Researchers at Cambridge University have used a combination of social media and transport data to predict the likelihood that a given retail business will succeed or fail.
Using information from 10 different cities around the world, the researchers have developed a model that can predict with 80 per cent accuracy whether a new business will fail within six months. The results will be presented at the ACM Conference on Pervasive and Ubiquitous Computing (Ubicomp), taking place this week in Singapore.
While the retail sector has always been risky, the past several years have seen a transformation of streets in various cities as more and more retailers fail. The model built by the researchers could be useful for both entrepreneurs and urban planners when determining where to locate their business or which areas to invest in.
“One of the most important questions for any new business is the amount of demand it will receive. This directly relates to how likely that business is to succeed,” said lead author Krittika D’Silva, a Gates Scholar and PhD student at Cambridge's Department of Computer Science and Technology. “What sort of metrics can we use to make those predictions?”
D’Silva and her colleagues used more than 74 million check-ins from the location-based social network Foursquare from Chicago, Helsinki, Jakarta, London, Los Angeles, New York, Paris, San Francisco, Singapore and Tokyo; and data from 181 million taxi trips from New York and Singapore.
Using this data, the researchers classified venues according to the properties of the neighbourhoods in which they were located, the visit patterns at different times of day, and whether a neighbourhood attracted visitors from other neighbourhoods. Whether a business succeeds or fails is usually based on many controllable and uncontrollable factors. Multiple tests were conducted to make sure they have covered every angle before they come out with their results.
According to the researchers, their model shows that when deciding when and where to open a business, it is essential to look beyond the static features of a given neighbourhood and to consider the ways that people move to and through that neighbourhood at different times of the day.
More details of the research can be found at: https://www.cam.ac.uk/research/news/social-media-data-used-to-predict-retail-failure