Finding Profitable Niches in Less than 10 minutes

Today let’s explore the Last Keyword Tool “golden niches”.   This is a default filter that is available to all levels of membership.

Golden Niches is designed to find profitable keywords that you can use to target a single page or small website.  To use this filter all you have to do is create a project, wait for the drill to complete and then apply the “Golden Niches” filter from the filter drop down menu.  It’s just that easy.

But wait, there must be more or this would be a twitter and not a blog post ;)

The Golden Niches filter tends to work best when you have created a project (i.e. drilled into a keyword) that is not too broad.  To demonstrate this, let’s take a look at what happens when we try to use this filter on the term “health”

health profitable niches

Notice only one result was returned, and not one that I’m particularly excited about.  Let’s drill into another term and try again – this time let’s look at the result for “health insurance”

health-insurance profitable niches

A little bit better, we’ve got three terms this time, though I’m still not jumping out of my chair, and I might start to doubt the usefulness of this filter, if I hadn’t read this article ;)

Now let’s look at “acai” – definitely a niche in the health industry.

acai profitable niches

Ok, here are some results that I can get excited about – but wait, before you run off to build one of those acai affiliate sites, hang tight and let’s get a little more technical  – folks subscribed at the deluxe and premium levels can modify the filters as they see fit, so it’s good to understand how they work and how to change them.   Roll up your sleeves and take a deep breathe – this won’t hurt, I promise ;)

Here’s the code behind this default filter:

keyword_lower_google_cost_per_click > 1 and
keyword_google_competing_pages < 500000 and
(((((keyword_bayes_score – min_bayes_score) / (max_bayes_score – min_bayes_score)) +
((keyword_lsi_score – min_lsi_score)/ (max_lsi_score – min_lsi_score) * 1.3))/2.3) * 100) > 39

Let’s tear this apart line by line

keyword_lower_google_cost_per_click > 1 and

this means that the results of this filter must be worth at least $1/click – quite expensive in some markets, meaning that we should get some good conversions off this keyword.  If you wanted to change this to say 50 cents, this line in the filter would look like this:

keyword_lower_google_cost_per_click > 0.5 and

I’ve only repeated the part that needed to be changed – you’d still need to include the rest of the code the way it is listed above.  Let’s move onto the next line:

keyword_google_competing_pages < 500000 and

This means that the number of competing pages as reported by Google must be less than half a million.  If you wanted to adjust this to make it less that one million it would look like this:

keyword_google_competing_pages < 1000000 and

Now for the last bit – the scary part ;)

(((((keyword_bayes_score – min_bayes_score) / (max_bayes_score – min_bayes_score)) +
((keyword_lsi_score – min_lsi_score)/ (max_lsi_score – min_lsi_score) * 1.3))/2.3) * 100) > 39

All this says that the LARI score (Local Aggregate Relevance Index) needs to be greater than 39.  LARI is a theme relevance index that uses the bayes and LSI values for the keyword measured against the averages for the entire list of terms returned to determine a relevance score.  A score above 60 is highly relevant, a score between 40 and 60 is largely relevant and under 40 is starting to get a bit tangent.

When we are looking for golden niches I set the value to be 40 and above because we are looking for some keywords that are under optimized and these often have a poorer LARI value.

Let’s say you want to adjust this and change the LARI value to be even more forgiving; let’s say you want to see everything with a LARI value above 20.  Your update would look like this:

(((((keyword_bayes_score – min_bayes_score) / (max_bayes_score – min_bayes_score)) +
((keyword_lsi_score – min_lsi_score)/ (max_lsi_score – min_lsi_score) * 1.3))/2.3) * 100) > 20

You’ll notice this wasn’t hard – the only thing I had to do was to change the number on the end.

Let’s paste all of our changes above together into a new filter:

keyword_lower_google_cost_per_click > 0.5 and
keyword_google_competing_pages < 1000000 and
(((((keyword_bayes_score – min_bayes_score) / (max_bayes_score – min_bayes_score)) +
((keyword_lsi_score – min_lsi_score)/ (max_lsi_score – min_lsi_score) * 1.3))/2.3) * 100) > 20

I wonder what the results of this new filter would look like – let’s take a look:

acai-more profitable niches

Wow!  The new parameters returned 65 keywords and it scrolled off the bottom of my capture screen.  You’ll notice some less relevant terms like “raspberry juice” now that LARI is set lower, but at the same time this gives us a whole bunch more keywords to work with.

For those of you who have gotten excited and can’t wait to tear into filter creation I’ll make it worse by giving you the link to all the different variables that you can use in TLKT.

And for those of you who haven’t tried it out yet, get moving and sign up for the FREE 7 day trial!

That wraps up this article.  Stay “tuned” for my next article when we dissect the competitive analysis for some of the more interesting golden niches for acai and figure out what it will take to rank for them and why.
–Sue and the Theme Zoom Team

Theme Zoom Krakken is an integrated application suite that radically combines Market Analysis, Competitive Analysis, Keyword DNA Creation, Automated Silo Structured Blueprint and Website Development.

For more information about The Last Keyword Tool visit Theme Zoom.

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Filed Under: Natural Language ProcessingTLKT FiltersThe Last Keyword Toolkeywords

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About the Author: Sue Bell is the founder, co-creator and mastermind behind Theme Zoom's 'Krakken' Search Engine Marketing Tool, The Last Keyword Tool and Domain Web Studio (DWS). Her expertise with system design and databases allowed her to back engineer popular search engines and discover methods for Search Market Analysis and Competitive Analysis that turn SEO and search engine rankings into a science. This resulted in the creation of tools which make the life of an SEO/SEM easier by automating both the difficult and mundane aspects of Market Research, Keyword Research, Website Site Structural Design and Implementation. Sue retired from her career supporting the Military in her thirties and recently came out of retirement to mastermind the programing behind our powerful and proprietary market analysis tools. She currently enjoys living in the arid Arizona desert, spends her spare time dominating online markets, and is available on a limited bases for online marketing consultation and training.

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