Beyond Average: An Analysis of Search Ad Position
An excerpt from my piece over on Search Engine Land today, if you missed it there:
Among the scores of data points search marketers have at their disposal, perhaps no other metric is as shrouded by mythology and misconception as ad position. Despite careful studies showing, including one by RKG in 2006, and confirmation by Google in 2009 that conversion performance doesn’t vary much by position, you will still hear pronouncements, even among enterprise level practitioners, that some specific position on the page generates exceptional performance.
This piece is not meant to rehash that argument, per se. Instead, we’ll examine how attributes of the current ad auction including: a Quality Score calculated at auction, broad matching, dayparting, increasingly personalized results, local competition and a changing competitive landscape, all conspire to scatter an ad across the listings regardless of anyone’s best efforts to “own” a particular position.
The Ambiguity of Average Position
Up until about a year ago, when Google introduced a ValueTrack parameter to pass along position for each ad click, this analysis wasn’t easy to do at scale. Those using Google Analytics have had a limited view of click position for some time, but for others, short of scraping the SERP, which may or may not have been above board, the best view of ad position was previously Google’s impression-based Average Position, usually viewed at the daily level. While the name itself clearly highlights it is just an average, and thus our ad may appear across multiple positions, it offers no insight on the extent to which that is occurring.
For example, if an ad has an Average Position of 3.5, did it appear 50% of the time in position 3 and 50% of the time in position 4? Or, did it appear in positions 2 through 5 25% of the time each. Furthermore, Google’s Average Position tells us nothing about which positions actually drove traffic to our site. For that we’ll need to assess the click position data we have via the ValueTrack parameter.
Actual Click Position Versus Daily Average (Impression) Position
Our sample here consists of over 120K ad clicks that took place over the course of a single day on about 50K unique keyword instances, i.e., identical keyword phrases in different ad groups or with otherwise different settings are viewed as separate from one another.
To keep the chart below readable, we’ve rounded the daily average impression position to the nearest integer. We’ve also grouped anything over position 10 in one bucket for both the click position and the average position. This includes any clicks from ads appearing on page 2 or higher.
The size of each circle here represents the percentage of all clicks for that daily average position that occurred at the given click position. Or, the same data in table form:
So, for ads with an average position of 1, 95% of clicks happened when the ad actually appeared in position 1, 3.6% happened when the ad appeared in position 2 and a small percentage of clicks occurred in lower positions.
A number of interesting elements jump out here, including:
- First and foremost, it’s clear that ad clicks really are taking place across a wide array of positions regardless of the average daily impression position. The spread escalates the lower the average position is, but is apparent throughout.
- Except for in position 1, most clicks do not take place at the same position as the average impression position.
- Click position is shifted upward from the average impression position. This makes intuitive sense as click-through rates increase as we move up the listings.
- We see a relative peak in clicks at position 3 and a relative drop off at position 9. The former likely has to do with ads moving from the side listings to the top of the page at that point. The latter may be a function of appearing below the fold.
- It isn’t until we get to an average position of 4, that the top position doesn’t generate a plurality of clicks.
For the full analysis, including a breakdown of Search Network and match type effects, check out the original post.