Over the past decade, the hospitality industry has embraced emerging technologies with intent to adapt to the constantly changing competitive environment, by partaking in the digital revolution. By 2016, more than half of all hotel bookings globally were made online.
As more and more travellers turn to book their holidays online, bypassing travel agents or manually handling it themselves, more and more data is being generated. With new technological tools available to process such big data becoming mainstream, the hospitality industry is harnessing them.
With the emergence of Big Data and Cloud, hoteliers are able to leverage AI and Machine Learning solutions at affordable prices, and build brand loyalty by offering personalized customer experiences. Today, Machine Learning is able to parse travel patterns from customer purchases, and enables hoteliers to tailor bespoke holiday offerings to customers. While at the hotel, chatbots and virtual assistants help in enhancing the customer experience.
Across the hospitality value chain, a range of stakeholders, from intermediaries to global brands to individual hotel properties are exploring opportunities to tailor service offerings throughout the customer journey lifecycle – from reservations to stay to post-stay.
This article looks at the key opportunity areas which AI is tapping into:
At Airbnb, artificial intelligence is used across various domains within the platfrom from personalized experiences through better search and discovery capabilities, to fraud prevention for guests, as well as enabling hosts with optimal pricing.
For guests, Airbnb uses machine learning capabilities to tailor the most relevant listings, Experiences and services, going beyond just finding a home or experience, but rather tailoring the perfect getaway for them.
For hosts, Airbnb has leveraged machine learning to come with Smart Pricing, helping hosts in predicting the probability that a listing will be booked at a given price at a given calendar date.
Airbnb is also exploring improving reviews using natural language processing (NLP), surfacing reviews of most relevance for the listing itself to provide the best experience for future guests. In addition, Airbnb is harnessing NLP to support advanced search capabilities, including open-text search for “a beach house in Bali for honeymoon”.
In September 2018, Airbnb had 9 job positions advertised for data science, ranging from Data Science Manager, APAC, to Data Scientist – Analytics, from Data Scientist - Analytics, Payments to Data Scientist – Inference.
Handling a million bookings per day in >43 languages across >million properties worldwide, Booking.com is the global market leader in the travel industry, handling over a million bookings per day. Two thirds of the daily bookings on Booking.com are made in a language other than English.
As such, Booking.com relies on machine translation to enable its partners and guests to consume and produce content in their own language, including hotel descriptions, customer reviews and customer service support.
In early 2017, Booking.com became one of the first companies to experiment with Neural Machine Translation (NMT). The Neural Machine Translation (NMT) developed in house at Booking.com outperforms all other Machine Translation, with consistent performance.
In addition, Booking.com built a Booking Assistant in-house that quickly identifies and automatically responds to a broad range of post-booking related questions from travellers, across their preferred device and platforms.
The Booking Assistant leverages NLP technology to identify the most frequently asked questions from customers, ranging from payment, to arrival and departure times, from Wi-Fi and internet availability, to a wide variety of greetings and thank-you messages. The Booking Assistant chatbot can currently respond to 30% of customers’ stay-related questions automatically in less than 5 minutes. In case the Booking Assistant identifies a question it is unable to solve, and depending on the nature of the query, the Booking Assistant leverages support from the Booking.com customer service team or the hotel property, adding their response directly into the conversation.
In September 2018, Booking.com had 17 job positions advertised for data science, ranging from Data Scientist Machine Learning to Data Engineer, from Sr. Product Owner – Platform Data Security to Product Marketing Manager.
Trivago’s travel platform keeps on improving with more customer interactions. As it keeps on learning about the preferences of its customers, it is best able to tailor results for them.
To harness Artificial Intelligence and Machine Learning, Trivago made key acquisitions to support its platform. Earlier this year, Trivago acquired TripHappy, an American startup that analyzes >25,000 neighborhoods in >10,000 cities to build and secure personalized bookings for travelers.
In 2018, Trivago also joined Plug and Play’s Travel & Hospitality ecosystem, focusing on partnerships that strengthen the search experience for its users. By sourcing innovative startup technologies aligning its long-term vision and strategy, Trivago aims to enhance its product and optimize the effectiveness of existing features and capabilities on its platform.
Previously in 2017, Trivago had bought Tripl, a platform that developed an algorithm to give tailored travel recommendations by identifying trends in users' social media activities and comparing it with in-app data of like-minded users. Tripl imitates the way a travel agent would recommend hotel experiences relevant to the customer, and combines it with the ease of online services.
As on September 2018, Trivago is hiring for positions such as Data Science Team Lead – User Profiling, to Data Scientist – Predictive Algorithms.