How Big Data Collected Via Point of Sale Devices in Textile Stores in Spain Resulted in Effective Online Advertising Targeting

Abstract

In these days of online commerce, we need to know the real behavior of consumers in physical stores: the points of sale must anticipate the purchasing decisions of consumers in order to be able to offer the best buying experience as well as tailor the marketing variables to the specific needs of each consumer. This is where retail intelligence emerges, especially in the textile industry, as a potential technology that makes use of extremely large data sets (“big data”) to engage potential customers better in order to increase company sales. The objective of this study is to show how big data can be effectively leveraged for direct and clear commercial purposes in textile stores. The development of research is based on the analysis of the application of systematic observation of consumer behavior in three main streets in Spain known for textile retail stores and interpreting their differences. The results show that data collected through various point-of-sale devices have a significant influence on retail revenue. The differences between commercial areas and the relative attractiveness of the textile trade in different cities are also borne out by the results. The main conclusions point to the need to improve the profitability of textile fashion stores on the back of promotional tactics that focus on the number of estimated customers and the possibilities of selling to them. All of the aforesaid have a significant influence on how advertising planning is carried out for retail stores.