Update README.md

README.md

@@ -1,46 +1,18 @@
 # The Pizza Sales Performance Analysis Report with Power BI and SQL
 
-<img width="100%" align="middle" 
-    src="https://github.com/NguyenDangXuanLinh/The-Pizza-Analysis-Report-with-PowerBI-SQL/blob/main/Pizza%20Analysis%202.png">
-
-## Introduction
-The purpose of this project is to help pizza business sustain and enhance their business growth. 
-The analysis will help business owner understands their current product performance and uncover valuable 
-insights to make data-driven decisions
-
-## Problem Statement
-To identify business growth opportunities, the pizza business owner needs to understand their product performance -
-their data sales in order to execute growth plans such as marketing, promotions, or product optimization.
-
-To generate insights, I developed Power BI dashboard monitoring key performance metrics using these
-questions:
-
-1. What is the total revenue over a specific period?
-2. How many pizzas have been sold in total?
-3. What is the average order amount?
-4. Which pizza names are the most and least popular by order?
-5. Which pizza names have the highest and lowest quantity sold?
-6. Which pizza sizes are most preferred by customers?
-7. Which pizza categories generate the most sales?
-8. What are the daily and monthly sales trends?
-9. How do sales vary across pizza categories and sizes?
-
-## Tools and Technologies Used
-- **MySQL** - This was needed to conduct **Data Quality Assessment** and also for **Data Cleaning processes**. The relational model is adopted to store and access structured information and I used tructured query language (SQL) to write and query data to:
-    - Perform data cleaning, aggregation, and then processing tasks within Power BI.
-    - Cross-validating analytical results between SQL query outputs and Power BI DAX-based calculations
-    - Documented the queries and their results in Google Docs to monitor and improve query performance and management tasks.
-
-- Power BI - This tool was required to explore data and create charts, graphs, visualizations to come up with a **Sales Performance Dashboard ** for the pizza business.
+ <img width="45%" src="images/Pizza Analysis 1.png" align="left">  <img width="45%" src="images/Pizza Analysis 2.png" align="middle" >
+
+# Product performance Metrics   
+- Revenue: specific period, total
+- Pizza sold: total, by amount, by category, by size
+- Pizza names: most order, least order, highest sold, lowest sold
+- Pizza sizes: most order
+- Sale trends: daily, monthly.
 
-## Click on the hyperlink to view the documents:
-- [Pizza Performance Analysis SQL Queries](https://docs.google.com/document/d/1wSnyjedEcRMVajsgZfbfodMxAo3hVIxO/edit?usp=sharing&ouid=107430840207084984872&rtpof=true&sd=true)
+# Click on the hyperlink to view the documents:
+- [Pizza Performance Analysis SQL Doc](https://docs.google.com/document/d/1wSnyjedEcRMVajsgZfbfodMxAo3hVIxO/edit?usp=sharing&ouid=107430840207084984872&rtpof=true&sd=true)
 - [Pizza Performance Analysis Power BI Dashboard](https://app.powerbi.com/view?r=eyJrIjoiMDMwMjRjMjQtYzJlNC00OTgwLWIxYmEtNDRkMjZkNjg3NDI0IiwidCI6ImRmODY3OWNkLWE4MGUtNDVkOC05OWFjLWM4M2VkN2ZmOTVhMCJ9)
-
-
-<img align=center width="100%"
-src="https://github.com/NguyenDangXuanLinh/The-Pizza-Analysis-Report-with-PowerBI-SQL/blob/main/Pizza%20Analysis%201.png" >
-## Key Findings
+# Key Findings
 The following are some of the key findings from the analysis:
 - The **total number of pizza orders** was approximately **21,000**.
 - The **total sales** of pizza were approximately **50,000**.
@@ -51,21 +23,20 @@ The following are some of the key findings from the analysis:
 - Sales Increase in **Friday, Wednesday and Saturday** as daily and **January,May, July** monthly.
 
 # Recommendations
-By implementing these recommendations, business owner can leverage sales data to make informed decisions that drive growth and profitability in their pizza business.
 
-### 1. **Optimize Satff anf Marketing Strategiess**:
+## 1. **Optimize Satff anf Marketing Strategiess**:
 Consider leveraging the noticeable trends by day (Friday, Wednesday, Saturday) and by month (January,May,July) to optimize staffing and marketing strategies. For instance, **increase staffing levels** on Fridays and **tailor promotions for the month** of July to capitalize on these trends.
 
-### 2. **Inventory and Production Planning**:
+## 2. **Inventory and Production Planning**:
 With a total of 50,000 pizzas sold, we can utilize this data to refine inventory and production planning processes.
 By identifying peak sales periods and popular pizza types, pizza owner can optimize stock levels to meet demand while minimizing waste.
 
-### 3. **Optimize Pricing and Promotions**:
+## 3. **Optimize Pricing and Promotions**:
 The average order amount of $38 provides valuable insights into **customer spending habits**.
 We can use this information to **fine-tune pricing strategies** and develop **targeted promotions** that resonate with customer base.
 For example, we can **offering bundle deals** or **loyalty programs** to incentivize higher spending.
 
-### 4. **Buid Customer Growth Programs for marketing, promotions and product optimization** 
+## 4. **Buid Customer Growth Programs for marketing, promotions and product optimization** 
 In terms of marketing efforts:
 
   - **Promote Customer Favorites** - 
@@ -74,18 +45,25 @@ In terms of marketing efforts:
   - **Prioritize Best-Selling Categories and Sizes** - 
         Given that Large Size and Classic pizzas dominate sales, we can **prioritize these sizes** and **categories** in **marketing campaigns** and **product offerings** such as introducing variations or promotions to further capitalize on     their popularity.
 
-### 5. **Utilize Percentage Sales Data**:
+## 5. **Utilize Percentage Sales Data**:
 By examining the percentage sales by pizza category, we can see that Classic, Supreme pizzas and Large (L) size pizza are more popular among customers.Armed with this information, some recommendations will help pizza owner maximizing profitability by:
 
   - **Allocate resources** - effectively by focusing marketing efforts and production resources **on the most popular pizza categories** and sizes will help **increase sales** and revenue **without significant additional expenses**.
   - **Optimize product mix** - for the **lower percentage of sales** in top 5 categories. For example, Chicken pizzas have a lower percentage of sales, it is worth to evaluate whether **adjustments to the recipe** or **marketing strategies** are needed to increase their popularity.
   - Or, we can introduce **new variations or seasonal specials** based on the popularity of certain categories and sizes of pizza.
 
-### 6. **Continuous Monitoring and Adaptation**:
+## 6. **Continuous Monitoring and Adaptation**:
 Continuously monitor sales data and customer feedback will help identifing emerging trends and customer preferences.
 This will help in maintain a competitive price on the market and sustain long-term profitability.
 
 
+## Tools and Technologies Used
+ **MySQL** :The relational model is adopted to store and access structured information to conduct **Data Quality Assessment** and also for **Data Cleaning processes**
+- Perform data cleaning, aggregation, and then processing tasks within Power BI.
+- Cross-validating analytical results between SQL query outputs and Power BI DAX-based calculations
+- Documented the queriesin Github and stored results in Google Docs to monitor and improve query performance and management tasks.
+
+ **Power BI**: create charts, graphs, visualizations to come up with a **Sales Performance Dashboard ** for the pizza business.
 ## Author
 - Nguyen Dang Xuan Linh - [GitHub Profile](https://github.com/NguyenDangXuanLinh)