Google Analytics is the world's most popular analytics platform – and Omm will never use it. Google Analytics collects more data than most people realise, and can infer users' specific location, age, gender, and other personal details by monitoring their browsing habits across the internet.
To assess the impact of marketing campaigns, blog posts, and app/website changes, the Omm contributors use Plausible.
Plausible is a privacy-friendly, open-source analytics tool. It doesn't place cookies on peoples' devices, and is fully compliant with GDPR, PECR, and CCPA privacy regulations in the EU, UK, and California, respectively.
The Plausible tracking script is smaller than 1KB, compared to 46KB for Google Analytics and Google Tag Manager combined, so Plausible is also a better choice for those who care about page performance.
Most web owners collect more information than they need or even have the skills to interpret. A better philosophy would be to collect what’s useful and nothing more. Plausible focuses on a few key data points: page views, unique users, bounce rate, and average visit duration. Learn more about each metric and how we interpret them.
Page views: the objective metric
Every time a visitor loads a page on the Omm marketing site, app, blog, or forum, Plausible registers a single page view. If someone visits the page 5 times, it'll register 5 views.
This seems self-evident, but it's a useful measure because it makes no assumptions about user activity (something we'll cover more below).
Unique users: a more nuanced measure
Unique users may seem like a more helpful metric — after all, how do we know that the 980 page views on 11 January weren't from one person refreshing the page 979 times?
The problem is that it can be hard to identify a unique user. Plausible does its best by using a visitor's browser and IP address to generate an identifier, which changes daily. If someone with the same browser and IP address visits again within the same day, they would register a second page view, but not a second unique user.
This approach is imperfect, as it makes assumptions about users. What if a user visits from more than one device (so they have more than one IP address)? What if they use a different browser each time? What if they have additional privacy protections that change their IP address?
Plausible can't address these scenarios, because it intentionally does not gather the information necessary to answer these questions. It must assume that if a user's IP address and browser don't match an already-recorded visit from the same day, they must be new.
Bounce rate: the most ambiguous stat
A 'bounce' is when a user lands on any page of a website, and then leaves without navigating to another page on that site. It sounds simple, and it sounds like bounces are bad – but it's more complicated than that.
First, what counts as a bounce? If a user lands on a page and then navigates to a different page 6 hours later, should it count as a bounce followed by a second visit, or a single visit with 2 page views?
To answer this, Plausible sets a 30-minute session time. If a user takes an action (like loading a page), then takes no further action for 30 minutes, they're considered to have ended their browsing session.
The above example would register as a bounce, with an average time-on-page of 0 seconds (because it's impossible to know exactly when they navigated away) – followed by a second visit.
But can a bounce be a good thing? Imagine a scenario where a user clicks through to the Omm website from some DuckDuckGo search results, and immediately sees the answer to their question. Satisfied, they leave without navigating to any additional pages. In this scenario, they've registered a bounce but the website has met their expectations, so the bounce is a mark of success, not failure.
So while we do track bounces, and all else equal we appreciate a lower bounce rate, it's not always possible to know if bounces are good or bad.
Visit duration: how engaged are visitors?
As with bounce rates, Plausible makes some assumptions to calculate a visit duration. If no action is taken within 30 minutes, a user is assumed to have left the site and their time on the last page is recorded as 0.
Sources and campaigns
Now that we've covered the four basic metrics, it's time to look at traffic sources. A 'source' is where a visitor came from. If they clicked through to omm.finance from some Google search results, then Google would be identified as the source.
These are the top 10 sources for omm.finance this year:
|Direct / None
Most are self-explanatory: 491 users came from CoinMarketCap, for example. But a couple need further discussion:
Direct / None are users who didn't come via another website. They likely either:
- Typed omm.finance directly into their address bar, or
- Clicked a bookmark/favourite in their browser, or
- Clicked a link in an email (or other app) which took them to omm.finance without indicating where they came from, or
- Have strong privacy settings that hide the referring website.
Dynamic reflects social media campaigns run by the Omm Monks. In Google Analytics, the word 'dynamic' would be automatically replaced with the name of the social network that hosted the campaign – but that's currently not possible with Plausible. However, the presence of 'dynamic' is useful, as it clearly shows that the Monks brought 1.7k users to omm.finance during their campaign period (mid-March to mid-April).
Using Plausible, we can isolate just the users that came via the Monks' campaigns:
These campaigns were successful at bringing new users to omm.finance, but overall they had a higher-than-average bounce rate (94%) and an average visit duration of 5 seconds.
This pattern is quite normal for social media advertising, so a 6% engagement rate (the percentage of users who didn't bounce) is reasonable. And if the bounced users are removed, the remaining 102 users viewed an average of 3 pages each, which shows a good level of interest.
Interpret with caution
Plausible can provide additional information (i.e. pages visited, device size/browser/OS, and general location), but the four key metrics – page views, unique users, bounce rate, and visit duration – are the most useful for spotting any sudden changes in traffic (good or bad), and identifying possible areas for improvement.
Apart from page views, all metrics are imprecise – even campaign tracking can be limited if users have strict privacy settings – so at best, web analytics can be seen as a useful guide to patterns and trends rather than a precise record of activity. But even with Plausible's privacy-friendly approach, we can still gain useful (anonymised) information to help us assess the impact of marketing campaigns, blog posts, and general website changes.
If you'd like to contribute some page views to Omm's anonymous, ethical, GDPR-compliant analytics, visit these websites anytime:
If you'd prefer to avoid being tracked by Omm or any other website, there are a few options: