Before you run your growth experiment, you should write down your best guesses as to what will happen. Do you think your email will have a higher or lower click-through rate than the emails you already send? Why do you think this? How much do you think the email will increase content creation over the next month? Will it single handily give you the 2x content creation goal you’re shooting for, or do you think it will get you part of the way there?
It may seem silly to write these kinds of things down when you can just send the email and find out what’s going to happen. If that’s your attitude, then you’re missing the point. Hypotheses are accurate reflections of your assumption before you are given a chance to rewrite the past to make yourself look like a genius.
For instance, imagine that you write down the hypothesis that the click-through rate will be lower because you already send users one email a week, and you think the second email will annoy them. Then you run the experiment, and it has a higher click-through rate. Hypothesis keeps you honest. Now, instead of trying to prove to everyone how smart you are, the discussion is about why your assumptions were wrong. You might come to realize that you underestimate the amount your users want to be in contact with you, and this insight has benefits which stretch far beyond email.
Lean from success and failure
Data is like publicity. There is no such thing as bad publicity, and there is no such thing as bad data. Even if an experiment fails, you will have undoubtedly gathered a lot of information about your product and your users that can be used in future experiments. Thomas Edison failed more than 1,000 times when trying to create his light bulb. When asked about it, Edison allegedly said, “I have not failed 1,000 times. I have successfully discovered 1,000 ways to not make a light bulb.” You can learn from successes, and you can learn from failures. You only stop learning when you give up.