Rich snippets are a feature of structured data frequently recognized for improving search results. Surprisingly, they are only the tip of the iceberg at this point. Structured data is essential to SEO as it helps the search engines understand and interpret the context of a website’s content.
It aids in transforming abstract “strings” of words into concrete “things” or entities, resulting in a more complex and interconnected web of information.
The semantic context of entities is established, and the relationships between various pieces of content on the internet are illuminated by using this standardized format to provide information about a page and categorize its content. The ability to assign particular meanings to entities makes it easier to comprehend online content more thoroughly.
Search engines like Google use structured data to interpret a page’s content and gather information about the wider web and the world. In terms of SEO, structured data is essential for assisting search engines in comprehending the core of content concerning user intent. The goal is to match our content with what users are looking for to increase the possibility that it will be presented as the most appropriate response to a user’s search query.
In this case, study, KitoInfocom looked at the impact of adding BoatTrip structured data to a travel website’s ferry route pages. Our objective was to create a convincing business case for allocating the necessary resources to implement this change across the website, in addition to measuring the impact of the change.
The SEO Split Test Results Hypothesis
Our primary assumption for this experiment was that adding BoatTrip structured data, a schema.org type that does not produce rich snippets, would positively impact the organic traffic going to the website under investigation’s ferry route pages.
This claim is based on a fundamental understanding of structured data’s function in search engine optimization (SEO), its potential to improve a search engine’s comprehension of webpage content, and its capacity to strengthen connections between pertinent pieces of online information.
Our hypothesis is supported by several underlying assumptions, which we will list here:
- Improved Search Engine Understanding: We give search engines explicit, useful information about the content on our ferry route pages by adding BoatTrip structured data. Search engines can index and comprehend the page’s content more effectively.
- Alignment with User Intent: The structured data may assist in better aligning the information on the ferry route pages with user search intentions. Search engines could more precisely match these pages to pertinent user queries by defining the content relating to boat trips and ferry routes.
- The advantage over competitors: Because not all websites use structured data to its full potential, adding BoatTrip structured data could give the website a boost in search results.
- Relevance of Traffic: Including structured data from BoatTrip may make website traffic more relevant. In other words, it might attract more users keen on ferry routes and boat trips.
To set up a controlled test environment on roughly 1,500 route pages of the target website for this experiment, we used SplitSignal.
In the world of structured data, specifically as described by Schema.org, the “BoatTrip structured data” concept is new. It is designed to give Google a precise and comprehensive set of information about commercial ferry trips, enabling travel websites to provide that information.
We created a unique script to integrate the BoatTrip structured data into these pages. This script was created to extract all pertinent data from every page and reformat it using the schema.org standard.
We chose to double-type the schema as both BoatTrip and Trip because the BoatTrip type is not formally a part of the core schema.org vocabulary and is currently found in the pending section. This strategy was used to ensure the accuracy and efficacy of our structured data, making it easier for Google to comprehend the page’s content.
The test was conducted over 28 days, giving search engines ample time to crawl and index the pages’ changes. We determined that Googlebot visited over 92% of the test pages during this time.
We found that including the BoatTrip structured data led to a 5.2% increase in organic clicks on the tested pages. This increase represents a significant improvement and provides convincing proof of the value of using structured data.
The statistical significance seen in the test emphasizes how strong our results are. When the blue shaded area in the cumulative view is below or above the x = 0 axis, the test is considered statistically significant at a 95% confidence level.
With such a high degree of confidence, we can say that including BoatTrip structured data will significantly improve organic traffic to the website’s listing pages.
We observed a steady increase in clicks to the tested pages throughout the test, reaching a confidence level of 99%. This increased level of assurance confirms the success of the change we implemented.
Remembering that this test was carried out on the website’s American version is crucial.
However, we also conducted similar experiments in nations like France and Germany and saw identical outcomes.
Please note that we are making a prediction based on historical data rather than comparing the actual control group pages to our tested pages. The model forecasts the outcome that would have happened if there had been no intervention. This is contrasted with the actual data. We use a set of control pages to provide the model with context for trends and outside influences. The model will recognize and consider any additional changes during our test, such as seasonality. We can see the true effects of an SEO change by filtering these outside variables.
Our study shows that the potential impact of using structured data to add rich snippets to search results is just getting started. SEO performance can be greatly improved by using structured data to give search engines context and connect web pages meaningfully.
The results of this test were more significant than just an increase in clicks to the tested pages, as shown by a closer examination of our data. We also noticed an increase in impressions compared to our modeled control group, which the SEO A/B test analyzer confirmed.
Further investigation revealed that this increase resulted from better page positioning and increased visibility across a wider range of search terms.
The relevance of the tested pages has increased due to this change. By implementing structured data, we allowed Google to assess each page and its content more, improving its capacity to match the content with a user’s search intent accurately.
It is important to remember that what works for one website might not produce the same results for another. Every website is distinct, with a unique audience, target audience, market, and objectives. Testing is the only surefire way to determine what is most effective in your particular circumstances.
By doing this, you can customize your SEO strategy to meet your specific needs and goals, increasing the likelihood of success.