Analyzing A/B testing results is one of the most important stages of an experiment. And while most experimentation platforms have built-in analytics to track all relevant metrics and KPIs, it's important that marketers – prior to analyzing an A/B test report – understand the following two important metrics: uplift and probability to be best.
In this post, learn more about test analysis, specifically:
- Basic experimentation analysis
- Secondary metrics analysis
- Audience breakdown analysis
- Why losing A/B tests are actually winning
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