An MVT is the most efficient and valuable way to collect data on a hypothesis.
On average, less than a quarter of experiments are successful.
In order to reduce the investment and time needed to get results, we start with minimum viable tests (MVTs). A MVT is the most efficient and valuable way to collect data on a hypothesis. Often, the data and insights gained from MVTs will be used to design more complex experiments, increasing their success rate. You can visit Optimize Hub.
We follow a few key guidelines when designing MVTM:
- Break down the hypothesis into as few possible hypotheses as possible.
- Plan to collect as much data as possible from the variants to improve the chances of finding key learning.
- Simplify the flows and user interface as much as possible.
- Plan to implement the simplest development possible.
- Analyze results to learn from them, not to declare winning ideas.
- Have a clear exit plan so you can stop the experiment quickly if things don’t go as planned.
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