Multi-channel shopping, along with the arrival of smartphones, is the most significant change that has taken place in retail lately. Mobile shopping behaviors are considerably different from the shopping behaviors of other existing channels such as offline, TV, and the Internet. However, initially, Korean retail companies had trouble coping with this market change owing to a lack of understanding of mobile shopping behaviors. Therefore, they espoused big data analytics, expecting to obtain customer insights on not only mobile shopping behaviors but also multi-channel shopping behaviors. This case study discusses a trial made by a leading Korean multi-channel retail company to implement big data analytics in its marketing. The company was confronted with two issues, which prompted it to embrace big data marketing. First, the company recognized that it is extremely important to understand customer behavior across the entire shopping process and accordingly conduct the targeted marketing. Second, the company seeked to encourage the customers who used only a single channel to use diverse channels for sales as well as retention. The company thus tried to develop its rules for triggered marketing by analyzing the behavioral characteristics of multi-channel customers. For this, behavioral data for three years, covering about 10 million customers, were gathered and analyzed. Lastly, the company came up with detectable customer metrics that were expected to forecast the sales. In addition, customer segments were derived from data clustering based on customers’ shopping pattern, and marketing strategies were developed accordingly. Furthermore, the big data analytics revealed the importance of returning customers, and recommended modification to the royalty program and promotion of specific product categories. This case study proved the merits and demerits of big data analytics. On one hand, it helps in understanding the market trends of complex environments such as multichannel retail, and the significance of developing marketing strategies accordingly and reaping immediate benefits. On the other hand, it analyzes only the data of a given condition; therefore, it is hard to forecast the results if the condition, such as product-related offers, changes considerably. Big data marketing seems to work more effectively when it is used in combination with other qualitative research. This case study shows the status of big data marketing in a Korean multi-channel retail company and highlights its potentials as well as limits in this industry. change that has taken place in retail lately. Mobile shopping behaviors are considerably different from the shopping behaviors of other existing channels such as offline, TV, and the Internet. However, initially, Korean retail companies had trouble coping with this market change owing to a lack of understanding of mobile shopping behaviors. Therefore, they espoused big data analytics, expecting to obtain customer insights on not only mobile shopping behaviors but also multi-channel shopping behaviors. This case study discusses a trial made by a leading Korean multi-channel retail company to implement big data analytics in its marketing.

Purpose – Retail companies are turning from one type of retailer to multiple business categories through various reasons, such as taking advantage of existed types of retailers’ channel distribution, information and know-how, and entering into new types of retailers. However, there is few research conducted about multiple type of retailers. Research design, data, and methodology – In this research, the references, data and new stores on E-mart will be collected. The dissertation, annual report, statistical data, land-book of E-mart will be also collected to confirm whether E-mart has made any acquisition towards another company. Results – There is a tendency to new stores expansion, store enlargement and prolonging the opening preparation of new stores, based on the core of new stores expansion of E-mart as a general supermarket type of retailers. Based on general supermarket type of retailers, E-mart begins to diversify its retail company’s type of retailers. Conclusions – As a general supermarket which is the most important type of retailers, E-mart is expanding into SSM type of retailers to seek new power of growth while slowdown growth is shown in general supermarket type of retailers. The expansion into SSM type of retailers would be a wise option as a retail company, E-mart is able to keep growing in the future.

In this paper, we codify the objective function that should be optimized by using Genetic Algorithm instead of Heuristic method to solve these problems. So, each bit that constitutes one structure can signify each commodity. Therefore, we can exchange customers without restriction if the traveling distance diminishes among the districts. Furthermore, even though the capacity of a customer's commodities exceeds that of a vehicle, the following vehicle can be allocated. Also, we obtained good result by testing with real data. To be brief, we can effectively allocate innumerable commodities, that have various magnitudes and weight, into restricted capacity of the vehicle by applying genetic algorithm that is useful in solving the problems of optimization.