65 results about "Client relationship" patented technology

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.

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.

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.

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.

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 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 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.

The invention discloses an enterprise information unified management system for multi-source heterogeneous data, and belongs to the field of data processing. Comprising a data processing module, a client matrix construction module, a model training and analysis output module, a retention strategy providing module, a closed-loop optimization module and a man-machine interaction module, all the modules work cooperatively, the crossing from static automation to dynamic intelligence is realized, and finally, the operation efficiency is improved while the real-time performance of the system is improved. And the accuracy of the retention strategy and the sustainability of the customer relationship are ensured.

The present application relates to the technical field of customer relationship management, in particular to a CRM customer data dynamic generation method and system based on geographic semantic analysis. The specific implementation steps include: parsing the natural language text input by the user and extracting semantic intent; based on real-time geographic space events and time window factors, combining industry prior semantic knowledge, dynamically weighting semantic reasoning of the semantic intent, generating context-aware geographic semantic query representation; performing semantic association calculation on the query representation and the discourse set containing business entity description, identifying candidate entities; aggregating multi-source heterogeneous discourses associated with the candidate entities, extracting event knowledge, and constructing a business semantic portrait; based on the portrait and the industry chain knowledge path generated by reasoning on prior knowledge, constructing customer knowledge entities in the CRM system. Through the geographic semantic analysis technology, the present application realizes intelligent dynamic generation of CRM customer data, and improves the efficiency and accuracy of customer relationship management.

The invention discloses a customer relationship evaluation model method based on decision chain dynamic analysis, which relates to the technical field of customer relationship evaluation, and comprises the following steps: establishing a multi-dimensional dynamic data acquisition framework, acquiring behavior tracks, language semantic features and time sequence signals of customers in real time in customer communication, contract execution and service feedback processes, and establishing a multi-dimensional dynamic data acquisition framework; and a dynamic information source capable of continuously reflecting the customer relationship state is formed. According to the method, a multi-dimensional dynamic data acquisition framework is established, behavior tracks, language features and emotion data of clients are acquired in real time, traditional static client portraits are broken through, potential intentions of the clients are accurately revealed, and accurate data are provided for decision support. An intelligent early warning mechanism combining a relationship viscosity index and a life cycle trajectory identifies potential risks and opportunities in advance, optimizes resource configuration and action schemes, realizes dynamic management of customer relationships, and improves a customer retention rate and a service expansion effect.

The invention discloses a customer group data processing method and device, electronic equipment and a storage medium, and relates to the technical field of big data or other related fields, and the method comprises the steps: obtaining transaction data of N financial customers from a financial database, and constructing a financial customer group network according to the transaction data, N being a positive integer; based on a preset threshold value formula and a spectral clustering algorithm, the financial customer group network is classified, the financial customer group network is disassembled according to a classification result, M sub-graphs are obtained, and M is a positive integer; solving a maximum independent subset for each sub-graph by adopting a quantum computing optimization strategy, and determining customer associated information corresponding to the maximum independent subset; and updating the current customer group information in the financial database by using the customer association information corresponding to all the sub-graphs. Through the method and the device, the technical problem that customer group information cannot be updated in time due to low processing efficiency of financial customer relationship data in related technologies is solved.

The invention discloses a multi-source data fused customer value evaluation and hierarchical management method, and belongs to the technical field of data analysis and customer relationship management. The method comprises the following steps: collecting and preprocessing customer information from various data sources such as transaction, behavior, social contact, service and outside, and constructing a customer feature matrix in a unified format; fusing different data source features by adopting a weighted fusion algorithm based on an attention mechanism to generate a customer comprehensive feature vector; calculating scores of the customers on indexes such as transaction contribution degree, activeness, loyalty, social influence and risk preference based on the comprehensive features, and determining weights of the indexes through an information entropy weight method to obtain a comprehensive value score of the customers; client layering is executed by adopting a clustering algorithm according to the comprehensive value score, and dynamic correction of the client value is realized in combination with a time decay factor; and parameter gradient updating is carried out based on client feedback data, and model self-learning and optimization are completed.

The system and method for guiding an Artificial Intelligence (AI) engine to automate the quality control process for generating quotes. The quote generation process involves receiving quote requests from a customer relationship management (CRM) system or a data structure entry point. Moreover, retrieving quote data associated with the quote request from data sources, including a CRM platform, via application programming interfaces (APIs) triggered by the submission of the quote request. Furthermore, the prompts are generated by a prompt generator to guide the AI engine in validating the retrieved quote data. The prompts are then transferred to the AI engine for validation, a process that involves analyzing the quote data against predefined rules and conditions, such as price structures, terms, and conditions. Subsequently, a quality control result is generated based on the validation, indicating whether the quote passes or fails the validation process.

