What is the best way Reinforcement learning, RNN or others to predict the best action we have to take to maximize sales? I have a dataset composed of few features :

customerId, actionDay1, SalesDay1, actionDay20, SalesDay20, actionDay30, SalesDay30

action can be : 


call
email
face 2 face
nothing


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?
I don't know if reinforcement learning is right for this problem.  A reinforcement learning model needs a simulated "game" environment where it can interact and select actions initially at random.  But real customer reactions are hard to simulate, and you don't want the model randomly sending out spam e-mails before it's trained.

There are other ways to train a model to recommend an action based on existing data.  If you want to keep it simple, you can perform a regression to estimate each action's impact on likelihood of a sale.  If you have more data and want to get more complex, perhaps look into recommender systems (Netflix, Amazon, etc.)