Delhi [India], March 31 (/BusinessWire India): To study the multi-objective personalization of the length and skippability of video advertisements, recently a research was conducted by Omid Rafieian, Assistant Professor at Cornell University - Cornell Tech NYC, Anuj Kapoor, professor at IIM Ahmedabad along with Amitt Sharma, Founder, and CEO at VDO.AI under the endorsement of Z1 Media. According to the research, video consumption decreased when users were presented with long, skippable ads. At the same time, ad consumption increased, posing a challenge for platforms seeking to optimize both outcomes. To address this issue, VDO.AI developed multi-objective personalization algorithms that utilize individual-level substitution patterns to optimize both ad and video consumption.
The results of the study show that multi-objective personalized policies can significantly improve both ad and video consumption outcomes over single-objective policies. The algorithm was able to increase ad consumption by 61 per cent at the expense of only a 4 per cent decrease in video consumption. Similarly, compared to the single-objective policy optimized for ad consumption, there is a multi-objective policy that increases video consumption by 47 per cent while decreasing ad consumption by just 13 per cent.
"We are excited to have partnered with VDO.AI on this study and to have contributed to the development of multi-objective personalization algorithms that can significantly improve both ad and video consumption outcomes over single-objective policies," said Omid Rafieian, Assistant Professor at Cornell University - Cornell Tech NYC.
Anuj Kapoor, Professor at the Indian Institute of Management Ahmedabad, added, "The study's findings have important practical implications for platform decision-making in real-time, and we are excited to have contributed to this important research."
Founder & CEO at VDO.AI, Amitt Sharma said, "We are thrilled to have collaborated with Cornell University and the Indian Institute of Management Ahmedabad on this groundbreaking study. Our multi-objective personalization algorithms will revolutionize the way digital video advertising is approached, and we are excited to see how they will be implemented in real-time."
The study was conducted using field experiments and utilized machine learning and causal inference techniques to analyze the data. The study's findings have significant practical implications for platform decision-making in real-time.
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