Sales Performance Insights
A Power BI report analysing first- quarter sales performance using transactional business data.
Embedded Files
Context
Context
The project was completed as part of the Generation UK Data Analyst Programme using anonymised training data representing business sales performance.
Aim
Aim
The aim for the project was to explore first- quarter sales performance and develop a Power BI report that answers key business questions and supports data-driven decision-making.
Approach
Approach
The project followed a structured process from preparing the data to presenting insights clearly:
🧹Data Preparation
🧹Data Preparation
Imported the CSV sales dataset into Power BI Desktop
Promoted the first row to headers to correctly structure the data
Reviewed and corrected data types to ensure dates and numerical values were formatted correctly
Finalised the dataset for analysis in Power BI
📅Date Handling
📅Date Handling
To support time-based analysis, I created additional date fields:
Extracted the month name for the transaction date to enable month-level reporting
Created a month number column to ensure months were displayed in the correct chronological order within visuals
📊Report Development
📊Report Development
Built visuals to answer key business questions about revenue, product performance, store performance, and monthly trends
Used KPI cards, bar charts, a tree map, and a line chart to present findings clearly
Added written insight summaries to explain what the visuals showed
Power BI report visualising Q1 sales performance across products, stores and time periods.
Key Insights
Key Insights
Revenue Trend
Revenue Trend
Sales increased from January to February before declining in March, suggesting a possible seasonal trend or short-term demand spike.
Top Revenue Category
Top Revenue Category
Laptops were the strongest category overall, with high products driving a large share of total revenue.
Store Performance
Store Performance
Store B had the highest number of units sold, indicating a stronger sales performance compared to other locations.
Best Selling Products
Best Selling Products
The Galaxy Tab, Charger, and Samsung S23 were the best- selling products, showing a demand across different product types rather than just one category.
Recommendations
Recommendations
Based on the analysis of the first quater sales performance, the following actions could support improved business outcomes.
💻Prioritise Top- Selling Categories
💻Prioritise Top- Selling Categories
Maintaining strong stock availability and targeted promotions for high-performing categories could help sustain overall sales performance.
🛒Support top selling products
🛒Support top selling products
Present more opportunities for top-selling products, such as strategic promotions, bundling, or feature placement, to maximise revenue.
📈Monitor Monthly Trends
📈Monitor Monthly Trends
Ongoing monitoring of monthly trends could help identify seasonal patterns and support more informed sales planning.
🏬Review Store Performance
🏬Review Store Performance
Investigate operational or promotional differences between store locations to identify successful approaches that could be implemented more widely.
Reflections
Reflections
This project improved my confidence in preparing raw data and designing reports that clearly communicate insights. I strengthened my time- based analysis and learned how thoughtful visual design can make findings more accessible to business users.
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