Start Date

11-8-2016

Description

While online shopping is one of the fastest growing sectors in the U.S. economy and is quickly surpassing traditional retailers (Enright 2014), shopper demand data used to place warehouses is either proprietary or expensive. To address this, we present an alternative approach to identifying where online shopping demand occurs in Los Angeles County and therefore where to most efficiently place warehouses for online retailers. Twitter data was harvested identifying the location of tweets about Amazon or EBay. This information was used as a proxy to model location of online shoppers. When compared with U.S. Census population data for ages 18 to 40, the Twitter-derived data was found to be a much more effective means to model the location of online shoppers and more efficiently place online warehouses of goods.

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Aug 11th, 12:00 AM

The Power of Social Media in Supporting Warehouse Location Decisions for Online Retailers Using GIS

While online shopping is one of the fastest growing sectors in the U.S. economy and is quickly surpassing traditional retailers (Enright 2014), shopper demand data used to place warehouses is either proprietary or expensive. To address this, we present an alternative approach to identifying where online shopping demand occurs in Los Angeles County and therefore where to most efficiently place warehouses for online retailers. Twitter data was harvested identifying the location of tweets about Amazon or EBay. This information was used as a proxy to model location of online shoppers. When compared with U.S. Census population data for ages 18 to 40, the Twitter-derived data was found to be a much more effective means to model the location of online shoppers and more efficiently place online warehouses of goods.