ML Response Prioritization for IVR Wait Time Reduction
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Solution Overview
Problem
Current automated response systems in communication networks, such as IVR, are inefficient, requiring users to navigate through menus, leading to increased wait times and low customer satisfaction due to lengthy query resolution processes.
Innovation Solution
Implementing a response handling arrangement with a trained machine learning module that processes user event and status data to provide real-time, prioritized responses, eliminating the need for menu navigation and reducing wait times by selecting appropriate responses based on user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a menu-driven IVR system is used to handle user queries, then the system can provide automated responses, but the user must listen to all menus to reach the desired option, increasing wait time and reducing user satisfaction
Solution Approach 1:
The system performs preliminary actions by analyzing user profile data, historical interaction patterns, and query intent before the user actually needs assistance. The machine learning module predicts the most likely desired option in advance and pre-configures the response path, so when the user initiates contact, the system already knows what to present first, eliminating the need for users to listen through entire menu trees.
Solution Approach 2:
The system enables self-service by automatically determining the user's needs based on their profile and interaction history without requiring explicit user guidance through menus. The machine learning module autonomously selects the most relevant response options and presents them directly to the user, allowing the system to serve itself in identifying user needs rather than relying on users to navigate the menu structure.
2Device complexity
If a menu-driven approach is used for query handling, then the system structure is simple, but the customer experience is poor and user patience is tested
Solution Approach 1:
The patent replaces the mechanical menu-navigation system with an intelligent machine learning-based response selection system. Instead of users mechanically interacting with menu options through voice or keypad input, the system uses machine learning algorithms to automatically determine and present the most relevant responses based on user profiles and interaction patterns, substituting the mechanical navigation process with an intelligent prediction and selection mechanism.
Solution Approach 2:
The system changes the fundamental parameters of how queries are handled by transitioning from a static menu structure to a dynamic, data-driven response selection mechanism. The machine learning module analyzes multiple parameters including user profile data, historical interactions, and query context to dynamically determine the optimal response path, transforming the rigid menu navigation into a flexible, adaptive interaction model.
3Productivity
If automated voice response systems handle all queries, then operational costs are reduced, but query resolution time increases due to lengthy menu navigation
Solution Approach 1:
The system implements feedback mechanisms where the machine learning module continuously analyzes user interactions, response outcomes, and interaction patterns to refine its predictions over time. This feedback loop allows the system to learn from actual user behavior and improve its ability to predict desired options, progressively reducing interaction time and enhancing query resolution speed as the model becomes more accurate.
Solution Approach 2:
The system introduces dynamics by making the response selection process adaptive and responsive to individual user patterns rather than static. The machine learning module dynamically adjusts the response path based on real-time analysis of user profiles, interaction history, and contextual information, allowing the system to optimize the interaction flow for each user individually and reduce overall interaction time through continuous adaptation.
Data Source
AI summary
A response handling arrangement comprises a response prioritizing device (26) with processing circuitry configured to obtain event data (ED) and/or status data (SD) in the communication network concerning a user, where the obtaining is triggered by the user initiating a connection to a response device (24) of the communication network, apply the event data (ED) and/or status data (SD) in a response selecting model (78) of a trained machine learning module (74), and obtain an indication (I) of a type of response to the user from the response selecting model of the trained machine learning module (74) based on the applied event data (ED) and/or status data (SD) for allowing a real-time response of the response type to be made to the user by the response device (24).