Identifying causation involves a combination of data analysis and controlled testing. Here are some steps to help identify causation:
A/B Testing: By comparing two versions of an email, you can see which version performs better and attribute the difference to specific changes. Controlled Experiments: Running controlled experiments can help isolate variables and determine their impact on campaign performance. Data Analysis: Advanced analytics can help identify patterns and relationships between different elements of your email campaigns and their outcomes.