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by gabemart
4249 days ago
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> The title of the post is "How we Figured Out What Makes People Love Ghost 1,000% More" - which I think is still pretty accurate :) I disagree. If you choose the bucket "people who subscribed to Ghost Pro", they would convert ∞% better than the bucket "people who didn't sign up for Ghost Pro". That doesn't mean that signing up for Ghost Pro makes people love Ghost ∞% more. More generally, how can you tell if people installing a custom theme makes them love Ghost more, or if people install a custom theme because they love Ghost more? Getting more people into the bucket of "people who installed a custom theme" doesn't actually help your bottom-line conversion rate until you can establish unequivocally that the people you've added to the bucket convert at the same rate as the bucket did before they were added to it. |
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This is incorrect. Let's say we have two buckets, A and B, where bucket A is people who haven't used a feature and bucket B is people who have. If some testing shows that people in bucket B convert far better than people in bucket A you make a change to get more people into bucket B. The change is successful if more people end up in bucket B and bucket B still outperforms bucket A and the total contribution coming from bucket B is higher. Bucket B doesn't need to perform equally well before and after the test.
Using the numbers from the link bucket B was performing at 10x the conversion of bucket A. Out of 10,000 users bucket B contributed 70 subscribers. After the changes bucket B received almost five times the users as previously and converted at four times the rate of bucket A. Out of 10,000 users bucket B contributed 884 subscribers. If the change had no impact we'd expect more users in bucket B but not more subscribers. These numbers aren't really accurate since I took them from the diagrams in the article and they change what they're looking at between the two. To be correct you need to compare the same thing before and after changing a feature.