Year: 2025
Type: article
Abstract: This project is a data visualization and analytics solution developed using Microsoft Power BI.The primary objective of this project is to provide an interactive and insightful overview of monthly sales performance on the Amazon e-commerce platform.The project utilizes historical sales data, including key metrics such as order volume, revenue, profit margins, product categories, customer segments, and regional distribution.Through the use of Power BI's robust visualization tools, the report presents dashboards that enable users to monitor sales trends, identify top-selling products, analyze customer purchasing behavior, and track regional performance. I. INTRODUCTIONThe Amazon Monthly Sales Report project is designed to efficiently manage, analyse, and present comprehensive sales data through the use of Microsoft Power BI, a leading business intelligence and data visualization platform.This project focuses on converting large volumes of raw, unstructured sales data into meaningful insights by creating interactive, dynamic, and visually appealing dashboards.These dashboards provide a clear overview of key performance indicators (KPIs) such as total sales, revenue trends, top-selling products, regional performance, customer behaviour, and return rates.By utilizing Power BI's powerful features like data modelling, DAX functions, and drill-down capabilities, the project empowers stakeholders-including sales teams, product managers, and business executives-to monitor sales performance in real-time, identify growth opportunities, and evaluate the success of marketing strategies or product launches.Ultimately, this project enhances decision-making by providing a centralized, intuitive platform that supports data-driven insights and strategic planning for continued growth in Amazon's competitive e-commerce environment. PROBLEM STATEMENTA. Unstructured Sales Data is Difficult to Analyse Amazon generates vast amounts of sales data every month, including transactions, customer orders, product performance, and returns.However, this data is usually stored in raw formats such as spreadsheets or databases, without any visual structure.Analysing such unorganized data manually is time-consuming, prone to human error, and lacks clarity.This makes it difficult for decision-makers to extract actionable insights quickly, leading to delayed responses to market trends, customer preferences, or product issues. B. Lack of Real-Time Sales MonitoringIn the absence of a real-time reporting tool, sales data is often reviewed only at the end of the month.This delay can prevent stakeholders from identifying issues or opportunities as they arise.Without timely insights, decisions may be based on outdated information, which can negatively impact sales performance and customer satisfaction. C. Inability to Visualize Trends and Patterns EffectivelyRaw data in tabular form does not easily reveal trends, seasonal patterns, or anomalies.Without visualization tools, it is difficult to identify which products are performing well, which regions are underperforming, or how sales change over time.Visualizations like graphs, charts, and heatmaps help turn complex data into intuitive insights. D. Limited Support for Data-Driven Decision-MakingWhen sales insights are not clearly presented or easily accessible, stakeholders may rely on intuition rather
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4414485650
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