I recently read an interesting article that included a Bear Stearns projection on in-video advertising:
To quote the old guy in the Conan the Barbarian movie – WRONG!
In order to model out your CPMs, you should never ever do a straight calculation of:
Wrong revenue = CPM * impressions / 1000
The reason is that brand advertising is typically demand constrained – meaning that you need to field a big NYC-based sales team in order to do your sell, and as a result, you can only sell some percentage of your inventory. You can think of this process more like an enterprise sell, which scales revenue up with the number of sales folks you have.
Another corollary to this fact is that if you have a good CPM to start out with on one of these ad networks, don’t assume that the high CPM will continue as you scale up revenues. It’s easy to "tap out" ad networks, which gets you high CPM brand campaigns and turns into really crappy direct response campaigns.
Modeling brand ad sales as an enterprise sell
The right way to model out inventory is a number of equations – I’ll pretend that a site has two types of inventory, their "brand" stuff and their "direct response" (aka remnant) inventory:
Brand revenue = # campaigns sold * average campaign size * brand CPM
Direct response revenue = (total impressions – brand impressions) * remnant CPM
Total revenue = Brand + remnant revenue
In an actual forecast, you could get a ton more detail in the brand revenues side, since what you really care about is the # of ad sales people you have, how many campaigns they’re selling per quarter, the size, etc. Again, think of this as an enterprise sell, and treat it as such.
Similarly, if you were doing this for an entire site, you’d want more granularity. You’d approach this channel by channel, and do the CPMs and %s for each one. Inventory has different characteristics depending on ad placement, where you are in the usage of the site, and other factors. If you incorporate this into a grid, you can start to get a sense for how your different channels differ. That way, you can make the most accurate prediction possible.
Perhaps I’ll do a longer post on that at some point, with Excel spreadsheet attached ;-)
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