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.