Machine Learning in the Travel Industry: Harnessing the Power of AI for Better Revenue Management

AI is transforming the travel industry by optimizing revenue management and enhancing customer engagement. 

In fact, it affects all segments of the travel industry and influences most aspects of the traveler's journey, including online searches and pricing of flights and accommodations.

In this article, we'll take a closer look at how machine learning can benefit your business. Specifically, we'll dive into how machine learning can help with personalized pricing and offer optimization, prevent fraud before transactions go through, and streamline the process of managing chargebacks after the fact.

Price forecasting and dynamic pricing for bookings

When searching for airline tickets online, there can be dozens, if not hundreds, of combinations of routes and fares. In order to determine which offers to present to a customer, airlines must balance personalization with revenue optimization.

To address this challenge, bright minds are already using artificial intelligence to create dynamic, real-time pricing for airline tickets and ancillary products.

Price forecasting

Machine learning for price forecasting is getting lots of attention these days because customers are getting savvy. They know they can find the best deals on flights and hotels by automatically monitoring the market. When the conditions are right, the app notifies them.

Hopper is a great example of this service. It helps users book cheap flights using analytics. By adding this tool to their portals, online travel agencies can engage customers and encourage them to book more trips.

Another good example is Skyscanner. Much like with Hopper, travelers can select their flight criteria and use filtering options to better help you  find ideal flights.

In fact, when you click the Search button, it gives you a list of the lowest prices possible and prioritizes the last eight days. It also tells you the lowest price by city, along with the average hotel price. 

Armed with this knowledge, travelers can pounce on flights when the lowest price available closely matches the hotel pricing average. After all, you want to save on both the flight and hotel, so saving on one but overpaying on the other completely defeats the purpose.

Demand forecasting

In more traditional terms, demand forecasting helps businesses make better-informed supply decisions by estimating future sales and revenues. However, in this particular case, it refers to the ability to predict future flight prices.

Hopper, for example, makes predictions about future flight prices. Users can also see if they should "buy now" or "wait for a better price" when tracking a flight.

And it does this by creating the world's largest flight database. Hundreds of billions of historical prices are included, along with an active feed of roughly 300 billion prices every month. Hopper also collects a variety of different measures to help predict future prices. Prices for jet fuel and capacity growth are two of these factors. 

Moreover, Hopper has built up a data feed that shows what people are searching and asking for across travel agencies, OTAs, and meta-search sites. Using this indicator, Hopper can predict how airlines will adjust their prices in the future. 

The combination of historical data, demand data, and other measures creates a powerful prediction engine rather than just a matrix of prices between different travel locations. As a result, they’ve been able to predict airfare prices with an accuracy of 95 percent up to a year in advance.

A note about dynamic pricing

Although it’s not a type of forecasting, dynamic pricing is important and deserves a special mention.

Think of it as a strategy that adjusts prices in real-time, based on different factors like demand, competition, and customer behavior.

The use of machine learning in dynamic pricing strategies, for example, can pinpoint buying patterns so accurately that airlines can synchronize their pricing strategies in real-time. This allows them to offer their customers deals that take into account consumers’ willingness to pay thereby maximizing revenue for the company.

With its data-driven technology, Hopper turns this on its head, using machine learning to encourage travelers to make smart purchases. 

There are a number of factors that affect every user, including preferable days and times to fly, the best time to buy airline tickets, convenience, etc. You simply enter those preferences, and the system automatically detects the best tradeoff between price and product features. Then, it delivers relevant offers at the most competitive price. It even suggests offers for destinations and times that you did not pick but that it judges likely to appeal to you based on a variety of variables.

In fact, around 25 percent of Hopper sales come from recommendations users did not ask for. Moreover, when the recommendation comes from AI, the conversion is almost three times higher than when Hopper responds to specific user queries.

Pre-transaction fraud prevention

Machine learning doesn’t just stop at flight price predictions. They also help travel companies tackle different types of fraud, such as payment fraud, fake account creation, content abuse, and account takeover. 

By analyzing a variety of data sources in real-time, a machine learning system can identify abnormal behaviors to create risk scores for each payment. There are a variety of companies that provide such solutions, including Forter, Riskified, Sift and others.

