145 results about "Client relationship" patented technology

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 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.

The invention discloses an intelligent system for customer service quality inspection based on multi-source data, and particularly relates to the technical field of customer service quality inspection. The method comprises the following steps: acquiring session texts generated in a customer service process and associated product purchase and consumption data in a customer account in real time, and performing cleaning and standardization processing; a large-model automatic marking algorithm is constructed to carry out category marking on customer problems, and recognition of problem backgrounds and customer appeals is realized through context correlation analysis; performing double evaluation on customer attitude and customer service quality by using an emotion analysis model and a customer service verbal skill analysis model; and generating a comprehensive evaluation result of the overall quality of the customer service through fusion analysis, finally predicting a customer conversion rate and a contract extension rate based on the overall quality evaluation result, and generating a targeted customer service optimization strategy, thereby realizing accurate evaluation of the customer service quality and intelligent management of the customer relationship.

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 a business decision-making method and device, equipment, a medium and a program product, relates to the technical field of artificial intelligence, and can solve the problem that accurate business decision-making results cannot be made for different customer groups. According to the specific technical scheme, N first group feature vectors are determined based on to-be-processed customer data of multiple customers, and the N first group feature vectors comprise a customer credit evaluation feature vector, a customer relationship feature vector, a business processing feature vector and other business factor feature vectors; based on M preset classification rules and the N first group feature vectors, dividing the to-be-processed customer data into M first customer group data sets; and for each first customer group data set, processing the first customer group data set by adopting the decision tree model of the business scene corresponding to the first customer group data set to obtain a business decision result of the customer corresponding to the first customer group data set. The method is used for making accurate business decision results for different customer groups.

1. Name of the product in this design: Graphical User Interface for Quotation Management in a Customer Relationship System for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photo that best illustrates the design points: Interface change state diagram 1. 5. Purpose of the graphical user interface: This graphical user interface is used for quotation management in the customer relationship system. The main view is the clue details interface. In the main view, when the user clicks the "Can Quote" button, a quotation is automatically generated and linked to the lead information, entering the interface change state diagram 1, which displays the quotation generation interface. In Interface Change State Diagram 1, when a user clicks on the quotation data under the "Quotation" column, they enter Interface Change State Diagram 2, which displays the quotation interface. In Interface Change State Diagram 2, the user clicks the "Edit" button in the upper right corner to enter Interface Change State Diagram 3, which displays the quotation editing interface. In the interface change state diagram 3, the user fills in the relevant information and clicks the "Save" button in the upper right corner to enter the interface change state diagram 4, which displays the process initiation pop-up interface. In the interface change state diagram 4, the user clicks the "OK" button to enter the interface change state diagram 5, which displays the approval interface. In the interface change state diagram 5, the user clicks the "Approved" button in the upper right corner to enter the interface change state diagram 6, which displays the approval approved interface.

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.

1. Name of the product in this design: Graphical User Interface for Customer Management in a Customer Relationship System for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are its graphical user interface content. 4. The image or photo that best illustrates the design points: Interface change state diagram 1. 5. Purpose of the graphical user interface: This graphical user interface is used for customer management in a customer relationship system. The main view is the marketing department's workbench interface. In the main view, when the user clicks the "Customer Information" column on the left, they enter the interface change state diagram 1, which displays the customer information interface. In Interface Change State Diagram 1, the user clicks the "Add Data" button in the upper right corner to enter Interface Change State Diagram 2, which displays the customer creation interface. In interface change state diagram 2, the user enters relevant customer information and clicks the "Save" button to enter interface change state diagram 3, which displays the customer information interface. In the interface change state diagram 3, the user clicks the double arrow in the first column of the data list to enter the interface change state diagram 4, which displays the customer details information interface. In the interface change state diagram 4, the user fills in the relevant information in the "Create Follow-up Record" card, clicks the "Submit" button, and enters the interface change state diagram 5, which displays the follow-up record saving interface. In the interface change state diagram 5, the user clicks the plus button ("+") to the right of the "Follow-up Records" card to enter the interface change state diagram 6, which displays the customer follow-up record interface. In the interface change state diagram 6, the user fills in the customer follow-up information and clicks the "Save" button in the upper right corner to enter the interface change state diagram 7, which displays the follow-up record saving interface.

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.

