beto-amaral/sql_eda

Brazilian E-Commerce

SQL_EDA

Brazilian E-Commerce Public Dataset by Olist

About Dataset Brazilian E-Commerce Public Dataset by Olist Welcome! This is a Brazilian ecommerce public dataset of orders made at Olist Store. The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. Its features allows viewing an order from multiple dimensions: from order status, price, payment and freight performance to customer location, product attributes and finally reviews written by customers. We also released a geolocation dataset that relates Brazilian zip codes to lat/lng coordinates.

This is real commercial data, it has been anonymised, and references to the companies and partners in the review text have been replaced with the names of Game of Thrones great houses.

Conclusions and Recomendations:

Query 1: Count and % of orders purchased in Jan18 with a 5 review score.

With this query, you can identify the quantity and percentage of orders made in January 2018 that received a review score of 5. Recommendation: This information can be useful for assessing customer satisfaction and the quality of products or services.

Query 2: Customer purchase trend year-on-year.

This query shows the count of delivered orders per month, separated by year, allowing you to analyze the customer's purchase trend over time.

Recommendation: Based on this query, you can identify which months and years had a higher volume of orders and detect possible patterns in customer behavior. This information can be used to adjust marketing strategies, seasonal promotions, or other targeted actions to drive sales.

Query 3: Delivery success rate across states.

This query calculates the delivery success rate by state, providing insights into the effectiveness of the delivery process across different regions.

Recommendation: Based on this query, you can identify states with high delivery success rates and states with lower rates. This can help direct efforts towards improving logistics efficiency in areas performing below average, ensuring a better customer experience and avoiding delivery issues. Additionally, this information can be valuable for negotiations with logistics partners and optimizing the geographical distribution of distribution centers.

Sources: kaggle.com and towardsdatascience.com