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Why I don't optimize for Quality Score

Published: March 27, 2013

Author: Susan Waldes


Larry Kim recently posted on the Wordstream blog about Quality Score having more importance than ever. Typical of Larry’s posts, the article was thorough, thoughtful, and compelling.

And I’m going to tell you why I’m ignoring it.

Quality Score is a Google-driven metric that is only loosely related to performance. Basically, it’s the wrong metric to chase – and on top of that, the metric itself is flawed. Let me break this down, bullet-point style. (Brace yourselves.)

Why is QS a flawed metric?

  • Google openly says that the “true” QS is more granular than the visible one.
  • Although Google says that QS is evaluated in real time, they don’t say that the UI-displayed QS is evaluated in real time, nor do they give the frequency with which the visible one is updated.
  • The above two points in isolation mean that whatever number you’re using to calculate “QS findings” has some flawed, inexact numbers for the QS itself. It’s fuzzy math, and I don’t need to tell you that’s one of the most toxic terms in SEM.

Why is QS the wrong metric to chase anyway?

  • It’s inarguable that QS and performance are related. There is a correlation. But the rigid assignment of any metrics showing lower CPCs is false data. Adding assumptions based on account data is faulty logic; it’s like saying, “Hey, there’s a correlation between higher-impression keywords and higher-conversion keywords” and coming up with numbers showing that driving more impressions on everything will get you X more conversions.
    Correlations are likely driven by your bidding strategy. So you’re combining flawed numbers with faulty logic and getting drivel.
  • If you smooth results with a big data set, the assumed correlation between higher QS and lower CPC is there. (Here comes the but.) But if it doesn’t work on the level of single keywords or keyword sub-sets, you’re just creating bigger pools of faulty data when you apply predictive numbers.

Here, by way of example of a false correlation, is a sub-set of my highest-performing keywords in a certain campaign. These are “super alphas,” in our Alpha Beta method of campaign structure (fill out a little form and download it; it’s worth it), which means they’re golden keywords that show in top positions 100% of the time. And they show a reverse correlation between higher QS and lower CPC or CPM:

reverse qs correlation

  • The stronger correlation is between higher CTR and higher QS…and this is so close that I propose the QS follows the CTR. In most cases (but not all), you should be aiming for better CTR anyway, so just go after CTR.
  • Except when: You have those cases when lower CTR is desirable – e.g. you’re eliminating unwanted clicks through exclusionary ad text focusing on high costs, OR you’re chasing tangential keywords with low CTRs but great conversion rates. If you’re optimizing for QS, you’re not using those highly valuable strategies.

The idea that QS is directly tied to true performance metrics is flawed. It’s like calories; it’s a correlated measure of something real that exists in a complex system of variables.

If you aim to lose weight, lowering calories is a good thing to do; likewise, increasing QS is a good tactic for secondary optimization benefits (more impressions, lower CPCs). But, like calories, QS is not formulaic. There’s some metabolism factor at play there, and just as you can’t say that eating 1,900 calories makes you gain half a pound, you can’t say a QS bump of 1 correlates to any specific lower CPC.

Yes, do the best practices (quick load times, good CTRs, highly relevant LPs). But understand that chasing QS – and especially creating flawed formulas on the impact – is a not only a waste of time but is selling clients on a bunch of hot air.

– Susan Waldes, Director of Client Services

1 S Wacker Drive
Suite 2250

Chicago, IL 60606(650) 539-4124


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