Machine learning for organic growth
How can you improve your link building strategy?
What modern techniques can help you boost organic growth?
What are the most important link metrics to consider for organic search visibility?
Google knows it, and its machine learning capabilities know it, but you?
With Google now able to understand the broader context of your content through machine learning and natural language understanding, we find relevance has a significant impact on your search rankings.
It’s time to use machine learning’s advancements in relevance to help you personalize your link building strategy.
On October 26, I hosted a webinar with Beth Nunnington, VP of Digital PR and Content at Journey Further, and Steve Walker, Chief Technology Officer.
Nunnington and Walker demonstrated how relevant branded content is key to increasing visibility and traffic to gain SEO performance, with machine learning in mind.
Here is a summary of the webinar. To access the full presentation, fill out the form.
Key points to remember
- Focus on improving keyword relevance through digital PR.
- Don’t rely on volume or “domain authority” metrics to measure success.
- Understand that the relevance of your link profile can give you a competitive advantage.
- Creative “Link bait” campaigns will only get you so far.
- Product-focused PR gets results and can be used to target specific areas.
[Get access to the full on-demand webinar] Access the webinar instantly →
Top Considerations for Organic Growth
Relevant links and content can boost organic performance.
And when it comes to building relevanceThere are four key areas of relevance you need to consider:
- Public interest.
- Brand authority.
- Relevance of keywords.
- Current relevance.
When it comes to developing your digital PR or content marketing strategy, topic relevance should be one of your main goals.
[See how Ikea does it] Access the webinar instantly →
The next step is link building.
Google thinks relevance is important, and John Mueller mentions that the total number of links doesn’t matter.
So if the number of links doesn’t matter, how do you measure relevance?
Machine learning can help with this. Here’s how.
How to use machine learning to measure relevance
Machine learning lets you measure and understand content at scale.
With the use of tools, you can get a quantitative score that can measure the relevance of an article.
By submitting your website’s entire link profile to these tools, the machine learning can then read all of those links, as well as any articles containing the links.
Then you will have get a list of the most common topics, keywords, entities, sentiments and scores.
With it you can get information on topics, themes and concepts that you can use to improve your strategies.
[See in machine learning in action] Access the webinar instantly →
Drive organic growth with relevance
Several studies have shown that relevance is positively correlated with organic market share.
The key to prioritizing quality over quantity is to focus on content that is relevant to the keywords you want to rank for and your target audience.
[Find out how a search engine and a robot view the backlink profile of a business] Access the webinar instantly →
[Slides] Smarter Link Building: How to Use Machine Learning to Accelerate Organic Growth
Here is the presentation:
Join us for our next webinar!
How to Build a Winning MarTech Stack in 2023
Join iQuanti’s VP of Digital Solutions Vishal Maru and Shaubhik Ray, Senior Director of Digital Analytics, along with Tealium’s Josh Wolf, Director of Partner Solutions Consulting, as they discuss the implications, benefits and disadvantages of the main MarTech platforms.
Featured Image: Paulo Bobita/Search Engine Journal