Voluum Documentation

Auto-Optimization: Use Cases

Voluum DSP DSP Campaign Optimization 

Below you can find examples of Voluum DSP campaigns running on Auto-optimization:

vlm1.png

The optimized segment - where our “auto-pilot” was applied remains profitable and at the same time the not-optimized segment is unprofitable.

Type of Auto optimization: iCTR.

Goal value: 0.35

vlm2.png

The optimized segment brings higher ROI than non-optimized.

Type of auto-optimization: iCTR.

Goal value: 0.2

So let’s dive deeper and check out how Auto-Optimization works on a daily basis for the following Voluum DSP Campaign:

Below, you can see a goal set  at iCTR – 0.32

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Campaign last 7 days performance:

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However, to better understand what actually happened, it’s better go on each day level:

vlm5.png

As you can see, the campaign has been created on 4th February 2019. However, after campaign creation, our auto-optimization model could not effectively optimize the campaign as there were not enough events to “learn” for the model. A sufficient number of events is usually between 100 and 400 (for iCTR such events are visits) so the campaign started to optimize itself on 4th day – 7th Feb. The goal value given by the advertiser was 0.32 iCTR and therefore, you can see a significant difference between the optimized and non-optimized traffic segment – 0.36% (optimized) to 0.17% (non-optimized) just within the first day.

Our model detected the most promising traffic sources on the combination below:

Application Bundle ID or Site (most promising), Creative ID, Connection Type, OS version and Device Model

Then, it started to apply higher bids for the most promising application bundle ids or sites (or decreasing bids if iCTRs were not met). You can see how adoption of the campaign looked like – in blue – data for optimized segment – in red – non-optimized segment:

Impressions & cost:

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Bid change:

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And finally - conversions:

diagram4.png

So in this scenario – optimizing placements with highest iCTR helped campaign get more conversions. Of course it can not be that obvious (high iCTR doesn’t always mean more conversions) but at least your campaign should limit spend on the lowest iCTR-placements.

Given that all adjustments above were performed without any manual bid adjustments, auto-optimization “pilots” your campaign towards your desired goal without you lifting a finger as soon as it has enough data to start.

To sum up, this example shows how we can reduce hours of campaign optimization via machine learning algorithms.