I have a dataset composed of few features :
customerId, actionDay1, SalesDay1, actionDay20, SalesDay20, actionDay30, SalesDay30
action can be :
- face 2 face
sales : amount of sales in $
my goal here is to predict the best action we have to take in any day (1, 20 or 30) to sale more.
some customer prefers email, some prefer to be called every time, some others prefer to not be contacted to buy.
the pattern is different for each customer. my first thought was to apply reinforcement learning for this problem, but I found some difficulty to do the right environment, but also the reward function. a reinforcement learning would work without data and the model will be different for each customer. I would like to have a model that can approximate the behavior of all customers using the dataset I have.
does anyone have an idea on how I can approach this problem using reinforcement learning or something different like Recurrent Neural Net?