Suburbia maps shopping trends for asset management community
Suburbia, a technology company specializing in alternative data solutions, today launches an offering for the investment community that leverages millions of anonymized transactions across Europe, to provide predictive insights into consumer goods companies.
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The solution is a multi-source platform with granular insights into brand performance, delivering daily signals on over a hundred publicly listed and large private companies. The data product has been built specifically for hedge funds, asset managers and other institutional investors to generate alpha and manage risk.
A key market that Suburbia is targeting is UK asset managers due to the region’s large market share. According to a 2019 report by the FCA over £1 trillion is managed for individual investors and £3 trillion on behalf of UK pension funds and other institutional investors.
“Investors have long been tapping into transactional data to anticipate trends and consumer behavior,” said Hamza Khan, CEO, Suburbia. “But we realized most of the existing data out there is generated by a panel of users which could lead to an opt-in bias and less accuracy. In addition, this data is much harder to come by in Europe because it’s such a diverse and fragmented landscape - every country has its preferred payment methods. We believe our unique approach has resulted in the industry’s most actionable dataset.”
Suburbia’s proprietary technology is capable of processing millions of consumer purchases from thousands of hospitality and retail channels across Europe, with a strong focus on Germany and the Benelux. No personal information is ever used or shared in the process. This data is updated on a weekly basis so investors can get up-to-date insights for making decisions faster.
Other highlights of this product include:
• Ticker mapping to easily see performance of publicly traded consumer packaged goods (CPG) companies over time
• Granularity such as product details, item pricing, basket composition, geography and time of transaction
• Historical coverage, with over two years of data available for backtesting