Analyzing experiment results and iterating on product features and marketing strategies

The true measure of an experiment’s impact lies in what happens after the data is collected. A thorough analysis of results provides pivotal insights into the efficacy of changes tested, illuminating what resonated with users and what fell flat. Armed with these learnings, teams can determine the next steps around iterating, pivoting, or scaling successful changes.

Let’s explore best practices for extracting actionable insights from experiment analysis to fuel ongoing optimization:

• Compare control and test groups: The crux of result analysis involves juxtaposing the performance of control and test groups against your defined success metrics. Did the test group exhibit a lift in conversion rates, engagement, or other KPIs compared to the control? Statistical significance testing quantifies the likelihood that results were due to chance.

  For example, an e-commerce site might...