📊 Food Delivery Analysis

📕 Table of Contents

• Problem Statement
• Objective
• Tools Used
• Dataset Overview
• Key Findings
• Recommendations
• Project Presentation
• Project Learnings
• Installation and Usage

❓ Problem Statement

Khao a food delivery company is facing challenges in improving sales and profit though their sales in increasing each year. They want to know why their sales has not been improving by doing data analysis of 5 months data. They want to know which factors like restaurant, driver, average delivery time, area etc. are effecting their sales and profitability. They are concerned about how much they are losing in refunds. Finally they are interested to know where they should invest their money and man power to boost their sales and profitability. They want detailed analysis and recommendations based on these insights.

🎯 Objective

The primary objective of this analysis is boost sales by actionable recommendation based insights. By examining different KPIs like total sales, total loss due to refunds, average delivery time. Also need to find - Monthly trend across various metric like (revenue, refund, average delivery time). Top performing drivers across different metric. Top performing restaurants across different metric. Top performing cities across different metric. Develop targeted strategies to increase sales and profitability.

🛠️ Tools Used

• Analytical & Visual: Python
  
• Presentation: Microsoft Power Point
  

📅 Dataset Overview

• Data source: Internet
• Time period: Jan 20 - Apr 20

🔎 Key Findings

• Total revenue is $7.24M with a refund amount of $63K and discount amount of $263K.
• Key findings also include
• Total 408 registered drives against 408 restaurants.
• Average order value is $100.15 with . Monthly average order value is increasing.
• Average delivery time 2 hour 18 minutes, It was decreasing from March to April but now it is increasing.
• Different driver excel at different dimension. Top 22 drivers have same tip to delivery ratio.
• Restaurant 7098 has the highest average order value.
• All cities have almost similar performance by different metrics.

💡 Recommendations

• Optimize delivery times through route planning and driver training.
• Implement a driver mentorship program based on high-performing drivers' practices.
• Partner closely with high-value restaurants to increase average order values.
• Encourage upselling and combo meals to increase average order values.
• Optimize discount strategies to maximize sales while minimizing revenue loss
• Analyze and address refund patterns to minimize revenue loss.
• Develop city-specific marketing strategies to boost local performance.
• Enhance customer loyalty programs with personalized, tiered rewards.

📌 Project Presentation

Slides

The detailed presentation slides for this project can be found here

🧠 Project Learnings

1. Data exploration.
2. Data cleaning.
3. Size reduction.
4. Data visualization.
5. Data storytelling.
6. Sharpened analytical and problem-solving abilities.
7. Actionable Insights Generation.

💻 Installation and Usage

• Download and run ipynb file using jupiter notebok.
• Or, Upload the ipynb file on Kaggle or google drive then run it.