I was reflecting on my self employment and how it compares with having a “job” in the “real world”.

I came to the conclusion that when I had a full-time job, it was as if every day was a grey-sky day, when that uniform blanket of dark grey cloud keeps the sun out, forever.  There is a dull normality about it all.  There may not be any thunder-claps or heavy downpours, but there is never any bright sunshine to warm the soul either.

By contrast, for me self-employment has been like an extended period of unpredictable, unseasonal and very unsettled weather.  When I look at my affiliate marketing website stats and see that my latest precious new website has captured no more than 10 visitors in a month; when I write a carefully considered comment on someone’s blog only to press submit and get the magic word “discarded” in big black letters; when I carefully apply to an affiliate scheme only to log into my Commission Junction account  and see my application in the “rejected” list – these things are like bad-weather days, cold, rainy, windy, stormy days.

Conversely, when I make a big sale (as I just did today), it’s as if the sun of affiliate marketing has suddenly come out from behind those black SEO clouds  and all is well in my little self-employed world for a few moments as I bask in the warmth of my little successes.

So which do I prefer?  Oh come on, you know me by now.  I would HATE to go back to the grey life; a life of endless sun-less days with no hope of a sunny spell.  Give me the turbulent, changeable climate of self-employment any day.

Conversion Rate Boost for Top Ranking Keywords

Well, I have been playing with the new version of Webmaster Tools and it’s really something.

The three new pieces of information that are really exciting to me are the number of impressions for a keyword; the average rank for the keyword and the clickthrough rate for a keyword – in a given period.

This is exciting stuff.

The ranking/clickthrough/impressions data is especially useful to me when I have a site under test because say I have a microniche site with a theme keyword ranking average position 4 in a month, with 500 impressions in a month and say 50 clicks. In theory, once I get to know the average conversion rate for a targeted site at a given ranking, I could extrapolate the data to forecast with some accuracy what I can expect to achieve when the site gets to no. 1.

And herein lies the first eye-opener!

Aside from any inaccuracy with the “impressions” number, and there seems to be some, the big revelation is the conversion percentages.  To date I have been using the old AOL table from 2006 to determine what percentage of traffic a site could expect to receive at no.1, no.2 etc.:

Now, though, judging by what I see in Webmaster Tools for my sites, it looks as if this old data, based on averages for all sites whether targeted to a keyword or not, is really not appropriate when it comes to forecasting the clickthrough percentage at a given ranking for targeted, focussed, Thirty-Day-Challenge-Style microniche sites. Recent examples from my stats seem to confirm an example in a new training video on this subject on Thirty Day Challenge Plus, which I cannot quote, but if I take an aggregate from all the data so far I get these very approximate expected conversion rates:

OK so it’s early days, and these results are VERY ROUGH and based on a TINY sample, but this looks very exciting. On the face of it, this means that I can expect much higher conversions from targeted niche sites than the old table led me to believe.

Note how the total is way over 100%.  This is likely because the highly relevant, targeted pages involved really do pull in the traffic far better than the global average page for that keyword – something that I, and many others, overlooked since that original table appeared in 2006.

If, through use of Google Webmaster Tools, we build up enough case history in the conversion rates at a given rank for targeted sites, this will lead to big changes in the assumptions we make.  For example, at present the Market Samurai tool, which I love, applies a 42% multiplier to expected traffic to give “SEOT” which is expected traffic at number 1.  This is in line with the 2006 AOL data.  It will be interesting to see if the tool is changed in time to reflect what we will learn.

Please let me know your views on all this by leaving a comment below.

I have just analysed the performance on 17 of my Thirty Day Challenge-style microniche sites to see how the actual traffic stacks up against the estimated traffic that I got from Market Samurai or the Google keyword tool (it’s all Google data anyway).  I had read that often the actual traffic is less than the estimates, and from my analysis, it seems so.

Before looking at the results, I want to clarify that I calculated MY traffic estimates by starting with the actual number of unique visitors now, taking into account the site’s current ranking for its keyword, and thereby estimating how much traffic the site would get at the ranking that I reckon I could achieve (usually no. 1).  This adjustment is made by using the well-known table that I believe came from AOL in 2006 (it seems we still have nothing better!)  that shows what percentage of traffic a site is likely to get in each of the top 10 slots in Google:

This means for example that if I have a site that’s getting 50 unique visitors at no. 2, I can hope for, say, 175 when it gets to no. 1.

So, based on the numbers, adjusted using this table, I reached the following rather interesting conclusion:

On average, these 17 sites, ONCE THEY ARE RANKED AS PREDICTED, are estimated to get unique visitors for the main keyword equating to ONE THIRD of the exact match SEOT and ONE SIXTH of the phrase-matched SEOT shown in Market Samurai (whose numbers are in turn based on the Google Adwords tool).

Of course, 17 sites don’t make an in-depth study.  However, it’s based on real evidence and I will definitely be using this when it comes to estimating return on investment as part of the keyword research phase.  I’ll also recalculate the numbers each month to see how they change.