Olist

Data

Data source:

1. Brazilian E-Commerce Public Dataset by Olist (https://www.kaggle.com/olistbr/brazilian-ecommerce#olist_order_payments_dataset.csv)
2. Marketing Funnel by Olist (https://www.kaggle.com/olistbr/marketing-funnel-olist/home#olist_marketing_qualified_leads_dataset.csv)

Both datasets are Brazilian ecommerce public datasets of orders made at Olist Store. The first 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.

In addition, The marketing funnel dataset has information of 8k Marketing Qualified Leads (MQLs) that requested contact between Jun. 1st 2017 and Jun 1st 2018. They were randomly sampled from the total of MQLs. Its features allows viewing a sales process from multiple dimensions: lead category, catalog size, behaviour profile, etc.

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.

Project Goals

In this project, I plan to fuilfill the following tasks:

1. Customer Segmentation
2. LTV Prediction
3. Churn Prediction
4. Sales Prediction
5. Customer Reviews Analysis
6. Recommendation System