Dataset Description

This dataset contains historical sales data for an e-commerce platform, including customer behavior, product preferences, and transaction details. The data is structured to support analyses aimed at understanding customer behavior, predicting product preferences, and improving overall revenue through strategic marketing and sales efforts. Full data: Brazilian E-commerce Public Dataset

Key Components:

• Customer Data: Includes demographic information, purchase history, and browsing behavior.
• Product Data: Details about the products, such as category, price, and sales volume.
• Transaction Data: Records of individual transactions, including timestamps, purchased items, and total amounts.
• Reviews Data: Customer reviews and ratings for products, useful for sentiment analysis and product feedback.

Usage:

The dataset is intended for: - Analyzing customer behavior to improve marketing strategies. - Predicting product preferences to enhance cross-selling and up-selling. - Automating reporting and creating real-time dashboards. - Implementing and testing machine learning models for sales prediction and customer retention strategies.

Format:

The dataset is provided in CSV format, with each file corresponding to a different aspect of the e-commerce data (e.g., customers, products, transactions, reviews). Each file includes relevant columns for the type of data it contains.

Brazil E-Commerce Market is Segmented by Business Model (B2C, B2B), Device Type (Smartphone / Mobile, Desktop and Laptop, and More), Payment Method (Credit / Debit Cards, Digital Wallets, and More), B2C Product Category (Beauty and Personal Care, Consumer Electronics, Fashion and Apparel, Food and Beverages, and More). The Market Forecasts are Provided in Terms of Value (USD).

I imported the two Olist Kaggle datasets into an SQLite database. I modified the original table names to make them shorter and easier to understand. Here's the Entity-Relationship Diagram of the resulting SQLite database:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2473556%2F23a7d4d8cd99e36e32e57303eb804fff%2Fdb-schema.png?generation=1714391550829633&alt=media" alt="Database Schema">

Data sources:

https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce

https://www.kaggle.com/datasets/olistbr/marketing-funnel-olist

I used this database as a data source for my notebook:

SQL Challenge: E-commerce data analysis

In August 2025, Mercado Livre's website (known in the rest of Latin America as Mercado Libre) received around *** million visits in Brazil, making it the most visited e-commerce portal in the country. Amazon's Brazilian site (amazon.com.br) came in second with about *** million visits. Regional e-commerce powerhouses While Mercado Livre maintains its stronghold in Brazil, the company faces stiff competition across Latin America. In Mexico, for instance, Amazon's local site outperforms Mercado Libre with ***** million monthly visits compared to Mercado Libre's *** million. This regional competition has contributed to Mercado Libre's brand value soaring to ***** billion U.S. dollars in 2024, a ** percent increase from the previous year. However, the company has experienced a decline in unique active users, dropping to *** million in 2024 from *** million the year before. Shifting retail landscape The rise of e-commerce is reshaping Brazil's retail sector, with traditional brick-and-mortar stores adapting to the digital era. In the first half of 2024, pure online players generated over ** percent of e-commerce revenue in Brazil, while brick-and-click stores accounted for more than a quarter of sales.

This dataset was generously provided by Olist, the largest warehouse in the Brazilian markets. Olist connects small businesses across Brazil to channels seamlessly and with a single contract. These merchants can sell their products through Olist Store and ship them directly to customers using Olist's logistics partners. To learn more, visit our website: www.olist.com

This dataset actually already exists on Kaggle, however I decided to create this version to fix certain things and make it easier to analyze.

Since this data comes from an online store, they are all separate from each other, which allows us to connect them and only the ID code. They also still need to be cleaned and processed, so I created this version, for anyone who wants to do an analysis without worrying about cleaning and how to merge them all into one data set. I also did attribution engineering, I think it's very useful for finding more insights.

1. order_id: Unique identifier of the order.
2. order_item_id: Identifier of the item in the order.
3. customer_id: Customer identifier.
4. customer_unique_id: Unique customer identifier.
5. customer_zip_code_prefix: Customer's ZIP code prefix.
6. customer_city: Customer's city.
7. customer_state: Customer's state.
8. product_id: Product identifier.
9. product_category_name: Name of the product category.
10. product_name_length: Length of the product name.
11. product_description_length: Length of the product description.
12. product_photos_qty: Quantity of product photos.
13. product_weight_g: Product weight in grams.
14. product_length_cm: Product length in centimeters.
15. product_height_cm: Product height in centimeters.
16. product_width_cm: Product width in centimeters.
17. seller_id: Seller identifier.
18. seller_city: Seller's city.
19. seller_state: Seller's state.
20. seller_zip_code_prefix: Seller's ZIP code prefix.
21. payment_type: Payment type.
22. payment_sequential: Payment sequential.
23. payment_installments: Number of payment installments.
24. installments_price: Price of installments.
25. price: Product price.
26. freight_value: Freight value.
27. payment_value: Total payment value.
28. shipping_limit_date: Shipping deadline date.
29. order_purchase_timestamp: Purchase timestamp.
30. order_approved_at: Order approval timestamp.
31. order_delivered_carrier_date: Delivery date to the carrier.
32. order_delivered_customer_date: Delivery date to the customer.
33. order_estimated_delivery_date: Estimated delivery date.
34. shipping_duration: Shipping duration.
35. day_of_purchase: Day of purchase.
36. month_of_purchase: Month of purchase.
37. year_of_purchase: Year of purchase.
38. month/year_of_purchase: Month and year of purchase.
39. order_status: Order status.
40. order_unique_id: Unique order identifier.

