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A causal impact analysis is used to measure the impact of a specific event or treatment. In this case, it’s measuring the impact of media on driving purchases/conversions. It is not intended to evaluate the cost of media on driving an outcome, assuming that is what you are asking. There are other analysis that can help evaluate the impact of spend on driving purchases, most notable analyses tied to ROAS or CAC.

I believe it's not metrics you need, but you need counterfactual populations that did not get affected by the marketing campaign. If the marketing campaign affected the whole population then causal impact is probably not recommended.

Since two campaigns adds noise, we would need to look at test and control groups. In the situation that you describe, you would want to find a control and test group. Example, let's say you are testing Facebook traffic: Test group #1 - people from campaign one Test group #2 - people from campaign one Control Group - people that came from FB but not from either campaign. Then you can compare the metric of Test Groups vs Control Group to see if the campaigns had an impact. Just need to make sure that the groups are closely correlated. This is good article on determining if campaigns are effective. towardsdatascience.com/how-can-i-tell-if-my-marketing-campaign-is-working-41cbf5c7dbc6

How did the interview go? I've recently been given this title and I don't really know the parameters.

Great question! You're correct in that the column purchases_A is the treatment group and purchases_B & purchases_C are the control groups. To answer your question, we have to think about what it is that we're trying to predict. We're trying to predict what purchases_A would look like if the treatment was never applied. To do that, we build a model based on the relationship between purchases_A and purchases_B/C during the pre-period, and then we use that model to predict what the treatment group would look like in the post-period if no treatment were ever applied. Then the CausalImpact tool determines the differences between the observed purchases_A and the predicted purchases_A. So in this case, our independent variables/covariates are purchases_B and purchases_C, and the dependent variable is purchases_A.

@@partandsum Amazing explanation ! Thanks so much.

Thanks Shane! Let us know if there are any topics that we could cover for you.

@@partandsum well funny you mention that, I invested today in learning about notion and I stumbled across feedback loops and other systems. Would you ever reccomend a specific set of systems to help businesses? Ive a tiny team so being agile is my greatest asset right now. Love your to hear your thoughts! Thanks! 😁

@@ArchitectofBrands Hi! Sorry for the delayed response. It's hard to say without knowing more of your business, so I'm going to tell you what we do. We use Trello and slack a lot for project management, feedback loops, etc. We also have weekly retros and project retros to reflect on what worked well and what we can improve. Those things work well for us. But its not a once size fits all. So make an informed decision on a system and try it out :)