Banner Ad: Please Fix Your Pacing Algorithm
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Tag Archive for campaign optimization

Which are the main challenges in real-time bidding facing Demand Side Platforms (DSPs) today?

[For demand side platforms,] is it the optimization of the bids, the allocation of budgets, managing potential conflicts between advertising campaigns from multiple customers and buying data? Or is it more related to other issues such as customer relations and getting ad networks out of competition?
This question was asked on Quora.com, below is my answer.

Demand Side Platform (DSP) ChallengesMature Demand Side Platforms (DSPs) have conquered the primary requirements to being in business in the online ad space, including: campaign pacing, optimization of bids, campaign goals and budget allocations.  The old guard is now well established.  New DSPs, presumably with novel approaches to the market, may encounter some of these basic challenges.  There are a lot of examples they can look at in the market for guidance. Read more

Using Proportional Control for Better Campaign Pacing

My post was originally published on the Rubicon Project blog. It was written with contributions from Dr. Neal Richter and Jonathan Zhuang.

Pacing algorithms come in a few basic forms at the Rubicon Project. The most basic is one called “as fast as possible” which can hardly be shown to do anything that resembles pacing. The Pacing controller is supposed to spread out impressions served for a campaign throughout the day. In general it takes the goal of impressions to serve in a given day as input and calculates a serving schedule for the campaign.

Estimated Curve of Available Impressions

A naive pacing algorithm will break the day up into 24 segments, let’s call them hours. It will allocate equal amounts of campaign impression for each hour and then recommend the campaign get impressions until the hour’s allocation is exhausted. This algorithm has a couple of pretty big flaws. Primarily it tends to serve the campaigns at the beginning of each hour and then once the allocated impressions are used up it stops serving until the next hour. The second flaw is that it has no understanding of the traffic distribution throughout the day. The 1AM hour doesn’t have the same amount of traffic coming in as the 10AM hour. If the inventory is relatively scarce the campaign will under-serve during the early hours, catch up during the peak hours, and then under-serve again in the later hours. Ultimately campaigns using this algorithm may not achieve their goals at the end of the day and it won’t serve evenly in a given hour. Read more

The Real Problem with Getting More Spend Online

Two weeks ago I participated in a couple of events during AdWeek in New York.  The first was Rubicon’s own #letsfixit event where agency advertising representatives joined a panel with publishers and the rest of the demand train to discuss the difficulties of running ads online.  The attention shifted from learning about the process to learning about where the process fails.

Familiarity

Spend OnlineBernhard Glock of Medialink, who was not on the panel, but chimed in with a profound thought.  Buying on television is buying something that’s familiar, with seemingly known or at least comfortable impact and expectations on results.  By contrast, buying online display is a complete mystery.  His solution, just buy on television because that’s what he understands.

In addition to all the great insight each panelist shared, this sticks out as an important point of the event.  If there’s something to be learned by this it’s that the Kawaja diagrams are illustrating the point – online is confusing to the buyers, and that’s bad.  Read more