A retail company wants to correlate the on-line behavior of its customers with what they purchase at its “physical” shops. They observed a typical pattern: the customers go first to the on-line shop to check the goods, their prices and then go to the shop to check more physical characteristics of the goods before getting to the purchase decision. A better understanding of this behavior could help the retail company either to provide a better buying experience at the physical store or on the on-line shop.
The main difficult under this scenario is to correlate two very different sources of information. The information regarding what the customers do at the “physical” shops are typically stored under ERP systems or simply under RDBMS. The click stream as generated by the web servers normally tracks the on-line behavior.
DataSquare provides simple ways for importing data coming either from RDBMS or from machine generated logs like the ones generated by the webservers. All the data regardless their original sources are represented as tables with proper column names and types providing a uniform view to the users. Then using the tools provided by DataSquare it is possible to query, correlate, and join tables even if they were originally coming from very different sources like in this case
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