Call Preparation Engine for CRM Tele-Agents
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Solution Overview
Problem
Conventional Customer Relationship Management (CRM) systems are large and lack infrastructure to fully utilize available information, leading to inefficiencies as tele-agents operate across multiple systems with different interfaces, failing to effectively present relevant data to improve performance and efficiency.
Innovation Solution
A system that includes speech-enabled devices, a triple server, and a voice server, utilizing natural language processing and semantic graph databases to recognize and process speech, transforming it into semantic triples for improved call preparation, integrating data from various sources to provide real-time insights and information to tele-agents through a call preparation cockpit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional CRM systems are used to manage customer interactions, then customer relationship management is achieved, but the systems are too large and lack infrastructure to fully utilize available information, leading to inefficiencies
Solution Approach 1:
The patent extracts and separates the intelligence assistant component from the conventional CRM system. This assistant specifically handles natural language processing, speech recognition, and semantic analysis tasks, allowing the main CRM system to remain focused on core customer relationship management functions while the extracted intelligence component processes and makes actionable insights from available information.
Solution Approach 2:
The intelligence assistant serves as an intermediary between the conventional CRM systems and the tele-agents. It processes information from multiple CRM systems, performs semantic analysis, and presents synthesized insights to agents through a call preparation cockpit, thereby enabling full utilization of available information without requiring direct integration of all underlying systems.
2Loss of information
If tele-agents operate across multiple CRM systems with different interfaces, then comprehensive customer data access is achieved, but efficiency decreases due to the need to navigate multiple interfaces
Solution Approach 1:
The intelligence assistant is designed as a universal interface that can access and process data from multiple different CRM systems simultaneously. It performs multi-functionality by handling speech recognition, natural language processing, semantic analysis, and information synthesis across diverse data sources, providing tele-agents with comprehensive customer data access through a single unified interface.
Solution Approach 2:
The call preparation cockpit acts as an intermediary that receives processed information from the intelligence assistant and presents it to tele-agents in a unified, easy-to-access format. This mediator layer eliminates the need for agents to navigate multiple CRM system interfaces directly, thereby maintaining comprehensive data access while significantly improving operational efficiency.
3Loss of information
If conventional CRM systems store large amounts of customer information, then data availability is improved, but the systems fail to present relevant data to improve tele-agent performance
Solution Approach 1:
The intelligence assistant applies local quality by performing semantic analysis and relevance assessment on the large volumes of stored customer information. Instead of treating all data uniformly, it identifies and extracts specifically relevant information for each tele-agent's context, such as customer intent, historical interactions, and product interests, thereby making data presentation tailored to local operational needs.
Solution Approach 2:
The system changes the parameter of data relevance by using natural language processing and semantic graph databases to transform stored customer information into actionable insights. It dynamically adjusts which data parameters are presented based on the specific call context, customer profile, and agent capabilities, ensuring that only the most relevant data is surfaced to improve ease of operation.
Data Source
AI summary
Call preparation engine for customer relationship management (“CRM”) is presented. Example embodiments of the present invention include invoking an intelligence assistant to retrieve lead details, customer information, and insights for use during a call between a tele-agent and a customer; administering tele-agent call preparation notes for use during the call between the tele-agent and the customer; and displaying, through a call preparation cockpit, the lead details, customer information, insights and tele-agent call preparation notes.