The publisher has an average timeout of 12% on different bidders. Its global prebid timeout parameter is 2000ms.
The publisher doesn’t want to increase the timeout parameter too much as he is afraid this would decrease the viewability of his inventory, and he also wants to avoid increasing his page loading time.
He decides to use Pubstack to optimize his timeouts without affecting his viewability nor the user experience.
With an average timeout rate of 12%, this publisher loses between 10% and 15% of its annual programmatic revenue which represents around 200k€ and 300k€.
The first step in this optimization process is to identify the source of these timeouts.
How do we spot it ?
With granular data, the publisher is able to identify very precisely the source of these timeouts.
|Share of voice||Avg. timeout rate per bidder|
We can clearly see that a large part of the said timeouts can be attributed to mobile traffic.
By increasing the timeout parameter in Prebid by 700ms, only on mobile, the timeout rate decreases to 10% on average.