From a bird's-eye view, machine learning can help travel companies track different types of fraud:

• Fraudulent payments involving stolen credit cards: This is when someone steals a person's credit card information and uses it to make unauthorized flight and hotel purchases.
• Creation of fake accounts: This is a common tactic used by fraudsters to take advantage of promotions, discounts, and other offers. 
• Abuse of content: This refers to the manipulation of user-generated content, such as reviews, ratings, and comments. 
• Takeover of accounts: Also known as loyalty fraud, account takeover occurs when a fraudster gains access to someone's travel account, changes the account information, and makes unauthorized purchases.

Post-transaction chargeback management

This refers to managing and resolving credit card payment disputes that arise after a transaction has taken place. The goal of this process is to ensure that merchants are protected against fraudulent activity and the financial losses that can result from chargebacks.

In the travel industry, chargebacks can occur due to various reasons such as disputes over the quality of a service provided, issues with the delivery or performance of a service, or customer dissatisfaction with a travel experience. To manage these disputes and reduce the risk of financial loss, travel companies use chargeback management tools and services.

With the use of chargeback management software, travel companies can improve their chargeback management processes by preventing and fighting chargebacks through the management of internal records, analysis of chargeback data, and integration with external tools like chargeback alerts. The software can be run by the merchants themselves or by third-party chargeback solution providers.

The software can provide automatic responses to incoming chargebacks based on the merchant's preferences and can also provide tracking and reporting functions that can give the merchant actionable information about how to win chargeback disputes and make operational improvements that will reduce chargebacks in the future.

For merchants who don't have the time to become chargeback experts, a common solution is to hire a chargeback management company to handle the problem. The right company will provide a positive ROI, and give merchants the tools and knowledge they need to make meaningful reductions to their chargeback rate.

Overall, post-transaction chargeback management aims to minimize financial loss and ensure a smooth and efficient resolution process for both the merchant and the customer. By using a chargeback management solution with machine learning, travel companies can  improve their chargeback management processes.

Types of chargeback protection services

• Chargeback alerts - Informs the business immediately if there is a payment dispute. Getting in touch with the customer and clarifying any misunderstandings can often resolve a dispute before it becomes a chargeback.

• Chargeback deflection - Automatic rules-based refunds for payments disputes before a chargeback has formally been filed. This helps avoid incurring chargeback fees and a higher chargeback ratio for the merchant.

• Chargeback mitigation solutions - Solutions like Justt that help retailers prevent chargebacks and minimize losses from these disputes after they occur. Choosing the right chargeback protection service

Chargeback protection is not a one-size-fits-all solution. Before choosing a service provider, consider the integration level and customer support effectiveness. 

It is also important to examine chargeback rates, dispute behavior, and transaction characteristics for root causes. Once the primary issues have been identified, they should be addressed in a targeted manner.

For instance, Justt's machine learning-based chargeback mitigation solution can provide businesses with advanced features such as automatic responses and win rate predictions. These are crucial in creating manageable chargeback levels in line with company goals.

Additionally, Justt's solution determines what evidence to submit in chargeback representments and how to optimize the presentation of evidence to maximize the likelihood that the issuer accepts the merchant's case, primarily through the use of A/B testing. 

By leveraging machine learning, businesses can effectively combat friendly fraud and increase the chances of successfully reversing chargebacks.

Here's a look at Justt as a viable option to suit your specific needs:

Using Justt's chargeback management solution, financial losses are minimized thanks to a reliance on cost-effective automation and revenue recovery optimization through machine learning.

Final Thoughts

AI and machine learning are making a profound impact on the travel industry by revolutionizing the way companies approach revenue management. With its ability to provide personalized pricing and offer optimization, pre-transaction fraud prevention, and post-transaction chargeback management, machine learning is an essential tool for travel companies seeking to stay ahead of the competition and achieve increased revenue.

Not only does machine learning optimize pricing strategies and provide valuable insights into customer behavior, it also enables travel companies to streamline and automate their operations. This results in more efficient and cost-effective processes, which is critical for large enterprise merchants in today's competitive business environment.

Ready to learn more about how machine learning can be used to manage chargebacks in the travel industry?

Contact us for more information or read more on the importance of catching travel related chargebacks.