How Sojern Uses Artificial Intelligence to Deliver the Perfect, Personalized Vacation

From automated personal assistants to self-driving cars, the adoption of Artificial Intelligence (AI) in APAC is estimated to skyrocket 46.9% between 2016 and 2022. This is especially true in travel marketing.

Companies like Sojern use the latest AI to transform the way they reach and engage in-market travelers. But the benefits of this trend extend beyond marketing. Greater levels of personalization can be achieved at scale, to travelers, to streamline and enrich their planning and travel experience.

Why AI and Travel Marketing Go Hand-in-Hand

Machine learning and AI is, in fact, transforming the digital marketing landscape as a whole. Travel planning is complicated. Finding and reaching travelers during the planning phase is increasingly difficult to achieve—especially manually. While historical path-to-purchase data can identify the behavioral and demographic characteristics that are precursors to a user moving down the travel planning path, such knowledge must be leveraged in real-time and at scale to be effective.

Planning a single trip involves numerous transactions, each preceded by distinct behavioral indicators of intent. For example, searching for the best flight is a strong indicator that a user will soon proceed to subsequent steps in planning. Reaching travelers with ads suited for each step along the way is where AI comes into play.

Through Leveraging AI and machine learning, we can identify, at scale, the behaviors that indicate a higher propensity of moving down the travel planning path. Leveraging historical data, learning the patterns of travel planning behaviors, and using this knowledge to predict the next step(s) within travel planning is how AI is transforming the travel industry. In effect, as travel marketers better predict a traveler’s next step and capture their attention by serving more relevant and value-laden ads, they are able to achieve more effective and efficient marketing at scale.  

The increased use of AI is not only limited to travel marketing—we already see corporate booking tools adopting AI. By leveraging automation and machine learning, travelers will be afforded superior and personalized user experiences. The result? Quicker and better bookings.

How AI is Making Travel Better—for Consumers and Marketers

But it’s not just having the data. Marketers must deliver a personalized, timely, and relevant experience for the traveler, whilst operating at a low cost. Doing both is, again, not easy to manage if done manually.

This explains the accelerating rate of AI in marketing, especially travel marketing. This technology will optimize the time, place, and manner in which we message to travelers. The possibilities are endless both during planning and while on the road. The market is also ripe for improvements, such as:

  • Travel recommendations during planning. For example, augmenting the trip planning experience by recommending add-ons based on the traveler’s previous bookings or recent trip planning behaviors. Such add-ons could include boutique hotels, local events, or unique attractions matching the traveler’s preferences.
  • In-destination, real-time recommendations. Taking AI one step further, marketers can target travelers mid-trip, leveraging real-time feedback pertaining to their travel experiences. Immediate feedback could suggest new experiences (e.g., restaurants, attractions, or even follow-on trips). These tailored real-time recommendations will undoubtedly enhance the travel experience as we know it.

How AI Can Make Cross-Device Targeting a Reality

In addition to granting travel marketers access to scale, AI is also helping to tackle cross-device challenges. One of the challenges facing marketers nowadays is marketing to users versus single browsers or mobile devices. Although the large walled gardens of Facebook and Google have enough marketshare to do a good job here, the rest of the landscape struggles.  

Historically, connecting cookies and mobile device IDs wasn’t easy. As a result, travel marketers failed to reach travelers with sequential storytelling across devices as they planned their trips.

In order to solve for this, cross-device consortia and open source cross-device graphs powered by AI seek to level the playing field. In the case of travel audiences, marketing across browsers or devices and delivery platforms will be highly impactful. It enables marketers to reach users at precisely the right time on the right device, thus reducing the need for superfluous ads and improving the user experience.

The Future of AI & Travel

We’re definitely on the path of marketers employing increasing amounts of AI and machine learning to their marketing strategies; from modeling the path to purchase, to identifying the preferences of particular users based on similar users’ behaviors and preferences, to tailoring the marketing messages to maximize user engagement. The sky’s the limit, especially as one envisions a seamless marketing strategy across browsers, devices and platforms.

While AI is certainly paving the future for the travel industry, there are still challenges to overcome. We need to avoid falling into a trap where our tools only suggest things that have worked in the past, at the expense of testing out new experiences or recommendations. This  balance requires constant refinement. Moreover, it is important that marketers tie revenue back to their efforts to assess campaign performance. 

The aforementioned challenges are in fact opportunities. Marketers who successfully marry their rich data with insightful applications of AI and machine learning will achieve extraordinary results. This is Sojern’s charter, namely, to leverage our rich traveler profiles,  updated in real-time, and apply AI and machine learning toward better marketing, throughout our travelers’ trip planning process.

Looking to understand more? Carl looks forward to connecting at the WIT 2017 Conference in Singapore on 23-25 October. Get in touch to arrange a meeting.

Alternative Text About Carl Livadas

An academic turned entrepreneur, Carl is an expert in using data, algorithms, and machine learning to deliver compelling business value and drive company growth. He leads Sojern’s data science team and focuses on leveraging the company’s rich data assets to develop new products and maximize online ad efficiency. Prior to Sojern, Carl led RTB optimization at Nanigans and managed the build-out of their RTB machine learning pipeline and bid evaluation tier. Before Nanigans, he held senior leadership positions managing engineering teams at inPowered and Kayak and worked on cyber security research at BBN Technologies and Intel Research. Carl earned a PhD in Computer Science from MIT.

View Linkedin

Carl Livadas About the author

An academic turned entrepreneur, Carl is an expert in using data, algorithms, and machine learning to deliver compelling business value and drive company growth. He leads Sojern’s data science team and focuses on leveraging the company’s rich data assets to develop new products and maximize online ad efficiency. Prior to Sojern, Carl led RTB optimization at Nanigans and managed the build-out of their RTB machine learning pipeline and bid evaluation tier. Before Nanigans, he held senior leadership positions managing engineering teams at inPowered and Kayak and worked on cyber security research at BBN Technologies and Intel Research. Carl earned a PhD in Computer Science from MIT.