Image credit: Field & Stream
A common exercise product teams do at the end of each year is goal setting and revision. We often see conversion rate goals / objectives being set like:
Increase the conversion rate from 6.7% to 7.4%
When goals measure absolute conversion rates across date ranges, like the above, teams may end up working against each other.
There's a reason tag managers are now the de facto for tag deployment.
Before tag managers, you'd embed tags directly into your application. It could take weeks or months to deploy them inside large, monolithic apps... Meanwhile, you'd be shifting precious developer time off high-value projects. And the practice of tagging the app just added further bloat/technical-debt to your heavy codebase.
...and then tag managers became popular.
Image credit: Blastam Analytics
Now, independent of the web application code, tags could be setup, QA'd and deployed before your coffee went cold. This led to an explosion in data collection and marketing efficiency.
This efficiency is critical in the fast-paced world of experimentation...
Update: We have just launched our documentation site for Mojito here.
We're excited to open source Mojito - the experimentation stack we've used to run well over 500 experiments for Mint Metrics' clients.
It's a fully source-controlled experimentation stack for building, launching and analysing experiments from your favourite IDEs.
Remember the good old days of JS errors? (Image credit)
Building large, complex experiments introduces new logic, new code and sometimes new bugs. But most A/B testing tools don't perform error tracking or handling for you. So when you launch your experiment and it tanks...
...did your awesome new idea just not work? Or did bugs torpedo your idea?