SQL + Python + Tableau · E-Commerce Analytics

Olist E-Commerce
Performance Analysis

9 relational tables joined via SQLite · 96,478 delivered orders · R$15.4M revenue across Brazil · 2016–2018

● 96,478 orders analyzed 27 Brazilian states Sep 2016 → Aug 2018 9 source tables · SQLite
Total Revenue
R$15.4M
across 96,478 orders
Avg Order Value
R$159.83
per delivered order
Avg Delivery Time
12.0 days
SP delivers in 8.3d
Top Category
9.2%
Health & Beauty share
Monthly revenue trend

Monthly Revenue — Full Time Series

Sep 2016 → Aug 2018 · annotated peak: Nov 2017 Black Friday spike (+53% MoM)
Revenue breakdown

Revenue by Category

Top 10 of 73 categories · underscores replaced with readable names

Top States by Revenue

SP alone = 37% of total · note delivery gap vs southern states
State Revenue Orders Avg del.
Operational insights

Delivery Speed vs Customer Rating

Fast deliveries (≤7d) score 4.22★ · very slow (>21d) drop to 2.55★ — a 1.67-point gap
Fast (≤ 7 days)
4.22 ★
4.22
26,803
Normal (8–14 days)
4.11 ★
4.11
42,891
Slow (15–21 days)
3.61 ★
3.61
16,843
Very Slow (> 21 days)
2.55 ★
2.55
11,203
Score axis: 1.0 (worst) → 5.0 (best) · bar width proportional to score

Payment Method Mix

Credit card dominates at 78.6% of revenue · boleto = Brazil's bank slip payment

Key Finding: Delivery Speed Directly Drives Revenue Quality

São Paulo (SP) achieves an 8.3-day avg delivery and accounts for 37% of all revenue (R$5.77M). States with >15-day delivery like Bahia (18.8d) and Espírito Santo (15.2d) show proportionally lower repeat purchase rates. The data suggests logistics investment in underserved states could unlock significant revenue — the delivery-to-review score correlation (r ≈ −0.94) is the most actionable finding in this dataset.