An analysis on Olist dataset which was provided as a Data Analysis Challenge on Twitter
Olist is a Brazilian e-commerce platform that facilitates connections between small and medium-sized businesses and customers across Brazil. As a marketplace, merchants can list their products and services, while customers can browse and make purchases online. The Olist sales dataset, available on Kaggle, comprises anonymized data about orders placed on the platform between January 2017 and August 2018. The dataset contains comprehensive information on each order, including order dates, product details, payment and shipping information, customer and seller IDs, and customer reviews. Additionally, it provides data on sellers listing their products on Olist, as well as customer behavior and demographics. The primary objective of this analysis is to help Olist gain valuable insights into their e-commerce platform, identify opportunities for growth, and optimize their operations.
To provide valuable insights to Olist, the analysis aims to answer the following business questions:
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Total Revenue and Revenue Trends: What is the total revenue generated by Olist, and how has it changed over time?
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Order Volume Variation: How many orders were placed on Olist, and how does this vary by month or season?
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Popular Product Categories: What are the most popular product categories on Olist, and how do their sales volumes compare to each other?
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Average Order Value (AOV): What is the average order value (AOV) on Olist, and how does this vary by product category or payment method?
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Active Sellers: How many sellers are active on Olist, and how does this number change over time?
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Seller Ratings and Sales Performance: What is the distribution of seller ratings on Olist, and how does this impact sales performance?
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Customer Repeat Purchases: How many customers have made repeat purchases on Olist, and what percentage of total sales do they account for?
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Customer Ratings and Sales Performance: What is the average customer rating for products sold on Olist, and how does this impact sales performance?
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Order Cancellation Rate and Seller Performance: What is the average order cancellation rate on Olist, and how does this impact seller performance?
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Top-Selling Products and Sales Trends: What are the top-selling products on Olist, and how have their sales trends changed over time?
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Payment Methods and Geographic Variations: Which payment methods are most commonly used by Olist customers, and how does this vary by product category or geographic region?
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Customer Reviews and Product Performance: How do customer reviews and ratings affect sales and product performance on Olist?
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Profit Margins and Category Optimization: Which product categories have the highest profit margins on Olist, and how can the company increase profitability across different categories?
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Marketing Impact and ROI Optimization: How does Olist's marketing spend and channel mix impact sales and customer acquisition costs, and how can the company optimize its marketing strategy to increase ROI?
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Customer Retention Rate by Geolocation: Calculate customer retention rate according to geolocations with high customer density.
The data dictionary provided contains information about the variables and their meanings, which will be used throughout the analysis to understand the dataset's contents.
(Note: The detailed data dictionary was not provided, but in your actual GitHub README, you should include the relevant columns and their descriptions from the dataset.)
The repository is organized as follows:
- Data: Contains the Olist sales dataset used for analysis.
- Notebook: Jupyter notebook containing data exploration, data analysis, and visualizations.
- Report: Additional reports generated during the analysis process.
- README.md: The main README file providing an overview of the project, business case, business questions, and repository structure.
The analysis and code provided in this repository are open-source and available for public use. Users are encouraged to read and adhere to the license and contribute to the project by providing valuable insights, suggestions, and improvements through pull requests.