Ubersuggest 5.0: Generate 1 Million Keyword Suggestions in 7 Seconds (Seriously)

Ubersuggest started out as a tool that provided suggestions through Google Suggest.

And although that’s great, everyone these days is using
Google Suggest to come up with keyword ideas.

There have to be more keyword ideas out there that get more search volume and aren’t competitive, right?

Well, there are, and now with the new Ubersuggest, you’ll get access to them.

Here are the 2 big changes I am making with this release…

Introducing a new keyword database

Because we have results in our database for more than a billion different keywords, I thought it would be fun to tap into it and provide you with even more keyword suggestions.

Now when you perform a search on Ubersuggest for any keyword, you’ll see a “related” tab with even more suggestions.

And each tab shows you how many keywords are in that group.

As you can see for the term “digital marketing”, just in the United States there are over 30,000 keyword recommendations.

And for terms like “dog”, there are over 1 million keyword recommendations.

dog

Don’t worry though, results from Google Suggest are still there under the “suggestions” tab but you can now see even more search terms if you click on “related”.

What’s cool is that you can even export all of the keywords via CSV.

And if you want to leverage the filters to fine-tune the results, you can easily do so.

filters

For example, I used the filters setting to find keywords with a minimum search volume of 400 searches a month and a maximum SEO difficulty of 50. Ubersuggest then fine-tuned the results to 489 keywords related to “digital marketing” that I should consider target instead of me having to manually go through 30,000 plus recommendations.

And with the CSV report, it adjusts as the settings with the filters change. So you can export the keywords that you want and ignore the keywords you aren’t planning to target.

Ubersuggest now has local search

Another big change that we made to Ubersuggest is that we
introduced local keyword research.

You can now pull up keyword stats and ideas on any city, county, region, or country.

For example, if I want to know the search volume and keyword
recommendations for West Hollywood, California, I can now do so.

From there, Ubersuggest shows keyword search volume, keyword recommendations, and even content ideas for a blog post.

On top of that, when you head over to the “keyword ideas” report, you’ll also notice that the SERPs results, which shows all of the sites that rank for that term, are now adjusted to also show the ranking sites within that region.

So, what’s next for Ubersuggest?

Well, speaking of keyword research, you’ll start seeing keyword recommendations based on questions, comparisons, and prepositions like Answers the Public within a month.

And, of course, as I promised earlier, next Tuesday I am releasing the rank tracking and dashboard features on Ubersuggest.

If you haven’t already, head on over to Ubersuggest to give the new keyword database a try.

And if you are trying to use the local SEO features, you may find that they only work once you are in the app.

I haven’t been able to make the changes to the main
Ubersuggest landing page yet, but once you type in a keyword and test it out,
you can then switch your location to any city.

So, what do you think of the changes?

PS: Make sure you test Ubersuggest out.

The post Ubersuggest 5.0: Generate 1 Million Keyword Suggestions in 7 Seconds (Seriously) appeared first on Neil Patel.

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New comment by deepmodel in "Ask HN: Freelancer? Seeking Freelancer? (July 2019)"

SEEKING WORK

Location: Germany, work world-wide

Remote: Yes, remote-only

Willing to relocate: No

Technologies: Keras/TensorFlow (recent arXiv models), Python, PySpark, mostly computer vision & large scale 3D processing and visualization

Résumé/CV:

– manufacturing defect detection for automotive industry with Deep Learning (<2% real-world false positive rate)

– detecting crime in progress from pair-wise spatio-temporal relationships of human pose estimates in continuous video feed using time-distributed attention-enhanced ConvNet-RNNs

– 3D indoor reconstruction using SLAM, randomized non-linear optimization, semantic segmentation and depth estimation from mobile video feed (ensemble)

– Mobile traffic anomaly/fraud detection (GBT + discrete VRNN)

– Image content filtering using Deep Learning (DenseNet)

– Diagnosing lung diseases from X-Ray images (CheXNet), surpassing human level performance

– End-to-end self-driving car control (NVidia Dave2Net)

– Top 10 US school MS education; detailed CV upon request

Email: deepmodel (at) protonmail.com