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Why I ditched Google Analytics & MixPanel for PostHog
It took me until I built my second product (Reciprocal.dev) to really understand the value of analytics. I’d always landed very much on the side of such tools as being an invasion of privacy and questioned the need for front-end scripts when proper configured server logs could provide the same information.
The hammer that hit the nail on the head was reading Lean Startup and learning how customer behaviour metrics are key to performing the iterative small tweaks needed to align the product to the customer needs.
Instead of analytics being used to count page views and traffic sources they should be used to understand the points the product has to make a decision and the outcome of that decision, so that the product can be changed to remove the friction.
With that new found understanding I started looking at how we’d implemented analytics in my existing products.
Google Analytics
Google Analytics had always been my go to analytics platform when I’ve wanted insights but this was out of lack of any real knowledge of the analytics space and just seeing what others had used.
In my personal opinion Google Analytics is just a mess.
There’s two different UIs for the same data (Firebase Analytics and Google Analytics), two different versions of tracking tag to pick from (v3 and v4 and some JS libraries only support v3 currently) and it’s Goals feature doesn’t lend itself easily to building up a clear picture of what users are doing as there’s either tables of data or very busy funnel charts to view.
The lack of value I was getting from Google Analytics led me to look for a secondary tool that would provide more qualitive data instead of the quantitive data I was getting from Google Analytics. For this I picked MixPanel as it’s funnel reports allowed me to see conversions easily (and admitted the UI looked good).
MixPanel
Initially MixPanel delivered the value we had hoped for. We were able to see how the app was being used, see which areas of the product weren’t converting as well as we’d hoped and use that data in discussions we had about improvements to the product.
