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First and foremost: beautiful and performant website. I don't understand how exactly the Volument product actually performs A/B testing (or distinguishes betwe

First and foremost: beautiful and performant website. I don't understand how exactly the Volument product actually performs A/B testing (or distinguishes between different variants). The only code docs [0] I've found are very, very light on details and doesn't mention how the active variant is detected. To be fair, the product hasn't launched yet so perhaps the docs are currently internal-only. That being said, very interesting idea of linking engagement to eventual conversion to get "results" faster. It certainly seems reasonable that there is a causation relationship (increased engagement, e.g. more scrolling and longer visit duration, eventually leading to increased conversion), but I don't know how statistically valid that is. [0]: https://volument.com/learn/client-api https://volument.com/learn/client-api

volument 7y ago
Here's how Volument works: https://volument.com/learn/volument-data-model https://volument.com/learn/volument-data-model A/B testing in Volument is quite different from, say Optimizely. You can compare a lot of things in apples-to-apples fashion: landing pages, inner pages, market segments, campaigns, site versions, etc. And you can compare both how much traction something generates, or how engaging a page is. The causation thing is just something we measure. We measure how people behave on their first visit, how intensively they return, and how much they convert. It's not about the statistical validity, it's about choosing metrics that are essential for conversion optimization.