Is there a key formula for measuring the value of Quality Score?
Magali Kajingu Enciso
advanced

Is there a key formula for measuring the value of Quality Score?

Here we are, diving into the second part of our series specially dedicated to Quality Score! In our previous installment, we exposed how important it is to fully understand a metric in order to extract relevant insights from it.

Since Quality Score is quite a controversial and sometimes misunderstood metric, we’ve decided to conduct a deep search to shed light on those grey areas. If you’re a PPC expert looking to make the most out of this metric, keep reading to discover everything about the rest of our findings!

Unveiling the secrets behind Google Ads Quality Score

Let’s start with a quick reminder of the mysteries already solved in the first part of this series. 

One of our first priorities was to set the basic definition of Quality Score, to ensure we were all on the same page regarding the superstar of this research.  We agreed on the following one - aka, the official one: “Quality Score is intended to give you a general sense of the quality of your ads. [It] is an estimate of the quality of your ads and the landing pages triggered by them.” We also agreed on the different elements composing it, the value they could take and the range of the final scoring. 

Then it got a little more complex... but also very interesting. We tackled two of the main issues about Quality Score. First, we addressed the lack of historical data about Quality Score, preventing PPC experts to lastingly observe the effects of their actions. We exposed how our findings lead us to create a powerful tool to help you make enlightened and more precise strategic decisions about where to invest your resources in terms of Quality Score optimization. The second issue we managed to solve was the limited span of analysis of this metric offered by Google. Now we know that it is possible to batch the values of the scoring in different clusters for a faster identification of variations across different levels. If you want more details about those concepts, don’t hesitate to take a look at our previous article Unveiling the secrets behind Google Ads Quality Score.

Now let’s move on to the next questions raised among industry experts. And let us tell you, these ones are probably among the most hot topics regarding Quality Score.

What is Google Ads Quality Score formula?

Is there a magic formula to determine the value of Quality Score? This is a question you probably already asked yourself at least once. And many articles, papers and other sources made you believe they had answered you. Does the following formula look familiar to you? 

Quality Score = 1 + Landing Page Experience + Ad Relevance + Expected CTR

Well… Let us tell you right away: it’s wrong. Our data team conducted an extended research, observing and collecting information from more than 100 of our clients accounts over 2 years. They applied that formula to every set of data and guess what? It didn’t work. The results show that this formula is incapable of computing correctly the value of the Quality Score. However, they did find that although there is no magic formula establishing a fixed link between Quality Score and its components, one can observe a constant relation between the values those elements can take

Every same combination of the possible values of each component always leads to the same Quality Score final result.  Let us clear that up with an example: if the value of your expected CTR, your Ad Relevance and your Landing Page Experience is “below average” for all three, the value of the Quality Score will always be 1. Knowing that, it was easier for our researchers to come to the following conclusion: based on the 27 possible combinations, it is possible to compute Quality Score knowing the value of its 3 components.

How is Quality Score related to Ad Rank?

As far as formulas are concerned, our findings don’t stop here. Before diving into the existence (or not…) of a formula linking Quality Score to Ad Rank, we wanted to clear up some misconceptions about those two metrics.  

Let’s replace each one of them in the right context. Ad Rank is calculated “instantaneously”, in the moment someone does a search that triggers your ad to compete in an auction. It takes into account real-time signals (query or user context for example) to give precise measurements of expected CTR, Ad Relevance and Landing Page Experience. Quality Score on the other hand, is calculated retrospectively, and gives you an estimate of the quality of  your ad based on your average past performance.  

With that being said, we understand that these two metrics are from different “nature”. And  we know from official documentation that Quality Score is not used at auction time to determine Ad Rank. But that same documentation mentions that quality factors, among other elements, are taken into account to compute Ad Rank. What are those quality factors then? Is Quality Score included in the list of those quality factors? That’s what we’ll try to verify.

Many sources claim Ad Rank can be defined as following: 

Ad Rank = CPC x Quality Score

The truth is that now that Google withdrew all information about the Average Position, it is even harder to prove that formula. To compensate that missing information, our data team used an estimate calculation setting Absolute Top Impression Percentage = Top Impression Percentage, assuming  Ad Rank (or the ad position, to be more precise) was a constant, to check if CPC and Quality Score values were inversely proportional. The conclusions of their observations show that there is no relation whatsoever between those two factors. And still, Google does affirm that quality factors are considered for Ad Rank computation, nurturing an ambiguous atmosphere around this metric.

As we’ve seen, there are many assumptions and attempts to frame Quality Score in a single magic formula capable of computing its final value based on other associated metrics. But the scientific validity tests and computations enlighten our understanding about the complexity of these metrics and show us that the reality is quite different. So, maybe we want to be careful when manipulating such formulas from now on... 

If this article sparked your curiosity and you’d like to know more about us and how we can help you take data-driven decisions regarding your ad campaigns, don’t hesitate to get in touch with us! And stay tuned, there are more revelations to come… 

We use cookies to provide the best experience. By continuing to use our website, you agree to our cookies policy.