Enzo Schitini

Data Scientist • Expert Bubble.io • UX & UI @ Nugus creator

This timeline shows the year-on-year e-commerce sales growth rates in Brazil from 2018 to 2025. It is forecast that e-commerce retail sales in Brazil will increase by approximately ** percent in 2021 compared to the previous year.

Discover the booming Brazilian e-commerce market! Our analysis reveals a $52.87B market in 2025, projected to grow at 18.91% CAGR until 2033. Learn key trends, top players (Amazon, Magazine Luiza, Casas Bahia), and regional insights. Invest wisely in this dynamic market! Recent developments include: May 2022: The Central Bank of Brazil granted Shopee, the Singaporean shopping app, permission to operate as a payment institution. Shopee can handle prepaid payment accounts in which funds have been pre-deposited., May 2022: Lojas Americanas, Brazil's retail and e-commerce company, implemented automated data quality verification to improve operations and customer experience. The goal is to make finding, prioritizing, and resolving data errors on all sales and customer data straightforward.. Key drivers for this market are: Growing Contactless Forms of Payment, Penetration of Internet and Smartphone Usage. Potential restraints include: , High Initial Cost During First Time Setup is Challenging the Market Growth. Notable trends are: Penetration of Internet and Smartphone Usage is Expected to Drive Brazil E-commerce Market.

Brazil E-Commerce Transactions: Average Order Value (AOV) data was reported at 244.807 USD in 10 May 2025. This records an increase from the previous number of 243.539 USD for 09 May 2025. Brazil E-Commerce Transactions: Average Order Value (AOV) data is updated daily, averaging 158.782 USD from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 718.941 USD in 30 Apr 2022 and a record low of 56.929 USD in 31 Mar 2020. Brazil E-Commerce Transactions: Average Order Value (AOV) data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Brazil – Table BR.GI.EC: E-Commerce Transactions: by Category.

The Brazilian e-commerce market is poised for remarkable growth, projected to reach a substantial market size of USD 52.87 billion by 2025, with an impressive Compound Annual Growth Rate (CAGR) of 18.91%. This robust expansion is fueled by several key drivers, including the increasing internet penetration and smartphone adoption across Brazil, particularly in emerging urban centers. Consumers are increasingly embracing the convenience and wider product selection offered by online platforms. Furthermore, the growing digital literacy among the population, coupled with the expanding reach of digital payment solutions, is significantly contributing to the surge in online transactions. The market is witnessing a notable shift towards mobile commerce, with a substantial portion of sales originating from smartphones, underscoring the need for businesses to optimize their mobile platforms and strategies. The e-commerce landscape in Brazil is characterized by dynamic segmentation, with categories like Fashion & Apparel, Consumer Electronics, and Beauty & Personal Care leading the charge in GMV (Gross Merchandise Value). The increasing demand for these goods online, supported by innovative logistics and delivery services, is a major growth catalyst. While the market benefits from strong consumer demand and technological advancements, it also faces certain restraints. These include ongoing logistical challenges in vast geographical areas, the need for continued improvement in last-mile delivery infrastructure, and evolving regulatory frameworks. However, the resilience and adaptability of key players, alongside government initiatives promoting digitalization, are expected to mitigate these challenges, paving the way for sustained and accelerated growth in the coming years. The competitive environment is robust, with both global giants and strong local players actively vying for market share. This comprehensive report delves into the dynamic Brazilian E-Commerce market, offering in-depth analysis and future projections from 2018 to 2028. The report will provide granular insights into market size, segmentation, key players, and influencing trends, leveraging an extensive dataset and expert industry knowledge. Key drivers for this market are: Growing Contactless Forms of Payment, Penetration of Internet and Smartphone Usage. Potential restraints include: , High Initial Cost During First Time Setup is Challenging the Market Growth. Notable trends are: Penetration of Internet and Smartphone Usage is Expected to Drive Brazil E-commerce Market.

The penetration rate in the e-commerce market in Brazil was modeled to be **** percent in 2025. Between 2025 and 2030, the penetration rate will rise by **** percentage points, continuing its consistent upward trajectory.Further information about the methodology, more market segments, and metrics can be found on the dedicated Market Insights page on eCommerce.

Discover the latest eCommerce statistics in Brazil for 2025, including store count by category and platform, estimated sales amount by platform and category, products sold by platform and category, and total app spend by platform and category. Gain valuable insights into the retail landscape in Brazil, uncovering the distribution of stores across categories and platforms.

