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Introduction
This is a Brazilian e-commerce public dataset of orders made at the Olist Store. The dataset has information on 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil. There are 8 tables in total including payments, products, orders, items, sellers, customers, geolocation, and reviews.
Objectives
From a store manager’s perspective, I tried to analyse sales, number of customers, popular payment method and geography locations of these customers. With an aim to find solutions for the store to boost sales and revenues in the future, I considered data on products, customers and orders.
Visualise and analyse the insights
Tableau:Â https://public.tableau.com/app/profile/lam.vy.tran/viz/BrazilE-commerce_16851995209700/Dashboard1
Analysis
Insights
- Overall, total orders, and total orders value increase over time, however, we can notice some peak months such as in May or in November. It can be easily understood that these months fall on the mid-year sale and last-year sales of many brands. However, we can not define whether this was the pattern for sales or just a trend in 2017, we should continuously collect and update data to get a better view.
- Comparing new customers and retaining customers, at this time, the retention rate is very low month on month. On the other hand, the growth rate experienced an increasing trend.
- There are some other findings in the most popular product
Dataset Limitation and Improvement
To provide more accurate and insightful solutions, I think the company should gather more information, particularly as follow:
- Collect customers' backgrounds ( age, gender, occupation, salary) to have a better customers' understanding.
Moreover, the datasets should follow some rules to make them easier to analyse.
- The time period we collect data should be a full year, and in a consistent way for easily compare year on year.