%201.png){kind=link}
.png){kind=link}
.png){kind=link}
This project focuses on analyzing sales, customer behavior, and operational performance data from a retail business dataset. The primary objective is to derive actionable insights that will assist in improving customer satisfaction, optimizing logistics, and enhancing sales strategies. The project explores various aspects of the business, such as revenue trends, delivery times, customer distribution, and payment methods.
The analysis is based on data from multiple tables:
- Orders: Contains details of all customer orders, including timestamps for order purchase, estimated delivery, and actual delivery.
- Payments: Tracks payment details for each order, including payment method and installment information.
- Order Items: Contains detailed information about the products in each order, including freight (shipping) costs.
- Customers: Contains customer information such as state and unique identifiers.
- Sellers: Details about the sellers and their associated orders.
-
Revenue Analysis:
- Monthly revenue trends were calculated to identify high and low-performing months.
- Total revenue and average order value were computed to understand the financial performance of the business.
-
Customer Analysis:
- The distribution of customers by state was analyzed to explore regional patterns in purchasing behavior and delivery times.
- Net Promoter Score (NPS) was calculated to assess overall customer satisfaction.
-
Operational Performance:
- The average delivery time per state was computed, comparing estimated vs. actual delivery times to uncover inefficiencies in logistics.
- Freight costs were analyzed, highlighting regions with higher-than-average shipping costs, which can be optimized.
-
Payment Behavior:
- Payment installment preferences were analyzed, revealing trends in how customers choose to finance their purchases.
- The average order value based on payment type provided insights into how different payment methods impact purchasing behavior.