Customer Lifetime Value Prediction

Business Problem & Purpose

Predicting Customer Lifetime Values with different ways

Thinking about the important metrics for Customer Lifetime Value

Predict the customers that will bring the most profit to the company

Project Steps

• Connect to the database and extract data

• Customer segmentation with RFM

• Calculation Customer Lifetime Value in basic concept

• Predicting Customer Lifetime Value with BG-NBD & GammaGamma models by adding the concept of time

• Export tables and forecast outputs of all models to the database

Dataset Information

Used Online Retail 2 dataset in this project.

This dataset contains the purchase values of a wholesale company's customers in UK between 2010-2011.

Features

InvoiceNo: Unique invoice number. C means refundees.

StockCode: Unique item code

Description: Item description

Quantity: Item quantity number

InvoiceDate: Invoice date time

UnitPrice: Item price (Sterlin)

CustomerID: Unique Customer Number

Country: Country name. The country where the customer lives.

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Libraries

datetime
pandas
pymysql
sqlalchemy 
sklearn
lifetimes

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Author

Oğuz Han Erdoğan - oguzerdo

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Reference:

VBO - Data Science and Machine Learning Bootcamp
www.veribilimiokulu.com