Intelligent Revenue Management: Automated Insights for the Transportation Industry with applied machine learning.

Business Case / Problem:
The travel industry is increasingly competitive, requiring innovative approaches to pricing, distribution, and strategic planning. This business case proposes an development of a machine learning (ML), or equivalent module to specifically analyze two types of telemetry data: pricing, booking across multiple distribution channels. By leveraging this data, the module will provide actionable insights for dynamic pricing models, automated channel rule management, and performance summaries, driving revenue optimization and competitive advantage. With this data our goal is to produce curated insights informing our customers and enabling rule based decision making in real-time.
Without such ML and data mining, study of this volume of data is impossible without slow and complex human involvement.

Opportunity:
Our platform’s wealth of historical pricing and booking data is a goldmine for optimizing revenue strategies in the travel sector. The opportunity lies in harnessing this data to:

Solution:
Refine dynamic pricing models, ensuring competitive and attractive pricing for users.
Automate distribution channel management, optimizing channel performance and profitability.
Generate comprehensive implementation summaries, providing clear insights into the effectiveness of adopted strategies on a monthly, quarterly, and annual basis.

Faculty Supervisor:

Xingdong Yang

Student:

Partner:

Zaui Software Ltd.

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

Simon Fraser University

Program: