Solving the Remnant Inventory Problem
By Ben Barokas
As an industry, it’s taken us more than 15 years to get a grip on dealing with remnant ad inventory, but I think there’s finally a light at the end of the tunnel. Over the past two or three years in particular, the R&D that’s gone toward solving this problem has begun to drive substantive results for publishers, boosting eCPMs and easing the burden on their perpetually overworked ad ops teams. We’ve moved far beyond the static daisy chain into real time optimization, but how does it all work, and what are we doing to stay ahead of the problem?
At its core, optimization is about “peeling off the layers” to reveal a clearer picture of what every impression is worth. Doing this at scale means accounting for a laundry list of ever-shifting variables (discrepancy, frequency, fill, user, content, geo) across countless sources of ad demand—and the problem isn’t getting any easier. The good news is, we’re on the cusp of another phase of innovation. Between Real Time Bidding (RTB) and a host of data infusion techniques, premium publishers in particular are poised to reap gains proportionate to the high quality of their content and audiences.
4. Creating Your Ideal Network Portfolio
Analyze your site
Optimize your Understand
portfolio network inventory
Integrate and
Find the right mix
prioritize
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Let’s use a simple example:Network A – Acerno, AudienceScience, typical behavioral network. High CPM but low fill. Network B – Typical network player like ContextWeb, Dotomi who has high CPMs and decent fill.Network C – RTB, done through APIs, not tags.Network D – Fixed deal on fixed frequency. Typical of interclick, specific media, intercept, etc.Network E – “Catch-all” no geotargeting restrictions, will take as much traffic as you give it, typical of AdSense.
1) Discrepancy= difference between what a network should be paying for and what they’re reporting they should be paying for.2) Frequency= the number of times a user has been shown an ad from a given network.3) Fill=the % of impressions we send to the network that they accept.
Latency primarily comes from latency of latency of the web in general, latency of ad networks, and ad servers. Often, legacy servers weren’t built to handle the high volumes and complexities (such as multiple passbacks), and have a hard time keeping up.
1) No discrepancy for RTB tag because RTB networks pay off AdMeld’s numbers.
1) Each network has its own optimization engine that chooses to serve the highest revenue ad first. On the second impression, a lower payout ad. As you move down the chain towards CPC and CPA deals, the payouts get even lower.
Tag A – In this example, our system applies a 60% frequency factor because this network has seen this user a couple of times already.Tag B – This network hasn’t seen this user yet, but they’re saying $1.30 is the average across a whole user session. Therefore, our system assigns a 120% frequency factor to valuate the first impression.Tag C – RTB, so this is what they’re actually paying, no frequency factor is applied on our end.Tag D – Fixed deal with 3x24 freq cap. They calculated the curve on their end so no need for us to apply a frequency factor to it.Tag E – This dbn
1) All the viable possible chains for this example
1) Pairing them reveals fill rates for each chain
1) Combining those fill rates with our valuations after calculating the frequency factors reveals the true expected value of each chain. AdMeld picks the one with the highest expected value.
The “winning” chain has an expected eCPM of $1.16, which is ~150% higher than the “common” choice, which is to pair the highest priced tag with the highest fill tag.