How Google Ads Quality Score drives your cost per click and what to do about it?
I was recently preparing my curriculum for a Performance Marketing course, at Hochschule für angewandtes Management Ismaning and I had to find an appropriate way to explain Google Ads Quality Score and how it is computed. Below you can see how I ended up doing it.
What is Google Ads Quality Score?
How does Quality Score impact Cost per Click prices for your ads?
How do Quality Score factors contribute to the final score?
What is Google Ads Quality Score?
Quality Score (QS) is Google’s simplified assessment (on a scale form 1-10) of three factors which reveal performance of your ads.
Ad Relevance: How well does your ad fit the keyword(s) you are targeting.
Expected Click-Through-Rate: The likelihood of your ad being clicked based on previous performance.
Landing Page Experience: How well does your landing page deliver towards searcher’s expectations (e.g. page load speed or content)
Each of those factors is classified as being either above average, average or below average and the combination of these factors finally results in a score between 1-10.
Example:
How does Quality Score impact Cost per Click prices for your ads?
As you might already know Google’s Paid Search placements are basically auctioned to every advertiser. This is based on QS and maximum price advertisers are willing to pay per click. So let’s try to dive a little bit deeper on the auctioning and what Quality Score’s role is here.
Let’s assume we have five different advertisers, bidding for top of page on the same keyword using same targeting options. First thing to determine is the advertiser’s individual Ad Rank (Google orders all ads for the auction) .
Based on Ad Rank all five advertisers are sorted as shown on below table:
Next thing to identify is the actual CPC to be paid for a click. As you can see the Max CPC is not necessarily what you will end up paying. Like with traditional auctions, you will not always pay what you would be willing to spend as your personal maximum.
The actual CPC is defined using this formula:
In the above example only advertisers one to four will be shown on Google (as there are only four ad placements on top of Google’s SERP). An important note to be taken is the fact that advertisers might end up paying different prices per click and those don’t always correlate with their ranking position. You can see advertiser one is being shown on position one and is paying the cheapest CPC. Did you already identify why this is the case? Yes, Quality Score is the reason for observed pricing. The higher your Quality Score is (denominator for actual CPC formula) the lower your actual CPC will end up being.
So hopefully we can agree so far, that Quality Score is a relevant factor for successful and cost efficient Paid Search execution. In order to emphasize this, see below chart it is showing normalized CPC prices for 2.7M clicks across various ads and campaigns. As you can see the higher your Quality Score is, the less money you will end up paying on average per click. Quality Score 5 is an outlier here. This is because QS 5 is the average score (across all ads for all advertisers), so most ads have this score and therefore we can observe a higher competitiveness (which leads to higher CPCs).
* All individual CPCs are devided by the maximum CPC in given dataset.
How do Quality Score factors contribute to the final score?
Taking into consideration that everyone’s time and monetary resources are limited, it is important to know where to focus first. Is it Ad Relevance, Expected CTR or Landing Page Experience? Based on over 6k Ads and a timeframe of 24 months I have reverse engineered the importance for each of the Quality Score factors. The result is not really surprising but worthwhile exploring. In order to understand impact of the individual factors I have used a linear regression to compute Quality Scores for all 6k Ads.
The result looks as follows:
QS = 1.15 + 1 x Ad Relevance + 1.57 x Expected CTR + 1.83 x Landingpage Exp.
Please note this equation might differ for your data as it seems to be influenced by devices and audiences (keywords) you’re advertising to. But after playing around with the data, one thing always remained stable. Landing Page Experience always has the highest impact, followed by Expected CTR and than Ad Relevance.
For those of you that did not enjoy linear equations in school, let me give you the quick summary of this equation. Let’s map the values below average, average and above average to numeric values (0,1,2):
Below average => 0 Average => 1 Above Average => 2
Example: Your example ad has a Quality Score of 1, which means all your three factors are 0 (below average). QS = 1.15 + 1 x 0 + 1.57 x 0 + 1.83 x 0 = 1 (rounded to a whole number)
Now we change one factor (Ad Relevance) to be 1 (average) QS = 1.15 + 1 x 1 + 1.57 x 0 + 1.83 x 0 = 2 (rounded to a whole number)
So increasing Ad Relevance by one step (below average -> average) results in a Quality Score increase of one. For Expected CTR this change would mean QS + 1.57 (rounded to 2) and for Landing Page Exp. QS + 1.87 (rounded to two). As Quality Scores are shown on a scale from 1-10 using whole numbers you would roughly end up having the following increase of QS for optimization of your individual factors:
So if you want to increase your Quality Score to save some money for your clicks, you should rather try to optimize your Landing Page Experience first, before trying to squeeze out the maximum of your Google Ads wording. Although you should also consider the individual effort you have to spend for those optimizations. Changing your ad texts might be way easier than changing your website’s architecture to increase your page speed (one of the relevant factors for Landing Page Experience).
So our results in a nutshell
What is Google Ads Quality Score? A value from 1-10 (constituted of the three factors Ad Relevance, Expected CTR and Landing Page Experience) which is intended to help advertisers understand performance of their Google Search Ads.
How does Quality Score impact Cost per Click prices for your ads? The better (higher number) your QS is, the less money you end up paying for your average click and the higher you will rank.
How do Quality Score factors contribute to the final score? The three factors all have different importance for your final score*. Ad Relevance (23%), Expected CTR (36%), Landing Page Experience (42%)
* Based on 170k ad and month combinations analyzed for this post.