Machine Learning Helps Bloggers Secure More Traffic with Long-Tail Keywords

Avatar
June 1, 2019
3,690 Views

 

Many bloggers get very frustrated after they have been working for a couple of months. They initially are excited about the possibility of making a six-figure stream of passive income. After they get started though, they discovered that the legwork can be overwhelming. The good news is that machine learning is making it much easier for them to create a successful blogging career, as Jeff Bullas points out.

One of the ways that machine learning has helped the most is with helping bloggers find profitable longtail keywords. A number of new keyword research tools are being released every year. They are not just available for bloggers trying to get more traffic on Google. They are also used for a variety of other platforms, such as Pinterest, Twitter and Instagram.

The importance of longtail keywords for blogging

New bloggers often have difficulty getting traffic to their websites. The problem is that they tend to focus on competitive, high-volume keywords. This is one of the things they learn to avoid when they start learning more about blogging.

The unfortunate reality is that they can be spending eons trying to get traffic this way. They would be better off focusing on a number of less competitive keywords that still provide a steady flow of traffic.

Here are two of the biggest benefits of targeting longtail keywords:

  • You can find lots of keywords that other bloggers are not targeting. As a result, you will have a much easier time getting to the top of the first page of Google for them.
  • Conversion rates for longtail keywords tend to be a lot higher because they are more specific. People searching for them have a more focused mindset and are often more committed to making a purchase. The average longtail keyword conversion rate is 36%, which is over three times as high as all keywords.

It is surprising how much traffic you can get from a long tail keyboard that seemingly has little volume. I used SEMRush to analyze the search engine rankings for a competitor of one of my websites. I noticed that this blogger was ranking for nearly 1000 keywords. However, the top five keywords were generating about 35% of all of her traffic.

Some of these keywords were surprisingly random and obscure, but they still brought in a strong amount of traffic and made her money with AdSense every day for the past eight years. One of her keywords was “how to build a wicker man,” which accounted for about 10% of all of her traffic.

When you see how well longtail keywords can perform, it is not surprising that many bloggers will use big data tools to discover them.

How machine learning can play a very important role in longtail keyword research

Previously, keyword research tools were not very good at identifying relationships between seed keywords and new keywords for marketing lists. Machine learning has helped keyword research tools find better contextual relationships between them.

For example, I run a blog about Wicca. I was using a keyword research tool called Keyword Shitter to find keywords related to love spells. Before these tools used modern machine learning algorithms, they would only find keywords that included the exact words that used to run my report.

However, this keyword research tool has much better machine learning capabilities. It provided keywords including the words “rituals” and “magic.” Even though they didn’t include the exact phrase that I used to start running my report, the tool was able to figure out that they were related.

This means that keyword research tools can provide bloggers with a much more exhaustive list of longtail keywords to choose from. Of course, they are still not perfect. Some keyboards are still omitted. Others that might be irrelevant might be included as well.

Despite the limitations, machine learning is still making these tools more effective than ever. The number of inappropriate results and relevant keywords that don’t get included will decrease as machine learning draws better correlations between various keywords.