IVR Route Wait-Time Estimation for Lower Call Traffic
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Solution Overview
Problem
Existing Interactive Voice Response (IVR) systems face challenges with improper load management and suboptimal route selection, leading to inefficient resource allocation, prolonged wait times, increased call abandonment rates, and network congestion, affecting both user experience and network resources.
Innovation Solution
Implementing an automated assistant that provides estimated IVR route times and dynamic updates, allowing users to make informed decisions about IVR routes, thereby optimizing call distribution and conserving device and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If IVR route information is not provided to users, then users cannot make informed decisions about route selection, but providing IVR route information increases system complexity and requires additional processing
Solution Approach 1:
The patent introduces an automated assistant as an intermediary component that mediates between the IVR system and the user. The automated assistant collects IVR route information from the IVR system, processes it to generate estimated wait times, and presents it to the user through a display interface. This intermediary layer simplifies the user's decision-making process while managing the system's complexity internally.
Solution Approach 2:
The system implements feedback by providing users with real-time or near-real-time information about IVR route wait times and call status. This feedback loop allows users to make informed decisions about whether to continue with a particular IVR route, switch to a different route, or abandon the call entirely, thereby improving ease of operation.
2Productivity
If users are directed to incorrect IVR routes, then call duration increases and network bandwidth is wasted, but improving route selection accuracy requires more sophisticated routing algorithms and data processing
Solution Approach 1:
The system performs preliminary actions by gathering and analyzing IVR route information before the user commits to a particular route. The automated assistant retrieves wait time estimates and route status data in advance, allowing users to make informed decisions before initiating or continuing a call, thereby preventing inefficient route selection and reducing network waste.
Solution Approach 2:
The routing system is made dynamic by continuously updating IVR route information based on real-time call volumes, agent availability, and queue status. Instead of using static routing tables, the system adapts routing recommendations based on current system state, improving call handling efficiency without requiring overly complex algorithms.
3Productivity
If call volumes are not accurately predicted and distributed, then some routes become overloaded while others remain underutilized, but implementing accurate load management requires sophisticated monitoring and prediction systems
Solution Approach 1:
The load management system uses feedback mechanisms by continuously monitoring call volumes, queue lengths, and agent availability across different IVR routes. This information is fed back to the automated assistant, which adjusts routing recommendations to balance load across routes, improving agent utilization efficiency without requiring overly complex prediction systems.
Solution Approach 2:
The system implements self-service load management by allowing the automated assistant to automatically adjust routing decisions based on real-time system state. The system monitors its own performance metrics and autonomously redistributes calls to optimize agent utilization, reducing the need for external intervention or complex centralized control.
4Loss of time
If users wait longer in IVR queues, then network resources are consumed unnecessarily and user experience deteriorates, but reducing wait times requires better load balancing and potentially more agents
Solution Approach 1:
The system reduces user wait time by providing real-time feedback about queue status and estimated wait times. Users can see current queue lengths and projected wait durations, allowing them to make informed decisions about whether to continue waiting, switch to a different IVR route with shorter waits, or abandon the call. This feedback mechanism optimizes network resource utilization by preventing users from occupying network resources in excessively long queues.
Solution Approach 2:
The system dynamically adjusts routing recommendations based on real-time queue status and wait time estimates. Instead of directing users to static routes, the system adapts recommendations as queue conditions change, automatically steering users toward routes with shorter wait times and better resource availability, thereby reducing overall network resource consumption.
Data Source
AI summary
Implementations disclosed herein optimize performance of phone calls where users interact with interactive voice response (IVR) systems. By utilizing dynamic wait time statistics and intelligent call management techniques, these implementations minimize wait times and reduce telephone network traffic. Call optimization systems can analyze real-time data to provide users with real-time wait time information to inform users decision-making prior to placing a call or at the time of placing a call. Implementations can present dynamic wait time statistics that consider personalized user experiences to provide more accurate information about any particular IVR navigation path that a user can select from. Selection and execution of a suggested IVR path can result in additional wait time data that can be utilized for refining wait time statistics for users.