Follow-Up Call Voice Updates for Peak-Time Contact Centers
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Solution Overview
Problem
Contact centers face inefficiencies during peak and non-peak times due to unpredictable inbound call volumes, leading to agent overload and customer frustration, with existing systems failing to distinguish between first and follow-up calls and provide personalized updates.
Innovation Solution
A computer-implemented method and system that identifies follow-up calls and predicts their timing, converting agent updates to personalized voice recordings using AI, allowing customers to receive status updates during peak times without waiting, thus optimizing agent workload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If extra buffer agents are reserved to handle unexpected increase in call volumes during peak-times, then the contact center can handle higher call volumes, but operational costs increase
Solution Approach 1:
The system performs preliminary actions by predicting follow-up calls in advance using machine learning models. Open tickets are analyzed and marked as predicted for follow-up calls with predicted dates and times. Status updates are prepared and converted to voice recordings before the actual calls occur, so that when follow-up calls come in during peak times, they can be handled automatically without requiring additional agent capacity
Solution Approach 2:
The system enables self-service by allowing customers to receive automated status updates through voice recordings played during their follow-up calls. The predictive model identifies which tickets are likely to result in follow-up calls, and the system automatically prepares and delivers status updates without human intervention, reducing the need for buffer agents
2Reliability
If all incoming calls are answered and routed to agents as they come in, then customers receive timely service, but agents become overloaded during peak-times causing increased wait times
Solution Approach 1:
The system segments incoming calls by identifying and separating follow-up calls from first-call requests using predictive modeling. Follow-up calls are detected through analysis of open tickets and customer interaction history, allowing the system to treat them differently from new service requests. This segmentation enables follow-up calls to receive automated status updates while first-call requests continue to be routed to agents
Solution Approach 2:
An automated voice recording system acts as an intermediary between the contact center system and follow-up customers. Instead of routing all calls directly to agents, the system inserts an automated status update delivery mechanism that provides customers with ticket status information before connecting them to agents, reducing agent workload and customer wait times
3Ease of operation
If agents handle all follow-up calls during peak-times, then customers receive personalized service, but agent workload increases and first-call request customers experience longer waits
Solution Approach 1:
The system creates copies of agent status updates by converting text-based ticket status information into voice recordings that sound like they are delivered by the original agent. The predictive model identifies follow-up calls, retrieves the relevant ticket status, converts it to speech, and plays it to the customer. This copying approach maintains the personalized feel of agent communication while freeing agents to handle first-call requests
Data Source
AI summary
A computer-implemented method for playing a personalized voice recording with a status update to follow-up customer on an inbound call in a contact center. The computer-implemented method includes marking one or more open tickets as predicted for follow-up inbound call and predicted date and time for the follow-up call in an inbound database; updating status of one or more open ticket marked as predicted for follow-up inbound call and predicted date and time; converting the updated status to a voice recording; and operating an inbound software to identify an inbound call as a follow-up inbound call of a customer received via a Voice over Internet Protocol (VoIP) network communicating with a customer's mobile device. The customer has an open ticket with an updated status and playing voice recording with the status update in the identified inbound call, thus reducing customers waiting time and agents' workload.