How Google’s Search Ads are Ranked and Priced on Google

For those that have been advertising on Google Ads for the past decade we’ve all likely been exposed to google’s explanation for calculating ad rank.

The quick explanation for most agencies and advertisers has been the following: ad rank = max cpc x eCTR

In other words your ad placement is based on the cost per click you set at the keyword level (This you can control with manual bidding) multiplied by what Google deems your expected click-through-rate (How likely users are to click on your ad).

It goes without saying that more recently regulatory bodies and the anti-trust law has been catching up with Google’s overall control of the advertising business. The CMA (Competition & Markets Authority) requested information back in February 2020 and more recently the briefing paper has been submitted for public filing. - Yay!

What does the public filing teach us about Google’s pricing?

First off, I’ll start this section by saying I took the time to read every detail on their submission however, without writing something that would be a literal multi-page essay, I’ve decided to consolidate what stood out and was missing from Google’s typical decade old explanation of ad rank = max cpc x eCTR.

Understanding Ad Rank

Advertisers all over the world hope that when they run ads on Google they are getting the best position in the SERP (Search engine results page).

Ad rank - Google’s system for determining an advertiser’s ad position, provides us a completely more in-depth view of the variables that impact an advertiser’s overall position.

Google used the example of the search query “insurance” for a bunch of insurance providers advertising on Google.

This is how they describe ad rank works: “Google identifies a few hundred ads in Google Ads system that are likely to be highly relevant to the user (we weed out, for example, ads that are unrelated to insurance as well as insurance-related ads that are ineligible to show).”

This is Google’s further description for how ads could be “weeded out”:

“Ads can be weeded out for a wide variety of reasons, such as a mismatch between the language of the query (English) and the language of the ad (French); a mismatch between the advertiser’s location settings (targeted at Los Angeles) and the user’s estimated location (San Francisco); the exhaustion of the advertiser’s daily or monthly budget; the expiration of the advertiser’s credit card; or a violation of our ads policies.”

Our synopsis for Ad Rank that we didn’t know previously:

Google then sends these ads to a system they call “Ad Mixer” that then assigns a score to each ad. This score is known to Google internally as “Ad Score” or “Long-term Value (“LTV”) Score.”

Ultimately, ads with a high enough LTV score are able to show on SERPs. As we all may assume these ads are then ranked on the SERP based on the ad rank score they each receive.

Google also confirms what they continue to teach advertisers (If you have ever worked with a Google Representative). LTV or ad score is heavily influenced by the advertiser’s bid, quality of the ad creative, and the quality of the landing page. Ad Score is a function of ensuring as the advertiser you are sending the user to the best quality ad and user experience.

Why is the Long-Term Value Important For Ad Rank?

This is likely the best take away from Google as they go on to describe the calculations for LTV and eligibility of ads showing or not showing. If Google did not have a function for LTV and ad score. They would be at risk in the short term of receiving revenue and clicks from users at the expense of longer-term profitability. For example, showing irrelevant and low quality ads which over time would condition users to look over them because of their irrelevance to their desired search experience.

Significant Changes Also Mentioned For Ad Rank

Google mentioned since January 2019, one significant improvement to Ad Rank was to allow thresholds to be dynamically set for each auction base on the threshold of closely-related auctions. This allowed their systems to optimize an advertiser’s ability to show its ads in related auctions for similar queries.

They also launched a randomized general second-price auction (rGSP) that will continue to set the stage for more machine-learning based auction dynamics that will have a profound impact on advertiser’s ability to show ads in auctions that would otherwise have had too high of a threshold.

In other words Google is creating more auction environments that allow them to make more money even if advertiser LTVs don’t keep up with increased competition.