Travel Data Unleashed with AI and ML, Explains Industry Experts

On 10th June, DataArt hosted the “Unleash Travel Data” webinar, the latest in an ongoing series addressing trends, technology and thought leadership in the travel industry. Joined by a panel of experts, Greg Abbot, Head of Travel, Transportation and Hospitality at DataArt, led a discussion around the opportunities and risks associated with deploying AI and ML inside travel organizations. In this post we’ve broken down some of the major areas debated during the webinar.
7 min read
06/29/21
By Alina Khodyakova
Marketing Specialist, Travel, Transportation & Hospitality
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Travel Data Unleashed with AI and ML, Explains Industry Experts

Introduction

The “Unleash Travel Data” webinar was hosted by DataArt’s Head of Travel, Transportation and Hospitality, Greg Abbott, who was joined by a group of travel industry insiders, including:

  • Anna Jaffe, CEO of Mobi Systems
  • Michael Reyes, Vice President of Offer Management at Sabre Corporation
  • Rick Seaney, CEO of 3Victors
  • Stan Boyer, Airline, Travel and Information Technology Advisor at DataArt
  • Dr Gene Kolker, EVP and Co-Director of DataArt’s AI/ML Center of Excellence 

The lively discussion revolved around the use of artificial intelligence (AI) and machine learning (ML) applications in the evolving travel landscape.

Video Webinar Read on for highlights on the major discussion points, or sign up to watch the full webinar

Discussion Point: Better Data Analytics in Revenue Management

The COVID crisis was the latest blow to traditional revenue management, and existing revenue management players and products are increasingly being paired with or replaced by newer, more nimble technologies. An example from the airline industry perfectly illustrates why this is: traditional product sets (like airline tickets) have been supplemented with numerous extra options (such as prepaid bags, meals, wifi, seat assignments, etc.). Now, no two customers are buying the same combination, and this spells trouble for traditional revenue management methodologies which rely on large comparable datasets to make statistically significant predictions. As there are fewer repeating observations that can be used to forecast the future accurately, managers are less certain about when to stop selling one product and start yielding to another.

This trend is happening across revenue management systems, simply because the market has changed.

Rick Seaney
The era of forecasting with two years of seasonally adjusted data is pretty much over.
Rick Seaney

So how does modern technology step into this gap? AI and ML allow for experimentation sampling, to supplement rather than replace older systems. For example, some airlines are getting smarter about deployment of their systems by augmenting long-term forecasts with data gleaned from recent online search and booking data, as well as holding regular meetings between capacity planning, operations, revenue management and sales departments to share information across data silos. This combined approach allows companies to make more accurate forecasts without abandoning standard practices.

Michael Reyes, an expert in revenue management, argued that although a more flexible, short-term data-driven approach is becoming popular, the traditional approach to revenue management isn’t obsolete yet: “I would say the old systems aren’t broken, but they're definitely not as useful as they have been historically.”

Get in touch with Travel & Hospitality experts at DataArt

Discussion Point: The Stifling Effect of Data Silos

The sharing of data is an important aspect in the deployment of newer technologies across revenue management, customer acquisition and retention, and operations, but is by no means a solved problem. Data silos in separate departments within an organization, and across competing organizations, means that accessing the most timely and appropriate information can become a hassle, and sometimes simply doesn’t happen.

The crux of this problem was perfectly encapsulated by Anna Jaffe: “It's like there's almost two parallel sets of information that they're trying to make business decisions off of.”

To solve this moving forward, our panel thinks that companies need to focus more on bettering their processes and algorithms, rather than ring fencing their data. Many in the industry still rely heavily on monetizing their data assets, which inherently stifles data sharing. But if companies instead share their data and compete on their algorithms, everyone would benefit. MITD, an example of historical data sharing, is proof of this principle in action. Although developed decades ago, MITD would cost far less to replicate now using more efficient solutions.

However, as Rick Seaney pointed out, this then brings data rights issues to the fore: “It's complicated commercially, because you want to put somebody's data in an algorithm that doesn't actually own it. The output is something new, but who gets the use rights? There’s a lot of legal requirements around this that still need to be fleshed out.”

Discussion Point: AI & ML Overuse

With the recent boom of AI and ML in the travel industry comes the inevitable pushback against their ubiquitous application. Some warn that using these technologies as a blanket solution is inappropriate. When it comes to digitalization and associative intelligence (used to perceive patterns and solve problems the human mind cannot), our panelists agree that there are multiple extremely useful applications within the industry. But AI and ML should not be used to try to predict every traveller’s every move, especially when this information is being willingly shared upfront.

A pattern seen across retail is consumers’ willingness to share their preferences and data, if it means they are more likely to get a product or solution they’re looking for. For example, in online retail, customers can search for the exact dimensions, colour, finish, materials and style of a piece of furniture they want in their home. They do this without hesitancy about privacy or data mining, because it gets them to the right product. The same is true in travel; customers will willingly share detailed information on what they’re looking for, making the application of complex new technologies meaningless.

Anna Jaffe
I think that it's really about using it in the right place at the right time. Otherwise you can get results that just don't feel at all relevant to the problem you're trying to solve.
Anna Jaffe

Discussion Point: Industry Acceptance of New Technologies

So is everyone in the travel industry prepared to offer their customers all of their options right from the get-go? Is everyone on board with data sharing, data mining and utilizing data analytics to facilitate business decisions? It seems that there is quite a bit of variation across the board, and our panelists have seen clients from both ends of the spectrum.

On data unification within organizations, Anna Jaffe reports: “We had zero traction from any of the airlines. Coming out of COVID, this is one of the things that we've started to see, from maybe 60% of the airlines that we've approached in the last two or three months.”

But on the analytics side of the equation, it seems there is more uptake. Sabre recently worked with an Asian airline to mine their existing bookings to look for people with long layovers, and combined this information with email marketing tools to reach out to those customers with promotions on hotels. This successful approach made it easier for the airline to see the value of deeper analytics, and thus justified their expense. This is just one example, but many other travel operators are using similar techniques and tools to supplement or replace existing mechanisms. However, industry-wide adoption is still a ways away, and at the moment very dependent on each individual company’s experience and capabilities.

Discussion Point: The Cost of Data Analytics

A large part of the reluctance some in the travel industry profess to adopting newer methodologies relates to their cost. Many industry operators are used to working on much longer timescales and outlaying far more on IT infrastructure than is needed, these days, to achieve similar levels of competitiveness.

Michael Reyes
Back in the day, computing, power and storage were precious commodities. That's not the case now; with cloud computing improvements in processing, we don't care as much about storage and computational load as we did even even 10 years ago
Michael Reyes

The advent of cloud infrastructure has been especially impactful in allowing revenue managers to see tech outlays as risk-appropriate; as the cloud is elastic, it allows flexibility, and AI/ML models can be run on machines bought on the spot market, reducing costs significantly over traditional hardware purchases.

Conclusion

According to industry data, COVID has changed consumer air travel, search trends and booking patterns, and the historical algorithms that rely on older data are less effective now than ever before. But the adoption of newer, more flexible and nimble technologies is uneven, and our panel warned that there is still a significant absence of alignment between data science, AI models, business priorities, business outcomes, and business use cases. Only when an industry player unifies its approach to data usage and technology across the board can reliable effects be expected.

The DataArt team has helped numerous companies in the travel and hospitality domains stay agile with custom technological solutions. If you are looking for technological expertise and travel domain knowledge, contact us.

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