GetResponse is a well-known email marketing software that operates internationally for more than 20 years. Because of its global approach, GetResponse has twenty-six location-specific versions of their site.
Twenty-six! That’s a lot. Especially if you want to optimize all of them for SEO since it’s your primary traffic source.
And let’s be clear here: GetResponse had done a fantastic job before they even heard about Surfer. However, their team loves innovating and looking for new ways to automate their work. There’s always room for improvement, right?
One of the ingredients of their SEO success is Natural Language Processing (NLP) optimization with Surfer.
Before GetResponse implemented NLP in their content processes for good, Oksana Kosachenko, SEO Content Manager, decided to experiment with a few valuable keywords to check if Surfer is worth the time and effort.
If you ever wondered whether optimization with Surfer works for non-English speaking markets, this article will give you the answer. We’ve seen English businesses succeeding with Surfer tools like Moon & Own, FLDTRACE, and astrology affiliate website.
Today we will focus on the SaaS marketing industry and the Russian market, so you’ll learn:
- How GetResponse utilizes NLP in their content creation and optimization
- How they improved their positions in the top ten for competitive, marketing keywords
- If optimizing content with Surfer works in the Russian market as well (spoiler alert: YES, IT DOES)
Quick reminder: What is NLP?
In October 2019, Google announced a new BERT algorithm that allows bots to understand the context and purpose better. BERT stands for Artificial Intelligence-based Bi-Directional Encoder, i.e., Google determines the meaning of a word or phrase using both the preceding and the following content.
This Google algorithm uses NLP, which affects on-page and off-page optimization (SEO). But to be more precise, NLP changes the way we understand queries in general and each word individually.
NLP in SEO comes down to the three most essential elements: sentiment, entities, and salience.
An entity is a word or phrase representing an entity that can be identified, classified, and categorized, such as people, products, events, numbers, organizations, etc.
Since Google distinguishes between these entities, the search engine can use the information obtained to satisfy the user and provide better search results. That’s the challenge of modern on-page SEO: how to find relevant entities and use them in the right places and density.
That’s where Surfer comes in.
Experimenting with NLP and Surfer for three articles
According to Oksana:
At GetResponse, we use Surfer to compile the semantic core and optimize existing content. The purpose of our experiment was to determine the influence of entities on the site's position in the search results.
Initially, we selected three texts that were already well-ranked in the TOP 10. We excluded the influence of other factors as much as possible and used Surfer to compare articles with our organic competition.
Using the SERP Analyzer tool, Oksana created three projects with the necessary keywords to find and analyze NLP words.
The tool automatically analyzes the query and creates a project. To find NLP entities, you just need to click on the Audit button next to the page of your choice.
I love that the process is fully automated, you don't need to do anything manually, and the list is ready in a few minutes. Otherwise, we would be forced to analyze top-ranking pages with Google NLP API, which would take at least an hour per one keyword.
Oksana analyzed the list of words and phrases and added as many NLP entities as possible within the suggested range.
A significant nuance should be emphasized – the text or fragments in which you add NLP words should be of value to readers. That is why the total number of words did not matter to us in this case, as we tried to supplement the publications with the most useful content.
For example, in the publication about Pinterest (screenshots below), I added fragments with words such as boards, social networks, Pins, trends, decor, online stores, etc. For some reason, these points were not touched upon in the article earlier, but they are important to readers. And now, even if we remove the main keyword (Pinterest) from the text, Google, analyzing the entities, will understand that we are talking about Pinterest.
The results of the experiment
The whole optimization process was straightforward and easy to implement.
To track changes, we used Serpstat. I wanted to be sure we understand the starting point and the results of the experiment. A week after we added the new content to our articles, we got quite a great outcome.
Just after two weeks, Oksana noted the following results:
- improved visibility of tested pages;
- approximately 90% of keywords have improved their positions;
- the content began to rank (in some cases even in the top 10) for new long-tail keywords. And they didn’t even use exact matches for them!
The sample of this experiment was small, but it proved that Surfer does the job, and it’s worth implementing on a broader scale. Content creation is much more complex than it has ever been. We need better content, an analytical approach, and an excellent presentation of everything we produce. We need more specialized tools that will help us deliver results. And yet, Surfer simplifies a big chunk of the work for us.
Of course, the number of words plays a significant role in SEO. But first of all, the texts should be useful for users and solve their problems. With NLP, we can create more relevant content that will meet our users and Google’s expectations. We keep on using Surfer and continuously get results from it. And I do not doubt that they will work for other SEO teams in Russia and other markets.