Tradeshift is a lean startup and as part of that we pay almost obsessive attention to the data generated by the companies on our platform. Which features do they find most useful? How much time do they spend on Tradeshift and with what frequency?
But when tracking so many factors, it’s getting to the point where one of our engineers has labeled the health risk ‘Death by data’.
Making sense of this very diverse data and using it to answer questions about how companies behave on our platform is the goal of ABinator. We wanted a tool generic enough that it could be used to analyze the results of our AB experiments but also flexible enough to answer a huge range of questions about our users’ behavior.
This mean tests like: What is the impact of being able to use Tradeshift in your native language? How can we make our onboarding emails work even more effectively for enterprises and their suppliers?
And this is how the ABinator was born…
Hasta La Vista, Testing
What we’ve created is an open source tool, hosted on github that helps you answer questions you have about your users’ behaviour. And you can get it going in three easy steps:
1. Define a list of metrics that matter to you, in plain SQL. That may be ‘time on site’, ‘money spent’, ‘activated feature x’ or whatever you want to keep track of.
2. Define segments of users to test, again in plain SQL. This could be based on country, their traffic source or whether they have been exposed to a certain experiment.
3. Sit back and get ready for results as The ABinator gives you a no frills statistical breakdown of how those segments performed in terms of the metrics you set.
This is a tool that allows you to ditch your uncertainty about whether something works and say “let’s find out”.
One simple purpose, one powerful execution. Check out the video above for more info or head over to github and get the source for yourself.
And remember, we’re always looking for the best engineers out there to join us and change the world. See current Tradeshift job vacancies here.