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superstore_sales_dashboard_powerbi's Introduction

๐Ÿšš "SuperStore" Sales Dashboard

๐Ÿ’ก Project Description

The dashboard utilizes sales data spanning two years to offer insightful visualizations and predictive tools, enabling the identification of sales trends.

Tech Stack Used

Untitled

PowerBI

๐Ÿ› ๏ธ Skills showcased in this Project

  • Data Extraction
  • Data Cleaning
  • Data Transformation
  • Data Visualization

๐Ÿ“…๐Ÿ” Project Approach

  • Connecting with datasources and shaping data in Power Query.
  • Data Cleaning in Power Query.
  • Creating calculated columns and measures using DAX formulas.
  • Designing interactive dashboard for visualisation.
  • Sales Forecasting

๐Ÿง ๐Ÿ’ก Key Insights

๐Ÿ’ณ Sales by Payment Mode: Cash on Delivery (COD) continues to lead the pack, closely trailed by online transactions and card payments.

๐Ÿข Sales by Segment: Our sales hierarchy is defined by Consumer purchases taking the lead, followed by Corporate and Home Office transactions.

๐ŸŒ Sales by Region: The West region emerges as our powerhouse, outperforming East, Central, and South regions consistently.

๐Ÿšš Sales by Ship Mode: Standard Class deliveries are the most popular choice, followed by Second Class, First Class, and Same Day shipments.

๐Ÿ“ฆ Sales by Category: Phones, Chairs, and Binders dominate our sales categories, showcasing strong market demand.

๐Ÿ“š Sales by Sub-category: Office Supplies, Technology, and Furniture sub-categories exhibit varying consumer preferences, providing valuable insights for inventory management.

๐Ÿ“† High Sales Months: September, November, and December witness a surge in sales activity, highlighting potential seasonal trends and opportunities for targeted marketing.

๐Ÿ’ฐ Profit Peaks: March, October, and December stand out as our most profitable months, emphasizing the importance of strategic planning during these periods.

๐Ÿ“ธ Data Visualisation

super1

super2

๐Ÿ“Š Project Learnings:

  • ๐Ÿ“ˆ Created an interactive dashboard for online sales data tracking and analysis.
  • ๐Ÿ” Utilized complex parameters for in-depth analysis through worksheets, enhancing customization via filters and slicers.
  • ๐Ÿ”„ Established connections, incorporated new tables, and applied calculations to manipulate data, facilitating user-driven parameters for visualizations.
  • ๐Ÿ“Š Employed various customized visualizations including bar charts, pie charts, donut charts, clustered bar charts, scatter charts, line charts, treemap and slicers for comprehensive insights.
  • ๐ŸŽฏ Sales Forecasting

End

Thank you for your interest and time. Feel free to give your valuable suggestions and connect with me on https://www.linkedin.com/in/harshitt-gahlaut/

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