OPTIMISATION OF THE ASSORTMENT THROUGH E-COMMERCE CHANNELS FOR AN FMCG RETAIL COMPANY USING DATA ANALYTICS TECHNIQUES ON BIG DATASETS

Abstract

The e-commerce market has been increasingly growing worldwide within the last years. With the spread of the COVID-19 pandemic and the strict lockdown conditions, many businesses, including retail firms, started moving to the online format. In Kazakhstan, the companies that are willing to gain a competitive advantage also adopted selling through online channels. The largest fast-moving consumer goods (FMCG) retailer in Kazakhstan, Magnum Cash&Carry, decided to enter the e-commerce market during the pandemic by launching Magnum Go online shopping mobile app. Due to the limited time constraints, the company did not adopt its wide range of assortment to the preferences of online customers. Thus, the primary goal of this paper is to improve the product assortment of Magnum Go, considering the findings of the literature review, comparison of sales, competitor's analysis, and benchmarking. Moreover, the further objectives include developing new categorisation and listing algorithm and evaluation of Magnum Go mobile app ergonomics. The methodology consisted of three main approaches: data collection and pre-processing, data analysis and inference, and data visualisation. Besides, this paper describes using practical data analytics tools such as Microsoft Excel and Tableau for data processing and representation. The literature review outlines the role of applying big data analytics in e-commerce, the importance of assortment management, categorisation and listing strategies, and web and mobile app ergonomics. The research results include the following parts: analysis of Magnum's offline and online sales, competitors' analysis, new categorisation and listing, mobile app ergonomics assessment, and interactive dashboard. The sales analysis of two different channels revealed the variation in customers preferences. The product assortment in offline and online stores, therefore, should also differ. Competitor analysis allowed comparing the company's assortment size with leading Russian FMCG retailers. The results showed that Magnum offers a considerably higher number of non-food products. Besides, this paper proposed implementing new categorisation and listing based on the extensive sales and competitors’ analysis. Finally, the assessment of mobile apps indicated some areas for improvement in the functionality and interface of Magnum Go.