Measure targeted audience traffic based on very large GPS mobility datasets in order to recommend retail spaces.
Development of a scalable tool (31 cities in the US) contributing to several successful investment rounds and client use-cases.
30 GB/day GPS mobility data processed on a fully cloud environment.
Nowadays, the process of finding the ideal spot where to place a shop is still largely driven by human experitse, painful and long.
The variety and heterogeneity of available data and the fragmentation of the market causes losses related to opportunity costs and the fact that most spaces remain unoccupied for longer times they should.
We are helping our US client ARetailSpace to build an intelligent retail recommender leveraging data from a variety of open and private datasets. We are in charge of designing, implementing and testing all the algorithmic and data technical exploitation of the product.
This includes the analysis of datasets, creation of data products, recommendation engines and user experience analysis.