ROI on Incremental Position Gains
Posted by Melanie Phung on Monday, August 21, 2006 at 11:17 pm
In the last 12 months as an in-house SEO, my Holy Grail of Web analytics has been quantifying what each gain in position (i.e., ranking on results page) is worth. It’s a hard thing to just test since there are so many variables beyond your control. Clearly the first position is more valuable than the third, which is more valuable than the seventh. But how much more valuable?
Each rise in rankings gets exponentially harder the closer you to the top. So, would my goals be better served if I prioritized moving a listing from the #11 spot to the #10 spot, or should I try to get a current #5 listing to move up position #4? Or what if I tried to get a bunch of Page 3 listings onto Page 2 — would half a dozen pages on the second page be worth more than a one position increase on the first page? If my optimizing a page a certain way moves it from the #5 spot to the #3 spot but causes a 1% drop in conversion, is it worth it? What about from #4 to #3 — would it still make sense to sacrifice one point in conversions to go after traffic?
Thanks to the AOL snafu, SEOs now have a little more visibility into search user behavior.
It should come as no big surprise that 50% of searches result in clicks on the top 2 results. That still doesn’t answer any of the questions I posed above.
But by analyzing AOL’s treasure trove of user data, and based on some data shared in EarnersForum.com the folks over at SEO Black Hat have come up with a tool that attempts to quanitify the value of each position change (in terms of traffic, not $).
From the forum:
Based on 9,038,794 and 4,926,623 total clicks:
- Ranking Number 1 receives 42.1 percent of click throughs.
- Ranking Number 2 receives 11.9 percent of click throughs.
- Ranking Number 3 receives 8.5 percent of click throughs.
- Ranking Number 4 receives 6.1 percent of click throughs.
- Ranking Number 5 receives 4.9 percent of click throughs.
- Ranking Number 6 receives 4.1 percent of click throughs.
- Ranking Number 7 receives 3.4 percent of click throughs.
- Ranking Number 8 receives 3.0 percent of click throughs.
- Ranking Number 9 receives 2.8 percent of click throughs.
- Ranking Number 10 receives 3.0 percent of click throughs.
- The rest of the Long Tail (ranks 11-1000) = 11.3 percent of click throughs.
To put it another way:
- Search Engine Ranking #1: 2,075,765 clicks
- Search Engine Ranking #2: 586,100 clicks = 3.5x less
- Search Engine Ranking #3: 418,643 clicks = 4.9x less
- Search Engine Ranking #4: 298,532 clicks = 6.9x less
- Search Engine Ranking #5: 242,169 clicks = 8.5x less
- Search Engine Ranking #6: 199,541 clicks = 10.4x less
- Search Engine Ranking #7: 168,080 clicks = 12.3x less
- Search Engine Ranking #8: 148,489 clicks = 14.0x less
- Search Engine Ranking #9: 140,356 clicks = 14.8x less
- Search Engine Ranking #10 147,551 clicks = 14.1x less
- Search Engine Ranking 11+: 501,397 clicks
If you then factor in market share owned by each engine, you can approximate how many clicks your various positions are getting — or at least that’s what SEO Black Hat’s tool tries to do. (Hint: you need to enter the frequency number Overture gives you for your particular keywords, no commas in the number.)
However (a big HOWEVER) AOL’s organic results don’t even show up above the fold on my screen, AND AOL users tend to be less tech savvy in general. Since the top of AOL’s results pages are more heavily PPC laden than Google’s, and because it’s been proved that AOL users’ search behavior differs from that of searchers using other engines*, I wouldn’t extrapolate too much. The data provide insight, not indisputable truths.
* Needs citation.
Hi Paul,
you’re absolutely right: Probably more important than the fact that AOL’s SERPs display differently is that AOL users tend to be far less tech savvy in general. Given how little market share AOL has in search, marketers need to be very careful about drawing conclusions about searchers across the board.
That said, this is the first time I’ve seen anything like this and however inaccurate the data might be, they are pretty fascinating. Still gives us something to work with that’s better than taking blind guesses.
Would you share some more of your practical observations with me?
p.s. Have you considered adding SprayOnSalt.com to your site?
I’ve thought about adding spray on salt to my site. I found it originally because Basking Shark asked me to look at it after I reviewed a bunch of social bookmarking/news sites on Performancing.com. There are a few technical thing I’d like to bring to light before I add it to my site.
I enjoyed reading your blog so I thought the list I could do was give you and sprayonsalt.com some link love. It seemed like a good time to do it 
Hooray Link Love! [In case anyone is reading these comments and thinking "That's a total non sequitur ..." and "gee, is that in fact how you spell 'non sequitur'?", to you I say, b) yes, that is how you spell 'non sequitur' and a) Paul put me on his list of top 6 "absolute must read" blogs and All About Content is on it. (Wheeee!) I thanked him, and that's what he's responding to in the previous comment.]
For one of my next post I’ll blatantly expropriate the idea of a must-read list but not put Paul’s site on it. Because I’m shameless that way.
she curses under her breath: Dagnabit! I hate not being able to edit comments once they’re posted.
1) I found you via SprayonSalt.com
2) I think you need to look carefully at the AOL data and the tech savviness of the people (whenever I get a potential client emailing me from @aol.com I want to cry).
Also Quadzilla’s tool is a little FUBARed…he even admits it himself
While, AOL search data gives us some insight really it is only in how AOL users search– I’d love to someday get the funding to run a serious test on what positions get the highest click rate. (On a few sites I worked with they seemed to do better in 9-10 than 5-6 but there are so many other factors involved I dont even know where I would begin to put together the data..and further who would pay for the testing and how to make avoid common testing pitfalls)