Customer Lifetime Value Prediction

FLO wants to determine roadmap for sales and marketing activities. In order for the company to make a medium long -term plan, it is necessary to estimate the potential value that existing customers will provide to the company in the future.

Introduction to create a CLI and custom Python package. The package contains some functions to apply RFM and BG-NBD.

Using real online retail data from a store, customer value was predicted and segmented using two probability models: BG/NBD and Gamma-Gamma. An example was used to illustrate the implementation process of the customer lifetime value analysis model and segmentation model.

DATA ANALYSIS

CLTV prediction, BGNBD, Gamma Gamma

A retail company wants to create a roadmap for its sales and marketing activities. To plan for the medium to long term, the company needs to predict the potential value that existing customers will bring in the future.

This project is based on the desire of an online store to segment its customers and determine its marketing strategies according to these segments.

Demonstrates how Python's lifetimes package can identify high-value customers and predict their future purchasing behavior. Utilizing the BG/NBD model to forecast purchase frequency and the Gamma-Gamma model to estimate transaction value, this repository aids in crafting targeted marketing strategies.