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by AJ007
4742 days ago
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#1 Its good to see a post using statistically significant sample sizes. Most articles on this topic that I have read either include sample sizes so embarrassingly tiny it raises questions if the author even understood his statistics class (30 to 300) to simply not including numbers at all. #2 If your audience is already pre-sold on your product when they visit your site, landing page changes won't be as meaningful. Possibly relevant to Jitbit, in this case. #3 When you do conversion optimization, eventually you hit a number that simply becomes unbeatable. Stating the obvious, you can't convert at over 100%. Depending on traffic source/quality/intention you may find that ceiling to be lower, around 60-70%. |
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#3. For real companies with completely unmotivated visitors, 60% conversion rates from visitor to paying customer is untenable. 5% is probably too much to hope for.
#2. Absolutely. A/B tests should be focused on actual balanced decision points. "Should I sign up?" "Should I open this email?" "Should I go back to this site?" But not on people who are already committed to doing what they were going to do anyways.
#3. On sample sizes, it is important to think carefully about the maximum effort you're willing to put into a test. Be very, very cautious about accepting test results that arrive early. No, 99% confidence is not enough to stop with 200 conversions, and 99.9% probably isn't either. Don't worry about statistical significance when you kill tests that have run too long to be worth continuing. This is a line of reasoning that is sadly rare in our industry. (I keep meaning to write my next article on that topic. But http://elem.com/~btilly/ab-testing-multiple-looks/part2-limi... explains one way to come up with such a strategy.)