Analyzing A/B Testing Results in Digital Marketing

Context: A/B testing in digital marketing, also known as split testing, is a method of comparing two versions of a marketing asset to determine which one performs better. It helps marketer appropriate marketing strategies. Result: For RQ1 we do not have sufficient evidence to reject the null hypothesis. This suggests that the amount spent does not have a statistically significant effect on the result rate at the 5% significance level. For RQ2 we do not have sufficient evidence to claim there is a significant difference in the CTR between Campaign A and Campaign B. For RQ3 we do not have sufficient evidence to confidently support the alternative hypothesis (H1) that the result rate is a better predictor of campaign success compared to the click-through rate. The analysis suggests that both metrics are similarly effective, supporting the null hypothesis (H 0). we fail to reject the null hypothesis. This means that, based on the t-test, there is no significant difference in the result rates between Campaign A and Campaign B.