By increasing awareness of product offers and availability in the consumer’s proximity, Location Based Marketing (LBM) increases relevance of placed advertisements. However, depending on how it is executed, such advertising can also be perceived as intrusive, irritating, or even violating consumer’s privacy. Existing knowledge does not offer clear directions for retailers, who are keen to know of LBM’s effectiveness on sales. In this paper, authors investigate the effects of LBM on application (app) driven revenues of 116 major mobile retailers from around the globe. In particular, we examine the contingency effects of the roles of device as well as privacy needs of the brand audience. Findings reveal that effects of LBM on app-based revenues vary by tactic (inbound vs. outbound), type of device (Tablet vs. Phone), and user type based on brand of app (Android vs. Apple). Overall, this research identifies critical factors for retailers to consider, in order to best monetize their location based efforts. Contributions of the analysis and managerial implications are discussed.

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.

This study assessed the effect of color marketing in the RTD coffee industry in Korea. In order to investigate the effect of color marketing, this study measured the characteristics of color marketing as well as brand image and attitude in accordance with behavioral intention to purchase. Data were collected using questionnaires, and a total of 310 questionnaires were distributed with 298 entered for data analysis. Frequency analysis, factor analysis, correlation, and multiple regression analysis were tested using SPSS. A total of seven factors were extracted, including brand attitude, purchase intention, association, identification, brand awareness, symbolism, and attention. Significances were found between brand awareness and identification (p<0.001) and attention (p<0.001). In the relationship between characteristics of colors and brand attitude, significances were found in identification (p<0.001), attention (p<0.001), and association (p<0.001). Further, brand attitude and brand awareness had a significant positive effect on purchasing intention of RTD coffee. Results of this study suggested that color marketing is a good marketing tool to persuade potential consumers to purchase RTD coffee based on brand attitude and brand awareness.