Dashboard: https://dailyli.github.io/olist_digital_marketing/
- To create visual dashboard on order number, sales, customer acquisition, etc. - see EDA.ipynb
- To make customer segmentation based on purchase behavior (RFM model) - see Segmentation.ipynb
- To build ML models for predicting customer lifetime value and next purchase day - see CLV_prediction.ipynb
The dataset was provided by Olist, the largest department store in Brazilian marketplaces. Olist connects small businesses from all over Brazil to channels without hassle and with a single contract. Those merchants are able to sell their products through the Olist Store and ship them directly to the customers using Olist logistics partners. See more on Olist website: www.olist.com
Business process: after a customer purchases the product from Olist Store a seller gets notified to fulfill that order. Once the customer receives the product, or the estimated delivery date is due, the customer gets a satisfaction survey by email where he can give a note for the purchase experience and write down some comments.
Source: https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce
- orders: the core dataset. From each order you can find all essential information and the FK to join other tables
- customers: customer and their location. Use it to identify unique customers in the orders dataset and to find the orders delivery location
- payments: orders payment options and payment amount
- items: items purchased within each order
- goelocation: Brazilian zip codes and its lat/lng coordinates (but not connected with specific customers or sellers)
The code was written in Jupyter Notebook with Python, using Pandas for data manipulation, sklearn for ML, and Plotly for visualization.