A python package to train & evaluate Customer Lifetime Value(CLTV) models using Neural Networks & ZILN loss(developed by google)

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Customer Lifetime Value Prediction

The data set named Online Retail II includes online sales transactions of a UK-based retail company between 01/12/2009 - 09/12/2011.

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.

FLO would like to set a 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.

Project for customer management in the Marketing Analytics Department of a large retail bank. The aim of this project is to know which marketing activity effectively retains customers. We have information about individual customer profitability (CLV) and a survey was conducted as well. A research model explaining/predicting individual customer p…

The goal is to predict the number of purchases from each customer in the next 30 days by using the customer lifetime model.

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In this section, I will perform RFM analysis and CLTV prediction on the dataset belonging to the FLO store.

In this section, I will perform RFM analysis and CLTV prediction on the online_retail_ii dataset.

CLTV_customer-lifetime-value-analysis

CLTV prediction, BGNBD, Gamma Gamma

FLO wants to determine a roadmap for its sales and marketing activities.

This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.

CLTV

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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 is a trained machine learning model deployed on Google Cloud Platform (GCP) for prediction of Customer Lifetime Value and Churn. This model is available for users to predict lifetime value of customers based on data and features such as recency, frequency and monetary/revenue, interact with the application and make informed business decision.