Project Two

Sales records and Performance of Fried Chicken Products

delves into the sales data of fried chicken products, showcasing the performance of different products across sales channels, the sales trends over time, and the specific impact of unit pricing on revenue.
It meticulously records each transaction, including product types, purchase quantities, and channel sources, and utilizes product cost data to calculate profits.
Through the advanced visualization capabilities of Power BI, this report not only provides deep insights into product popularity but also analyzes the efficiency of sales channels and the overall profit margins,
thereby offering interactive data support for sales strategies and product pricing. This enhances the decision-making process by optimizing strategies based on comprehensive data insights.

  1. Date: Records the date of each order, providing a basis for analyzing sales trends over days, months, and quarters.
  2. Time: Logs the specific time when an order is placed, helping analyze sales performance during different times of the day.
  3. Product: Lists the name of the product purchased in each order, offering data for product sales performance analysis.
  4. Quantity: Indicates the number of products purchased in each order, aiding in calculating total sales volume and average sales quantity.
  5. Price per unit: Shows the unit price of each product, which, combined with quantity, can be used to calculate the total revenue of each order.
  6. Revenue ($): Directly provides the total revenue of each order, serving as a basis for revenue analysis and profit calculation.
  7. Channel: Records the sales channel of each order, such as Food Panda, Open Rice, etc., aiding in analyzing the performance of different sales channels.
  • Sales Trend Analysis: Using date and time data to analyze sales trends over time, including daily sales and seasonal variations.
  • Product Sales and Revenue Analysis: Analyzing the sales volume and revenue of different products to identify the most and least popular products.
  • Channel Efficiency Analysis: Comparing the efficiency of different sales channels to find out the most effective sales channel.
  • Profit Margin Calculation: Combining product list cost data to calculate the profit margins of products, analyzing which products contribute higher profits.
    1. Time Series Analysis:
  • Build time series analysis using time data to identify peak and trough periods of sales activities.
  • Analyze sales patterns and trends within specific time frames to predict future sales performance.
    2. Product Analysis:
  • Compare the sales volume and revenue of different products to analyze product popularity and sales contribution.
  • Evaluate market performance of products, providing data support for product development and sales strategies.
    3. Sales Channel Analysis:
  • Analyze sales data of different channels to identify the most effective promotion and sales channels.
  • Optimize sales strategies based on channel analysis to improve sales efficiency and customer satisfaction.
    4. Profit Analysis:
  • Calculate profit margins of each product by combining cost and sales data, identifying the products with the largest profit contributions.
  • Adjust product mix and pricing strategies based on profit analysis to maximize profits.