Brazil Revenue: E-Commerce Categories: Electronics data was reported at 10.400 % in 2024. This records a decrease from the previous number of 10.530 % for 2023. Brazil Revenue: E-Commerce Categories: Electronics data is updated yearly, averaging 11.450 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 17.300 % in 2009 and a record low of 7.480 % in 2004. Brazil Revenue: E-Commerce Categories: Electronics data remains active status in CEIC and is reported by Brazilian Association of Eletronic Commerce. The data is categorized under Brazil Premium Database’s Domestic Trade – Table BR.HF001: E-commerce: Revenue.

Brazil E-Commerce Transactions: Volume data was reported at 200,957.000 Unit in 10 May 2025. This records a decrease from the previous number of 239,835.000 Unit for 09 May 2025. Brazil E-Commerce Transactions: Volume data is updated daily, averaging 195,313.500 Unit from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 1,362,626.000 Unit in 05 Jan 2024 and a record low of 16,537.000 Unit in 01 Jan 2019. Brazil E-Commerce Transactions: Volume data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Brazil – Table BR.GI.EC: E-Commerce Transactions: by Category.

Brazil E-Commerce Transactions: Value data was reported at 49,195,626.526 USD in 10 May 2025. This records a decrease from the previous number of 58,409,219.174 USD for 09 May 2025. Brazil E-Commerce Transactions: Value data is updated daily, averaging 29,064,876.048 USD from Dec 2018 (Median) to 10 May 2025, with 2324 observations. The data reached an all-time high of 338,114,503.738 USD in 05 Jan 2024 and a record low of 1,285,533.534 USD in 01 Jan 2019. Brazil E-Commerce Transactions: Value data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Brazil – Table BR.GI.EC: E-Commerce Transactions: by Category.

Dataset

This dataset was created by SANDEEP ADALAJA

Contents

This is the Brazil's E-commerce Dataset.

In Brazil E-Commerce Software Market is projected to grow from USD 9.8 billion in 2025 to USD 22.4 billion by 2031, at a CAGR of 14.7%

Brazil E-Commerce Transactions: AOV: Lifestyle: Beauty & Cosmetics data was reported at 66.519 USD in 10 May 2025. This records an increase from the previous number of 65.438 USD for 09 May 2025. Brazil E-Commerce Transactions: AOV: Lifestyle: Beauty & Cosmetics data is updated daily, averaging 43.475 USD from Dec 2018 (Median) to 10 May 2025, with 2293 observations. The data reached an all-time high of 152.726 USD in 09 Dec 2020 and a record low of 24.662 USD in 14 Jan 2022. Brazil E-Commerce Transactions: AOV: Lifestyle: Beauty & Cosmetics data remains active status in CEIC and is reported by Grips Intelligence Inc.. The data is categorized under Global Database’s Brazil – Table BR.GI.EC: E-Commerce Transactions: by Category.

Comprehensive dataset containing 3,983 verified E commerce agency businesses in Brazil with complete contact information, ratings, reviews, and location data.

Forecasts predict that the Brazilian retail e-commerce market will generate nearly ** billion U.S. dollars in revenue in 2025. Statista's Digital Market Insights estimates that this revenue will surpass ** billion U.S. dollars by 2029. Statista’s Digital Market Insights offers forecasts, detailed market insights and essential performance indicators of the most significant areas in the Digital Economy, including various digital goods and services for 150 countries worldwide.

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.

Join it With the Marketing Funnel by Olist

We have also released a Marketing Funnel Dataset. You may join both datasets and see an order from Marketing perspective now!

Instructions on joining are available on this Kernel.

Context

This dataset was generously provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on our website: www.olist.com

After a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.

Attention

1. An order might have multiple items.
2. Each item might be fulfilled by a distinct seller.
3. All text identifying stores and partners where replaced by the names of Game of Thrones great houses.

Example of a product listing on a marketplace

https://i.imgur.com/JuJMns1.png" alt="Example of a product listing on a marketplace">

Data Schema

The data is divided in multiple datasets for better understanding and organization. Please refer to the following data schema when working with it: https://i.imgur.com/HRhd2Y0.png" alt="Data Schema">

Classified Dataset

We had previously released a classified dataset, but we removed it at Version 6. We intend to release it again as a new dataset with a new data schema. While we don't finish it, you may use the classified dataset available at the Version 5 or previous.

Inspiration

Here are some inspiration for possible outcomes from this dataset.

NLP:
This dataset offers a supreme environment to parse out the reviews text through its multiple dimensions.

Clustering:
Some customers didn't write a review. But why are they happy or mad?

Sales Prediction:
With purchase date information you'll be able to predict future sales.

Delivery Performance:
You will also be able to work through delivery performance and find ways to optimize delivery times.

Product Quality:
Enjoy yourself discovering the products categories that are more prone to customer insatisfaction.

Feature Engineering:
Create features from this rich dataset or attach some external public information to it.

Acknowledgements

Thanks to Olist for releasing this dataset.