17 results about "Client relationship" patented technology

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 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 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.

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 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 relates to the technical field of customer relationship management, and discloses a customer contact relationship evaluation method and system based on multi-feature quantization, and the method comprises the following steps: recognizing a plurality of key persons in a customer organization; and carrying out quantitative evaluation on seven types of features of each key person, wherein the seven types of features comprise relationship closeness, scheme recognition degree, price acceptability, one-ticket overbiter, one-ticket approver, report relationship and influence relationship. According to the customer contact relationship evaluation method and system based on multi-feature quantification, a'seven-feature 'quantification model (relationship closeness, scheme recognition degree, price acceptability, one-ticket overtaker, one-ticket approver, report relationship and influence relationship) is created, so that an originally fuzzy and subjective customer relationship judgment standard is converted into a unified and objective numerical index; and subjective judgment which varies from person to person is eliminated, so that the customer relationship state becomes measurable, comparable and traceable, and a data foundation is laid for fine management.

A first event stream maintains a plurality of privacy preference data from two or more customer relationship management (CRM) systems. A second event stream maintains a plurality of solicitation preference data, each solicitation preference data in the plurality of solicitation preference data determined by applying a set of rules to filter a respective privacy preference data from the plurality of privacy preference data. A request for a current solicitation preference of a client is received from a CRM system of the two or more CRM systems. One or more entries in the second event stream are accessed. The current solicitation preference of the client is determined from the one or more entries in the second event stream. The current solicitation preference associated with the client is provided to the CRM system.

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.

Systems and methods provide social media management capable of identifying parties on social media networks and providing social media and business records related to those parties.

A service provider system receives customer case data from a customer service system. Vector data is collected from the case data through integration and aggregation. Anomalies or opinion signals are detected from the integrated and aggregated vector data using machine learning. These signals are verified, integrated, and associated with case, contact, and customer object types. A user interface presents the verified and integrated signals to the user, who then takes proactive action based on these signals. The user interface includes dashboards, notifications, and indicators.

A system for real-time marketing automation employs adaptive artificial-intelligence (AI) models to process user behavior data from a plurality of sources such as customer-relationship-management systems, social-media platforms, websites, and mobile applications. A processor executes instructions stored in memory to perform marketing-automation tasks including predicting user-engagement trends, generating personalized content, and monitoring compliance with data-privacy requirements. Output data produced by one AI model is used as input to another to adjust marketing parameters in real time. Engagement data received across multiple communication channels is fed back to the AI models to update stored user profiles and campaign parameters. An integration interface registers metadata describing additional AI models and synchronizes the additional models with existing data pipelines without altering the underlying system architecture, enabling adaptive, compliant, and scalable marketing operations.

A system for identifying false positives about a user and closing resulting alerts before the alert consumes system resources may use machine learning techniques including clustering and multi-labeling classification to effectively categorize prior text-based notes. The categorized notes may be used to identify and automatically close false positive alerts. Moreover, weak labeling, AI transformers / sentence transformation, and / or k-means cluster analysis may be used to condense large quantities of textual data into ML model components with improved interpretability. Customer relationship management (CRM) platform and Risk Management Supervision (RMS) note analysis captures past work and leverages it to reduce redundancies in future expert user / supervisory / customer advisory efforts. The various ML techniques disclosed herein output numerical features for improved alert triaging.

A method and a device for transmitting CRM content from a medical institution client assigned to a hospital or a doctor are provided. The medical institution client selects CRM content to be transmitted and, when an input of identification information of a recipient who is to receive the CRM content is received, performs CRM transmission. The CRM transmission is performed using a possible means regardless of whether a recipient has a membership and whether a service application has been installed, and is collectively managed in the future.