Unlocking hidden revenue streams with market models
Sponsored
In partnership withFetcherr
Each day, an airline transports tens of thousands of passengers on hundreds of flights. Often these are not straightforward point-to-point routes, with passengers requiring multiple connections. The airline can consider potentially hundreds of variables to price each of these journeys: demand, season, time of day, current events, global markets, and competitor airline activity to name just a few. It is a nuanced process that must constantly adapt to the goings on in the wider world.
Generative AI-powered market models are emerging as a means of handling complex tasks like this in real time. These deep learning models are trained on high-resolution numerical data and designed to analyze, simulate, and predict complex financial dynamics. Rather than relying on historical trends or static rules, the market model acts as an AI “brain,” consolidating a variety of data to simulate different market environments and make dynamic commercial decisions, such as pricing, inventory, or revenue management.
DOWNLOAD THE REPORT
“It helps us make better, faster, more granular commercial decisions,” says Dominic Kennedy, senior vice president of revenue management, sales, and e-commerce at Virgin Atlantic about the market model his team is using to drive their generative pricing engines in some markets.
“It considers, on a real-time basis, a plethora of different inputs, whether it be demand, capacity, or booking. It has a really sophisticated way of evaluating our positioning relative to competitors, market conditions, and a whole raft of other things that have significance in how demand is manifested,” he adds.
Download the full report
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Deep Dive
Artificial intelligence
A fundamental flaw leaves LLMs strikingly vulnerable to attack
It makes it easy to trick them into doing things they shouldn’t, such as telling you how to sabotage an aircraft’s navigation system.
By Will Douglas Heavenarchive page
Anthropic found a hidden space where Claude puzzles over concepts
A new technique has let the company probe deeper than ever into the weird workings of an LLM.
By Will Douglas Heavenarchive page
Claude Science is Anthropic’s newest flagship product
The company is doubling down on AI for science.
By Grace Huckinsarchive page
Here’s why AI agents lie and cheat to reach their goals
The misbehavior is called reward hacking. This is what you need to know.
By Grace Huckinsarchive page
Stay connected
Illustration by Rose Wong
Get the latest updates fromMIT Technology Review
Discover special offers, top stories, upcoming events, and more.
Enter your email
Privacy PolicyThank you for submitting your email!
Explore more newsletters
It looks like something went wrong.
We’re having trouble saving your preferences.
Try refreshing this page and updating them one
more time. If you continue to get this message,
reach out to us at
customer-service@technologyreview.com with a list of newsletters you’d like to receive.
