Join agencies and companies at the cutting edge of SEO inovation
Structure your site accordingly
to customer psychology
and only create landing pages you need
Structure your site by customer search intent rather than using a ‘traditional’ site structure with a landing page for every product and category.
Simply use our API to find out which of your keywords belong together on the same landing page and which don’t. Then use those insights to create and optimise your landing pages.
The result? Less spent on SEO that doesn’t deliver, and more time spent creating pages that rank highly and convert.
Quickly optimise large, multilingual websites
If you need to optimise a site with 1,000, 5,000 or even 10,000 keywords then manual inspection of SERPs isn't cost effective. Analysing 10,000 keywords will only take Syntent 1.5 hours.
Our API looks at the content on pages as well as at SERPS, meaning our results are as reliable as possible, no matter what European or Pan-Asian languages you're working with.
Natural language processing and maths is the only way to understand the search intention of users, which is the key to creating pages that rank highly and convert.
Get the edge on your competitors
Use actual Google results
Google’s results are a live test of content that satisfies the search query. So with Google results you get a quick and accurate feedback loop about what your customers need. This is why we use Google results to make sure our results are as reliable as possible in terms of judging search intent.
Simply comparing internal pages of your website, which is a less advanced technique still used by SEO specialists, will give you much less reliable results. The feedback loop will be very slow compared to Google simply because your site gets a lot less traffic than Google and you have fewer resources to run experiments.
Move beyond TF-IDF to ML
Although you can use Term Frequency Inverted Document Frequency (TF-IDF) to tell the difference between two pages, we wouldn’t recommend it. Using TF-IDF means you aren’t able to capture position in text, semantics, co-occurrences in different documents, and so on.
You also can’t feed in other information such as content layout and pagespeed. This is why we use machine learning (ML), which is the most robust and reliable way to compare and analyse content.
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