Dynamic Pricing Implementation through Data Science: Price Optimization Strategies

Background

This is a part two article following the introduction of dynamic pricing from my previous article. In this article, we’ll go through the step-by-step implementation of dynamic pricing. In terms of dynamic pricing for price optimization, we’ll have these two main steps

1. Building the baseline model(Xgboost Regressor)to predict initial prices.
2. Implementing a price optimization logic based on the business goal. To build a price optimization logic based on the predicted prices of each customer, we formulate an objective function that represents the business goals (e.g., maximizing revenue, profit) subject to certain constraints (e.g., pricing rules, number of seats left, ).

In this article, we will utilize flight data as our primary example to illustrate the implementation of price optimization. However, it’s important to note that the methodologies discussed here can be adapted and applied across diverse industries. Nonetheless, it’s imperative to grasp the intricacies of each specific industry and collaborate with subject matter experts to identify suitable price optimization strategies. Moreover, the availability of data plays a pivotal role in the implementation process. It’s essential to ensure that the data used is abundant, reliable, and accurate.

For a deeper understanding of the context, I recommend reading my previous article. Finally, you can find the complete code implementation and dataset linked at the end of this article.

Introduction

This dataset from Kaggle comprises details of flight bookings gathered using web scraping methods. It encompasses booking dates, journey dates, airline class, departure and arrival timings, flight duration, total stops, and prices. We’ll clean this scraped data, preprocess it, and use it for analysis and our dynamic pricing problem.

Business Goal

It’s crucial to understand the business goal from the start. This is important because it helps us…