The Sales Behavior Analysis and Precise Marketing Recommendations of FMCG Retails Based on Geography Methods

Abstract

With the rapidly increasing of people’s purchasing power, the fast moving consumer goods (FMCG) industry is supposed to grow dramatically. In order to gain more market access and profile, it is important for the FMCG manufacturers and retailers to find the preferences and provincial characteristics of consumers, to develop more suitable goods distribution strategy. Based on retails marketing data with geographic characteristics, this paper proposes a new combination of geography methods to solve the problems in distribution of FMCG. Via multiple K-means clustering and cross validation of KNN half off, the mesoscopic sales features are extracted through the classification of retails, which can indirectly grasp the consumer behavior characteristics. Based on space division and Moran’ I spatial autocorrelation arithmetic, two strategies are developed to satisfy consumer’s needs and promote sales, including conservative and positive strategies. According to our analysis, the total sales volume of the regions will increase by 5.1% and 10.3%. This study can be applied to the provide purchase strategies for FMCG retails according to their locations. The research can explore the consumption potential of different areas, thus improving the profile of retails and the development of economy in more mesoscopic scale.

Crisis Marketing in the FMCG Sector: A Review of Strategies Nigerian Brands Employed During the Covid-19 Pandemic

“…Furthermore, the role of data-driven insights in shaping marketing strategies cannot be overstated. Wang et al (2017) underscored the value of using geographic methods to analyze sales behavior and develop precise marketing recommendations for FMCG retails. By harnessing the power of data analytics, FMCG brands can gain a deeper understanding of market trends, consumer preferences, and competitive landscapes.…”

Spatial and aspatial clustering analysis of PM2.5 concentrations in Temuco, Chile using mobile measurements

“…For example, [22] concluded that integrating K-Means clustering and the spatial Getis-Ord G * i statistic resulted in a superior technique for identifying clusters of orchards. In another study, [23] employed multiple K-Means clustering and Moran's I spatial autocorrelation method to find the preferences and provincial characteristics of consumers in the retail industry. In [24] Scrucca implemented K-Means clustering and measures of spatial autocorrelation to incorporate the spatial structure of the labor market data in Umbria, Italy.…”