Vrinda Store - Data Analysis in Excel

This project involves analyzing sales data for Vrinda Store from 2022.

🎯 Objective:

The goal is to generate an annual 📊 report that highlights performance metrics for 2022, providing insights into customer trends 🔎 and helping develop strategies to improve sales in 2023.

🔮 Roadmap:

  1. 📚 Collecting Data
  2. ❓ Sample Questions
  3. 🧰 Data Cleaning
  4. ⚙️ Data Processing
  5. 🔝 Data Analysis
  6. 🎨 Creating Dashboard
  7. 🔦 Insights
  8. 🌐 Next Steps

📚 Collecting Data:

The dataset was provided in the file Vrinda_Store_original.xlsx

❓ Sample Questions:

Here are some questions to guide the analysis:

  1. 📈 How do sales compare to orders in a single chart?
  2. 🌄 Which month had the highest 📈 sales and orders?
  3. 💃 Did men or women purchase more in 2022?
  4. 🔎 What were the different order statuses in 2022?
  5. 🌎 Which 10 states contributed the most to sales?
  6. 🌀 How are age ⏳ and gender 👩👨 related to order numbers?
  7. 🌐 Which sales channel contributed the most revenue?
  8. 🎪 Which category had the highest 💸 sales?

🧰 Data Cleaning:

  1. 🔢 Transforming the data into a table: The data was turned into a table to enable filters and make it easier to analyze. (You can also activate filters using Alt + D + F, but tables are more reliable.)

  2. 🧠 Checking for anomalies: I checked for blank or null cells. Luckily, none were found. If there were any, they would have been ❇ excluded using the filter options.

  3. 🏛️ Standardizing gender entries: The gender column had inconsistent entries (Men, M, W, Women). These were standardized into two categories: Men and Women.

⚙️ Data Processing:

To address trends over time, a new column was added to extract the 🔢 month from the Date column.

🔝 Data Analysis:

Analysis steps include the use of 📈 charts, pivot tables, and calculations to answer the questions and uncover trends. ✨✨✨

🎨 Creating Dashboard:

The dashboard includes:

  • 📈 Bar Charts:
    • Orders vs. Sales
    • Top 10 States by Sales
    • Age vs. Gender Orders
  • 🔹 Pie Charts:
    • Order Status
    • Channel Contribution
    • Gender Comparison (Men vs. Women)
  • 🔂 Slicers:
    • 🎪 Category
    • 🌄 Months
    • 🌐 Channel
    • 🔢 Age Group

🔦 Insights:

Key findings from the analysis include:

  1. ♀️ Women made up about 65% of total purchases.
  2. 🌎 The top states for sales were Maharashtra, Karnataka, and Uttar Pradesh.
  3. ⏳ Adults aged 20-40 years contributed to ~50% of orders.
  4. 🌐 Amazon, Flipkart, and Myntra were the top-performing sales channels, accounting for ~80% of revenue.

✅ Recommendations:

To boost sales in 2023:

  • Focus on ♀️ women aged 20-40 in 🌎 Maharashtra, Karnataka, and Uttar Pradesh.
  • Use 📲 targeted ads, 🎉 promotions, and 🎁 coupons on channels like Amazon, Flipkart, and Myntra.

📥 Downloads:

  • 📂 Download dashboard in Excel file.
  • 📂 Download raw dataset in Excel file to practice.

🌍 Resources:

🙏 Special Thanks:

  • Rishab Mishra for his guidance 🤝 and for providing valuable resources. ✨

🔗 Useful Links:

  • GitHub – Explore related projects and 🔎 resources.
  • YouTube Channel – Tutorials 🔄 and additional learning materials by Rishab Mishra. ✨✨✨

🎥 Project Course: