The deep understanding of publishers' ad stacks made possible with Pubstack, allowed in this case, to identify that Prebid was not bidding on auto-refresh ads, thus impacting revenue directly.
For one of our publishers, Prebid was not bidding on auto-refresh ads. This was directly impacting his revenue because the competition on refresh ad requests was only taking into consideration the bids from GAM.
Through Pubstack’s granular data, we were able to notice an abnormal fill rate.
In Pubstack, we consider that the number of auctions in Prebid equals the number of auctions in GAM. In this case, we were seeing a fill rate of more than 100%. We were then able to conclude that this fill rate was due to the fact that GAM was bidding on more auctions than Prebid.
This led us to understand that Prebid was not bidding on auto-refresh ads, thus impacting the publisher’s revenue directly.
Pubstack’s support team helped fix the publisher’s code which allowed Prebid to bid on auto-refresh ads again and increase revenue.
Increasing competition on every refresh ad request had a direct impact on the Publisher’s revenue. We could attribute a 2.5% uplift in annual programmatic revenues to this optimization.
⇒Publisher’s annual programmatic revenues: 1.800.000€
⇒Annual programmatic uplift= 1.800.00€ x 2.5% = 45.000€
And this just one optimization out of several others Pubstack allows you to achieve.
To complete your knowledge on setting up a proper Header Bidding strategy, we advise that you continue reading with this Business Case : Header Bidding Integration issue.
Finally, if you’d like to know more on Pubstack’s capacities when it comes to troubleshooting, we’ve interviewed Webedia France’s Ad Ops Director Alban Clochet to explain how our platform has allowed them to unleash their ad stack’s full potential : Use case : Webedia.