Frequently Asked Questions
Last updated
Last updated
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.
Yes. All clustering credits are applied upfront.
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.
No, credits do not roll-over. Any unused credits will be reset at the end of the billing cycle.
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.
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.
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.
Please watch this video as it explains this in more detail. https://snippet.wistia.com/medias/oaeqllw2a4
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.
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 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.
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.
Here is the list of supported languages.
English
German
Norwegian
Swedish
Danish
Spanish
French
Dutch
Ukranian
Russian
Hebrew
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.
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.
Most tools see how much volume a keyword gets to create an opportunity score, but they don't take into account two important things:
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.
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).
It's likely that you're currently using our older subscription plan, priced at $9.99 per month. We've made some changes, and we no longer offer support for this legacy plan. While we have ceased billing your account for this plan, we've ensured that you still have access to all your remaining credits for clustering. Our updated features no longer accommodate a universal credit system, so now, in order to continue using our services, you'll need to switch to a monthly subscription.
Your account has been flagged as spam by Google. Google's system is quite sensitive and can occasionally generate false positives. We apologize for any inconvenience this may have caused. Please reach out to our support team through live chat or email, and we will promptly remove the block. Thank you for your understanding.
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 .