Cloud Callback Platform Predictive Multi-Channel Routing
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Solution Overview
Problem
Existing contact center systems are inefficient due to cumbersome voice prompt menus and lengthy waiting times for clients attempting to connect with service representatives, as they often require navigating complex menus and waiting on hold, which exacerbates customer frustration.
Innovation Solution
A cloud callback platform is introduced, comprising a profile manager, callback manager, interaction manager, media server, context analysis engine, and context aggregator mechanism, which allows users to request callbacks when initial connections fail, using predictive logic to determine the most effective communication channel for reconnection, thereby reducing wait times and menu navigation burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice prompt menus are used to channel callers to service agents, then callers can be routed to the appropriate service group, but the menu navigation process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs preliminary routing decisions using predictive logic and machine learning models before the caller needs to navigate menus. The predictive callback system analyzes caller data, historical patterns, and service requirements to pre-determine the optimal service agent or group, eliminating the need for time-consuming menu navigation while maintaining accurate routing.
Solution Approach 2:
The patent introduces a predictive callback system as an intermediary between the caller and the traditional voice prompt menu system. This intermediary uses machine learning algorithms to analyze caller information and predict the most appropriate routing, thereby replacing the manual menu navigation process with an automated intelligent system that maintains routing accuracy while reducing time loss.
2Reliability
If callers wait on-hold in queue for connection to service agents, then connections can be established when agents are available, but waiting time increases customer frustration
Solution Approach 1:
The system performs preliminary actions by predicting agent availability and callback timing using machine learning models before the actual callback occurs. The system analyzes historical data, current queue status, and agent performance metrics to determine the optimal callback time, ensuring connections are made when agents are available while minimizing customer waiting time and frustration.
Solution Approach 2:
The predictive callback system enables a form of self-service where the system automatically manages the callback scheduling and execution without requiring customers to continuously wait on-hold. The machine learning model autonomously determines when to initiate callbacks based on predicted agent availability, thereby maintaining connection success rates while eliminating the need for prolonged customer waiting.
3Reliability
If traditional callback systems are used, then customers can be called back when agents are available, but the system cannot determine the optimal communication channel for reconnection
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting communication channel selection based on multiple variables including customer preferences, historical interaction data, current availability, and service type. The machine learning model analyzes these parameters to determine the optimal communication channel (voice call, text message, email, etc.) for each callback, thereby maintaining reliable callback delivery while enhancing adaptability to different customer preferences and situations.
Solution Approach 2:
The system implements local quality by customizing the communication channel selection for each individual customer and callback scenario rather than using a uniform approach. The machine learning model analyzes specific customer characteristics, past interactions, and current context to determine the most appropriate communication channel for each case, thereby achieving both reliable delivery and high adaptability to diverse customer needs.
Data Source
AI summary
A system and method for a cloud callback platform, comprising at least a profile manager, callback manager, interaction manager, media server, context analysis engine, and context aggregator mechanism, allowing users to call a business, agents in contact centers, or other users who are connected to cloud callback platform, and, failing to connect to the individual they called, or by request, allow for an automatic callback object to be created, whereby the two users may be automatically called and bridged together at a time when both users are available, and a context summary report is sent to one or more user. The system and method implement predictive logic to determine the most successful alternative communication channel to use to bridge the two parties.