Paper Type

ERF

Abstract

The streaming industry is rapidly adopting AI-driven Virtual Product Placement (VPP), a technology that allows brands to be inserted into video content post-production. Unlike static legacy placements, AI-VPP enables dynamic, programmatic, and personalized advertising inventory within back-catalog content. However, the trade-off between maximizing dynamic revenue yield and maintaining user immersion remains underexplored. This research-in-progress proposes a mixed-method study to investigate how scene congruity and disclosure levels in AI-VPP affect both advertiser ROI (brand recall, purchase intent) and user experience (intrusiveness, reactance). Drawing on the Persuasion Knowledge Model and Processing Fluency Theory, we aim to provide a theoretical framework for sustainable monetization strategies in the streaming era.

Paper Number

1752

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Aug 15th, 12:00 AM

Seamless or Deceptive? Optimizing Dynamic Revenue and User Experience in AI-Embedded Advertising

The streaming industry is rapidly adopting AI-driven Virtual Product Placement (VPP), a technology that allows brands to be inserted into video content post-production. Unlike static legacy placements, AI-VPP enables dynamic, programmatic, and personalized advertising inventory within back-catalog content. However, the trade-off between maximizing dynamic revenue yield and maintaining user immersion remains underexplored. This research-in-progress proposes a mixed-method study to investigate how scene congruity and disclosure levels in AI-VPP affect both advertiser ROI (brand recall, purchase intent) and user experience (intrusiveness, reactance). Drawing on the Persuasion Knowledge Model and Processing Fluency Theory, we aim to provide a theoretical framework for sustainable monetization strategies in the streaming era.

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