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How Does Social Media Data Help To Forecast Retail Failure?

A brand-new model has developed by researchers at University of Cambridge and that uses a combination of social media as well as transport data to forecast whether a retail business in a city will fail within 6 months.

How Does Social Media Data Help To Forecast Retail Failure?
How Does Social media Data Help To Forecast Retail Failure?

A research team led by the University of Cambridge have come with a brand-new model that actually uses a combination of social media as well as transport data to forecast whether a retail business in a city will fail within 6 months.

The model was built based on machine learning and the developers have already used data from 10 different cities around the world to predict retail failures.

The researchers claimed that the prediction was 80% accurate.

According to an article published in University of Cambridge website, lead author Krittika D’Silva stated, “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.”

The Metrics researchers used to predict retail failure.

D’Silva and her colleagues used over 74 million check-ins from the location technology platform 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.

Considering the collected data, the research team classified places 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.

The data showed that across all ten cities, venues that are popular around the clock, rather than just at certain points of day, are more likely to succeed.

Moreover, venues that are in demand outside of the typical popular hours of other venues in the neighbourhood tend to survive longer.

The data also suggested that venues in diverse neighbourhoods, with multiple types of businesses, tend to survive longer.