International spending by Chinese tourists has skyrocketed in the past 8 years, showing increases of over 150% from $38B to $261B with the growth of the Chinese middle class. The tourism market is currently very fragmented, as would be expected due to the number of countries and people involved, there is an opportunity to integrate. Traditionally, Chinese tourists travel with their families or through travel agencies that offer group tours. Due to the data capabilities and market share of major Chinese companies such as Tencent and Baidu, we believe there is a chance for them to quickly disrupt the tourism market by offering personalised recommendations and trip planners using prediction models as well as matching algorithms.
AI and machine learning techniques will be used to create a powerful trip planner and recommendation tool that is personalized to individuals. Building off the kind of matching techniques that are used by platforms such as say, Netflix, machine learning can play a large part in the recommendations section. The AI solutions will be powered by continuous real-time data through surveys, reviews and location tracking. Therefore, the application will identify travel-companions and restaurant recommendations by overlaying these techniques on top of traditional filtration tools specified by the users. The solution required must be able to combine different kinds of data types, for example users may have an image of a tourist spot with a comment about how much they loved it, therefore the company would need to apply image recognition techniques. This could be combined with multiple searches in the week prior to travelling on the Baidu search engine, for best restaurants in a certain district of a city. Thus, combining and profiling using such data would create an optimal route and itinerary for the traveller.
Design of Demonstration:
To understand the commercial promise, we must focus on the pain points of customers and see how the product addresses these. One of them is logistics and excessive paperwork to track all kinds of reservations, compounded for families. With the centralized dashboard (pictured below), these can be compiled in one place, and this can be done through automatically pulling data from e-mail and other communications. A proof-of-concept has already been offered by Google on its Trips app.
Another pain point can often be the recommendation system. Younger travellers often want to skip the touristy spots and go somewhere authentic. This is often not possible due to the language barrier, but by utilizing crowdsourced recommendations (already a part of these companies’ infrastructure) and filters, it will be easy to save time and money finding suitable places.
We have talked about many features, so a pilot would focus on rolling out some core features in a few cities. Say, we pick Paris, a popular tourist destination with a lot to see and a language barrier for most. We would beta test with young travellers going to Paris, and initially propose itineraries and recommendations based heavily on crowd-sourced data and location tracking. In this service, the main user is not the main source of revenue, and thus we would have to focus on user experience. As more users are attracted to the platform, there would be various ways to monetize, the most popular of which we foresee as being a B2B model, where businesses are charged for giving deals to travellers and advertising on the platform. Moreover, as travellers take multiple trips, machine learning techniques can be employed more heavily to solve prediction problems related to planning itineraries and suggesting establishments.