Transforming tourism: aggregated travel services and intelligent personal assistants
Challenge identifier: SC6-2017
Proposed byMASAI is an open community offering specifications and tools that enable simpler, seamless, more personalised travel experiences. Rather than having to access solutions separately, MASAI enables a personal concierge in everyone’s pocket. ONLIM is an Austrian company that develops technology to help businesses manage their social media and marketing activities.
DescriptionWith more than 2.3 million enterprises, employing 12.3 million persons (a significant proportion of which are young people), and generating over 9% of the EU GDP, tourism is a major economic sector in Europe. With the growth of the Internet, a considerable change in travel planning and travel behaviour took place. Nowadays, 4 out of 10 Europeans are using the Internet as a major communication tool to look for travel information and over 1 in 3 are purchasing tourist services online. The activities supported under this topic address the general technological and systemic challenges that concern entire data value chains in the travel and tourism domain. Despite the large public appetite for the use of online travel services, the user experience leaves much room for improvement. Smart aggregation mechanisms that combine travel and leisure options for tourists visiting particular destinations are often missing, especially for local offerings. An end-to-end, seamless integration of relevant functionalities is not available – consumers must make multiple searches and purchases to put together their trips. Chatbots and intelligent personal assistants (IPAs) could help, by enabling novel, simplified and more natural and intuitive ways for people to discover relevant information and book services, both prior and during the trip (for example, restaurants, tours etc. at their destination). However, the quality of these technologies depends on the quality of the underlying data and on their ability to analyse the data to derive better answers and learn to understand customer preferences. The aim is to improve the ability of European touristic service providers (from small accommodations and hotels to large hotels chains to touristic associations) to build innovative conversational applications that leverage open and proprietary touristic data sources and services; and to find solutions that seamlessly integrate travel services across Europe, for instance train and long distance bus services, first/last mile transportation such as local and regional buses, or taxis and transfers with tourist tours, museums and attractions, local commerce, parking, etc.
DataApplications are expected to make best possible use of tourism-related data ranging from corporate, closed to open data from the European Union Open Data portal and/or other European and national open data sources. Examples include, but are not limited to:
- points of interest
- weather data
- flight data
- events and event registries
- tourist and leisure data.
- Algorithms and apps that integrate different sources of data in interesting, novel ways to offer a seamless customer experience while planning and/or executing a travel.
- Natural language and speech processing algorithms going beyond state-of-the-art AI technology, trained and successfully applied in a tourism context.
- Innovative chatbots and intelligent personal assistants apps and services, leveraging the multitude of touristic data sources and matching micro-moments and demands of user requests.
- Seamless travel experience enabled by tools combining different technologies;
- Improved user engagement, allowing a user to dialogue with a chatbot for up to 10 minutes without being aware they are interacting with a machine;
- New products and services fostered by the availability of new feeds of data;
- Commercial take-up and/or wide deployment of chatbots and IPAs with clear demonstrated applicability in tourism;
- Demonstrated, significant increase of accuracy of chatbots and answers as measured against relevant benchmarks.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement 732506. Unless otherwise indicated, all materials created by the Data Pitch consortium are licensed under CC-BY 4.0