Abstract
<p class="0abstract"><strong>—</strong> 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.</p>
Highlights
The economic recession has been devastating Spain since 2008, with GDP plunging 3.1% in 2009 and unemployment rates of more than 20%
In the eyes of González [14], observation is about systematically and carefully contemplating how social life develops. She alludes to a set of methods laid down for the direct observation of events that occur in a natural way. This definition brings in two main considerations: firstly, that the data is collected when the event occurs; this does not, in any way, preclude the possibility of recording or other means of collecting this data for later analysis; secondly, it means that the event is not staged, created, maintained or finished exclusively for the benefit of the research. (A little later in this narrative, we will be talking about the so-called experimental method [14])
By knowing the number of potential customers who pass in front of the window of a textile store or point of sale, it should be possible to control other indicators such as the cost per potential customer (CPC)
Summary
The economic recession has been devastating Spain since 2008, with GDP plunging 3.1% in 2009 and unemployment rates of more than 20%. Grim note; physical stores posted a 12% slump in sales and the number of premises shrunk by a whopping 40,000 while 90,000 people were rendered jobless [1] In such a gloomy scenario, would people visit any textile store at all and even if they do visit, what are they most likely to shop? When a shopper walked past the beam, there was a “break” in the beam of light, and every such break was recorded by the device as a single customer. They were two such devices – one at the entrance, the other, at the exit, and the final count of shoppers was obtained by merging data from both sources [2]. The cash tickets were counted to get a ballpark count of shoppers
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