Gem Logic AI

πŸͺ Olist Marketplace

SQL E-Commerce Causal Paths 96k Orders

Overview

Public dataset from the Brazilian Olist marketplace (Kaggle, 2016–2018): ~96K orders joined across 8 relational tables.

Data Structure

TableWhat It Captures
OrdersOrder lifecycle: created, approved, delivered, estimated dates
Order ItemsProducts per order: price, freight, seller
SellersSeller metadata: location, zip code
CustomersCustomer metadata: location, unique ID
ReviewsCustomer review scores (1–5) and comments
PaymentsPayment method, installments, value
ProductsProduct category, dimensions, photos
GeolocationLat/long for zip codes

Causal Reasoning Model

Three nested paths converge to explain bad customer reviews (score ≀ 2):

1. Logistics Delays: late shipment β†’ delivery past estimate β†’ customer frustration 2. Product Quality Gaps: category mismatch, missing photos, description issues 3. Payment Friction: high installment count, payment method issues

Sample Questions

Try It

Ask Gem Logic: Why did this customer leave a bad review?

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