A/B testing experiments on your Header Bidding setups

Importance of header bidding in Advertising: Header Bidding is the most important AdTech enhancement in recent years since RTB. This helped publishers to maximize the the bids they would receive for their most important inventory. With so many modules at our disposal, this is a good time to dive down to different experiments we can perform on Header Bidding.

What is A/B Testing? Why it is helpful?: A/B testing (or Split Testing) is a process of comparing two or more variants simultaneously under the same conditions and limitations. We can think A/B testing as a battle of ideas on a neutral ground. This is a especially helpful if you want to compare performance or test your hypothesis. For every A/B Test we must define the reason behind this test, the outcome expected out from it & a metric to compare it against. Also the traffic that gets associated to this experiments should be truly random. A lot of these are prerequisites for a successful analysis. A/B Testing today will be at the center of our experiments and exhibits perfectly why it’s so important.

Floor Price A/B testing : Predbid.js allows floor modules to setup floors based on Adunits, creative size, domain, etc. This can be especially useful if the publisher expects a minimum floor CPM to his inventory. But testing multiple floors can be a cumbersome process to bare. Using custom analytics adapter, publishers can perform A/B testing on multiple floors and test in real time the performance of the rates they set. Tercept’s Analytics Adapter further saves log level data that can be used to analyze data at bid level. Publisher could take advantage of this to understand which header bidding partners adjust to change in CPMs and by how much. Furthermore, using Key-Value Pair the adapter will be able to pass values custom values to the Ad Manager that can be used to compare performance against various different dimensions.

Partners A/B Testing: Today we have loads of header bidding partners to choose from, statistics show that the more number of partners you have integrated the more yield you can potentially generate. But this in itself presents another major problem to the publisher; delayed ad loads that could lead to lower revenue. There is another problem of user experience if the page doesn’t load in certain amount of time. A/B Testing against your best partners could be a good solution to understand the top partners you should be working with, you could potentially rule out partners giving the lowest CPM or taking more page load time that could directly affect the user experience.

These experiment are primarily performed with an idea to facilitate the publisher to take easier decisions.

Author
Tejas Potti
Business Analyst, Tercept

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