NOTE: After set up, it takes about half an hour for Schema App to schedule the crawl. Every 500 pages will take approximately 1 hour to crawl.
ACTION: If you haven’t already done so, we recommend benchmarking your site’s current performance before you’ve implemented schema markup so that you have a starting reference point. Many of our customers capture:
Segment the data by specifying a time period. To view a complete month, select the “Custom” field.
Once a timeframe has been selected, it will be displayed as follows:
Google Search Console has some predefined comparison periods. Look under the “Compare” tab (within the “Date Range” selection tool).
Be cognizant of trends that may occur on certain days, such as increased site traffic on a Saturday, Sunday or holiday. If this pertains to your business, manually select a start and end date to align with a day of the week (e.g. Saturday or Monday). Be sure to adjust your start date to account for this even when you are comparing year over year data.
We recommend calculating the change in growth rate by determining the difference between the average growth (month-over-month) before and after schema markup.
Be sure to use the same number of months before and after for an accurate representation of this change.
Example: Compare the ‘rich results’ click-through rates to the click-through rate of the site as a whole. We can assume that if the rich results have a higher click-through rate, then they are outperforming the other pages and skewing the sitewide click-through rate up.
The first chart shows the performance with rich results, while the bottom chart shows the performance of the entire site.
In this example, the rich results have a higher click-through rate and achieve a better average rank position than the other results.
If the pages weren’t previously receiving rich results you will likely see a steep increase from zero. Rich result page performance can be compared to other page performance to view the impact.
The “Query” filter allows you to filter out queries containing (or not containing) any given keyword. Using this functionality, you can differentiate “branded” versus “non-branded” content. But note that this can be tricky.
For example, if Schema App wanted to filter using, “Queries not containing” > “Schema”, we would inadvertently omit valid non-branded queries that are about schema markup and not Schema App specifically. Therefore, filtering out “Schema App” would likely be the best approach to see only non-branded site performance.
Blog posts can be key drivers of traffic and can typically be compared using non-branded queries when compared to other site pages. To capture the metrics, combine a “Query” filter and a “Page” type filter to see how the non-branded traffic has changed over time, as shown below.
Sometimes errors show up in Google’s Search Console that aren’t showing up in Google’s Structured Data Testing tool. When this happens it is difficult to know which tool is providing you with accurate information. Our team has uncovered some misattributed errors in the Search Console. If you are running into a similar issue, check out our blog, “How To Resolve Misattributed Errors In The New Google Search Console” for a complete walk-through.
Now you can better understand your visitor behaviour, by pulling any of the schema.org properties you’ve marked up, into your Google Analytics.
Google Analytics allows you to track and report website traffic at the URL level. Schema App’s Enhanced Analytics reports allow you to dive deeper and learn about your traffic at a more granular level. Imagine being able to understand which elements on a page are generating the most interest! By adding your schema entities into Google Analytics, you can get more information out of your site and understand your visitor behaviour in greater depth. The opportunities are endless!