How to optimize your drip campaigns with multi-armed bandit testing?
Experimenting with your contents is a must-have thing if you are a marketer. But there are some cases when the well-known split testing methodology doesn’t provide satisfying results. Therefore you need to utilize multi-armed bandit testing! Here you will know what it is, how it is different, when it is better and how can you use it in practice.
What is multi-armed bandit testing?
The multi-armed bandit problem has a famous analogy in probability theory: a gambler goes into a casino and sees a row of one-armed bandits (slot machines). The gambler naturally wants to play on the machines that have the biggest chance of winning.
So he needs to know in which order and how many times he should play on the machines. When he starts to play with them, every machine has a specific probability distribution that will give him a reward. The gambler’s goal is to maximize his overall reward in the end of the day.
The same problem applies when it comes to optimization in marketing: you have different assumptions that need to be tested at a time. So you just “pull the levers” (CTAs, images, copies, headlines, subject lines, etc.), experiment with them and try to maximize your “rewards” (conversion rate)…