Proactive Customer Support Sorting for Service Outage Call Reduction
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Solution Overview
Problem
Customer call centers face increased operational burdens and delays due to rising call volumes, necessitating a solution to minimize unnecessary calls by predicting potential communication needs based on service network operational states and caller behavior.
Innovation Solution
Implementing a system that predicts whether a caller will contact a call system using machine learning techniques to analyze historical operational states and caller behavior, and transmitting pre-emptive notifications to minimize call volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If call volume increases to meet customer service demands, then customer service coverage is improved, but operational burden and costs increase
Solution Approach 1:
The system performs preliminary action by predicting potential customer calls before they occur and proactively notifying customers about service outages or issues. This allows the call center to prepare responses in advance and reduces the need for reactive call handling, thereby decreasing operational burden while maintaining service coverage.
Solution Approach 2:
The system implements feedback mechanisms by analyzing historical call data and service operational states to continuously improve prediction accuracy. The feedback loop involves monitoring actual call patterns versus predictions and adjusting the machine learning models accordingly, enabling more effective call volume management.
2Adaptability or versatility
If call volume increases to address customer issues, then customer service quality is improved, but processing time and delays increase
Solution Approach 1:
By predicting calls before they occur and notifying customers proactively, the system enables agents to prepare solutions in advance rather than reacting to calls after issues arise. This preliminary preparation significantly reduces processing time and delays when customers actually contact the call center.
Solution Approach 2:
The system extracts and separates predictable call patterns from random call traffic by using machine learning to identify callers who are likely to contact based on service operational states. This allows the call center to pre-notify these customers and filter them from the main call queue, reducing overall processing delays.
3Adaptability or versatility
If call center operations expand to handle more calls, then customer service coverage is improved, but costs increase
Solution Approach 1:
The system reduces operational costs by performing preliminary notifications to customers who are likely to call about known service issues. This proactive approach prevents unnecessary calls from reaching the call center, allowing existing staff to handle fewer calls while maintaining service coverage quality.
Solution Approach 2:
The system enables self-service by automatically predicting and notifying customers about service outages and issues. This automated approach reduces the need for manual call center intervention for routine outage notifications, thereby reducing operational costs while maintaining comprehensive service coverage.
Data Source
AI summary
Systems and methods for automation of a customer support sorting process are described. A probability that a call from a potential caller will be received by a call system may be determined based at least in part on historical status information and call history information. The historical status information may indicate one or more past operational states of a service network. The call history information may indicate one or more calls received by the call system, as well as a subject matter of each of the one or more calls. If the determined probability satisfies a threshold, a notification to the potential caller may be transmitted. The notification may inform the potential caller of a current operational state of the service network.