My Developed Version of KEI Keyword Effectiveness Index for SEO

In one of my previous SEO related articles, I had talked about KEI (Keyword Effectiveness Index) and how you should use in SEO. If you don't know what KEI is, first read this article and come back here to explore a better version.

Original KEI Formula :

s = Number of Monthly Searches

r= Number of Competing Pages (Search results)

KEI = s^2 / r

Retrieving New Formula Step by Step :

Most of the new data comes from Google Adwords keyword tool. You have to choose important fields in the related keywords section according to your phrase and download the list as a csv excel file. Then add three more columns including Average PageRank of First Page Results, Average Google Backlinks of Competing Pages, Number of Search Results

The screenshot of the excel page with sample keywords data. Notice that I have filled columns for several fictitious keywords. Now, I will explain how I retrieved the final formula and this sample data. You can click the image to see in full size and continue reading below.

CPC and Advertiser Competition Relation

There can be a keyword with high CPC of 16 but not enough advertiser competition (0,10) as in keyword a . It means that it is not guaranteed to have continuous high valued clicks for this keyword. To normalize this situation, I have developed a relational factor of CPC and Advertiser Competition.

Remember that we had taken the square of s (Number of Monthly Searches) to give high importance on this factor. Now, we use cube root to decrease the effect on KEI final formula which you will see at the end of the article.

cpc = Estimated Average CPC

adv = Advertiser Competition

cpc-factor = CubeRoot (cpc * adv * 100)

Notice that a keyword with 16 CPC and 0,10 competition is less effective than 3 CPC and 1 competition.

Google Backlink Normalization

I also want to normalize the backlink data by decreasing its value. If I didn't do this I would end up huge differences in final KEI. For example a backlink value of 450 would be almost 25 times important than a backlink value of 18. Now, the difference normalized into 3 times which is much more relevant.

back : Number of Backlinks

back-factor = CubeRoot (back)

No Normalization on PageRank

I didn't normalize this data because it is already much relevant. If you look at the screenshot, you will notice that average pagerank for fictitious keyword c is 7. It is almost impossible to compete for this PR eventhough there are 5,400 searches. So, it stays like this.

pagerank-factor = Average Page Rank

Final Formula

While cpc-factor positively effects the KEI, the other newly added PageRank and Backlink numbers will be negative. So;

newKEI = KEI * cpc-factor / back-factor / pagerank-factor

It is my first version of newKEI and currently using it on my SEO keyword choices. When I will have more time, I will refine individual factors more to retrieve final KEI more relevant.

You can also exclude some values completely not falling the selected ranges. For example advertiser competition can be chosen only for values bigger than 0,50. Or set a rule like Average Google Pagerank should be less than 3 before entering the new KEI calculations. Using Excel with filters will help you on these choices.

Retrieving average pagerank and backlink values can be a time consuming task for comparison of huge lists. With the help of a programmer, you can have your own automatic KEI calculator coded.