Why is schema important?
To start this off on the right foot we need to first establish and understand why schema is important. Schema is a structured data vocabulary that helps search engines better understand the information on your website. When adding schema to your website, you get “rewarded” by having rich snippets in search engine result pages such as those on Google. These “rewards” take the form of visual changes that make your search engine results take on a new visual appearance, for the better. Here is an example using “Teddy Bear” as the search query:
Normal Search Result
Enhanced by schema result
Which one of these results feels better to you? Which result provides you more information? Which result would you rather click on?
The inclusion of images provides a LOT of context. The above examples don’t include review information, meaning there’s room for improvement even! Users who are informed, are more likely to engage with your product. Furthermore, the user gets a good idea of what you offer via these enhancements as a “picture is worth a thousand words”, which means the users who do visit your website are that much more likely to want your products.
Think of it this way: Schema is the digital equivalent of adding a window display to your brick and mortar store.
How do we measure the results of schema?
Now that we’ve defined why we want schema (pretty search results = more clicks, better user experience, more money), the real question becomes how do we define success with schema? This can be tricky as in general you’re not going to be ONLY adding schema to a site for a large chunk of time, which means other edits and optimizations may make the data harder to interpret. There are several areas that you can find good information from however, naturally these are Google Search Console and Google Analytics (be it GA4 or UA).
In Google Search Console you can measure:
CTR (Click Through Rate)
In Google Analytics you can measure:
Average Time on Page
It’s important to know that using these two sources of data is equivalent to looking at two halves of a user journey. Google Search Console covers the user journey as they explore and try to find an answer to their query in Google Search. Google Analytics covers what happens once a user has left search engine result pages and has entered your website.
Starting with Google Search Console, the simplest place to check for improvement would be your CTR. CTR, however, is one of the most finicky data points to work with. Almost ANYthing can influence your CTR, including macro and micro-economic trends all the way down to a typo in your meta description. A very common example of why CTR seems simple, but is deceptively complex, comes into play when you start to implement supporting keywords for (for example) products or service pages.
“But why would adding new keywords mess with my CTR?”
When you add supporting keywords and alternative keywords, you introduce an entirely new subset of queries to your query rank for the page in question. In doing so, especially early on when you’re establishing a solid SEO campaign, you usually will improve your clicks and on-page metrics, but it will appear that you have less CTR. To put it simply: You increased your visibility at the perceived cost of lowering your CTR. This makes it hard to define the ROI behind schema “at a glance”.
A Quick Schema Case Study
With the above information taken into consideration, we have the ability to look into a project we began back in November of 2022. A Baotris client on a Shopify store, was doing fairly decently, and historically had no formal SEO support.
So one of the first things we did was implement scalable schema. If you’re still manually coding every single schema item you add to a page, you can look into automating schema via either apps or you can use custom fields in most website CMS to create it yourself. Don’t know how to do this yourself? Reach out to us, we can help set your online store up for success regardless of the platform.
The dates of our case study will be:
January & February of 2023 v. November & December of 2022.
Two setup months as a baseline, v., two “implemented” months to see results.
Schema was successfully deployed and all errors resolved by the end of November. Furthermore, we added supporting keyword variants to the existing products on-site. All in all, we completed overhauling the keywords and schema for this specific client in the first two months of service. Now we can use Google Search Console to see if we’ve made a difference by comparing January & February of 2023, with the months of November & December of 2022.
Here’s what we get:
As you can see, the CTR did slightly dip! However, the average position also increased. These are the tell-tale signs of keyword variations being indexed and changing the playing field. Understanding the data is one of the single most important aspects of working with your website. If you ONLY look at CTR and Average Position (AKA: rank), you would think we’ve actually harmed the website. That said, clicks grew by around 400 more clicks, and impressions have increased beyond the 100k mark. We know we’re getting more visibility and clicks, which is due to the marriage of keyword variations as well as implementation of schema.
Now, let’s double down on our research. We know “good” things are happening on the website in search, but what about the pages users land on? Does schema really help improve a user's experience before they get onto your site? Does schema help improve your on-page stats by ensuring the right users find your website?
Note: The final data point in the above example is unbalanced. Since we’re comparing a range of 61 days (before) v. a range of 59 days. Despite being at a two day disadvantage, we still have positive momentum and can see plenty of improvement!
Here’s what the data says:
Average Time on Page:
We use this to measure a user's engagement. The more time spent on-page is, in general, better. There are niche situations where this does not apply, however.
A single user can generate multiple Sessions. We use this to measure engagement as well, it can help us understand how many times a user revisits your website. Essentially, the more sessions the better.
Bounce Rate is the only metric we want to actively see decreasing in this scenario. A “bounce” is when a user leaves a website without taking any actions on page. It implies a user simply isn’t interested, and leaves. It is the website equivalent of realizing you’re in the wrong shop.
As you can see, the data tells the story far more effectively than I can. ALL of the on-page metrics we can most closely associate schema with are showing improvement.
This implies that we’re not only improving visibility for this website by adding keyword variations, but that our new schema is also providing important information that prequalifies users before they even reach this website. This leads to improved retention of users, more sales, and a slew of other benefits. Furthermore, when set up properly you rarely have to modify your schema! That means your schema will just keep working its magic in the background while you expand your product or services provided!
Need help with your schema set up?
That’s ok, we can help! We’ve run the gambit with schema issues before and we will do so again. Schema should be easy to set up, scalable via automation, and overall an item that you update only when necessary. If you’re not finding this is the case for your website feel free to reach out to us and inquire about either a one-time project or our SEO services.
Frequently Ask Schema Questions
What is schema?
Schema refers to a structured data markup that is used to help search engines understand the content and context of a webpage. Schema markup is a type of micro-data that can be added to the HTML code of a webpage to provide additional information about the content on that page. This information can include things like the name of the page, the author, the date published, the organization that published it, etc.
How do I fix schema errors?
It depends on the error! Usually, you can use this handy tool provided by Google to source the specific errors and test the fix you’ve applied.
Does all schema work in Google?
Yes… And no. There is a subset of formally accepted schema that Google engages with. This information can be found in their Google Search Central documentation, I’d recommend starting with this page. In most cases you should be able to either request support from the app you use for schema, OR, you can manually edit your schema as well.
Does Google also support this concept?
Yes! Google fully endorses the schema that they accept. Our case study is meant to be an introduction into a much deeper topic, for further reading I’d recommend you take a look at one of Google’s own case studies about SEO investment and schema implementation!