Abstract
It is quite intricate for a buyer to reach the publisher’s advertising slot with many market players in the programmatic era. Auction Duplication, internal deals between Demand & Supply side platforms, and rife fraudulent activities are complicating the existing complex process - leading to a single impression being sold through multiple routes by multiple sellers at multiple prices. The dilemma: Which path should the buyer choose, and what should be the fair price to pay? has been staying put for years. The framework suggested in this paper solves the problem of choosing the best path at the right price in the Video Advertising Landscape, a significant contributor compared to other advertising channels. This framework embraces two techniques named Data Envelopment Analysis, where an unsupervised data set is ranked by estimating the relative efficiencies, and a statistical and machine learning hybrid scoring method based on Classification Modeling to help us decide the path worth bidding. These models’ results are compared with each other to choose the best one based on campaign KPI, i.e., CPM (Cost per 1000 impressions) and VCR (Video Completion rate of the video ad). An average of 6%- 12% reduction in CPM and 1% - 4% increment in VCR is observed across 10 live video ad campaigns. The zenith improvements in CPM reduction give rise to a better return on investment(ROI) than the heuristic approach.
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Musku, U., Yadav, P. (2021). Supply Path Optimization in Video Advertising Landscape. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_44
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