Tired of searching for profitable traffic sources? Pausing placements and adjusting bids taking too long? Thanks to years of development (and bunch of smartass data scientists) we’ve solved all your problems at once, by creating our Auto-Optimization feature. It’s a free to use, built-in feature for Native ads optimization in the Voluum DSP platform.

Voluum DSP is a high-performance platform oriented towards getting your native campaigns the best payouts. Thanks to exclusive traffic from Revcontent, MGID, Outbrain, Polymorph, and Liveintent among many others you can easily run campaigns on multiple Ad Networks at the same time. Moreover, we offer a huge variety of optimization features, giving you – the user – complete control over your spending strategy with full transparency in where your traffic is coming from. Additionally, we focus on satisfying performance-oriented advertisers, where we know, each conversion counts. Therefore not only can you fully adjust your campaign targeting to your specific needs, but you can also support your campaigns with our Auto-Optimization machine learning algorithm which optimizes campaigns for you.

So what exactly is Auto-Optimization and why is it so awesome?

The usual journey of any media buyer is a cycle of running a campaign, making mistakes, learning from said mistakes, adjusting targeting options and repeating. This cycle can equate to thousands of dollars spent just to “probe” the traffic and collect whitelists.

The ultimate weakness of this cycle is how much time we waste on the testing process. Each action is supported by hours of checking reports and analyzing which traffic segments bring the best results.

This step by step optimization revolves around analyzing traffic segments which are responsible for campaign performance. An example of traffic segments for a campaign is for example “sites” as displayed below:

In the above diagram, we see a simplified form of the optimization process where the advertiser will pause Site X and Site Z for not delivering optimal results.
In the below diagram, however, we can see extended granularity by adding “Browser” type to each “site”. Instead of pausin ...

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