Objective

The project aims to create an intuitive Sales Dashboard using PowerBI to extract valuable insights. This dashboard will incorporate advanced charting, interactive filtering, and slicers for enhanced user experience. Additionally, it will include forecasting capabilities to predict sales for the upcoming 15 days. Ultimately, the aim is to equip the superstore with data-driven tools to facilitate informed decision-making.

Data Preprocessing

The dataset contains historical sales and profit data for two years i.e. 2019 and 2020, customer information, product information, regional data, and order information. Conducted comprehensive analysis of sales data for a superstore using Excel and applied data cleaning and transformation methods within Power Query in Power BI. This involved addressing data inconsistencies and handling missing data.

Insights

1. The sales analysis identified substantial holiday season peaks, particularly during year-end. To easily examine and compare these trends over two years, a stacked area chart is employed, offering a clear visual representation.
2. In terms of sales, the technology and office supplies categories take the lead, while the subcategories of phones and chairs emerge as top performers. These insights can be effectively visualized and analyzed using a bar chart.
3. The consumer segment and the western region stand out as the most profitable, and this information can be effectively visualized using a donut chart, which provides a clear and concise representation of the data while allowing for easy comparison between categories.

Dashboard

Leveraged PowerBI visualization tools and DAX functions to design insightful charts and user-friendly dashboard. Users have the flexibility to apply real-time filters, enabling a targeted analysis of specific metrics and facilitating deeper insights.

Implemented a line chart or time series plot for forecasting sales trends over the upcoming 15-day period.

Challenges and Learnings

1. Proficiency in Chart Selection: Recognizing the appropriate chart types and their respective applications.
2. Regional Analysis: Recognizing the importance of regional-specific insights, necessitating context-aware analysis through filters and slicers.
3. Boosting Customer Engagement: Acquiring an understanding of customer preferences to improve engagement and enhance the dashboard's visual appeal.