Trabajo Final correspondiente a la Especialización en Ciencia de Datos ITBA 2019

Propose CRM strategies by segmenting customers into clusters and identifying purchase patterns and give recommendations separately for each group.

A Python project examining customer lifetime value (CLV) for Door Bell, Inc. This analysis investigates the influence of autopay on customer retention, providing valuable insights for strategic marketing decisions.

Predicting Customer Lifetime Value

Customer Lifetime Value estimation model

In this project we predict the Customer Lifetime Value (CLV) for an Automobile Insurance Company in USA.

Calculate CLV using the BG/NBD and Gamma-Gamma models.

The purpose of this project is to recommend personalized products for segments by finding product associations.

Explore the world of data-driven customer analysis and lifetime value estimation. This project dives into customer segmentation, geographic analysis, time series insights, stock trends, and product descriptions. Join us on our journey of data exploration and optimization.

Built a customer lifetime value (CLV) model using parametric models.

Customer Lifetime Value

This project creates a user-friendly customer lifetime value (CLV) prediction engine able to take in transaction data and return important CLV predictions with a high degree of accuracy for a merchant's entire customer base and individual customers over a selected period of time in the future.

Customer lifetime value analysis is used to estimate the total value of customers to the business over the lifetime of their relationship. It helps businesses make data-driven decisions on how to allocate their resources and improve their customer relationships.

TimeSeriesAnalysis-AR-MA-ARMA IBM Dataset