I was hoping for some consultation and direction with how to go about the following:

To give context, I work for an agency that manages advertisements on social media for general motors - specifically their car sharing branch called "Maven". We run ads to get people to register on their Maven mobile App, and subsequently to get them to rent a vehicle. One of our key performance metrics is called the rental rate = rentals/registrations.

I have daily performance metrics data in terms of daily registrations and rentals that occur for various ads. What I would like to do is build three models that will give me a daily probability that a given ad will meet a specified rental rate at the end of t days after launch of the ad. I want to build a model for t = 60, t = 90, and t = 180 days after launch. For example, I want to be able to look at maybe day 10 of 180 days, and get the probability that the rental rate after 180 days will be 0.05.

I was thinking a logistic regression model would be of use here, but I can't wrap my head around how to go about building the proper model to achieve my goal. Any advice would be greatly appreciated!!!!

I have daily data for these ads. I have computed a cumulative registration and cumulative rental variable, as well as a cumulative rental rate variable. I have used a binary variable putting 1 to indicate days where the rental rate goal was met and 0 where it was not. I built a logistic regression using cumulative rental rate and day index to predict the binary variable but I really don't think this is the proper model I am looking for! Please help me out!


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