I have a situation at my work that I want to take as a chance to learn more about pricing and stats. In a nutshell, I work for a company that buys several products and then charges a margin (we have unlimited supply of our product and our competitors do as well). Different clients consume our product at different rates but eventually they come back to buy more. Our e-commerce site allows clients to request a quote and it automatically provides it to them once they enter the quantities they like.
So, I have details on clients, details of their quotes and details of when they purchased. Within the details I have date/time, amount requested and product requested. I'm sure I can get other information if needed.
I wanted to build a pricing model to suggest the optimal margin to charge a client (currently each client has a sales rep who does these adjustments and they rely on their instincts and not any type of technology to assist, which is what I want to propose). That is, we want to make as much from each client as possible while also continuing to generate business. So I wanted to try to find a balance between setting the price low enough to encourage people to buy while maximizing the price to get the most profit.
My original idea, was to just keep increasing the price until they stopped buying then lower it as needed but I don't like that idea much because you could possibly lose the client. I'm wondering if there are any research papers or methods that I could use to learn about pricing models to get a better understanding?
Do models like this exist or am I to think of possible situation and include it (e.g., they always buy in the morning so prices can increase in the AM and reduce them in the PM, etc.). I've been looking up dynamic pricing models and most of the models (used by amazon, etc..) are private and super secret. I don't want to know how anyone else is pricing, I'm more just looking for a statistical framework I can use to start with and then customize to my problem.