Amazon Sales Report Analysis - Innobyte Services Internship

Introduction

As part of my Data Analyst internship at Innobyte Services, I was tasked with analyzing Amazon’s sales data to derive actionable insights and support strategic decision-making. The dataset contained critical information on sales transactions, order fulfillment, product distribution, and customer behavior. My analysis aimed to uncover patterns and provide clear, data-driven recommendations to enhance sales strategies, customer satisfaction, and operational efficiency.

Throughout the project, I applied my attention to detail and problem-solving skills to uncover trends and provide recommendations that enhance business operations and customer satisfaction.

Project Objectives

• Sales Overview:

  Analyzed overall sales performance, trends, and patterns over time to highlight key fluctuations and potential growth opportunities.

• Product Analysis:

  Evaluated product categories, sizes, and quantities sold to identify popular products and optimize inventory management.

• Fulfillment Analysis:

  Investigated the effectiveness of different fulfillment methods in delivering orders, pinpointing areas for operational improvements.

• Customer Segmentation:

  Segmented customers based on purchasing behavior and geographic location to tailor marketing strategies and enhance customer satisfaction.

• Geographical Analysis:

  Explored the geographical distribution of sales across states and cities, helping identify regions for growth and targeted outreach.

Key Insights

• Sales Performance Trends:

  • In April, the dataset reveals the highest number of orders, indicating a peak in customer activity.
  • A significant decline in order volume is observed immediately after May.

• Product Popularity:

  • T-shirts, shirts, and blazers are top selling categories.
  • Watches and shoes had fewest sales

• Fulfillment Method Efficiency:

  • Amazon fulfils more orders and generates more revenue than Merchant.

• Customer Segmentation:

  • Maharashtra shows highest order frequency with multiple high-value customers.
  • Many customers show consistent spending pattern, with order amounts ranging between Rs 3K to Rs 7K.
  • Customers placing the highest number of orders (e.g., 11 to 12 orders) are consistently generating high revenue.

• Geographical Sales Distribution:

  • Maharashtra and Karnataka have larger sales.
  • Bengaluru and Hyderabad have much bigger sales.

• Order Status Analysis:

  • The vast majority of orders are successfully delivered.
  • A large proportion of purchases are cancelled or returned, suggesting a problem with product quality or consumer expectations.

Recommendations

1. Implement targeted promotions during high-sales periods (e.g., April and May) to capitalize on the sales spike.
2. To keep loyal consumers, provide loyalty programs to frequent purchases.
3. Ensure that top-selling categories such as T-shirts and shirts have enough supply levels to fulfil demand.
4. Stock more of the popular sizes. Consider offering special specials on less popular sizes to help balance inventories.
5. Analyse and fix issues in underperforming categories such as watches and shoes.
6. Investigate the causes of high cancellation and return rates to increase customer satisfaction.
7. Continue to use Amazon's fulfilment efficiency while looking for methods to increase Merchant fulfilment.
8. Optimize stock levels based on geographical sales data to meet regional demand efficiently.

Deliverables

• A comprehensive analysis report AMAZON SALES REPORT.docx summarizing the findings and recommendations.
• Data visualizations (charts and graphs) that illustrate key insights across sales, fulfillment, and customer behavior.
• Actionable recommendations on improving sales strategies, inventory management, and customer experience.

Conclusion

This project showcases my ability to dive deep into data, extract meaningful insights, and present them in a way that drives business improvements. By focusing on attention to detail, problem-solving, and data storytelling, I helped unlock strategic recommendations to boost sales and optimize operations for Amazon's marketplace.

The analysis and recommendations provided here showcase my capability as a data analyst who not only works with data but also turns it into meaningful stories that guide strategic decisions.