Leveraging GenAI in short-form video ads: Strategies for maximizing reach, engagement, and customer loyalty
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| Publicado en: | MATEC Web of Conferences vol. 411 (2025) |
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| Autor principal: | |
| Otros Autores: | , , |
| Publicado: |
EDP Sciences
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| 022 | |a 2274-7214 | ||
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| 024 | 7 | |a 10.1051/matecconf/202541104002 |2 doi | |
| 035 | |a 3274908786 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
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| 100 | 1 | |a Joshi, Ram | |
| 245 | 1 | |a Leveraging GenAI in short-form video ads: Strategies for maximizing reach, engagement, and customer loyalty | |
| 260 | |b EDP Sciences |c 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a The rise of short-form video formats like TikTok, Instagram Reels, and YouTube Shorts has fundamentally transformed digital advertising, presenting both challenges and opportunities for brands. These platforms, designed for rapid engagement, often struggle to accommodate traditional advertising methods, which are increasingly viewed as intrusive and ineffective in capturing fleeting attention spans. This study aims to explore the integration of Generative AI (GenAI) in short-form video ads to enhance brand reach, engagement, and customer loyalty. By utilizing GenAI’s capabilities in hyper-personalization and real-time content adaptation, the research identifies innovative strategies that brands can leverage to overcome challenges in the fast-paced, attention-driven digital landscape. Data was analyzed using the RACE methodology (Reach, Act, Convert, Engage), highlighting GenAI’s role in improving ad performance across these stages. Findings demonstrate that hyper-personalized content significantly increases user engagement and conversion rates while addressing ethical concerns related to data privacy and algorithmic manipulation. The study’s originality lies in its focus on applying GenAI to short-form video ads, offering actionable insights for marketers seeking to optimize their digital strategies. Implications include a balanced approach to personalization, ensuring ethical responsibility while enhancing consumer-centric experiences. | |
| 653 | |a Customers | ||
| 653 | |a Attention | ||
| 653 | |a Brand loyalty | ||
| 653 | |a Advertising | ||
| 653 | |a Real time | ||
| 653 | |a Ethics | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Optimization | ||
| 700 | 1 | |a Panyam, Sriram | |
| 700 | 1 | |a Gujar, Praveen | |
| 700 | 1 | |a Paliwal, Gunjan | |
| 773 | 0 | |t MATEC Web of Conferences |g vol. 411 (2025) | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3274908786/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3274908786/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |