Our first survey was a success! First, we’d like to thank all 43 respondents for their participation. Below are the results of the survey. Tomorrow I will post an analysis of the survey results; and on Thursday I will post the extended comments we received.

The attributes featured in the survey were culled mainly from two sources: Bill Slawski’s discovery of US patents filed by Google that discuss the AdWords ranking model; and Google’s official AdWords help section. The list of attributes that could contribute to the AdWords Quality Score has not been determined exactly but it could easily be quite a large number. However, taking into account our experience with the quality score, the ‘official’ information released by Google, and the recent US patents, we truncated the list down to what we believe are the 12 most important attributes of the overall quality score.

The 12 attributes of the AdWords Quality Score listed in the survey were ranked between “Very Important” to “Not Important” in relation to how each contributes to the overall quality score. A 5 point scale was applied to the rankings:

  • 1 point for “Not Important”
  • 2 points for “Fractionally Important”
  • 3 points for “Important”
  • 4 points for “Moderately Important”
  • 5 points for “Very Important”

The rankings were determined by the average response to each attribute.

And here are the survey results:


AdWords Quality Score Attribute


Very Important

Click-Through Rate: An advertisement’s click-through rate within Google’s search results page.


Moderately Important

Ad Text/Keyword Relevance: Static placement of keywords within an advertisement’s body copy and headline.


Moderately Important

Historical Performance: An algorithm that collects multiple quality scores for a single keyword in order to determine whether a keyword’s performance is increasing or decreasing.



Landing Page Keyword Relevance: The placement of core keywords on each landing page.



Keyword Bid Price: How much the advertiser is willing to pay per-click.



Overall Ad Group Performance: The tabulation of every keyword’s quality score within an ad group which determines the ad group’s score.



Predictive Performance: A calculation that predicts how a keyword will perform in the future by combining past, current and predicted quality scores.



Keyword Match Type: Using broad, exact and phrase match within your ad group for each keyword.


Fractionally Important

User Behavior in Relation to other Paid Listings: An algorithm that incorporates the number of paid listings a user visits before they take an action on your landing page.


Fractionally Important

Human Review: A review conducted by real-live Googlers who review and determine if an advertisement is relevant to a specific search.


Fractionally Important

Landing Page Conversion Rate: The number of conversions generated directly by a pay-per-click advertisement.


Fractionally Important

User Behavior in Relation to Organic Listings: An algorithm that incorporates the number of organic listings a user visits before they take an action on your landing page.