As always with SEO there are several workable solutions - each with its all sets of pros and cons and necessary actions. This is true for dealing with multinational SEO as well.
In this post I would like to share some of my experience and I very much hope you will share yours too
Even though Shopify support multi-language/region/domain shops within a single install as I understand it that is most often not the best solution - for reasons such as:
The more detailed you can adjust each of your international webshops the better. It can not only give you more visibility in search but also better usability and conversions.
There are several areas that can and should be locally adjusted:
Setting up individual shops to target each country appear to be - all in all the best strategy on Shopify. This will give you the best options to adapt locally. However, this also introduce some challenges. One of them is Hreflang data.
Hreflang data is structured data and as such have to be 100% correct to work. Even minor errors can have a dramatic negative impact. I was just dealing with a huge global B2B client that had most of their international sites completely disappear in Google. I found the errors in the Hreflang data we fixed and they are now back - more visible than ever.
But with a multi install structure how do you deal with Hreflang?
If you do not localize URL it is easy to crawl all sites, with a service such as Hreflang Builder, an have them create XML-files with the Hreflang-data. However, if you do localize URLs - as I prefer to do, then that becomes more difficult.
One of the solutions for this that I am working on is appending SKU (which is most often the same across local shops) to the end of URL's and then setup a custom filter for matching.
But maybe there are other/better ways. What do you do?
You make many great points about the localization of content, common mistakes, and technical limitations of using a single Shopify store.
Hreflang tags for a business with a multi-store global strategy
is was their greatest limitation to good SEO. Google's John Mueller described hreflang tags as easy to implement as meta tags, but it quickly becomes one of the most complex aspects of SEO.
For Shopify, many businesses think you can have good hreflang tags with some liquid code that uses the canonical url tag. This complexity reveals itself real quickly:
This is just a sample of how messy hreflang tags can get for Shopify stores who want to do their SEO well.
To solve it, we created Multi-Store Hreflang Tags app this year and made it public. It solves these messy issues all the while letting stores save days in work with automatic mapping and an easy to way to keep this up-to-date.
I think you'll find the app's FAQ helpful in understanding more about these SEO challenges and solving them in Shopify.
Just wanted to address some of your points ...
I agree - Hreflang-data can be one of the most complex issues of SEO to get right. That may be the reason so many sites makes damaging mistakes with this. In fact, the majority of the many sites I have analyzed make mistakes - such as wrong ISO-codes, missing handshaking, references to dead pages and lack of X-default.
I also agree that it's best practice to not include paginated pages in your your Hreflang and in most cases that is also true for Product variants. That is, unless you implement a strategy and structure where variants are optimized with unique content and allowed indexing. This is, for some shops a better strategy if there are many searches specifically for variants.
As far as I can see your app implement the Hreflang-data as embedded tags. Correct?
I do very much prefer to have it in XML-sitemap format. The problem with embedding is that Google need to crawl all pages to verify all data. For large webshops this often create handshaking issues because of the time lack in crawling. Having it all in one XML-sitemap remove that problem as Google can see all data at once. It also makes it a bit easier to debug for us.
In the case I refered to that was in fact part of my solution. We moved from embedding to XML and resolved many of the problems.
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