rahulgaikwad2010/real-time-first-price-auction-machine-learning

Setting a reserve price induces this by causing bidders to lose at lower bids which encourages higher bidding and more publisher revenue. However, since most of these take place through automated systems, there might be an unknown delay in setting reserve prices & reducing the win rate of bidder & bidder changing their bid shading algorithm & increased publisher revenue.

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This project helps digital publishers optimize their revenue from real-time ad auctions. By analyzing historical auction data, it provides insights into optimal reserve prices. The output helps publishers understand potential revenue ranges, ensuring they maximize earnings from their ad inventory. Ad operations managers or monetization strategists at digital media companies would find this useful.

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Use this if you are a digital publisher seeking to understand how to set effective reserve prices in first-price ad auctions to maximize your advertising revenue.

Not ideal if you are an advertiser looking to optimize your bidding strategy or if you are not involved in real-time ad inventory monetization.

digital-advertising ad-monetization publisher-revenue programmatic-advertising ad-operations
No License Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 5 / 25
Maturity 8 / 25
Community 8 / 25

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Mar 26, 2021

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