Looks like some of the SERP position tackers are noticing a relatively big change, in SEMRush I can see a 5.5/10 volatility score in the US, it was relatively stable in AU with a 1.5/10 in Australia. Check out how some of the SERP Fluctuations tools reacted to it: Mozcast, Algoroo, Advanced Web Rankings. Keep an eye on your ranking folks, we will have a better idea about the impact in the next few days.

Looks like Google might be testing a new layout on desktop SERPS for the People Also Search For SERP feature. In this new test, it appears on the right-hand side similar to how a Knowledge Panel would appear.

Sample Query: Try searching for Sample Query on Desktop. I was NOT able to trigger the result from Australia or USA.

This looks like a new format for Google News where you have multiple topics each with their own carousels.

Sample Query: Try searching for Axis Bank on Mobile or Desktop. I was NOT able to trigger the result from Australia or USA.

A lot of tests on Google today, this is a test on Goole Maps where you have a scrollable carousel for the various businesses that matches your query. I think this gives users an easy way to switch between the different results, iF this gets rolled out make sure you have a really good image to improve your CTR.

Bill Slawski breaks down a newly granted Google patent Ranking Image Search Results Using Machine Learning Models that sheds some light on how Google might rank images based on a combination of the query used, features of the images and features of the landing page using various Machine Learning models.

As always I highly recommend you go check out Bill’s breakdown for the full details, this is just a short summary:

Features Extracted from the Image Search Query

• Language of the search query
• Some or all of the terms in the search query
• Time that the search query was submitted
• Location from which the search query was submitted
• Data characterizing the user device from which the query was received
• So on

Features Extracted From the Image:

• These features can include vectors that represent the content of the image.

Features Extracted From the Landing Page: 

• The date the page was first crawled or updated
• Data characterizing the author of the landing page
• The language of the landing page
• Features of the domain that the landing page belong to
• Keywords representing the content of the landing page
• Features of the links to the image and landing page such as the anchor text or source page for the links
• Features that describe the context of the image in the landing page
• So on

Features Extracted From The Landing Page That Describes The Context of the Image in the Landing Page

• Data characterizing the location of the image within the landing page
• Prominence of the image on the landing page
• Textual descriptions of the image on the landing page
• Etc.

Check out Bill’s post on more info on how it all comes together and how the initial ranking scores are adjusted.