90 results about "Customer relationship management" 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.

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.

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.

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.

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.

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.

The application discloses a composite machine production method, relates to the technical field of computers, and obtains various production instructions by analyzing production requirements of the composite machine through an order assembly system; generates a sales order through an enterprise resource planning system; splits the sales order through a customer relationship management system to obtain a first material list, and determines a second material list based on a material damage ratio; locks target material inventory based on a supply chain planning and optimization system through the enterprise resource planning system, splits the production instructions based on the locked target material inventory to obtain various machine production work orders; and performs corresponding machine production on the various machine production work orders through a production execution system and a production line to obtain the composite machine, improves the efficiency and accuracy of composite machine production, and realizes efficient management, control and tracing of the composite machine order.

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.

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 communication that includes identifying information; processing the identifying information within the communication to identify a sender and an intended recipient for the communication; and matching the communication with an existing sales lead for one of the plurality of dealership entities associated with the dealership group based, at least in part, upon the sender and the intended recipient for the communication.

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 application discloses an automatic execution system of enterprise system and artificial intelligence agent fusion, and aims at the problem of unnecessary change of enterprise state caused by purposeless redundant action of sufficient execution control of forward time sequence, decoupling of success criterion and user intention. First, the target result state predicate set is independently derived from the user intention and is suspended as an authoritative reference, and then the agent action is degraded to a candidate causal explanation of the predicate set, and a minimum sufficient action subset which is not sufficient is taken as an action effective threshold: only the action belonging to the subset is recognized, the redundant and purposeless action is not structurally effective and does not produce enterprise side side effect, and the execution result is constrained to converge to the intention. The application can be superimposed on heterogeneous enterprise systems such as enterprise resource planning, customer relationship management, workflow engine and work order system, and does not need to be invasively modified.

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.

The invention belongs to the technical field of marketing automation and customer relationship management, and discloses a multi-channel personalized automatic marketing arrangement and optimization method thereof, comprising: a time zone and situation adaptive scheduling module; a multi-dimensional dynamic weight decision module; an enterprise organization level discretization and hierarchical scheduling module; a multi-round intention analysis and evolution identification module; a dynamic channel reachability and cost benefit optimization module; the A / B test and closed-loop optimization module is used for carrying out A / B experiments in groups based on a touch strategy, a priority and an intention label, monitoring experiment effects in real time and outputting feedback for system optimization; and a system integration and full-process automation module. According to the method, client address analysis is matched with industry work and rest, a dynamic time zone identifier is established, and a sliding queue is combined to ensure that a marketing task is executed in a local effective time period of a client; the system comprehensively considers the enterprise matching degree, the level weight, the historical connection frequency, the warehousing time and the conversion performance.

The invention provides an aquatic product merchant wholesale management interaction method and system, electronic equipment and a medium, and relates to the technical field of intelligent agricultural trade, and the method comprises the steps: integrating five controls of aquatic product information editing, income and expenditure record query, goods in and out management, supply chain management and customer management through a wholesale management interaction main interface. In the aspect of information management, aquatic product traceability information can be systematically input, income and expenditure records can be conveniently inquired, and a traceability system and financial management can be conveniently constructed. In the aspect of inventory management, inventory can be monitored in real time and early warning can be performed when the inventory is lower than a threshold value, so that inventory problems are avoided. In supply chain management, suppliers can be effectively managed, replenishment is performed according to inventory requirements, and stable supply is guaranteed. In terms of customer relationship management, customer information and transaction records can be comprehensively managed, and the satisfaction degree can be improved through accurate marketing. On the whole, a plurality of key management links are integrated, a one-stop solution is provided, the process is simplified, the efficiency is improved, and digital and intelligent development of aquatic wholesale business is promoted.

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.