The invention discloses a customer relationship maintenance optimization system based on comparative learning, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing multi-source behavior data associated with a target customer; the customer characterization module is used for training the standardized customer data set by adopting an improved MoCo-v3 contrast learning method to generate a customer characterization model; the customer state analysis module is used for carrying out feature coding, similarity calculation and state marking on the target customer; the maintenance instruction generation module is used for determining maintenance content and an execution mode based on the client state information; the model updating module is used for collecting actual response behavior data and performing incremental training on the customer characterization model; and the maintenance optimization module is used for outputting a customer relationship maintenance optimization result based on the updated customer characterization model. According to the invention, the improved comparative learning method is adopted to realize the intelligent maintenance of the customer relationship, and the method has the advantages of accurate feature representation, adaptive strategy optimization and high maintenance efficiency.

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.

To provide an information processing system, method and program for properly supporting input of detail information related to customer management, regarding extraction items or input items.SOLUTION: An information processing system includes a terminal device and a server device. The terminal device includes: a business negotiation sound recording unit which records sound of business negotiations with a customer; and a sound recorded data transmission unit which transmits sound recorded data obtained by the business negotiation sound recording unit to the server device. The server device includes: an association processing unit which associates text information of an input item managed in a customer relationship management system, as title information of an extraction item; a derivation control unit which controls execution of detail information derivation processing to derive text information corresponding to detail information of the extraction item, based on sound recorded text data obtained by turning the sound recorded data into text, and the title information of the extraction item; and a presentation processing unit which performs processing to present, to a user, minute information including the text information derived by the detail information derivation processing, through a screen of the terminal device.SELECTED DRAWING: Figure 8

Artificial intelligence (AI) oversight may be added to a multiparty engagement, such as a conference between devices having user interfaces. The AI may apply visual indicators, e.g., informational elements, recommendations, or other data, on one or more user interface. Visual indicators assist with presenting or participating in a conference. Visual indicators may highlight, e.g., AI-determined attributes of participants in the conference. Attributes may be derived physiological characteristics of participants, such as mood, apparent interest, paying attention, as well as AI-derived data from analyzing data sources, such as data about the participant, past engagements, time zone, name / title and other information to facilitate interaction with the participant. The AI may also review data for past deals, resume, project plans, social media, customer relationship manager data, and the like and develop models to allow it to monitor a conference and provide recommendations to facilitate conference goals.

The invention discloses a resource exception management method and system. The method comprises the following steps: receiving resource abnormity alarm information from a resource monitoring subsystem, and determining an abnormal resource; a data management subsystem is called to determine an affected service corresponding to the abnormal resource, a mapping relation between a plurality of resources and a plurality of services is stored in the data management subsystem, and the mapping relation comprises a calling link from a service layer to a service layer and then to a resource layer; operation and maintenance personnel corresponding to the abnormal resources are determined, and a work order management subsystem is called to send a resource abnormity processing work order to the operation and maintenance personnel; and calling a customer relationship management subsystem to determine a customer corresponding to the affected business, and sending resource abnormality notification information to the customer. According to the invention, the technical problem that a traditional resource exception processing scheme can only position exceptional resources and cannot position products and businesses influenced by the exceptional resources is solved.

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.

The invention provides a service resource allocation method which can be applied to the technical field of artificial intelligence. The service resource allocation method comprises the following steps: acquiring transaction behavior data of a user in a preset time period by using a customer relationship management model; based on the transaction behavior data, extracting a last consumption time interval feature, a consumption frequency feature and a consumption amount feature of the user; determining the marketing value of the user according to the last consumption time interval feature, the consumption frequency feature and the consumption amount feature; according to the marketing value, utilizing a principal component analysis model to extract principal component characteristics of the user, and generating a user portrait according to the principal component characteristics; and distributing corresponding service resources according to the user portrait. The invention further provides a service resource allocation device and equipment, a storage medium and a program product.

The invention belongs to the field of customer relationship management based on big data, and particularly relates to a customer relationship management system based on big data, which comprises a multi-source sensing and acquisition module; a scene modeling module; a scene prediction and decision module; a strategy execution and closed loop optimization module; according to the method, a closed loop of acquisition-modeling-prediction decision-execution-attribution retraining is constructed, multi-source data is opened to form global customer portraits and scene representation, and a strategy list of human, time and place is generated according to individual probability evaluation and risk score and is executed in real time, so that personalized marketing is realized, resource waste is reduced, and the marketing efficiency is improved. And furthermore, customer loyalty is remarkably improved, a wastage rate is reduced, supply and demand matching is improved, inventory turnover is accelerated, promotion homogenization is avoided, and stable increase of revenue is promoted.