Ad Yield Ops 104: Building A Price Floor Strategy

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Now let’s talk about how to combine the fundamentals we learned about in the last video to create custom price floor rules for specific situations.

In order to both build a comprehensive price floor strategy, and to find ways to get around limitations on the number of unified pricing rules you can create, you need to change the way you’re looking at price rules.

We mentioned that they can’t be thought of as individual rules but instead as a set of interconnected rules that govern a comprehensive price floor strategy. Today, we’re going to show you how to do that.

First, we’ll break our strategy into two parts: AD UNIT BASE PRICES + TARGETING CRITERIA LEVERS.

The way to build this strategy is to think of your ad units first. You’ll set base floor prices for each ad unit that govern their “starting point”.

Then, you’ll view the targeting criteria as levers that adjust the ad unit’s base price. Each of the different options for targeting criteria should apply a different percentage increase or decrease from the base price.

From there you can simply multiply all of the criteria levers with base ad unit price to come up with a custom price floor rule for a specific situation.

This can be a complex idea in theory, but becomes very clear when we look at a specific example.

So, let’s demonstrate with an example (keep in mind that the numbers used are entirely random and simply used to demonstrate the concept):

We’ll start by setting “Ad Unit A” with a base price floor of $0.50 to begin, based on past performance.

Now we’ll use 2 levers in our example: Browser and Country.

Let’s say our options for browsers are: Google Chrome, Safari, Firefox, Android, and Microsoft Edge. We’ll assign a percentage that will increase or decrease the price floor from the ad unit base price based on each factor:

We’ll assign:

• Google Chrome: 110% (meaning that a chrome visitor actually increases the value of the view from the base price)
• Then, Safari: 75%
• Firefox: 75%
• Android: 85%
• And, Microsoft Edge: 90% (meaning that each of these browsers slightly decrease the value of the view from the base price)

Now, let’s tackle country. 

Because we have hundreds of options for countries, we’ll have to break the countries into groups in order to build our strategy. For argument’s sake we’ll break them into continents, and assign percentages which will increase or decrease the price floor from the ad unit base price:

We’ll assign:

• Countries in Asia: 75%
• Countries in Africa: 80%
• Countries in North America: 100%
• Countries in South America: 50%
• Countries in Antarctica: 2%
• Countries in Europe: 87%
• And, Australia: 90%

Now, to create a custom price rule for Ad Unit A being shown on a Safari Browser in a country in Africa, we’d simply multiply the correct items to get our custom price floor rule.

This leaves us with $0.50 X 75% X 80%, resulting in a $0.30 price floor rule for this specific combination of circumstances.

Now, if we are managing this in spreadsheet form, we also can easily build custom price rules for 35 different combinations of all of these factors (by simply multiplying out all the different combinations).

All of the sudden, we are looking at a comprehensive strategy that can be maintained centrally.

Say you want to change the percentage increase or decrease allocated to Countries in South America, because you are seeing ads performing well in that geo; now all you have to do is adjust the percentage assigned to it, and all of your corresponding rules that include Countries in South America as a targeting criteria can be updated simultaneously.

An important part of this strategy involves grouping targeting criteria.

Let’s cover how you can creatively try to work around some of the limitations on the number of custom rules you can create.

You’ve already seen this strategy in action in the example above, where we grouped countries together rather than setting percentages for each individual country. To lessen the sheer number of variations there are, you can group like criteria options together.

For instance, on the Browser example we previously covered, you’ll notice that both Safari and Firefox were set to 75%. If we view these two browsers as equivalent, we can combine them into a single criteria in order to reduce our total number of criteria options.

The breakdown would then look like this:

• Google Chrome: 110%
• Safari & Firefox: 75%
• Android: 85%
• Microsoft Edge: 90%

This now brings down our total number of rule variations we can create from just the combination of the two criteria of Browser and Country from 35 down to 28.

As often as you can, you will need to look for opportunities to group criteria options that are close enough together in order to maximize what you can get from your, at most, 200 allowed price rules in Google Ad Manager.