The invention discloses a service intervention-based fixed telephone voice quality inspection anti-fraud method and system. The method comprises the following steps: collecting fixed telephone user data information; constructing a client hierarchical management and control system for executing different monitoring strategies for different clients; when a user initiates an outbound call, the session boundary controller calls the user to the telephone traffic subsystem by adopting different modes according to the first-level management and control system, and the telephone traffic subsystem monitors the user according to the second-level management and control system and collects the call voice of the corresponding user and the call ticket of a client; the automatic voice recognition subsystem converts call voice into text information and sends the text information to the risk control subsystem, and the risk control subsystem performs business risk recognition by using a fraud semantic analysis model according to the converted text information and a customer ticket; and the voice service management subsystem sends an instruction of whether the user is shut down or not to the customer relationship management system according to the risk identification result, and the customer relationship management system shuts down the corresponding fixed telephone user. According to the invention, the identification accuracy and recall rate of the fixed phone fraud can be improved.

A multi-tenant, cloud-based Software-as-a-Service (SaaS) manufacturing platform 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)—and implements associated architectural features that address a number of issues relating to data sharing, security, scalability, and other concerns.

The application discloses a sinking private network data security management method and system based on a 5G commercial network and a medium, and the method comprises the following steps: if a secret key distribution instruction is received, a card management system generates a system initial secret key and sends the system initial secret key to a customer relationship management network; the customer relationship management network distributes the system initial secret key to corresponding terminal equipment; the card management system generates an encryption verification secret key corresponding to user subscription data and sends the encryption verification secret key to the customer relationship management network; the customer relationship management network authenticates activation information from the terminal equipment; if the authentication result is passed, an access instruction is sent to a corresponding service subnetwork to control the service subnetwork to access the terminal equipment. The application belongs to the technical field of data security, and the sending of the system initial secret key and the encryption verification secret key is flexibly selected according to different service subnetwork types, which can be simultaneously applied to intensive service subnetworks and sinking service subnetworks, realizes the intensive management of the commercial 5G customized network, and reduces the data security management cost of the commercial 5G customized network.

A customer relationship management (CRM) system can leverage a generative machine learned model to extract customer insights from conversation data. In some examples, the generative machine learned model may be a large language model (LLM) that is trained to extract customer contact reasons from data associated with different communication channel types. The system may generate input data to input into the LLM. When generating the input data, the system can identify non-essential conversation data associated with the different communication channel types. Responsive to a prompt, the LLM may disregard the non-essential conversation data and extract a primary contact reason consistently across the different communication channels.

This invention relates to the field of enterprise business data processing technology, specifically a business collaborative management system based on multi-dimensional data fusion. The system includes modules for data extraction, data cleaning, correlation fusion, analysis cube, and collaborative decision-making. The data extraction module extracts business master data from enterprise resource planning systems, customer relationship management tools, and supply chain management platforms to form corresponding datasets. The data cleaning module removes duplicate records and outliers to purify the data. The correlation fusion module establishes logical mapping relationships between production work orders, sales orders, and purchase contracts in the business data lake, generating a business object correlation graph. The analysis cube module constructs a multi-dimensional data cube with specified dimensions, and the collaborative decision-making module extracts key indicators through multi-dimensional operations to form an indicator set. This system breaks down the isolation of cross-source data, achieving full-domain correlation of business data and targeted extraction of collaborative decision-making indicators.

The present invention relates to a cloud-based workforce development and readiness platform supporting career exploration, workforce development, skills enhancement, and personal growth for students and young adults using a gamified learning approach. The platform comprises a dual SaaS / WaaS™ architecture, including a Software-as-a-Service (SaaS) module for learners and a Workforce-as-a-Service (WaaS™) module for employers, educators, and partners, enabling bidirectional data flows across the ecosystem. The platform further includes a modular AI-powered assistant (GT3S), implemented as a knowledge-based large language model trained on accredited and proprietary workforce data sources, configured to provide personalized guidance, mentor-matching signals, curriculum alignment, and predictive workforce analytics. Users engage with structured programs based on four developmental pillars, earning points and incentives tied to workforce readiness outcomes, including certifications, internships, and employer-aligned skills benchmarks. The system also provides multi-stakeholder dashboards for learners, mentors, educators, and employers, integrating customer relationship management, learning management, e-commerce, donation management, and recruitment functions on a secure cloud infrastructure to create a comprehensive ecosystem for workforce pipeline development.

