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1. Revenue & Budget Variance Analysis Dashboard

Objective:

This Power BI dashboard analyzes revenue and budget variances, providing insights into expected vs. actual sales performance across different years, quarters, and product categories.

Steps Taken:

Data Preparation:

Imported the Budget and Actual dataset from an Excel file.

Cleaned and transformed the data using Power Query, ensuring consistency in column names and formats.

Data Modeling:

Created relationships between tables to enable efficient data analysis.

Defined calculated columns and measures using DAX for key metrics like Variance, Variance %, and Budget Goal.

Dashboard Development:

Designed a summary section displaying key financial metrics:

Expected Revenue

Actual Revenue

Variance & Variance %

Created a Budget Variance by Month area chart to visualize fluctuations in budget variance over time.

Developed a Sales by Month line chart to track trends and compare actual vs. expected sales.

Built a Product-wise Sales Comparison bar chart to analyze product performance.

Added a Quarterly Variance Table with color-coded variance % (green for positive, red for negative).

Used a heatmap-style variance percentage table to highlight major discrepancies.

Interactivity & Filters:

Implemented slicers for Year, Quarter, and Product to enable dynamic filtering.

Included a tooltip feature to display additional insights when hovering over visual elements.

Key Insights:

The overall variance was -0.42%, indicating a slight underperformance.

Some quarters showed positive variance, while others exhibited significant negative deviations.

Product sales distribution highlighted areas of improvement and potential growth opportunities.

Tools & Technologies Used:

Power BI (Data Cleaning, DAX, Visualization)

Excel (Data Source)

2. Sales Performance Dashboard (1997-1998)

Objective:

This Power BI dashboard provides a comprehensive analysis of sales performance across different regions, products, and time periods, helping businesses track sales trends, customer retention, and product returns.

Steps Taken:

Data Preparation:

Imported datasets (Customers, Products, Regions, Returns, Stores, Transactions 1997 & 1998) from CSV files.

Merged multiple transaction datasets to create a consolidated sales dataset.

Cleaned data using Power Query, ensuring proper date formats, category mappings, and missing value handling.

Data Modeling:

Established relationships between different tables (Customers, Products, Transactions, Returns, etc.).

Created calculated measures using DAX for key metrics:

Total Sales

Return Rate

Customer Retention Rate

Sales Growth Percentage

Dashboard Development:

Designed an interactive KPI section displaying:

Total Sales (£1.76M)

Return Rate (1%)

Customer Retention Rate (33.36%)

Created a Top-Selling Products bar chart to identify best-performing products.

Developed a Sales by City funnel chart to visualize regional sales distribution.

Implemented a Product Return Trends bar chart to highlight products with the highest return rates.

Added a Total Sales by Quarter line chart to track seasonal trends.

Integrated a geospatial map (Microsoft Bing Maps) to show store locations and total sales by city.

Interactivity & Filters:

Added a year filter (1997-1999) to analyze sales trends over time.

Implemented a store type and city slicer for granular insights into different locations.

Used drill-through functionality to explore product performance in-depth.

Key Insights:

Sales Growth (112.18%) indicates strong performance, with Q4 being the most profitable period.

Customer retention (33.36%) suggests a need for improved engagement strategies.

Certain products have higher return rates, highlighting potential quality or customer satisfaction issues.

Tools & Technologies Used:

Power BI (Data Modeling, DAX, Visualization)

CSV Files (Data Source)

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بطاقة العمل

اسم المستقل محمد ف.
عدد الإعجابات 1
عدد المشاهدات 10
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