Analisis Dan Visualisasi Data Transaksi Brazilian E-Commerce Menggunakan Looker Studio Untuk Mendukung Keputusan Tim Sales

Abstract

The growth of e-commerce in Brazil experienced a significant increase in 2016, with revenue rising by 7.4% compared to the previous year, reaching a total of 44.4 billion BRL (Brazilian Real). Approximately 48 million consumers, or nearly a quarter of Brazil’s population, made at least one online purchase during the year. This figure reflects a 22% growth in active consumers compared to the previous year. This rapid growth indicates a shift in consumer behavior toward digital platforms, highlighting the need for in-depth analysis to examine sales patterns, understand customer behavior, and identify market trends. This study aims to analyze and visualize Brazilian e-commerce transaction data using the Exploratory Data Analysis (EDA) method and the Looker Studio visualization platform. The analysis process involves data collection, data cleaning, data analysis, and interactive visualization to support decision-making. This approach is intended to provide deeper insights into consumer behavior, sales trends, and potential market growth across various regions in Brazil for the sales team. The results of the analysis reveal several important findings, including monthly sales trends, geographical distribution of sales, the proportion of product categories sold, and seller distribution. The peak of sales occurred in November 2017, with the largest contribution coming from the city of São Paulo. Meanwhile, regions with low sales activity present significant opportunities for market expansion. The Bed, Bath, and Table category became the best-selling product category, while other underrepresented categories indicate growth potential that can still be optimized. In addition, the most dominant payment method used by customers was credit cards. Based on these findings, several recommendations can be proposed to support the performance of the sales team. First, it is important to expand promotional activities in cities with low sales levels, such as offering special discounts during local celebrations or national holidays. Second, a product bundling strategy can be implemented by combining low-selling products with popular products into affordable package deals. For example, electronic products with lower demand can be bundled with household products that have a high purchase rate. Furthermore, the third recommendation is to develop a customer loyalty program by providing shopping points that can be redeemed for discounts, cashback, or exclusive rewards. This strategy aims to improve customer retention while attracting new customers. Fourth, incentives should be provided for new sellers in cities with growth potential, such as reduced platform fees and free training programs. This initiative may encourage prospective sellers to start their businesses. Finally, alternative payment methods such as vouchers and debit cards should be promoted through 5–10% discounts in order to encourage customers to use these payment methods more frequently. Keywords: Exploratory Data Analysis, Data Visualization, Sales Strategy