Cross-Channel Communication Management with Real-Time ML Routing
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Solution Overview
Problem
Conventional online customer service methods fail to seamlessly transfer communication channels, leading to misrouting and loss of context when customers switch from one channel to another, such as from text chat to voice call, resulting in inefficient service and customer dissatisfaction.
Innovation Solution
Implementing a system that uses real-time machine learning scoring algorithms to determine the appropriate customer service representative for channel transfer, ensuring seamless communication session continuity by retaining customer information and session details, and prioritizing outbound call requests to minimize human errors and misroutes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional communication channel transfer methods are used, then customers can switch between channels, but context is lost and misrouting occurs
Solution Approach 1:
The system performs preliminary actions by capturing and storing communication session data (including context, customer information, and interaction details) in a database before channel transfer occurs. This ensures that when the customer switches channels, the new agent can immediately access the stored session data and continue the conversation without loss of context.
Solution Approach 2:
The system introduces an intermediary component (the server with database) that acts as a mediator between communication channels. This intermediary captures, stores, and manages session data, enabling seamless transfer of context between different communication channels and agents without direct loss of information.
2Ease of operation
If manual agent routing is used, then customers can be transferred between agents, but human errors cause misrouting
Solution Approach 1:
The system implements self-service by using automated algorithms and machine learning models to determine the most appropriate agent for each customer query. The system analyzes session data, customer preferences, and agent expertise to automatically route customers to the most suitable agent, eliminating human error in routing decisions while maintaining ease of operation.
Solution Approach 2:
The system incorporates feedback mechanisms where routing decisions are continuously evaluated and improved. By analyzing transfer outcomes, customer satisfaction metrics, and session data, the system refines its routing algorithms to increase routing accuracy and reduce misrouting over time.
3Productivity
If agents communicate with multiple customers simultaneously, then service coverage increases, but context is lost due to human limitations
Solution Approach 1:
The system uses the server and database as an intermediary to store and manage context information for multiple customer sessions simultaneously. This allows agents to communicate with multiple customers at once while the system maintains the context of each interaction, retrieving relevant session data as needed without relying on human memory.
Solution Approach 2:
The system creates digital copies of customer context and session data that can be instantly retrieved and transferred. Instead of relying on human agents to remember and switch between multiple customer contexts, the system maintains accurate copies of each interaction's details, ensuring context is preserved regardless of agent multitasking capacity.
4Reliability
If real-time monitoring and auditing is implemented, then misrouting can be reduced, but system complexity increases
Solution Approach 1:
The system performs self-monitoring and self-auditing through automated algorithms that track routing decisions, analyze outcomes, and identify patterns. This self-service approach to monitoring reduces the need for complex manual oversight systems while maintaining high routing accuracy through continuous automated evaluation and adjustment.
Data Source
AI summary
Disclosed herein are systems and methods capable of establishing a communication session between a user and an analyst. The contents of the communication session are analyzed to make recommendations of goods and services to the user. Otherwise, the communication session may be redirected from one channel, for example, chatting to another channel, for example, voice call, to another analyst. The user's information and the communication session details are retained, and provided to another analyst before the user is redirected. Such systems, apparatuses, methods, and computer program products use real-time machine learning scoring algorithm to determine which analyst the users should be transferred to, and thereby saving a lot of time for both the analysts and the users, and significantly reduce misroutes by eliminating human errors.