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Frequently Asked Questions

How do I tell when my credits are added?
For monthly/yearly subscribers, new clustering credits will be added at the end of their billing cycle.
Click the User icon to see when the credits are refreshed.
Do I get credits upfront when I buy an annual subscription?
Yes. All clustering credits are applied upfront.
What happens to my credits if I cancel my subscription?
After your subscription is cancelled, your monthly clustering credits will be available until the end of your billing cycle. After the end of the billing cycle, these credits will be reset.
Your pay-as-you-go credits will never expire and will be continuously available.
Do unused credits roll over every month?
No, credits do not roll-over. Any unused credits will be reset at the end of the billing cycle.
Can I try it before I buy it?
Yes, you can try our $1 trial for 4 days. It comes with 6000 keyword clustering credits, 3 Keyword discovery searches, 1 Content Brief and Pro versions of SERP Similarity, and SERP Explorer.
What happens after my $1 trial expires?
Your $1 trial will expire in 4 days. After this, your clustering credits, briefs and keyword searches will be reset to zero. Your freemium tools PRO versions will be downgraded to a Free version with limited features. You will not be charged anything.
How do special characters in my language affect the output?
Special characters are common in many languages. Such as Norwegian, Thai, Swedish, German etc.
If you're using Microsoft Excel or Mac Numbers, Please make sure to export your files with UTF-8 encoding. Else your special characters will not be exported correctly, and Keyword insights will show you an error.
When possible, we highly recommend you upload an excel XLXS file when dealing with special characters instead CSVs. The CSV encoding can convert special characters into random unreadable text, and it will cause our clustering and hub/spoke algorithm to produce incorrect output results.
Why do I have no_cluster in the output?
If the keyword it's not clustered with any keywords, it now either means one of two things:
1) I need to do more research around this word. It's interesting to me, so I'll set it aside and do more research on it in the future. I'm clearly not going into enough depth with my current keyword list.
2) This keyword is just incredibly specific and requires a deep dive on just one page.
Solution.
To improve your results you could add more keywords to your upload. No_cluster simply means those keywords don't share at least 4 URLs with any other keyword so, if there are any in there that you wanted to write about or are interesting to you or have a lot of volume, our recommendation would be to do more research around those terms.
You'll also sometimes see some surprising results (i.e. keywords you THINK should be in a cluster as they're really similar to others, like pluralised versions of the same keyword) but if you actually test them, you'll see Google does in fact show very different results.
We recently came across one cluster "50mm skate wheels" and then a keyword in the no_cluster "50mm skate wheel" (singular version) and we thought it was a bug. We searched both and, right enough, the results were very different.
Some of my clusters appear really similar - why is this?
Please watch this video as it explains this in more detail. https://snippet.wistia.com/medias/oaeqllw2a4
What languages does your keyword Context feature support?
We currently support English and German.
Please submit requests for new languages via our contact form. We prioritise new languages based on demand.
Our current queue is as follows:
  • Danish
  • Swedish
  • Norwegian
  • French
  • Spanish
  • Italian
Unfortunately, we do not have an ETA for new languages going live. We have to build and train language machine learning models for each new language, which takes a lot of time and resources. We appreciate your patience.
What languages are supported by the Topic cluster feature?
Here is the list of supported languages.
  • Afrikaans
  • Albanian
  • Arabic
  • Aragonese
  • Armenian
  • Asturian
  • Azerbaijani
  • Bashkir
  • Basque
  • Bavarian
  • Belarusian
  • Bengali
  • Bishnupriya Manipuri
  • Bosnian
  • Breton
  • Bulgarian
  • Burmese
  • Catalan
  • Cebuano
  • Chechen
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Chuvash
  • Croatian
  • Czech
  • Danish
  • Dutch
  • English
  • Estonian
  • Finnish
  • French
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian
  • Hebrew
  • Hindi
  • Hungarian
  • Icelandic
  • Ido
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Kirghiz
  • Korean
  • Latin
  • Latvian
  • Lithuanian
  • Lombard
  • Low Saxon
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Marathi
  • Minangkabau
  • Nepali
  • Newar
  • Norwegian (Bokmal)
  • Norwegian (Nynorsk)
  • Occitan
  • Persian (Farsi)
  • Piedmontese
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Scots
  • Serbian
  • Serbo-Croatian
  • Sicilian
  • Slovak
  • Slovenian
  • South Azerbaijani
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tagalog
  • Tajik
  • Tamil
  • Tatar
  • Telugu
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Volapük
  • Waray-Waray
  • Welsh
  • West Frisian
  • Western Punjabi
  • Yoruba
What's the Topic cluster?
What's the Hub/spoke model, and how is it different from clusters? Once you have your keyword clusters, it may be helpful to know how closely related those clusters are to other clusters.
By grouping similar clusters together, we create Hubs and Spokes. Using these, you'll be able to produce what we call "hub articles" which link to "spoke articles".
In essence, our "hub and spoke" tab will make your content planning easier, allowing you to quickly identify and comprehensively cover a given content topic.
Bonus tip: Pull through the your keyword rankings and you'll be able to see internal linking opportunities, if you have relevant existing content.
In this example, "Hawaii vacation" will form the "Hub" content piece. This could be a category page or a long-form article.
Here we are show all the "clusters" in the Spoke column that are contextually related to the hub.
So, you can produce content around "Where to stay in Hawaii" or "Planning a honeymoon to Hawaii" and internally link to our main "Hawaii vacation" hub .
What countries does your keyword clustering feature support?
Keyword clustering is not language-specific but rather geo/country-specific. We support all the countries in the world.
Let's take a look at a few examples.
I have an SEO client in France, and I'm targeting users in France. When I prepare my keyword list, I will select all the keywords and search volume data for those keywords. I will choose France under the country dropdown when I run my order through Keyword Insights.
Keyword Insights will then scrape google.fr and build the clusters based on how keywords are ranking in google.fr.
You can choose to upload French keywords, English keywords, or any other language. Regardless of language, Keyword Insights will scrape google.fr for those keywords.
As you can see, the language has no influence whatsoever on our clustering process.
Do you have a public product roadmap?
We do not have a public roadmap. However, if you're a paying customer, you will get access to our private Facebook group. We often show our early previews and engage with our customers. You can also make feature requests. You can apply here to join the group.
Do you have live chat support?
We provide live chat support for all of our paying customers. For users, we have a support ticket system. Our operating hours are between 9 am - 5 pm GMT.
How is the opportunity score calculated?
Most tools see how much volume a keyword gets to create an opportunity score, but they don't take into account two important things:
  1. 1.
    A realistic number of visitors based on volume can be determined by looking at the CTR study by AWR. Based on this study, if you rank in position 1, you can expect to get 38% of the clicks, position 2 gets 13%, position 3 gets 8% etc.
  2. 2.
    It doesn't consider where you currently rank (and, therefore, the opportunity).
So we calculate the opportunity score by doing the following.
If someone ranks in position 1 for a keyword, the opportunity score will be "0", as they already rank for it. However, if they rank in position 2, it will be the difference between the percentage scores of position 1 and position 2. So we have 1 column, "maximum opportunity", which is position 1 (or 38%) multiplied by the search volume. Then another column called "current estimated traffic", which is whatever their current position is, multiplied by the volume. So if we were in position two, it would be 13% multiplied by the volume. The opportunity is then the difference between the two.
For example, "cats" has a search volume of 100. We rank in position 3.
Maximum opportunity = 38 (38% x 100) Current Estimated traffic = 8 (8% x 100) Opportunity = 30 (30 - 8).