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Structured data is often recognized for its ability to enhance search results through rich snippets, yet this only scratches the surface of its true potential. In the realm of SEO, structured data plays a critical role by enabling search engines to understand better and interpret the context of website content. It helps transition from mere “strings” of words to meaningful “things” or entities, thereby creating a more intricate and interconnected web of information.
This standardized format is used to provide information about a page and categorize its content, thereby establishing the semantic context of entities and illuminating the relationships among different pieces of content across the internet. This capacity to attribute specific meanings to entities facilitates a deeper understanding of online content.
Search engines like Google leverage structured data to comprehend the content of a page and to amass knowledge about the broader web and the world at large. From an SEO perspective, structured data is critical in helping search engines understand the essence of content in relation to user intent. The aim is to align our content with what users are searching for, increasing the likelihood of our content being presented as the most relevant answer to a user’s search query.
In this case study, we sought to examine the effect of incorporating BoatTrip structured data into the ferry route pages of a travel website. Our goal was not only to measure the impact of this change but also to build a compelling business case for dedicating the necessary resources to implement this change across the website.
Our central hypothesis for this experiment was that implementing BoatTrip structured data, a schema.org type that doesn’t produce rich snippets would positively affect the organic traffic directed towards the ferry route pages of the website under scrutiny.
This hypothesis stems from the basic understanding of structured data’s role in SEO, it’s potential to enhance a search engine’s comprehension of webpage content, and its ability to create stronger connections between relevant pieces of information on the web.
Our hypothesis rests upon several underlying assumptions, which we will detail as follows:
- Improved Search Engine Understanding: By adding BoatTrip structured data, we provide search engines with explicit, meaningful information about the content on our ferry route pages. This could lead to better indexing and understanding of the page content by search engines.
- Alignment with User Intent: The structured data may help align the content of the ferry route pages more closely with user search intent. By clearly defining the content as pertaining to boat trips and ferry routes, search engines could more accurately match these pages to relevant user queries.
- Competitive Edge: Since not all websites utilize structured data effectively, implementing BoatTrip structured data could give the website a competitive edge in search results.
- Relevance of Traffic: Incorporating BoatTrip structured data might increase the relevance of the traffic to the website. That is, it could attract more users who are specifically interested in ferry routes and boat trips.
For this experiment, we employed SplitSignal, to set up a controlled test environment on approximately 1,500 route pages of the target website.
BoatTrip structured data is a new concept in the area of structured data, particularly as defined by Schema.org. It is meant to provide a specific and detailed set of information related to commercial ferry trips, allowing travel websites to give precise and comprehensive data about such services to Google.
We developed a custom script to incorporate the BoatTrip structured data into these pages. This script was designed to extract all relevant information from each page and reformat it into a standardized schema.org format.
Given that the BoatTrip type is not officially part of the core schema.org vocabulary and currently resides in the pending section, we decided to double-type the schema as both BoatTrip and Trip. This tactic was employed to maintain the validity and effectiveness of our structured data, facilitating Google’s understanding of the page content.
The test was run over a period of 28 days, providing a substantial timeframe for search engines to crawl and index the changes made to the pages. During this time, we could ascertain that Googlebot visited over 92% of the pages included in the test.
We discovered that adding the BoatTrip structured data resulted in a notable increase of 5.2% in organic clicks directed toward the tested pages. This increase signifies a substantial improvement and offers compelling evidence of the effectiveness of implementing structured data.
The strength of our results is underscored by the statistical significance observed in the test. In the cumulative view, when the blue shaded area falls either below or above the x=0 axis, the test is deemed statistically significant at the 95% confidence level. This level of significance indicates that we have high confidence that the addition of BoatTrip structured data will have a positive impact on organic traffic to the website’s listing pages.
As the test progressed, we saw a continued rise in clicks to the tested pages, culminating in a confidence level of 99%. This elevated confidence level further solidifies the efficacy of the change we implemented.
It’s important to note that this test was conducted on the U.S. version of the website. However, we also ran comparable setups in countries like France and Germany, witnessing similar results.
Note that we are not comparing the actual control group pages to our tested pages but rather a forecast based on historical data. The model predicts the counterfactual response that would have occurred had no intervention occurred. We compare this with the actual data. We use a set of control pages to give the model context for trends and external influences. If something else changes during our test (e.g., seasonality), the model will detect and consider it. By filtering these external factors, we gain insight into the true impact of an SEO change.
Our experiment demonstrates that using structured data to enrich search results with rich snippets is just the beginning of its potential impact. Using structured data to provide search engines context and interconnect web pages semantically can significantly enhance SEO performance.
A closer look at our data reveals that the impact of this test extended beyond simply increasing clicks to the tested pages. Compared with our modeled control group, we also observed a rise in impressions, as the SEO A/B test analyzer corroborated.
Upon further analysis, we discovered that this surge resulted from improved page positioning and enhanced visibility across a broader range of search terms.
This change has heightened the relevance of the tested pages. By implementing structured data, we enabled Google to evaluate each page and its content better, thereby enhancing its ability to match the content with a user’s search intent accurately.
However, it’s essential to remember that what works for one website might not yield the same results for another. Every website is unique, with distinct content, audience, and goals. The only definitive way to ascertain what works best for your specific situation is to perform your tests. By doing so, you can tailor your SEO strategy to meet your unique needs and objectives, maximizing the potential for success.