Goal: Many clients we speak to in partnership with the AWS Travel and Hospitality team have the goal of providing personalized digital experiences that span across the entire travel journey of their customers. This strategic shift enables a brand to go from a sophisticated service provider to ‘expert vacation host.’
Challenge: To enable this shift, companies need high quality data on what their customers would likely enjoy doing throughout an entire travel journey. This data empowers marketing to be significantly more effective, helps operations serve guests to their exacting standards, and vastly improves digital experiences for customers. However, there is a Catch 22: how does a company provide personalized experiences, with little—if any—historical data for content that surrounds their core services? In other words, how does a hotel know what concerts to show to a guest, without previous insight into what music they like? The hotel likely has years of data on what type of hotel room they prefer and in what location, but it knows little to nothing about anything else, such as a concert preference.
Solution: This is where Mobi comes in. We have designed our B2B AI solutions to work with travel and hospitality businesses to gather powerful preferential data within seconds. This data empowers Mobi’s customers to serve their travelers personally curated digital experiences at every stage of their journey. Moreover, as a B2B partner, the powerful data insights we collect are all available to your organization, to ensure marketing, operations, sales, finance, and all other stakeholders have access to the complete guest preference profile.
To give you a better feel for how this works, here’s an example of the data insights that are generated through an eight minute interaction with our hypothetical user, Jane, as she experiences a digital micro-vacation from her endless back-to-back work meetings.
Quiz 1: Jane’s Travel Daydreaming (t=0:00)
Jane begins interacting with quiz content and gets swept away in a world of possibility, excitement, and adventure. Within 30 seconds, this experience exposes the best and most beautiful things our world has to offer: things your brand can unlock for Jane.
After this brief interaction we know this about Jane’s preferences:
Quiz 2: Dream stay (t=0:57.34)
In this quiz, Jane adds browses different types of hotels and stays. In addition to logging the content Jane clicks, we also store the content she ignores. After Jane selects items, the quiz lets her drag content into order of priority. Overwater bungalows are at the top, so now we know to start tracking for deals and discounts on flights to those locations. 34 places Jane might love to stay are added to the map.
Jane also adds how often she wants to visit these different types of stays.
Quiz 3: Wild Animals (t=1:36.12)
Based on her first responses, Agent knows Jane is interested in wild animals, so Agent offers her a quiz about which animals she’s most interested in. This is a fun interaction—showing her creatures she didn’t even know existed—and Agent can draw on these answers to recommend different destinations in the future.
This time, in addition to selecting what she likes, Jane adds the friends and family members she wants to see each animal with, as well as how often she wants to see them. Now Agent knows that Orcas are always worth recommending, whereas Kingfishers are more relevant when they are convenient.
Quiz 4: Landscapes (t=3:37.21)
Jane opens the next recommended quiz on landscapes, and chooses her 6 favorites. There’s only 1 pink lake we know of in the world, and only 5 places we recommend for sand dunes. All together, her answers result in 21 recommended destinations which we’ll be able to use in the future to inspire travel.
Quiz 5: Airport Preferences (t=4:26.03)
Because Jane expressed that she is very particular about airport experiences, Agent asks her to share more specifically what she likes and dislikes. Jane’s answers to the airport quiz help Agent recommend a Starbucks along the way, while prioritizing flights with shorter security lines.
Before moving on to the next screen, Jane puts her phone down to make some tea. This is pretty typical: 80% of Agent’s users engage for 3-4 minutes at a time. Agent knows and expects this, so these breaks in interaction are interpreted, and your data about engagement stays clean.
Trip Planning: San Francisco (t=6:41.46)
Jane re-opens her profile and opens the card for her future trip to San Francisco. Trip cards are created any time a user saves a trip. The saved trip already has dates and a few preferences from the original search: flight (non-stop flight, JetBlue preferred), Thai massage (once), hiking (2 days), Four Seasons (for breakfast, once), stay with sauna.
When Jane opens the card, she navigates to the Explore view and touches the stay panel. She adds budget ($200-$400 per night) and an infinity pool to amenities. 3 recommended stays appear:
Trip Planning: Dining (t=7:12.03)
When she opens the dining tab, she adds her ‘Always True’ preferences to the trip from the profile and a collection of recommendations that match those preferences appear. At the top of the recommendation stack is Restaurants by chefs from Chef’s Table and Dominique Crenn’s Atelier Crenn appears. There’s only one reservation available for two people on day 3 of the trip. The restaurant is added to her itinerary. The day view briefly fades as the restaurant is added. Jane opens the card and clicks confirm. She scrolls down but does not engage with any of the other recommended dining content. This is consistent with typical user behavior: users wait to add dining content to a trip until just before they arrive, or even while they are mid-trip, unless the restaurant is rare and requires advance reservation. Trip Planning: Experiences (t=7:41.50)
Trip Planning: Experiences (t=7:41.50)
Jane clicks into her preferences, which reveals the Orca Whales tag that she saved earlier. It’s Orca season, and a recommendation for Fast Raft out of Half Moon Bay appears, along with two other alternative whale watching trips.
Jane saves FastRaft but otherwise does not engage with the recommendations. Instead, she opens the preference panel and adds different contexts to her interests
As the clock runs out on our 8 minutes, we leave Jane to continue trip planning.
Conclusion
This single micro-engagement accomplished this for Jane:
- Provided an escape from her monotonous day
- Excited her about the possibility of travel
- Helped her to start planning out what she wants to do,
moving from dreaming to booking
Without a single historical data point, this digital experience has provided this data summary of Jane’s preference and intent:
- What she is most interested in seeing when she travels
- Who she likes to travel with and how that effects her decisions
- 562 specific likes and dislikes
Through future interactions, customer insight will continue to deepen, constantly improving the digital experience provided to them. This leads not only to increased booking and conversion, but also unlocks the strategic goal of moving from a service provider to whole-trip host.