New Insights Into The Google Auction
Granted, we’re geeks. We get really excited anytime the engines give us new information to analyze or tools with which we can fine-tune our approach. Google has just given us a treasure trove of new information: click level data on an ad’s position. In the past, we’ve had to rely on observations and average position […]
Granted, we’re geeks.
We get really excited anytime the engines give us new information to analyze or tools with which we can fine-tune our approach.
Google has just given us a treasure trove of new information: click level data on an ad’s position. In the past, we’ve had to rely on observations and average position day to day to get a sense of where an ad is appearing on the page. But we know that averages lie.
By giving us information identifying the position on the page for each click we get a whole new window on auction dynamics.
How It Works
Similar to the Google Click id, gclid, by flipping a switch in your adwords account, Google will append an ad position parameter called ‘adpos’ to your destination url. We can then parse this information as it’s passed to our clients’ sites and analyze from there.
The values passed in the adpos parameter reveal the page of the results off of which the ad was clicked, whether the ad appeared on top of the organic listings or to the side, and its overall position rank on that page. All of this is passed in one text string.
- 1s3 means: page 1, on the side of the page, third position among ads.
- 2t1 means: page 2, on the top of the organic listings, first position.
What It Reveals
Matt Mierzejewski, our VP of Paid Search, flipped that switch immediately for one of our clients to start gathering data, and I couldn’t resist digging in, even though we only had a couple of days worth of data.
To wrap my arms around this, I narrowed my study to 8 ads representing 4 different keywords to reveal among other things what impact match type might have on ad serving.
Of the 471 clicks in my sample: 97% happened when the ad was on page 1; 2% on page 2 and less than 1% on page 3 or more.
The academic in me wonders: if an ad is in position 2 on page 2, how does that figure in the average position calculation at the end of each day? Does that count as position 2, or position 12 or whatever it might be given that the page 1 ads were ‘above it’? I suspect the former, but in truth given that 97% of the time we’re looking at page 1, it probably doesn’t matter.
Fascinating, fascinating, fascinating!
Keyword 1. Adwords reports that Keyword 1 on broad match had an average position of 3.7
But take a look at the positions from the perspective of click traffic:
This is just jaw-dropping, IMHO!
The Average Position reported is 3.7, but the weighted average of the clicks by position is 2.6. Bear in mind, the reported Average Position is based on impressions served, not ads clicked. The click-wise view will always be a lower ordinal number reflecting higher position on the page. This is a product of the higher click-through rates of higher positions.
That said, for a reported average position of 3.7, 67% of the clicks came when the ad was in position 1 or 2!
Boy-oh-boy, do averages not tell the whole story!!!
Let’s take a look at Keyword 2, also on broad match. Average reported position for the days covered: 4.9
Even more dramatic! The average position of ads clicked is under 2.1!
These results are so startling to me that I wonder if I’m comparing apples and oranges. The average position data comes in day long chunks, and the click data I’m looking at starts midday and ends midday.
I also wonder if Google appends the parameter for network partner clicks…I think the answer is yes, but…Also worth noting that our own day-parting activities could be driving this as much as auction dynamics.
This view of data will have us all scrambling to revisit what we thought were settled questions about paid search. One obvious example: we long ago concluded that conversion rates don’t appear to vary much by position. I’d be surprised if we found variance given the new view.
Hal Varian’s study, with the crystal clear view of the auction that only available to Google, suggested that conversion rate is relatively invariant with position, and that’s actually somewhat contrary to Google’s best interest — they’d benefit from saying higher positions on the page convert better. Still, the new visibility means we can and should take another look.
For ads that are primarily in position 1, what fraction of the clicks come from impressions when the ad is on top of the organic listings versus on the side?
Keyword 3 on broad match had 114 of its 140 clicks placed when the ad was on top of the organic listings. The same keyword on exact match saw all 112 of its clicks on top of the organic listings.
This seems to be the trend. There is somewhat less variance in both position and placement when the ad is on exact match than when it’s on broad match. That makes sense, as the competitive landscape is likely different for every broad match variant.
It’s worth pointing out that we don’t get a perfect view of the page layout from this.
We don’t know whether 1s3 means the ad was third from the top on the right, or at the top of the right column with 2 ads served over the organic listings, or whether it was below Product Listing Ads. Nevertheless it gives us much more information at the click level than we’ve had before.
Well, that’s a hard question in this case.
What are we going to do with this data? I don’t know…yet. Perhaps it will shed light on how national ads compete in various local markets?
We may be able to see using geographic overlay data that clicks from certain locales are coming from significantly lower on the page than the national average, indicating stiff competition from local brick and mortar stores. That might suggest creating a geo-targeted campaign for that region with separate bid logic and copy to provide a competitive edge.
That’s just one idea. The point is that every time Google has given us greater visibility and greater flexibility the leaders in the industry have been able to figure out ways to raise the performance bar.
I expect this to be another opportunity for differentiation.
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