Mapping Olist Brazilian Ecommerce Dependence

Project Objective

This case study was built to move beyond top line revenue and test how marketplace performance is distributed across sellers, categories, and activity patterns The goal was to evaluate whether Olist’s growth appears broad-based or whether seller concentration, category leadership, and activity patterns reveal meaningful dependence on narrower revenue drivers.

To make that visible, I built a two-page Power BI dashboard designed to separate executive-level monitoring from deeper driver analysis.

Data and Analytical Approach

The case uses the public Olist Brazilian ecommerce dataset and focuses on the model components most relevant to marketplace revenue analysis

Top: Initial Model State

Bottom: Final Analytical Model

The workflow included:

• selecting and narrowing the tables most relevant to revenue activity

• structuring relationships across orders, order items, products, sellers, and customers

• translating Portuguese category labels into English for reporting clarity

• adding a dedicated Date table for time-based analysis

• developing DAX measures for revenue, orders, active sellers, revenue share, and concentration indicators

The aim was not just to clean the data, but to convert a raw public dataset into a usable marketplace intelligence model that could support both high level monitoring and deeper business interpretation.

Dashboard Design

Page 1: Executive Overview

The first page supports quick performance monitoring by combining marketplace scale, activity trends, and concentration signals in one view.

It includes:

• total revenue

• total orders

• average order value

• active sellers

• top 10 sellers’ revenue share

• category contribution

• monthly revenue trend

• monthly order trend

This page helps establish the overall performance picture before moving into deeper breakdowns.

Page 2: Marketplace Driver Analysis

The second page was designed for closer inspection of seller and category dependence.

It includes:

• seller-level revenue ranking and revenue share

• category-level revenue ranking and revenue share

• supporting bar charts for seller and category comparison

• a shared Year-Month slicer to move from full-market view to period-specific analysis

This page shifts the analysis from monitoring to diagnosis by helping identify whether revenue is spread broadly or meaningfully shaped by a narrower group of marketplace drivers.

What the Dashboard Shows

Taken together, the dashboard suggests that Olist’s marketplace growth is not dominated by a single seller or category, but it is also not fully diffuse.

Performance is broad enough to show meaningful participation, while still reflecting visible concentration in leading sellers and categories

The dashboard supports a more balanced reading of marketplace health:

• growth is not purely dependent on one extreme driver

• concentration is present and measurable

• activity breadth matters alongside revenue scale

Key Insights

1. Seller concentration is meaningful, but not extreme

The top 10 sellers contribute 13.15% of total revenue.

This indicates that leading sellers matter, but marketplace performance is not overwhelmingly captured by a tiny seller group. Revenue concentration exists, but it is not severe enough to suggest dependence on only a handful of dominant sellers.

2. Category leadership is visible, but revenue is not captured by a single category

The top category contributes 9.26% of total revenue.

This shows that category concentration is present, but revenue remains spread across a wider product mix. Marketplace performance is shaped by category leaders, but not dictated by one category alone.

3. Growth appears to be supported by activity breadth, not only higher-value transactions

Revenue and order trends rise together over time, suggesting that marketplace expansion is linked to growing transaction activity rather than being driven only by a small number of unusually large orders.

This matters because it points to broader participation in marketplace growth rather than isolated high-ticket revenue effects.

4. Marketplace performance should be judged through both scale and concentration

Topline growth alone would not fully explain marketplace health. The dashboard shows that seller share, category contribution, and activity patterns add important context to revenue growth.

marketplace growth can remain healthy while still becoming structurally dependent on narrower drivers, Olist appears broad based enough to avoid extreme dependence, but concentrated enough that leading sellers and categories still materially shape performance.

Power BI and DAX Contribution

This project was not only about visualizing data.

Power BI was used to turn a raw ecommerce dataset into a business intelligence model built for analysis, monitoring, and interpretation.

DAX played an important role in making the analysis usable by enabling:

• revenue and order aggregation across time

• active seller measurement

• seller concentration tracking

• category contribution analysis

• revenue share calculations for comparative interpretation

These measures helped move the dashboard beyond descriptive reporting into a more analytical view of marketplace dependence.

Business Interpretation

From a business intelligence perspective, the main takeaway is that Olist’s performance should not be judged on topline revenue alone.

The dashboard suggests three practical interpretation rules:

• monitor whether seller concentration rises over time

• track whether category contribution becomes more concentrated

• evaluate revenue growth alongside order activity to distinguish broad expansion from narrower dependence

This creates a more defensible way to judge marketplace performance, especially in multi-seller environments where concentration risk can sit beneath healthy topline trends.

Key Outcome

The final outcome of this project is a clearer and more defensible view of marketplace dependence.

By moving beyond topline revenue into seller concentration, category contribution, and activity-based growth patterns, the dashboard makes it easier to judge whether Olist’s marketplace expansion is broadly supported or meaningfully dependent on narrower drivers.

As a BI case study, this project demonstrates the ability to:

• structure a relational Power BI model from raw public data

• develop DAX measures tied to business interpretation

• build an interactive dashboard for both executive monitoring and driver analysis

• translate marketplace data into a business-facing analytical conclusion