A B2B Dynamic Pricing Solution

for the Client’s Revenue Management System

  • Client Location:USA
  • Project Duration:2017 - 2018
  • Team Size:5
  • Tech Stack:Python, Flask, scikit-learn, PostgreSQL, Elasticsearch, MongoDB, Redis, Google Cloud, Docker, RabbitMQ

The Challenge

Dynamic pricing, also referred to as demand pricing or time-based pricing is a strategy that has taken over ecommerce. Our client was looking to create a solution that would apply this concept to predicting airfare volatility and offering highly customized pricing to its customers in order to maximize revenue and at the same time provide more value to its clients.

The Solution

Our partner was looking for skilled Python developers to join their distributed team in coming up with a predictive airfare calculation tool. Within the framework of the collaboration, our team worked on developing a pricing solution that would utilize all of an air carrier’s real-time and historical data (flight schedules, seat availabilities, fare structures, promotional calendars, etc.) in order to calculate fares dynamically.

By leveraging machine learning capabilities, we created a solution that takes into account all possible circumstances at the time of an airfare request, such as market conditions and the buyers’ specific behavior in order to produce a highly customized price.

By anticipating the buyers’ willingness to pay for not only in-air but also non-air products and services, the solution calculates the dynamic price as well as combines it into lucrative bundles for customers (e.g. hotel deals, event tickets, etc.), thereby boosting customer experience.

Key Benefits

  • Automatically adjust for competitive conditions
  • Dynamically adjust by competitive segment, channel, or market
  • Legacy compatible
  • New delivery capacity
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