I've written some follow-up posts to this one, as a series called Peeking into the Black Box, so I'm grandfathering this post in as "Part 0" of the series.
I read an interesting post on the AppNexus tech blog about their campaign monitoring tools and the screenshots there almost exclusively contained various pacing measurements. Some of the graphs there looked a lot like the ones I had sketched up while trying to solve the pacing problem for our real-time bidding (RTB) client. Here are the basics of the problem: if someone gives you a fixed amount of money to run a display advertising campaign over a specific time period, it's generally advisable to spend exactly that amount of money, spread out reasonably evenly over that time-period. Over-spending could mean you're on the hook for the difference, and under-spending doesn't look great if you want repeat business. And if you don't spend it evenly, you'll get some pissed-off customers, like this guy who had his $50 budget blown in minutes. Sounds obvious, right? Apparently it's harder than it looks!
Doin' it wrong
When Datacratic first dipped its toe into RTB (real-time bidding, characterized in this sister post), the main tool at our disposal to control budget/pacing was a daily spending cap. It took about 48 hours for us to figure out that that wasn't going to work for us: depending on the types of filters we used to decide when to bid at all, we'd either not manage spend the daily budget (more on that at the bottom) or blow the budget very quickly, usually the latter. In practice this meant that we would start buying impressions at midnight (all times in this post are in Montreal local time: EST or EDT) and generally hit our cap mid-morning.
On a daily spend graph over a 30-60 day period resulting from such a policy, this would probably look just fine: you're spending the budget in the time given, and spending exactly the right amount per day. But having this sort of 'dead time' where we don't bid at all bothered us, so as soon as we spun up our own bidder we added options like "bid on X% of the request stream" and when we looked at the resulting data we noticed something really interesting: the impressions we were buying before were actually among the most expensive of the day! The average win-price graph below (i.e. what the next-highest bid was to our winning one) from a high-bidding campaign shows a pretty clear pattern: if you start at noon, the dollar cost for a thousand impressions (the CPM) generally drops until precisely midnight, then jumps something like 40%. It fluctuates a bit, hits a high in the morning and then drops down again until noon to start the cycle again. This pattern repeats day after day:
So what's with that midnight jump? This is supposed to be an efficient marketplace, but are impressions a minute past midnight really worth 40% more than impressions 2 minutes earlier? Had we not had our initial experiences described above it might have taken us a long time to puzzle out! Our current working theory is in fact that there must be lots of bidders out there that behave just like our first one did: they have a fixed daily budget and they reset their counters at midnight. This would mean that starting at midnight, there's a whole lot more demand for impressions, and thanks to supply and demand, the price spikes way up (alternately, odds are that at least one of the many bidders coming back online is set up to bid higher than the highest-bidder in the smaller pool of 11:59PM bidders). And as bidders run out of budget throughout the day, at different times, depending on their budget and how quickly they spend it, the price drops to reduced demand. It's not obvious from the above graph but when you plot the averages over multiple days together there is a similar step at 3AM, when it's midnight on the West Coast, where a lot of other developers live and work and code:
Now there might be some other explanations for this price jump. We're in the Northeast and that impacts the composition of our bid-request stream, so maybe this is just a regional pattern (do other people see this also?) although the 3AM jump suggests otherwise. Midnight also happens to be when our bid-request volume starts its nightly dip, but that dip is gradual and isn't enough to alone explain that huge spike. Maybe some major sites have some sort of reserve price that kicks in at midnight. Or maybe everyone else is way smarter than us and noticed that click-through rates are much higher a minute past midnight than they are a minute before. I have trouble believing those explanations, though, so if anyone reading this has any thoughts on this I'd love to hear about them by email or in the comments below. Whatever the reason, it looks like if you're going to only be bidding for a few hours a day, don't choose the time-period starting at midnight and ending at 10AM!
Closed-Loop Control to the Rescue
So let's assume that this post gets a ton of coverage among the right circles and that this before/after-midnight arbitrage opportunity eventually disappears, or maybe you just want to actually spread your spending evenly throughout the day, how would you do it? As I mentioned above, we have the ability to specify a percentage of the bid-volume to bid on. The obvious way to use this capability is to see how far into the day (percentage-wise) we get with our daily budget and bid only on that percentage of the bid-request stream, thereby ensuring that we run out of budget just as midnight rolls around. In practice, though, bidding on a fixed percentage of all requests can and does result in a different amount spent each day, sometimes higher than the daily budget (if you remove the daily cap) and sometimes lower. Maybe some days you get a different volume of bid-requests, because your upstream provider is making changes. Maybe there's an outage. Maybe the composition of your bid-request stream is changing such that all of a sudden the percentage of all bid-requests that's in the geo-area you're targeting goes way up (or down). Maybe your bidding logic doesn't result in a consistent spend. All of these factors could amount to a pretty jagged spend profile:
So what's the solution? For us it's to look at this problem as a control problem and to move from open-loop control to closed-loop control. If you theorize about the appropriate bid probability, set it up and let things run for a week and look at the results later, that's basically open-loop control. If every morning you look at what the spend was the day before and adjust the coming day's bid probability accordingly, you're doing a form of closed-loop control: you're feeding back information from the output into the input. It's pretty easy to automate (at least we thought so), and once that's done, there's basically no reason not to shorten the feedback loop from daily to every few minutes. Our RTB client was designed from the ground up to facilitate this sort of thing so we were able to control spend to within 1-2% of target with the following simple control loop: every 2 minutes, set the bid probability for the subsequent 2 minutes to that which would have resulted in the 'correct spend' over the preceding 2 minutes (maybe with a bit of damping). So if for whatever reason we get 20% fewer bid requests, for example at night, the bid probability will rise so that the spend rate stays roughly constant. There are more sophisticated ways to do closed-loop control, like the PID controller which I hacked into my espresso machine, but this really basic system works pretty well:
Like any good optimizer, the scheme described above does exactly what you ask it to: it tries to keep the error between 'instantaneous' spend rate and the target as close to zero as possible. If there's a full-on outage where no bidding occurs for an hour, this system will not try to make up the spend, it will just try to get the spend rate back to where it should be according to the originally-specified target, once bidding resumes:
In order to deal with outages, as well as over- or under-spending due to accumulated error or drift from the instantaneous control system's imperfect responses, we have to dynamically adjust this spend-rate target as well. The way we've come up with to deal with this is to define the error function in terms of the original problem: continuously work to keep the spend rate at a level that will spend the remaining budget in the remaining time. This means that if there is an outage for an hour or a day, the spend rate for the rest of the campaign will be marginally higher to make up for it, without human intervention. This system also has the nice property of automatically stopping right on the dot when the remaining budget is zero:
Now obviously you might not want to always have exactly the same spend rate all the time, in fact I almost guarantee that you don't. A closed-loop control system can support pretty much whatever spending profile you like. You can also use one to manipulate other variables than the bid-probability (think: the actual bid price, or the targeting criteria, or whatever other scheme your quant brain can conjure up). Finally, the astute reader will notice that the approach described above is only really useful in situations where there is more supply than your budget permits you to buy, and so you need to allocate your budget over time. The approach above won't help at all if the optimal bid probability to meet your needs is up above 100%, that is to say that even when you bid on and win everything that matches your targeting criteria, you're not able to spend your budget. That's probably a topic for a different blog post.
Follow-up: I also posted this question on Quora, where I got a few responses.
Follow-up posts: This post is now Part 0 of a series I'm calling Peeking into the Black Box. Check out Part 1 and Part 2 of the series, where I show how to really take advantage of other bidders' behaviour.