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Information technology’s introduction of online retail has deeply influenced methods of doing business. However, offline retail has not changed as radically in comparison to online retailing. Recently, studies in computer science have suggested new technology that can support offline retailers, including sensors, indoor positioning, augmented reality, vision, and interactive systems. Retailers have recently shown interest in these technologies and rapidly adopted them in order to improve operational efficiency and customer experience in their retail shops. Marketing studies also address immersive marketing that employs these technologies in order to change ways of doing offline retail business. Even though there is much discussion concerning new trends, technologies, and marketing concepts, there is, as of yet, no investigation that comprehensively explains how they can be combined together seamlessly in the real world retail environment. This paper employs the term “smart store” to indicate retail stores equipped with these new technologies and modern marketing concepts. This paper aims to summarize discussions related to smart stores and their possible applications in a real business environment. Furthermore, we present a case study of a business that applies the smart store concept to its fashion retail shops in Korea.

Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability. Acknowledging the importance of indoor positioning systems in store layout optimization, we conducted a field experiment for 11 months in order to develop algorithms for connecting indoor positioning data with customer transaction data. Using fingerprinting as a primary data collection technique, we compared positioning and transaction data before and after critical store layout optimization decisions in order to identify which customer movement patterns generated the highest sales. In contrast to previous works on indoor positioning systems, which focused solely on developing algorithms or techniques to increase accuracy rates, our algorithms in principle integrate computing and marketing perspectives. Our findings can be applied to store layout optimization and personalized marketing.

We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market.

The advancement of the Internet and technology has made it possible to purchase and use different types of products and services online instead of offline. In particular, as the scale of online shopping malls is rapidly increasing, new functions have been tried and introduced to compensate for the limitation of not being able to directly wear clothes in an online mall. Among them, 3D virtual try-on is an innovative service and its technology is being advanced with continuous interest. Technological advances and interest in 3D virtual try-on have led to a variety of related studies. Most previous studies on virtual try-on have been conducted on the virtual fitting technology from the perspective of making clothes, or the effects and customer behavior from the customer perspective. However, there has been no research based on actual data of customers using 3D virtual try-on to show how virtual try-on affects sales. Therefore, this study understands the fundamental meaning of virtual 'try-on' as a customer experience and examines the effects of 3D virtual try-on on online sales. We create a 3D body model complemented by adding more diverse body shapes and sizes, and investigate the effects of virtual try-on on online sales through actual data. In addition, qualitative data including interviews are used to complement and interpret the results. The results show that virtual tryon affects the sales results: the average sales per customer increased by 14,000 won (13USD). The most important finding is that the return rate decreased by 27% by filtering out incorrect sizes and fits. Virtual tryon may very well replace physical fitting rooms. This study presents an advanced technology of 3D virtual try-on and shows that virtual try-on is an effective tool to boost sales and decrease customer's returns in a case study of women's casual L brand. INDEX TERMS virtual try-on (VTO), 3D clothes digitization, customized body, customer experience, online sales

Many companies operate e-commerce websites to sell fashion products. Some customers want to buy products with intention of sustainability and therefore the companies need to suggest appropriate fashion products to those customers. Recommender systems are key applications in these sustainable digital marketing strategies and high performance is the most necessary factor. This research aims to improve recommendation systems’ performance by considering item session and attribute session information. We suggest the Item Session-Based Recommender (ISBR) and the Attribute Session-Based Recommenders (ASBRs) that use item and attribute session data independently, and then we suggest the Feature-Weighted Session-Based Recommenders (FWSBRs) that combine multiple ASBRs with various feature weighting schemes. Our experimental results show that FWSBR with chi-square feature weighting scheme outperforms ISBR, ASBRs, and Collaborative Filtering Recommender (CFR). In addition, it is notable that FWSBRs overcome the cold-start item problem, one significant limitation of CFR and ISBR, without losing performance.

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In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the flow of a typical film process was ascertained. Second, a sustainable plan for real-time forecasting in a process that cannot be automated was developed using the semi-automation method flexible structure production control (FSPC). Third, statistical early selection of the process variables that are most probably responsible for failure was performed during data preprocessing. Then, a new, unified dataset was created using the link reordering method to transform the time sequence of the continuous process into one time zone. Fourth, a sustainable prediction algorithm was developed using the association rule method along with traditional statistical techniques, and verified using actual data. Finally, the overall developed logic was applied to new production process data to verify its prediction accuracy. The developed real-time early warning system for semi-automated processes contributes significantly to the smart manufacturing process both theoretically and practically.