How can you apply OR models to optimize pricing in data analytics?

2 Nonlinear Programming

Nonlinear programming (NLP) is a generalization of LP that allows you to deal with nonlinear objective functions and constraints. NLP is useful for pricing problems that involve complex relationships between the variables, such as nonlinear demand functions, nonlinear cost functions, or nonlinear pricing rules. For example, you can use NLP to optimize the price of a subscription service that has different tiers and features. You can also use NLP to optimize the price of a product that has network effects, such as a social media platform or a software application. NLP can be solved using various algorithms, such as the gradient method, the Newton method, or the sequential quadratic programming method.

3 Integer Programming

Integer programming (IP) is a special case of NLP that requires some or all of the variables to be integers. IP is useful for pricing problems that involve discrete choices, such as the number of units to produce, the number of customers to serve, or the number of products to offer. For example, you can use IP to optimize the price of a product that has limited inventory or capacity. You can also use IP to optimize the price of a product that has multiple variants or options. IP can be solved using various algorithms, such as the branch-and-bound method, the cutting-plane method, or the branch-and-cut method.

4 Dynamic Programming

Dynamic programming (DP) is a powerful OR technique that allows you to solve complex problems by breaking them down into smaller and simpler subproblems. DP is useful for pricing problems that involve sequential decisions, uncertainty, or learning. For example, you can use DP to optimize the price of a product that has changing demand or cost over time. You can also use DP to optimize the price of a product that has uncertain customer behavior or preferences. DP can be solved using various algorithms, such as the value iteration method, the policy iteration method, or the Q-learning method.

5 Game Theory

Game theory is a branch of OR that studies the strategic interactions between rational agents. Game theory is useful for pricing problems that involve competition, cooperation, or negotiation. For example, you can use game theory to optimize the price of a product that faces competition from other products or firms. You can also use game theory to optimize the price of a product that benefits from cooperation or coordination with other products or firms. Game theory can be solved using various algorithms, such as the Nash equilibrium method, the Stackelberg equilibrium method, or the Shapley value method.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?