More feature flags related terms

Conversion rate


Conversion rate is a key performance indicator in digital marketing and e-commerce that measures the percentage of users who take a desired action out of the total number of visitors. This action can vary depending on the context, including making a purchase, signing up for a newsletter, downloading a guide, or completing a registration form.


The conversion rate is calculated by dividing the number of conversions (desired actions taken) by the total number of visitors, then multiplying the result by 100 to obtain a percentage. Conversion\:rate = \left( \frac{Number\:of\:Conversions}{Total\:Number\:of\:Visitors} \right) \times 100%


Understanding the conversion rate helps businesses gauge the effectiveness of their website and marketing campaigns. A higher conversion rate indicates successful engagement strategies and website design, reflecting that the business is effectively persuading visitors to take the desired action. Conversely, a low conversion rate may signal the need for website optimization, better targeted marketing efforts, or an improved user experience.

Strategies for Improvement


The conversion rate is a crucial metric for measuring the success of online platforms and marketing strategies. It provides insights into how well a site converts traffic into desired actions, serving as a guide for continuous improvement. By focusing on user experience, content relevance, and targeted marketing, businesses can effectively increase their conversion rates, leading to higher customer satisfaction and increased revenue.

Confidence interval

Confidence intervals in A/B testing provide a range of plausible values for the true difference in performance metrics between variations, guiding decision-making and interpretation of results.

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Correlation quantifies the degree to which two variables are related, ranging from -1 to +1. It's useful for analysis but doesn't imply causation and has limitations like not capturing non-linear relationships and being sensitive to outliers.

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CUPED is a technique that uses pre-experiment data to reduce variance in A/B testing, improving result sensitivity and reliability by focusing on the true effects of the experimental changes.

Learn about CUPED

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