Personalized Hold Media Selection via Machine Learning
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Solution Overview
Problem
Interactive voice response systems (IVRs) often play hold music that users find unenjoyable, leading to increased likelihood of terminating calls, consuming unnecessary resources, and failing to notice when the hold status is lifted, resulting in wasted communication session resources.
Innovation Solution
A communication platform uses a machine learning model to identify and present customized media items to user devices during hold times, based on user information, to enhance user engagement and reduce resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standardized hold music is played to all users, then resource consumption is minimized through a single media stream, but user engagement decreases and call termination rate increases
Solution Approach 1:
The system transitions from a uniform hold music approach to personalized media selection based on user characteristics. Each user receives media tailored to their preferences, demographics, or historical behavior, thereby improving engagement while managing resources through targeted delivery rather than blanket distribution.
Solution Approach 2:
The system dynamically changes media parameters (genre, artist, track selection) based on user-specific data. By adjusting media characteristics according to user profiles, the system maintains high engagement levels while optimizing resource allocation through intelligent selection rather than exhaustive provisioning.
2Reliability
If personalized media items are presented to each user during hold time, then user engagement increases and call termination decreases, but system complexity and processing requirements increase
Solution Approach 1:
User profiles and media preferences are pre-established before the hold period begins. The system prepares personalized media selections in advance based on stored user data, eliminating the need for complex real-time analysis during the hold period and reducing processing complexity while maintaining personalization benefits.
Solution Approach 2:
The system uses existing user profile data and media metadata as templates for personalized selection. By leveraging pre-existing data structures and information about user preferences, the system avoids creating entirely new complex processing pipelines, thereby reducing system complexity while achieving personalization.
3Reliability
If hold time is extended to provide better service, then user satisfaction may improve, but resource consumption increases and user attention decreases
Solution Approach 1:
Instead of continuous resource-intensive media playback throughout extended hold periods, the system employs periodic media changes or intermittent engagement strategies. Media is refreshed at intervals based on user attention patterns, maintaining satisfaction while reducing cumulative resource consumption compared to uninterrupted playback.
Data Source
AI summary
A device communicates with a user device via a communication session, determines user information relating to the user device or a user of the user device, and determines that the user device is placed on a hold status. The device determines, using a machine learning model, a type of media item to be presented to the user device, where the machine learning model has been trained to identify types of media items to present to user devices when the user devices are placed in the hold status, and selects a media item corresponding to the type of media item. The device presents the media item to the user device via the communication session, determines that the user device is no longer placed on the hold status, and causes the media item to cease being presented.