When using the ContentDNA in the content of your website, take into consideration the recommendations below. While not obligatory for getting a good result, we have achieved the best results so far by taking them into account.

We strongly recommend only optimizing for one search term per web page. This is a general SEO rule. There is no advantage at all to be gained from optimizing for different search terms on the same page.

If you want to optimize your website for different search terms, then create a different web page for every single search term and use the corresponding ContentDNA for each page.

Example:

You have checked the most popular search terms with Wordtracker or Goodkeywords related to the search terms “Brussels, Hotel, Centre, Airport”. The term “Brussels Hotel” is the most searched for, but this is also the most competitive one, so you have decided to optimize on the following three additional search terms:

“Hotel Brussels centre”
“Cheap hotel Brussels”
 “Hotel Brussels airport”

This is what the sitemap of your website could look like:
www.brussels-by-night.com

The content of your home page, based on the ContentDNA of “Brussels hotel”, will differ from the content on the other pages optimized for “Hotel Brussels Centre”, “cheap hotel Brussels” and “Hotel Brussels airport”.

Of course, part of the ContentDNA will be the same for the different search terms, but the text on each page will be unique.

This will achieve the best result for two reasons:
 

1. The general term “Brussels hotel” is a ‘difficult’ term to optimize for since there are already many hundreds of websites optimized for this phrase. Optimizing for the less-popular related search terms will generate ‘easier’ traffic.


2. When you optimize several pages of your website for terms related to your main target search term (“Brussels Hotel”), it raises the ranking of that search term. This is called cross-content optimization.

If you optimize for a search term which has a lot of commotion or hype around it, you will have to update your content more often than for subjects that are more stable. When there is a lot of news value around a subject or a search term, it results in a lot of posts on the internet within a short time span. As a result, the ContentDNA of that search term will evolve rather quickly.

Consider the search term “global warming”. Today there is so much focus on this subject – and so many new articles are published daily on the internet about it – that its ContentDNA changes relatively fast.

For example, words like “North Pole” and “new oil deposits” have gradually gained importance in the ContentDNA of “global warming”. Other words will consequently have become less important.

Other search terms are relatively stable: take, for example, “Cuban Cigars”. Words like “Fidel Castro”, “fine selection”, “Havana”, etc. have held a fixed position in the ContentDNA of “Cuban Cigars” for the past few years. 

But should a world-startling event happen – for example, the US lifting its import embargo – then the ContentDNA of  “Cuban Cigars” would change considerably overnight.

So, depending on the business you are in, and depending on the search term you have optimized for, you will have to update your ContentDNA more or less often.

Monitor extreme events related to your search term

Any extraordinary event related to your search term, be it positive or negative, will result in a change in its ContentDNA. This means that you will have to update your ContentDNA for this search term.

Example: If you have a web page optimized for the search term “weight loss drugs”, which has a relatively stable ContentDNA, you would normally only need to update the text 3 or 4 times a year. But if Pfizer were to suddenly announce the discovery of the ultimate weight-loss drug called “Fataga”, you can be certain that the words “Pfizer” and “Fataga” would acquire a very important position in the ContentDNA for “weight loss drugs” overnight.

Conclusion: When extraordinary events occur related to your search term, you will have to update your ContentDNA accordingly.