We’re always thinking about travel, a just-completed study from Think with Google tells us. Most of those thoughts, the report says, lead to booking decisions that have nothing to do with price, and machine learning can help. (Note: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.)
The average traveler spends 13 percent of his/her time online conducting travel-related activities.
That won’t surprise anyone who’s ever started planning an
Alaskan vacation after scrolling through a friend’s wilderness photos or
researched Rio hotels after watching a documentary about Carnival.
When it comes to travel, inspiration is everywhere. As a result, the purchase path is full of twists and turns. It ranges from days to months, stretches across thousands of touchpoints, and generates a mountain of data in the process. In the survey that produced its report, Google asked more than 7,000 travelers from six countries how they plan a trip from beginning to end, starting with what inspired them to travel.
Source: Google/Bain, Global (AU, BR, DE, JP, U.S., U.K.), “Infinite Paths to Purchase,” 2019.
Even after completing a booking, many travelers continue to research and find inspiration.
But people don’t act on every inspiration. Each traveler has underlying needs that vary by trip.
When a brand shows it can meet those needs, people usually respond by taking an action. While needs can be emotional or functional, they’re the considerations that matter most to each traveler — often more than price.
Samples of underlying need as reflected in comments: “I need a dog-friendly hotel.” “I need things to do in Kyoto.” “I need a wellness trip to recharge.”
New research reveals that leaving these needs unmet will more likely deter a booking than price.
And needs-based bookings are more valuable than price-based ones, lifting revenue across major categories.
Before they booked, travelers showed interest in comfortable hotel beds, time-of-day availability, and airplane movie screens, signaling their unique needs: sleep quality, activities that fit their schedule, and in-flight entertainment. By catering to those needs, brands can earn more valuable bookings: +27 percent for international flights,⁷ 22 percent for hotels,⁸ and 14 percent for tour packages.⁹
Travelers’ unique needs are important for marketers to address. But how do marketers know what those needs are?
Clues from anonymous traveler IDs appear across searches, sites visited, videos watched, images viewed, tickets purchased, tours booked, and more. That volume makes it hard to tell what’s relevant.
Samples of “anonymous” traveler IDs:
—Traveler read an article titled “Best Hotels for Couples 2019”
—Searched “How long to train a dog.”
—Searched “Best dance lessons in Italy.”
—Visited local library website.
—Searched “When is feast of Santa Rosalia.”
—Booked October flight to Sicily.
—Browsed hotels in Palermo.
—Watched videos about homemade dog food.
—Viewed photos of the Amalfi Coast.
(The type of traveler above needs a couples vacation.)
By reviewing anonymized and aggregated data points in real time, machine learning can identify clues to understand what really matters to travelers. This allows brands to prioritize relevance across ads, while the data itself remains private and secure.
The chaos of the travel journey is an opportunity for marketers. It may look intimidating, but with the right tools, marketers can use these signals to deliver a more meaningful experience to every traveler, trip by trip.
• Focus on needs, not price: Today’s travelers need help planning not just any trip, but the right trip experience for them. So, whether your travelers value “adventure,” “health and wellness,” “family time,” or something else entirely, knowing what motivates them can inspire new products, value propositions, and marketing strategies, which can all help to build customer loyalty.
• Let consumer behavior guide your strategy: Since there is no single path to purchase, today’s travel brands should think holistically about all potential interactions with a traveler. After all, there are opportunities to re-engage someone even after they’ve completed a booking. A data-driven approach can help a brand find those opportunities and appear at the right moments of influence.
• Build relationships early with search: One of the best ways to engage travelers at the start of their journey is through search. Destination searches indicate that people are beginning to narrow down their travel ideas, even if they haven’t yet settled on all the details. Search also makes it easy to test and iterate, so you can experiment with how to reach more travelers and quickly identify the right audiences.
• Connect across the journey with machine learning: Machine learning makes it possible to analyze millions of nuanced interactions within seconds, and an ML-first marketing strategy can help future-proof your brand. With the power to automatically tailor messaging to different audiences, customize creative, and even free up time for other areas of the business, machine learning can help you connect with the people who were looking for you all along—whether they knew it or not.
1-9Google/Bain, Global (AU, BR, DE, JP, U.S., U.K.), “Infinite Paths to Purchase,” 2019.