My first data analysis project is used by Power BI to create dashboard and spreadsheets to clean datas.
Source: Rikkei Academy (Group FB)
Lastest update: 2021
Usage date: Oct - 03 - 2026
Rows: 3.889
Data fiels:
The dataset used in this project contains retail beverage sales transactions from 2021.
For analytical purposes, the fields are categorized into Dimensions and Measures.
| Field | Description |
|---|---|
| Retailer | Name of the retail company selling beverage products |
| Retailer ID | Unique identifier for each retailer |
| Invoice Date | Date when the sales transaction occurred |
| Region | Geographic sales region |
| State | State where the transaction took place |
| City | City where the sale was recorded |
| Beverage Brand | Brand of the beverage product |
| Field | Description |
|---|---|
| Price per Unit | Selling price of a single beverage unit |
| Units Sold | Number of units sold in a transaction |
| Total Sales | Total revenue generated from the transaction |
| Operating Profit | Profit generated after operating costs |
| Operating Margin | Profitability ratio derived from sales and operating profit |
The project was developed using the following tools:
- Power BI – Data visualization and dashboard creation
- Excel / Spreadsheet tools – Data inspection and preparation
This project analyzes retail beverage sales data from 2021 and presents key insights through interactive dashboards built in Power BI.
The objective is to transform raw sales data into clear business insights that help stakeholders understand sales performance, product demand, and regional distribution in the beverage retail market.
This analysis aims to answer several key business questions:
- How did beverage sales perform throughout 2021?
- Which products and categories generated the highest revenue?
- Which regions contributed the most to total sales?
- Are there any seasonal patterns in beverage consumption?
The Power BI dashboard is designed to provide an overview of beverage retail performance in 2021 through several analytical perspectives.
The top section presents key performance indicators summarizing overall business performance:
| KPI | Description |
|---|---|
| Total Sales | Total revenue generated from beverage sales |
| Units Sold | Total number of beverage units sold |
| Operating Profit | Total profit generated after operating costs |
| Operating Margin | Average profitability ratio across all sales |
These KPIs provide a quick snapshot of the overall financial performance.
Total Sales by Month (Line Chart)
This visualization tracks monthly revenue performance throughout the year, helping identify:
- Seasonal sales patterns
- Peak sales periods
- Sales fluctuations across months
Units Sold by Beverage Brand (Donut Chart)
This chart shows the distribution of units sold across different beverage brands, allowing users to quickly identify:
- Top-performing brands
- Brand contribution to total sales volume
Total Sales by Retailer (Bar Chart)
This visualization compares sales performance between retailers and helps highlight:
- Retailers generating the highest revenue
- Market dominance among retail partners
The geographic map visualizes where beverage sales occur across cities, enabling users to identify:
- Key sales locations
- Geographic distribution of demand
The treemap provides a hierarchical view of sales volume across cities, making it easier to compare relative sales contributions between locations.
The dashboard includes slicers that allow users to dynamically filter the analysis:
| Filter | Purpose |
|---|---|
| Quarter | Analyze sales performance by specific quarters |
| Region | Compare sales across different geographic regions |
These filters enable more flexible and interactive data exploration.
Analysis by beverage brand shows that Coca-Cola leads both in total sales and operating profit, making it the most significant contributor to overall business performance.
This indicates that Coca-Cola functions as the core revenue driver within the product portfolio.
Implication:
Maintaining strong distribution coverage and marketing investment for this flagship product is essential to sustain overall revenue growth.
Brands such as Dasani Water and Diet Coke demonstrate strong sales and solid operating profit, playing a key role in maintaining stable revenue.
Meanwhile, products like Sprite and Powerade show moderate sales and profitability, indicating potential growth opportunities through improved pricing strategies, promotion, or product positioning.
Implication:
Optimizing the product mix can significantly improve overall profitability.
Sales trends reveal a strong seasonal pattern. Revenue begins to increase from May, peaks in July during the summer season, then declines slightly before rising again in December during the holiday period.
Implication:
The company should strengthen inventory planning, marketing campaigns, and distribution capacity during peak seasons to maximize sales opportunities.
Regional analysis shows that the West region generates the highest revenue, while the South region achieves the highest operating margin.
This suggests that different regions follow different strategies:
- West: market expansion and high sales volume
- South: stronger cost control and higher profitability
Implication:
Adapting regional pricing and cost strategies could help balance revenue growth with profitability.
Retailer analysis shows that Sodapop contributes more than 50% of total sales, making it the dominant distribution partner.
Implication:
While Sodapop plays a crucial role in driving sales, heavy reliance on a single retailer also creates concentration risk. Expanding partnerships with other retailers could improve long-term distribution resilience.
- The dataset only includes sales data for the year 2021.
- External factors such as marketing campaigns or economic conditions were not included in the analysis.
Possible extensions for this project:
- Add multi-year sales data for trend comparison
- Include profit and margin analysis
- Develop sales forecasting models
Personal Data Analysis Project