Location Intelligence being an integral part of spatial analytics, allows businesses to identify areas with market commonalities using layers of data.
A 2015 Pew research study found that roughly eight in ten Americans shop online. With this figure edging 80% of the population and rising, businesses must begin taking for granted the fact that real business growth today is a far riskier proposition than 20 years ago. It is not enough for a company to rest on its brand’s laurels and head straight to market. Consumers are being offered products that are custom tailored to their specific needs, and sometimes even meet them right at their doorstep. With services like Blue Apron and Amazon catering successfully to a demanding and fickle buyer, retailers offering the traditional brick-and mortar interface must be certain they are hanging out their shingles in exactly the right place with the right layout.
Knowing the best next location to open, expand, or redesign means being aware of many different data sets simultaneously, such as area incomes, education levels, and individual buying habits. The more data layers there are, the harder it is for companies to manually sort through them. So, most businesses don’t perform the kind of holistic analysis necessary to understand what will make a new branch more successful in Fort Lee, New Jersey versus downtown New York City. These areas may be only ten miles apart, but the stark demographic differences between those ten miles can make or break a new coffee shop, franchise or grocery store. Spatial analytics automates the entire process of figuring out where to open and expand next by creating a digital workflow for placing consumer data into the context of location.
The big hassle of Big Data
この記事は Geospatial World の September-October 2017 版に掲載されています。
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この記事は Geospatial World の September-October 2017 版に掲載されています。
7 日間の Magzter GOLD 無料トライアルを開始して、何千もの厳選されたプレミアム ストーリー、9,000 以上の雑誌や新聞にアクセスしてください。
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