Dynamic Call Queue Positioning Using Sensor Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Callers in on-hold queues at call centers often experience long wait times, and existing systems lack the ability to efficiently identify and prioritize calls from users who may require immediate assistance due to emergencies, technical difficulties, or other urgent situations.
Innovation Solution
A call center system that determines a weighted level of importance for a caller's call by analyzing factors such as call history, GPS data, accelerometer data, and user profile information, allowing for adjustment of wait times and prioritization of calls based on identified needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If callers are served in first-come-first-served order, then queue management is simple, but callers in urgent situations experience long wait times
Solution Approach 1:
The queue position is made dynamic rather than static. The system continuously monitors multiple data sources (GPS location, accelerometer data, call history, user profile) and adjusts queue positions in real-time based on changing conditions. Callers can move up or down in queue priority depending on their current situation, allowing urgent cases to be identified and prioritized without a fixed hierarchical structure.
Solution Approach 2:
The system performs preliminary analysis of caller data before assigning queue position. By evaluating GPS data, accelerometer patterns, call history, and user profiles in advance, the system can predict urgent situations and pre-position callers appropriately in the queue before they even speak to an agent, reducing wait time for genuine emergencies.
2Reliability
If all calls are treated equally, then resource allocation is straightforward, but urgent cases cannot be identified or prioritized
Solution Approach 1:
The system uses a universal multi-functional analysis platform that processes multiple types of data (GPS coordinates, accelerometer readings, call history records, user profile information) through a single integrated system. This multi-functional approach allows the same system infrastructure to handle various data sources and analysis tasks, improving reliability without proportionally increasing complexity.
Solution Approach 2:
The system implements continuous feedback loops where queue position assignments are monitored and adjusted based on ongoing data collection. As callers provide more information or their situation changes (detected through GPS movement, accelerometer patterns, or updated call history), the system receives feedback and dynamically recalculates priorities, improving identification accuracy of urgent cases.
3Reliability
If multiple data sources are analyzed to determine call importance, then urgent cases can be identified, but processing time and system complexity increase
Solution Approach 1:
The system applies partial analysis initially, evaluating only the most critical data sources first (such as GPS location and accelerometer data that can quickly indicate emergencies). Less critical data sources are analyzed progressively or in parallel, allowing the system to make initial prioritization decisions faster while still incorporating comprehensive data analysis for final confirmation, thus balancing speed and accuracy.
Data Source
AI summary
Technology for determining a queue position of a call from a user can include receiving, at an enterprise, the call from a user device associated with the user. The user device includes an application associated with the enterprise. The call is placed into an on-hold queue. A weighted level of importance associated with the call is determined based on call history information associated with the user device and call frequency associated with the user. The weighted level of importance is compared to at least one weight threshold. A wait time for the call is decreased based on the weighted level of importance meeting or exceeding the at least one weight threshold.