Customer Lifetime Value

Overview

Trained a Beta Geometric-Negative Binomial Distribution (BG/NBD) model that explains how frequently customers make purchases while they are still “alive” and how likely a customer is to churn in any given time period, using customer transactions of E-Commerce store Olist public dataset

Model Outcome

Trained Model Predicts Number of Purchases with an RMSE of 0.144 and is able to predict 99% of purchases in the test set.

Model vs Actual - Cumulative Transactions and Daily Transactions

About Olist Dataset

The dataset has information of 100k orders from 2016 to 2018 made at multiple marketplaces in Brazil, the orders are divided into 9 .csv files in a relational database schema.

For this study, I have aggregrated data using the following 3 .csv files out of the 9 in the zip file.

• olist_customers_dataset.csv
• olist_orders_dataset.csv
• olist_payments_dataset.csv

Source : Olist Dataset