Brazilian E-Commerce Public Dataset by Olist

Brazilian E-Commerce Public Dataset by Olist

Datasets

Brazilian E-Commerce Public Dataset by Olist

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Brazilian E-Commerce Public Dataset by Olist

Use Case

Brazilian E-Commerce Public Dataset by Olist

Description

Explore the Brazilian E-Commerce Dataset by Olist, featuring 100k anonymized orders from 2016-2018.

Description:

This dataset was generously shared by Olist, Brazil’s largest department store on online marketplaces. Olist connects small businesses across the country to e-commerce channels through a single contract, simplifying the sales process. Merchants can list their products on the Olist Store, while Olist logistics partners ensure seamless shipping directly to customers.

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The Brazilian E-Commerce Dataset by Olist provides an extensive collection of 100,000 anonymized order records from 2016 to 2018, spanning multiple marketplaces in Brazil. This dataset is a powerful resource for analyzing e-commerce trends, customer behavior, logistics performance, and product attributes. With features that allow multi-dimensional analysis, the dataset includes information on order status, pricing, payment methods, freight logistics, customer reviews, geolocation data, and more.

Additionally, this dataset is complemented by a Marketing Funnel Dataset, enabling researchers and businesses to analyze orders from a marketing perspective.

Key Features

  1. Comprehensive Order Data:

    • Track orders through their lifecycle: order status, pricing, payment details, and delivery performance.
  2. Customer Reviews:

    • Includes customer satisfaction scores and written feedback, anonymized by replacing brand mentions with Game of Thrones great houses.
  3. Geolocation Insights:

    • Geolocation dataset maps Brazilian zip codes to latitude and longitude coordinates, enabling regional analysis and logistics optimization.
  4. Product Attributes:

    • Detailed product-level insights, including category, dimensions, and performance metrics.
  5. Multi-Item Orders:

    • Analyze orders containing multiple items, each potentially fulfilled by different sellers, for a deep dive into marketplace dynamics.

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