Dynamic Advertisement Scheduling via User Interaction Composite Values
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Solution Overview
Problem
Playlist-based media consumption providers face challenges in monetizing their services through advertising and in providing content that aligns with user preferences, as they struggle to determine optimal times and types of advertisements based on user interactions, such as skipping or delaying media assets.
Innovation Solution
A system that monitors user interactions with media assets, associating different values with 'skip' and 'delay' options on a playlist menu, and generates advertisements based on a composite value that reflects user behavior, adjusting the playlist dynamically to maintain user interest and reduce ad frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If advertisements are played frequently to maximize monetization, then revenue increases, but user attention and interest are lost
Solution Approach 1:
The system dynamically adjusts advertisement scheduling based on real-time user interactions with the playlist. The advertisement schedule is not fixed but adapts to user behavior patterns, allowing the system to optimize monetization while preserving user attention by playing ads at moments when users are least likely to disengage.
Solution Approach 2:
The system monitors user interactions (skips, delays, playlist modifications) and uses this feedback to adjust advertisement timing and frequency. By continuously learning from user behavior, the system can determine optimal moments to display ads that maximize revenue without excessively disrupting the user experience.
2Productivity
If user interactions are monitored closely to optimize ad timing, then advertisement effectiveness improves, but system complexity increases
Solution Approach 1:
The control circuitry that manages playlist playback is extended to also handle user interaction monitoring and advertisement scheduling decisions. By making the existing control circuitry multi-functional, the system avoids adding separate complex monitoring and decision-making systems, thereby reducing overall system complexity while still achieving effective ad timing.
3Productivity
If advertisements are played when users are attentive, then ad effectiveness improves, but user engagement with content may decrease
Solution Approach 1:
Instead of playing advertisements continuously or at every possible opportunity, the system uses partial action by selectively playing ads only at specific moments when user attention is inferred to be available. This partial approach to ad delivery maintains user engagement with the primary content while still capturing advertising opportunities effectively.
Data Source
AI summary
Methods and systems are provided herein for determining a dynamic advertisement schedule. These methods and systems are provided by way of associating a first value with a selectable delay option on a playlist menu, and associating a second value with a selectable skip option on the playlist menu. Each time the selectable delay option is selected, the first value may be added to a composite value, and each time the selectable skip option is selected, the second value may be added to a composite value. The composite value may be compared to a threshold value, and when the composite value corresponds to the threshold value, an advertisement may be generated for display on a display screen and the composite value may be reset.