Forecasting Number of transactions a customer would make using Beta Geometric-Negative Binomial Distribution, a BTYD-Probabilistic Model.

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 capture 99% of customer historical transactions with a frequency less than or equal to 4, and only less than 1% of customers have greater than 4 repeated purchases in the dataset.

Model vs Actual - Cumulative Transactions and Daily Transactions

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

To reproduce this study, use modules in 'src' directory of this repo. (setup instructions below) and walk-through of the package is presented in the final report

This repository has been tested on Python 3.7.6.

1. Cloning the repository:

git clone https://github.com/abhijitpai000/customer_lifetime_value.git

2. Navigate to the git clone repository.

cd customer_lifetime_value

3. Download raw data from the data source link and place in "datasets" directory

4. Install virtualenv

pip install virtualenv

virtualenv clv

5. Activate it by running:

clv/Scripts/activate

6. Install project requirements by using:

pip install -r requirements.txt

Note

• For make_dataset(), please place the unzipped raw data (from data source) in the 'datasets' directory.