AI Case Study

United Airlines manages overbooking issues and last minute changes in flyer travel plans with machine learning

United Airlines leverages big data and machine learning technology to upgrade its overbooking strategy. By being able to predict which passengers are like to change their travel plans, the company aims to respond better and faster to these changes and make personalised offers.

Industry

Transportation

Airlines

Project Overview

"United is using machine learning to respond to issues such as inclement weather more quickly, or manage overbooking with greater precision. Unlike traditional manual methods, analytics can better predict which flights are most likely to have no-shows and target customers with personalized offers.

Both tasks require getting data and compute power closer to the customer, Linda Jojo, executive vice president of technology and chief digital officer at United, told The Wall Street Journal.
UA wants to make internal processes more efficient, easier and better prepared to handle site traffic during high volume periods." (wsj)

Reported Results

Results undisclosed

Technology

data analytics and machine learning

Function

Marketing

Customer Management

Background

"After a series of fiascoes in 2017, such as the delay of about 500 flights after suffering a second glitch in its computer systems in just over two weeks, the airline was having problems with customer service." (wsj)

Benefits

Data