a plurality of database tenants accessing customer relations management services. The system may also include a database system storing one or more database records including the information characterizing the customer and being stored in a profile corresponding with the customer. The database system may receive a request to determine content to provide to the customer in association with an interaction between the customer and a second database tenant. A recommended content item may be determined based at least in part on the one or more database records. A message including an instruction for presenting the recommended content item in a user interface may be transmitted from the database system to a client machine associated with the customer.