Great marketers experiment.
Want to be one of them? Let's do this.
If you are anything like us, you're probably hungry for well documented experiments that you can replicate and use in your own marketing strategy.
We run both our own experiments and experiments for our community. We also follow industry leaders who run experiments and share these experiments with you.
The Experiment digest is free. You can unsubscribe anytime. Your email address won't be used for anything else but the digest.
Each experiment you run will not only help improve your website or application but will also teach you something about your users.
Experiments not only help you improve conversion rates, they help you own conversion rates.
No, it's so much more than that. AB testing is simply the method used to validate experiment data. A well documented experiment is much more and includes the whys, the whats and the hows.
Have you ever thought why replicating AB testing almost never gives the same results? It's because too often we try to replicate the methods and not the conclusions. Conclusions, if valid, should always be the same, no matter how many times they are replicated.
The goal is to build a library of experiments that other marketers can use.
Each monthly newsletter will contain the details of a specific experiment that will include:
Each experiment starts with a hypothesis which the experiment will prove or disprove.
Experiments are not about testing website elements but about testing user reactions to them. It’s important to have a clear understanding of what is being tested and why it is thought to have an impact on users.
It's critical to define clear success metrics based on which we can declare the experiment a success or not. It's what other peers will try to replicate.
Each experiment will be well documented in terms of how it was implemented and tracked. If you are to be able to challenge the experiment yourself, all these details will be crucial.
Experimenting is learning, learning about your audience, technology, your competitors or field of work. It is the goal we are after.
That's where your role starts, as judges of the experiments we run. Is it possible that we did not notice possible errors in the experiment? The more eyes on the experiment, the more chances for accurate conclusions. .