Olist Brazilian E-Commerce: Interactive Sales and Customer Review Dashboard

Overview

A multi-page analytics dashboard built on the Olist Brazilian E-Commerce dataset. It covers sales performance, product categories, and customer satisfaction in one interactive report, so business owners can see what sells, when it sells, and how customers feel about it.

Dashboard pages

Overview and Sales: Revenue and order performance.

Products and Categories: Which categories drive revenue.

Sales and Sellers: Seller-level performance.

Review: Customer satisfaction analysis.

KPI cards (on every page)

Total Revenue (9.7M), Total Reviews (79,398), Total Orders (66,629), and Total Customers (66,629).

Sales and category analysis

Top Categories: A ranked bar chart of categories by revenue, led by watches and gifts (872K), then computer accessories, home decor, and housewares.

Sales over time by weekday: Order counts and revenue for each day of the week.

Sales over time by month: Monthly orders and revenue, showing seasonality and peaks in July and August.

Review and satisfaction analysis

Most Rated Products: Product categories ranked by review score, split by year (2016, 2017, 2018) to show how ratings change over time.

Review Scores donut: The share of 1 to 5 star ratings.

Loyalty of States Map: A map of Brazil showing average review score by state, from 3.56 to 4.33, to find the strongest and weakest regions.

Review distribution chart: The number of orders per rating, which shows that most customers give 5 stars.

Interactivity

Filters for year of order and review score update every chart on the page, and navigation buttons link all the pages.

Key insights

Which categories and products bring in the most revenue and the best ratings.

The best days and months for sales, which helps with campaign and inventory planning.

Which Brazilian states have the most satisfied customers, and where service needs improvement.

Overall customer satisfaction and how it relates to sales.

Tools used

Tableau (with Mapbox maps), SQL for extraction and cleaning, and Excel / Python for data preparation.

Skills demonstrated

Data cleaning and modeling, KPI design, geographic analysis, interactive dashboard design, and business storytelling.