How to Increase Google AdSense Earnings
You might have read my first two tutorials on increasing Google AdSense earnings relating to the importance of keyword research, blocking irrelevant ads and identifying high AdSense potential websites in your channel. This tutorial will go deeper, with tips and techniques not covered in the previous articles.
AdSense is not a source of easy money - if I calculated how much I was earning per hour, I wouldn't bother with the website at all - I don't - my website is something I do for fun - initially, the money from AdSense was just to cover the domain and hosting costs (AdSense more (way more) than covers that for me).
Don't waste time on SEO etc. Work on the content of your website and your income from AdSense will look after itself...
Lesson I've learn about aDSense:
Don't put all of your eggs in the same basket, not jut income but traffic also. Don't allow Google rule your business. Usually if you receive good money for your current visitors, sooner or later Google might invade your niche and make sure they convert directly those costumers and not through your website.
Google Ranking Positions and %CTR: Estimating Search Engine Visitors
Have you ever wondered how many visitors you'll attract if you manage to bump your site up a few ranks on Google for particular keywords -- and how that will affect your click-through rate? Now that Google Webmaster Tools offers more accurate data, you can get that answer, and see if the increase will be worth the work you'd need to put in.
I like what you have done here and I like the logic you have followed but unfortunately I believe it is flawed. When plotting a curve to points of data, for each point on the X axis you need to consider spread.
For position 2 on Google your CTR ranges from c. 4% to c. 78%. with no particular clustering. So it is just as likely if I achieve positon 2 that I will get a 4% CTR as it is that I will get 78%. In certain instances the data suggests lower positions are better.
That's not to say that your theory is not accurate and it could be that there is just not enough data to draw anything conclusive.
Finally I would be interested, and might even try, to see the difference between industries - is there a similar pattern in health related searches compared to IT related searches - otherwise you might be mislead using the behaviour of different users in incorrect places.
But thanks for a very interesting idea!
Quick explanation for the following if you don't do stats: R-square is a statistical output that says "according to the line I made fit this graph, what percentage of times will using the line as a predictor be successful". If R-square is .5 that means there is a 50/50 chance that the line that the data makes will make an accurate forecast.
Now when I used a Power Curve I got an R-square of .54. When I used a regression line R-square was .52. That means my data could be a Power Curve or a straightline regression. There is so much noise that neither is a good indicator or predictor of the data.
I also saw your R-square is .52 when fitting a Power Curve. That means as a predictor it is only true half of the time - same as my data.
I would not want to advise clients to invest based on a model that has a 50% chance of being right.
I actually think what both your data and mine show is the opposite of what you want to prove. I believe the data say:
- there is little difference between being Google #1 and Google #5
- there is little difference between being Google #5 and Google #8
- Google #11 and #12 may be more valuable than #9 and #10 - I've read alot of speculation about this but it needs more data