![]() ![]() ![]() This would lead to a staggering 5 trillion potential bid requests and a massively wasted infrastructure. Suppose we get 50 billion impressions, within a defined set of time, and send the corresponding bid requests to 100 DSPs without any bid throttling. To appreciate the scale of the problem bid throttling addresses, consider the following example. Bid throttling allows PubMatic to send traffic to DSPs they actually want instead of sending every impression to every DSP connected on our platform. This is where PubMatic’s response of automated bid throttling powered by machine learning (ML) comes in. It doesn’t do justice to the complexity and dynamic nature of real-time bidding (RTB). #Request was throttled meaning manual#However, a manual optimization can go only so far as it usually revolves around broad and somewhat static constructs such as geography, platform, publishers, etc. To that end, DSPs sometimes work with exchanges to pre-filter inventory they don’t want. It’s well understood that not all DSPs bid on all impressions. Thus, there is lot of focus on optimizing the supply path to make it more efficient and sustainable. This essentially means that SSPs and DSPs have to improve the way they manage the increased volume of impressions. The reason for this is SSPs are now receiving every impression on the page, while DSPs are actually getting even more impressions from multiple SSPs who are plugged into their wrapper solution. ![]() As the growth of header bidding and wrappers climbs, the infrastructure costs have also increased as SSPs and DSPs are bombarded with ever more traffic. Header bidding and wrappers have pretty much redefined the ad-tech supply chain. ![]()
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