From Data to Actionable Insights: Harnessing the Power of Business and Social Analytics
HKUST’s Center for Business and Social Analytics (CBSA) generates quality insights from massive amounts of social data.

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British mathematician Clive Humby famously coined the phrase “Data is the new oil” in 2006. Since then, many organizations have scrambled to embrace this maxim, ensuring they have a robust data strategy to inform decision-making for every function from finance to marketing and logistics to production.
Today, data is being generated, stored, and analyzed at staggering rates worldwide. According to a Forbes study in 2018, over 2.5 quintillion bytes of data are produced in the digital universe every single day. Along with this rapid growth of data, the rise of social media has encouraged researchers and data users to tap unconventional data sources and methods to explain and predict economic or behavioral outcomes in various domains such as finance (e.g., stock price), marketing (e.g., brand reputation, consumer purchasing behavior), and politics (e.g., voting behavior).
Unconventional data, more commonly referred to as big data, is unstructured and undefined. Such data is characterized as high-volume, high-velocity, high-variety, and/or highly semantic, and advanced techniques and applications, as well as high-performance computing, are often required to collect and process it. Big data analytics, including data mining methods and machine learning algorithms, allow users to gain a deep understanding of behavior.
The Internet has become a part of modern life, and it is almost second nature for people to express their views and preferences on social media. The emergence of platforms such as Facebook, Uber, Taobao, and peer-to-peer applications, and new computing and communications technologies such as Internet of Things (IoT) and 5G cellular networks, has generated tremendous amounts of new data that opens up the possibility to study a wide range of human behaviors.
Against this backdrop, the Center for Business and Social Analytics (CBSA) of the HKUST Business School leverages massive amounts of social big data to generate quality insights that aid business and social decision and policy making. To date, a majority of academic and industry research is based on conventional data from opinion polls, surveys, and lab or field experiments, which suffer from sample selection bias or lack generalizability. The Center takes advantage of social big data to tackle complex business and social problems by listening to what the crowd actually says. The following are sample projects undertaken by the Center.
Hong Kong Tourism Index
The tourism industry is one of the four pillar industries in Hong Kong. Mainland China is the major tourism market of Hong Kong, accounting for nearly 80% of visitor arrivals in 2019. In view of the tendency of Chinese tourists to share their travel experiences and sentiments on social networks, the Center has collaborated with Wisers, the world's leading expert in big data and AI analytics, to construct a series of predictive tourism indexes for government monitoring and other business interests.
We collect massive volumes of data from digital platforms frequently used by Chinese travelers, including Sina Microblog, Baidu Tieba, Douban, Mafengwo, Xiaohongshu, and Douyin. The total amount of data extracted from these platforms exceeds 10 million observations per day. To account for the seasonality in tourism demand and key sociopolitical events in Hong Kong, we focus on the period of January 2018 to December 2020. It is imperative to gain a systematic understanding of the various factors impacting Hong Kong's tourism industry.
We draw on the latest natural language processing technologies and advanced statistical models to process and quantify the data, and establish a multivariate time series forecasting model and a series of predictive indexes that are updated in real time. With the error rate as low as 4% (in terms of Symmetric Mean Absolute Percentage Error, sMAPE), these indexes predict the occupancy rates of Hong Kong hotels, the number of visitors to Hong Kong, and the average daily rates of Hong Kong hotels.


