A/B testing combined with funnel analysis is a highly interesting technique to support data driven decision making. This thesis outlines a scalable architecture that gathers custom defined events and applies funnel analysis to gain valuable insights about user behaviour. The insights of the users are discussed from an A/B testing point of view, however, these insights are just as valuable for scenarios outside A/B testing as well. Custom defined events together with A/B testing is an interesting combination, since it provides opportunities to test different versions of an application against each other, based on relevant metrics. Where the vision is to determine which of the tested application versions that is best. The power to make smart data driven decisions lies in the hand of good data analysis of the end-users. Having pre-defined metrics such as counts, of some sort, is one way to do it. However, it reduces the flexibility to let e.g. application managers to "dig deeper" into what is actually happening. Funnel analysis is a technique to analyze sequences, and can be used to analyze user behaviour in a sequential matter. There are different techniques to provide such tools, e.g. with Google Analytics, users can pre-define funnel steps that they want Google Analytics to register when events are logged. This thesis will instead strive to not require anything being pre-defined and also make it possible, at high scale, provide dynamic low latency queries. A proof of concept architecture has been presented in this thesis, to support two problematic ends of a spectrum, that is; at scale, both to store custom events, and at the same time be able to interactively run dynamic low latency funnel analysis on the events.