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1. Introduction
Advances in technology have enabled collecting, storing and processing a large amount of data. Automated collection of human behavior is one of the recent developments in data collection. Companies can analyze the behavior data to get insight into interests and desires of individuals. The technology can be used to detect movements of employees to understand common routes and optimize facility location in production systems; it can also be used to monitor potential customers in stores for marketing purposes. The technology is very important for customers to understand and communicate with their customers. Nowadays, customers expect to see personalized proposals from both production and service companies especially stores such as companies like Netflix, Amazon and Spotify (Celikkan et al., 2011; Merad et al., 2016). In a recent study by the Infosys Company, it was reported that 78 percent of consumers would be customers again if a retailer offers customer-targeted and personalized offers (Infosys, 2013). In another similar study, Microsoft states that 73 percent of customers prefer to shop with brands that offer a personalized buying experience (Shave, 2016). By 2020, customer experience is expected to pass price and product as a brand separator (Walker Corp., 2017). These findings underline the importance of understanding customers.
It is easier to generate personalized recommendations that allow customers to return to internet-based businesses. Each click or cursor movement is recorded, and the operators send the chip, code, advertisement or other material based on this data. According to RetailNext, 84 percent of customers believe that retailers need to do more to integrate their online and offline channels (RetailNext, 2017). Accordingly, physical stores should provide customers with an experience that matches the specific service they receive from the internet and should establish an unproblematic relationship with digital channels (Wu et al., 2015). The problem of integrating online and offline channels has been solved by tracking customers in the store using different technologies (Merad et al., 2016; Wu et al., 2015; Hurjui et al., 2008; Oosterlinck et al., 2017).
The traditional method to understand customer behavior is to use questionnaires, interviews and observations. With the development of technology, new data collection methods have emerged. The common feature of the new methods is that the...