Magnetic is an artificial intelligence company that operates a variety of AI-driven advertising solutions, including a fully-automated, AI-powered DSP. The machine decides who to target and how much to pay for an ad. This decision is based on Magnetic’s rich consumer profiles, and how well each profile “correlates” with a typical converter for one of our live campaigns.
Our machine learning methods have been performing exceptionally well since Magnetic embraced AI years ago. We brought more decisioning under our AI’s control over time, and significantly scaled back the manual parts of media buying.
The next step in Magnetic’s journey to full AI-automation is well underway. AI now pilots our exchange partners to control our media supply.
Ad impressions: too much of a good thing
Managing exchanges was still a highly manual task in late 2017 and early 2018. We had to hold monthly internal meetings to review QPS (queries per second) allocated to exchanges, blacklists, brand safety levers, potential publishers deals, and overall performance metrics.
Managing exchange-specific lists of preferred publishers became especially time consuming. The number of ad opportunities Magnetic received doubled while overall value stayed the same, thanks to header bidding and exchanges reselling impressions between each other. We applied short term fixes to optimize our path to supply, but they could not be our long term solutions.
Please, only send consumers we care about
Last October, we committed to largely automating supply management by 2019.
We reached out to our exchange partners to see if they could “pre-filter” ad opportunities based on rules generated by our AI. Enough of our existing exchange partners had such whitelisting capabilities to reach critical mass. None, however, had done pre-filtering at such scale.
Our first step was to identify all consumers our AI may target. We maintain about 350 million consumer profiles for North America. Each exhibits human (i.e. repeated and consistent) behavioral patterns over the last 330 days – such as searching multiple times for travel related terms across one of 350K+ partner sites.
We trimmed down the user whitelist in 2 ways. First, we used a probabilistic model to exclude low value profiles unlikely to be considered by at least one of our running campaigns. Low value users account for less than 5% of predicted conversions, but represent the majority of traffic.
Second, we customized the whitelist for each exchange by removing unmatched users (those who do not have a linked cookie with the exchange).
Then, we extended each whitelist to include device IDs belonging to any of these target consumers, as per our cross device graph. Because in-app conversion measurement is unreliable, we only target mobile app users if they are predicted to convert on a website.
The final lists of 500MM+ cookies and device IDs was synced with each exchange. Time is of the essence here, as we update our profiles continuously and the value of any newly added consumer can tail off in a matter of minutes.
The tweaks that matter
More tweaks were needed to ensure maximum performance.
First, we still needed to receive some unbiased traffic to continuously A/B test the performance of our whitelisted traffic.
Second, we had to whitelist placements that generated a high level of clicks. We run a fair amount of click-optimized campaigns, and we did not want to miss out on some high-CTR domains even when the consumer is unknown to us.
For a similar reason, we chose not to filter video traffic. Most video campaigns are optimized toward completed view, and the context of the ad often matters more than the consumer seeing the video.
Spiking win rate
As soon as an exchange enables supply shaping, we saw a significant hike in gross win rate (the win rate based on all received ad opportunities). Some of these gains eroded as we launched the tweaks described above to preserve clicks. But in general, gains have been holding pretty well.
Such gains translated immediately to cost saving, since processing ad opportunities is the biggest infrastructure cost.
But more importantly, for a company like Magnetic, supply shaping enabled us to remove the QPS cap entirely and address the entire internet. For us, this is the guarantee of delivering maximum possible conversion rate to our clients.
Exchanges must embrace DSP’s supply path optimization
We are slowly rolling out supply shaping and consumer whitelisting to all of our exchange partners that can support it. In 12 months, Magnetic will no longer do direct business with any exchange that does not have adequate supply shaping capabilities.
Magnetic may be a pioneer, but I expect most media buys to be AI-driven in a few short years. Exchanges will need to beef up their capabilities to enable DSPs to optimize their supply path.
Magnetic is an artificial intelligence company that uses machine learning to deliver smarter, faster, and more effective advertising. Our powerful AI platform continuously analyzes the attributes of over 350 million live user profiles alongside real-time inventory supply and bid opportunities to deliver highly performant audiences and profitable campaigns for our clients.