This project aimed at developing a benchmarking and customer analysis system for commercial areas. Leveraging online feedback, data mining, and AI, it evaluates spaces against industry standards and projects like Miami Design District and Hudson Yards. This approach aids in strategic decision-making across planning, design, and operations, enhancing commercial viability and consumer engagement.
For a detailed demonstration of the project, please refer to the slides: Phase I and Phase II.
The Idea
Is it possible to gauge public sentiment towards the operational strategies of commercial business sites, particularly in an era where enticing offline shopping has become a paramount challenge for many shopping districts? These areas have significantly invested in enhancing their landscape’s appeal. The question arises: how can we measure the return on investment for these temporary or even permanent enhancements and infrastructure in terms of value to developers? Evaluating the success and failure of space design is crucial. With the wealth of information available on social media platforms, can we harness this publicly accessible data and compile it into a comprehensive public opinion pool? By identifying effective strategies and contrasting them with unsuccessful examples, we can glean insights that are invaluable for future decision-making processes.
Data Process
Tons of the data from social media are collected from a wide variety of both China and US social media platform. We usually follows comments and photos from user. For the shopping area cases in Phase I, we mainly collected 14M records (Famous international cases), and 7M in Phase II (Domestic newly built cases).
Key Results
Analysis of Miami District
Hot search index of each platform of Miami District.
Brand check-in rhythm.
The investment of leading merchants in space design and the resulting influence of influcers checking in.
Usage and comparison of public facilities in the main space.
Analysis of Hudson Yards
Hot search index of each platform of Miami District.
Prediction of the structure of the visiting crowd
The analysis of social comments on Vessel.
The analysis of social comments on Edge.
The analysis of social comments on the Shed.
