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We have been discussing setting up analytics on the website for a while (forum: Add Plausible Tracking to DjangoProject.com?). At this point I think there is a pretty clear case for it and it’s more of a matter of deciding what tool(s) we could use and setting them up.
So I think opening this as a GitHub issue will help make this more of a task that we want to happen, not just an idea.
Tasks
Discuss analytics use cases and potential problems
List possible approaches
⌛️ Try out 2-3 promising approaches/ tools
Check with ops team on self-hosting
Pick top 1 and top 2 options
Set up the most relevant options on the site
Access for website WG, fundraising WG, Board, and any other relevant stakeholders
Tools considered so far
For the dedicated analytics / tracking tools, likely options:
We want something that does not require cookies, is compatible with privacy laws (GDPR, CCPA), ideally has minimal to no impact on performance for site users, ideally allows us to get good reports with little efforts. I think there are three approaches that are viable:
Reach out to Fastly to see if we could get access to their analytics product, and use that.
Set up a dedicated JS-based analytics beacon
I have no experience with Fastly analytics / logs tools, but from my experience with Cloudflare’s equivalent this is pretty viable to understand popular pages and high-level geographic audience details. That’s about it. Big benefit is it doesn’t require any additional, potentially invasive tracking.
Those come with the clear drawback of loading more code for users, and tracking more data than we would often want. But I think there are ways to mitigate that - picking the least-intrusive option, or only having tracking turned on occasionally.
Use cases
Just reiterating the use cases we have - here is what we want to know:
Rough geographic distribution of our audience across countries
Page views / Session counts site-wide
Top landing pages
Hits per search queries
Bounce rate of specific key pages (fundraising, docs, etc)
To help illustrate the benefits of this kind of data, I wrote a blog post about the Python docs analytics: What we can learn from Python docs analytics. For Django, we would use this data to:
Know what information is so popular it might warrant more attention / restructure
Decide which translation efforts to encourage
Revamp the docs based on common searches
Understand where there is friction in our donation flow
Relevant existing issues that this data would support:
We have been discussing setting up analytics on the website for a while (forum: Add Plausible Tracking to DjangoProject.com?). At this point I think there is a pretty clear case for it and it’s more of a matter of deciding what tool(s) we could use and setting them up.
So I think opening this as a GitHub issue will help make this more of a task that we want to happen, not just an idea.
Tasks
Tools considered so far
For the dedicated analytics / tracking tools, likely options:
Google Analytics (industry standard but requires cookies)Requirements
We want something that does not require cookies, is compatible with privacy laws (GDPR, CCPA), ideally has minimal to no impact on performance for site users, ideally allows us to get good reports with little efforts. I think there are three approaches that are viable:
I have no experience with Fastly analytics / logs tools, but from my experience with Cloudflare’s equivalent this is pretty viable to understand popular pages and high-level geographic audience details. That’s about it. Big benefit is it doesn’t require any additional, potentially invasive tracking.
Those come with the clear drawback of loading more code for users, and tracking more data than we would often want. But I think there are ways to mitigate that - picking the least-intrusive option, or only having tracking turned on occasionally.
Use cases
Just reiterating the use cases we have - here is what we want to know:
To help illustrate the benefits of this kind of data, I wrote a blog post about the Python docs analytics: What we can learn from Python docs analytics. For Django, we would use this data to:
Relevant existing issues that this data would support: