Retail analytics means to make use of big data in order to optimize the selling of goods to the public. There is the believe that there is a process that explains the retail data one observes in shops daily. The detail of this process generating such data is not known. But performing analytics on customer-specific data uncovers that the customer behavior is indeed not completely random. For example, supermarket customers do not buy goods at random. When those customers buy beer, often they tend to buy also chips in order to prepare for popular soccer game TV events. Also, the seasons can have an impact. Customers buy more often ice cream in summer periods while Gluehwein is mostly part of the shopping baskets in winter seasons and not in summer.
Given the examples above one can observe that there are certain patterns in the data. Analytics techniques may not be able to identify the process and patterns completely, but they can generate a quite good and useful approximation. Being just an approximation it will not explain everything about retail customers. Nevertheless it still be able to account for selected parts of the retail datasets. Although that retail analytics can not identify the complete process it can still detect certain patterns or regularities in the data. Those identified patterns help to understand the buying habits and process generating the customer data and can be further used for predictions. Of course this would require that the near future is not very much different from the past where the datasets have been collected and analyzed. When analytics are performed on such recent datasets the future predictions of customers buying retail products is possible.
Retail Analytics Details
We refer to the following video about this subject: