Understanding Travel Patterns to China in 2019
Last updated on 2020-01-10
Charting a journey based on the experiences of fellow travelers offers invaluable insights. This comprehensive analysis, our third consecutive tourism report, scrutinizes the choices of the numerous guests we catered to in 2019. It provides a clear look into the patterns of Western tourists in China, with the intent to aid future visitors in customizing rewarding itineraries.
Optimal Seasons for Visiting China
Deciding on the perfect time to travel is pivotal. Our data confirms that spring and autumn, owing to moderate climate, lead as the preferred seasons to explore China, reflecting in the travel patterns of 65% of our clientele. Conversely, the cooler winter months saw a mere 7% of the tourist influx, notably due to the chilly conditions and holiday time.
China's Exemplary Destinations in 2019
A staggering 90% of travelers in 2019 included Beijing in their plans, drawn by iconic landmarks such as the Great Wall and the Forbidden City. Shanghai and Xi'an followed, boasting their own unique attractions like the bustling financial district and the Terracotta Army, respectively. Guilin/Yangshuo, Chengdu, Suzhou, Zhangjiajie, Hangzhou, Pingyao, and Luoyang rounded off the top ten favored locales.
Multi-City Ventures: Popular Tours and Duration
Multi-city itineraries often featured Beijing, Xi'an, and Shanghai, with the 'Golden Triangle of China' tour being the most prevalent choice. Travelers on this circuit spent an average of 8.6 days, while varied combinations with other cities such as Guilin, Chengdu, and Zhangjiajie tailored to diverse interests.
Booking Trends: Planning Your Trip
Analysis of booking lead times highlighted a direct correlation between the complexity of the trip and planning duration. Longer voyages necessitated more extensive prior arrangement, with bookings typically made 28 to 85 days in advance, ensuring a wider selection of amenities and potential cost savings. Such preparation guarantees a smoother experience in China's dynamic landscapes.