The invention discloses a client clue dynamic label processing method and system, a medium and a product, and the method comprises the steps: obtaining a client clue data flow; generating corresponding customer main clue data for each customer entity; performing feature extraction on the target customer attribute field and the target field value in the customer main clue data, and determining an individual intention feature of the customer entity; constructing a customer relationship graph; performing aggregation enhancement on the individual intention features to obtain a target feature vector of the customer entity; performing incremental clustering on the target feature vector in each preset time period to obtain a plurality of customer clusters, and calculating a dynamic evolution index of each customer cluster in a plurality of continuous preset time periods; a customer tag is generated for each of the customer entities based on the dynamic evolution metrics. According to the invention, the accuracy of dynamic label processing can be improved.

Systems and methods are provided for a unified platform hosting multiple workspaces. A customer success workspace is hosted through the unified platform. The customer success workspace stores data within a first object of a customer relationship management (CRM) database. A helpdesk workspace is hosted through the unified platform. The helpdesk workspace stores data within a second object of the CRM database. A user is provided with access to the customer success workspace and the helpdesk workspace of the unified platform through a workspace user interface. The helpdesk workspace is provided with access to the first object. The customer success workspace is provided with access to the second object.

The invention discloses a bank ODSB system. The system comprises five layers of architectures: a data source layer comprising a core business system, a customer relationship management system and the like; the data integration layer performs data cleaning, conversion and loading through an ETL process and comprises an ODSB online data warehouse; the data service layer is used for providing services such as report generation and data analysis in a DataMarts form; the application service layer has the functions of report service, system management and the like, and interacts through a WebServices interface; and the user interaction layer is used for presenting the analysis result in the form of charts and reports. The system adopts a J2EE technical system to construct a front-end framework, and a rear end comprises three stages of ETL processing, data platform management and CIS integration. The physical architecture is configured with a DBSERVER server, an ETLSERVER server, a WEBSERVER server, a CognosSERVER server and the like, and an online storage system and an offline storage system. Unified standardization of branch data models is realized, a whole-bank integrated data service mechanism is established, information islands are broken, repeated construction is reduced, data sharing efficiency is improved, and technical support is provided for digital transformation of banks.

The application belongs to the technical field of customer relationship management, and particularly relates to a target customer dynamic extraction method based on multi-source data. A dynamic time window condition is set, when a marketing activity is officially started, data is inquired according to the set dynamic time window condition to generate initial target customer data; the initial target customer data is preprocessed to obtain preprocessed target customer data; based on the preprocessed target customer data, different sources of multiple customer identifiers are associated to the same target customer through a graph association algorithm, and deduplication is performed in the target customer extraction process to obtain a seed customer group; after the seed customer group is obtained, a potential customer group highly similar to the seed customer in characteristics is searched from a full-amount customer library to form a target customer group; whether the target customer group exists in other marketing activities or has been excessively reached within a preset time is detected, and the target customer group is excluded according to the detection to obtain a final target customer list.

The invention discloses a communication operator-oriented intelligent number asking method and device and a storage medium. The method can be applied to a data analysis and service decision scene of a communication operator, an industry knowledge graph covering full-chain data of a communication network, a service support system, an operation support system, customer relationship management and the like is constructed, and semantic understanding and reasoning capabilities of a large language model are combined; accurate conversion from a natural language problem to a structured query statement is realized, and intelligent data insight of scenes such as network operation and maintenance, marketing, customer service, operation analysis and the like is supported.

Techniques for enterprise data aggregation and collective insights generation are disclosed, including: receiving multiple sets of enterprise-specific customer relationship management (CRM) data, respectively, from enterprise-specific CRMs; aggregating the sets of enterprise-specific CRM data, to obtain collective CRM data; receiving a request to generate a collective insight that is applicable to a particular subset of the collective CRM data; responsive to the request, selecting a particular machine learning model from multiple machine learning models, wherein each machine learning model is configured to generate collective insights for a respective subset of the collective CRM data; generating the collective insight that is applicable to the particular subset of the collective CRM data, using the particular machine learning model.

Disclosed are techniques to facilitate Customer Relationship Management (CRM) data utilization and display to suit organizational and team goals, methods, requirements, and more. A system includes a CRM page building engine, a layout building engine, a rendering engine, a request handling engine, a CRM datastore, an element datastore, and a layout datastore. For example, a design system is implemented as a no-code builder that facilitates creation through a drag-and-drop interface, which is intuitive and user-friendly. A layout building engine processes the CRM element data, which comprises the absolute position of the CRM element on the canvas. The layout building engine performs grouping and / or sub-grouping process to wrap the CRM element in the layout. The layout building engine outputs layout data for each element. The resulting element set is a separable element set or a non-separable element set. The resulting set is aligned to place its elements in the exact position or order. On successful fetching of all the required data, the layout data will be mapped with the CRM data and its application elements, based on its type and the action to be performed.