This case study is the perfect example of how impressive results in SEO can be achieved without huge budgets or hustling for backlinks. Instead, it took a genuine desire to provide value, combined with a smart strategy.
Evocon is an Estonian startup that offers a cloud-based production monitoring solution. Their goal is to help manufacturers remove excess waste in their production so we can all move toward a more sustainable future.
Evocon’s production monitoring software
It’s not your classical raise-a-shitload-of-money-and-then-grow-like-crazy startup. Evocon is fully bootstrapped and profitable and they intend to keep it that way. So they can’t afford to solve SEO by simply throwing money at it.
Evocon’s website aims to be a thought leader in a competitive global market of improving manufacturing efficiency. Whenever a production manager goes looking for ways to boost efficiency, they should find all the information they need on Evocon’s blog. The cherry on top is the monitoring system that can actually accelerate this process significantly.
In SEO, our goal was to break into the first pages of Google results for keywords relevant to Evocon in topics such as OEE, machine monitoring, root cause analysis, production downtime and technical availability. This means we need to challenge established US brands with millions of VC money, dedicated content teams and more headway.
Evocon, however, had no full-time marketing people. The content production was managed by the Head of Design and Service together with a designer and an expert in the field of manufacturing with the part-time role of writing content.
At most, this team had the resources to produce 1 high-quality article per month. When trying to make a dent in a competitive market, this is not a lot.
We had a good idea of the keywords we needed to be relevant for. Evocon already had organic impression data from more than 30k unique search queries in their Google Search Console account. Add to this low-funnel search term data from extensive Google Ads campaigns, and additional keyword research for new topics and a list of roughly 40k keywords.
We could filter that down to 10k by removing branded keywords, irrelevant terms and keywords with non-existent search volume. But 10 000 keywords is still a lot to comprehend and covering all that while writing 12 articles per year is a real challenge.
To achieve this, we need to drop the old mindset of “one page - one target keyword” that tools such as Yoast have so successfully established as the standard in SEO.
So we know it can be done, but the question then becomes - which keywords exactly?
Can an article explaining the process of “oee calculation” also rank for the keyword “oee calculator”? Or would you need to separate these ideas into 2 articles? Maybe you could cover every aspect of OEE in one mammoth piece that ranks well for all of the 800 keywords that we’ve targeted in this topic?
It might be surprising, but these questions have a definitive answer - you can tell if the same page can tackle any two keywords if you look at the similarity of their SERPs.
Let's do it.
Take the same example keywords: “oee calculation” and “oee calculator”.
Google the first and make a note of the results you see. Then google the other one. Do the same results come up?
- Yes - This means Google considers the search intent to be similar and you’ll have no problem ranking for both with one page because you can see other websites doing just that.
- No - Google knows that the first SERP does not provide information relevant enough for the second search term. It’s highly unlikely that your page will be an exception.
This methodology allows you to assign a numerical value on a scale of 0-100 for the similarity between any two keywords based on the number of elements in the SERP that match.
The similarity between “oee calculation” and “oee calculator” is surprisingly low - 9/100. “oee calculation” and “oee formula” has a much higher similarity of 32/100. In fact, “oee calculator” has a low similarity with most of the other terms we targeted.
It’s clear that we’ve got to create two pages here - one that would cover the theory of how to calculate the metric and another page dedicated to the actual calculator.
We went through all of the keywords and calculated the similarity between each. Then, we ran a clustering algorithm on this dataset, which grouped together similar keywords.
The generated structure was optimised so that we’d get as few groups as possible, while still ensuring that keywords within each group had a similarity above a certain threshold.
This allowed us to create a roadmap of articles to be produced so that each article would be highly relevant for not just one target keyword but hundreds of variations of it with similar search intent. At the same time, the overlap between pages was minimised so that we wouldn’t waste time repeating ourselves.
Visualising all the article ideas in this manner gave us the clarity we needed to prioritise the ones that we felt we could offer the most value. The writers then knew exactly which keywords to focus on and which ones to leave out.
All this gave us a significant competitive advantage because a content strategy with this level of detail can not be created manually.
Evocon’s organic search traffic increased from 19k sessions to 59k sessions in the first 8 months. Meanwhile, traffic quality was kept high (the conversion rate did not decrease). Target keyword rankings increased by 10 positions on average. The number of unique target keywords in the Top 3 rankings globally increased by 5x.
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