I was hoping for some consultation 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 (make an account) 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 (lets call them x, y z). 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 know the probability that the rental rate after 180 days will be 0.05.
After building the model, I hope to use this model to predict these probabilities for future advertisements we choose to roll out so that we can measure their performance.
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 advertisements. 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!