Dynamic User-Agent Matching via Real-Time Interaction Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional customer service systems rely solely on static user and agent information for matching, neglecting personality and real-time data, leading to unsatisfactory user experiences due to outdated information and inadequate skill-based routing.
Innovation Solution
A personalized agent matching system that collects real-time user data and agent profiles to dynamically match users with agents based on personality, user value, and interaction context, using algorithms to refine user journeys and provide customized services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static user information and static agent information are used for matching, then the system is simple to operate, but the matching accuracy and user satisfaction deteriorate due to outdated information
Solution Approach 1:
The patent transforms the static matching system into a dynamic one by continuously collecting real-time user interaction data and updating user profiles. The system now uses dynamic user information including interaction context, personality traits, and real-time behavior patterns instead of relying solely on static demographics, thereby improving matching accuracy while maintaining operational simplicity through automated data collection and processing.
Solution Approach 2:
The system performs preliminary data collection and user profile creation before the actual matching process. Interaction monitors continuously gather user interaction data in advance, and user profiles are pre-computed with personality traits and interaction patterns, so that when matching is needed, the system can quickly utilize this pre-prepared information for accurate matching without complex real-time analysis.
2Productivity
If only skill-based routing is used to connect users with agents, then the routing process is efficient and quick, but the user experience deteriorates due to lack of personalization
Solution Approach 1:
The patent segments the agent selection criteria into multiple dimensions: skill-based factors (traditional routing), personality traits (user-agent personality match), interaction context (current user needs), and user value (customer lifetime value). This multi-dimensional segmentation allows the system to balance routing efficiency with personalized user experience by evaluating agents across multiple independent criteria rather than relying on a single skill-based metric.
Solution Approach 2:
The system changes the parameters used for agent selection from purely skill-based static parameters to include dynamic parameters such as user personality traits, interaction context, and real-time agent availability. The matching algorithm adjusts these parameters based on user value and interaction history, transforming the routing process into a personalized experience while maintaining efficiency through automated parameter optimization.
3Measurement precision
If real-time data collection and dynamic matching algorithms are implemented, then user satisfaction and matching accuracy improve, but the system complexity increases
Solution Approach 1:
The system implements self-service mechanisms where interaction monitors automatically collect user interaction data without manual intervention, user profiles are automatically created and updated based on collected data, and the matching algorithm autonomously processes real-time information to connect users with appropriate agents. This automation reduces the operational complexity burden on users and administrators while enabling sophisticated real-time matching capabilities.
Solution Approach 2:
The patent introduces an interaction monitor as an intermediary component that sits between user interactions and the matching system. This intermediary automatically captures interaction data, processes it into structured user profiles, and feeds it to the matching algorithm, thereby simplifying the overall system architecture by centralizing data collection and processing functions rather than distributing complexity across multiple components.
4Adaptability or versatility
If comprehensive user data and interaction context are collected, then personalized service delivery improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data collection and user profile creation in advance, continuously gathering interaction data and computing user profiles with personality traits and interaction patterns before matching is needed. This pre-computation allows the system to quickly utilize prepared user profiles during actual matching operations, reducing real-time processing time while maintaining comprehensive personalization capabilities.
Solution Approach 2:
The system implements a hierarchical data collection approach where essential user information and interaction patterns are collected and processed first for immediate matching needs, while more detailed comprehensive data is collected subsequently or in parallel. This partial action approach enables the system to deliver personalized service with sufficient data quality without waiting for complete data collection, thereby reducing processing time while maintaining personalization effectiveness.
Data Source
AI summary
A communication session between a user and an agent to discuss content provided by a client. A first interactive event occurred during the communication session. A first data collection package associated with the first interactive event is identified. The first data collection package includes a plurality of queries, each query being associated with one of a plurality of workflow stages of a data collection workflow. For each of the queries in one of the workflow stages, a data collection rule corresponding to a current workflow stage is examined to determine whether the query should be sent to the user, the query is transmitted to the user device of the user based on the examination, and a user response is received from the user device in response to the query. The user profile and the agent profile are updated based on user responses.