Sales Data Visualization for Rumah Berkebun Shopee Store Using Business Intelligence and Google Data Studio - Dito Ramadhani; Muhammad Adie Syaputra (International Journal Software Engineering and Computer Science (IJSECS), 2025)
This study focuses on the analysis and visualization of sales data from Rumah Berkebun through a Business Intelligence (BI) approach, facilitated by the Google Data Studio platform. Utilizing an interactive dashboard, the research uncovers sales trends for key products such as durian and avocado seeds while identifying prominent seasonal demand variations. The dataset spans a two-year period (2022-2023) and incorporates critical metrics, including total sales, transaction volume, and order cancellation rates. Findings indicate notable seasonal fluctuations, with peak sales recorded in June 2022 and December 2023, alongside dominant market contributions from South Sumatra and Lampung. Conversely, regions like Bali and Nusa Tenggara exhibited substantial declines in sales performance. Quantitative insights were derived using statistical methods such as linear trend analysis and geographic heatmaps to map sales patterns and regional disparities. A significant challenge lies in the elevated order cancellation rates, largely attributed to payment-related obstacles, which hinder customer satisfaction. The adoption of BI has demonstrated its value in optimizing operational efficiency, enabling targeted stock and promotional strategies, and bolstering Rumah Berkebun competitive edge in digital and e-commerce landscapes. These results underscore the critical role of BI technology in fostering data-driven decision-making for online businesses.