IVR Route Time Estimation for Lower Hold Times and 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 resource utilization.
Innovation Solution
Implementing an automated assistant that provides users with estimated IVR route times and real-time updates, allowing informed decision-making on IVR routes, thereby optimizing call distribution and conserving device and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If existing IVR systems use traditional call routing without real-time route estimation, then the system structure remains simple, but users experience prolonged wait times and poor resource allocation
Solution Approach 1:
The system performs preliminary actions by calculating estimated IVR route times before the user actually places a call. The automated assistant queries historical data and predicts wait times in advance, allowing users to make informed decisions about whether to place a call and which IVR route to select, thereby reducing actual hold time without requiring complex real-time monitoring during the call.
Solution Approach 2:
The system implements feedback mechanisms by providing users with real-time estimated route times and actual wait time comparisons. This feedback loop allows users to see the difference between estimated and actual wait times, enabling them to adjust their call behavior accordingly. The feedback also feeds into continuous improvement of the estimation model, refining accuracy over time.
2Productivity
If IVR systems do not provide real-time route information to users, then network traffic remains high with more calls being placed, but users cannot make informed decisions about call routing
Solution Approach 1:
The automated assistant performs preliminary queries to historical data sources to obtain estimated IVR route times before the user places a call. This advance information retrieval and processing allows the system to provide users with actionable route information without requiring complex real-time data processing during the actual call, thereby improving call handling efficiency while maintaining information availability.
Solution Approach 2:
The automated assistant acts as an intermediary between the user and the IVR system. It queries historical data, processes route information, and presents simplified estimated times to the user. This intermediary layer translates complex IVR routing data into user-friendly estimates, enabling informed decision-making without requiring users to interact with complex system data directly.
3Loss of energy
If entities do not implement load management strategies, then the system operates with fewer controls, but resource allocation becomes inefficient and network congestion increases
Solution Approach 1:
The system performs preliminary load assessment by querying historical call data and predicting current network conditions before calls are placed. This advance planning allows entities to anticipate traffic patterns and adjust routing strategies proactively, reducing network resource consumption during peak times without requiring complex reactive control systems that would need to monitor and respond to every individual call in real-time.
Solution Approach 2:
The system implements feedback loops that continuously monitor actual wait times and compare them against estimated times. This feedback mechanism provides entities with actionable intelligence about network conditions and user experience, enabling data-driven load management decisions. The feedback also feeds into continuous refinement of estimation models, improving accuracy over time without requiring overly complex control systems.
Data Source
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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.