I seek some guidance regarding which method(s) are best suited for evaluating a dataset I'm working on. My data consists of newly launched pharmaceuticals (~100 products), with sales data for these between Q1 2015- Q3 2018 (4 meassurements per year, so 15 meassurements). Some of these have data for the whole interval, some only started selling later (for example in Q2 2017).
Together with this, I have ~10 different events (Event1 - Event10) that can have happened at any time, sometimes several times, for the product in the time interval 2015-2018. I am basically trying to figure out if an event causes a change in sales, and in which case what event affects the sales the most.
Right now I have An Excel-sheet with rows of product names, lets say of length m, with sales data for Q1 2015 to Q3 2018, basically an m x 15 matrix (Example in image below).
The event data are for each product in 2 columns, 1 for the event type, and one for the date of the day the event occured, basically m number of these matrices, with 2 column but of different lengths (depending on how many events occured for that specific product) An example of how the data looks like is in the image below.
My question, I think, comes in two parts:
First, both the sales data and event data are as a function of time, only that they don't take the same time step. I assume that this has to be altered for the datasets to be evaluated against eachother. Any tips on a good way of doing this?
Secondly, what methods should I look into for evaluating if an event at one point in time causes a change in sales going forth? Basically, for each product a combination of events 1-10 can have occured at some time during the time interval, and I want to figure out if a certain event somehow affects sales for that same time interval.