BG-NBD ve Gamma-Gamma ile CLTV Tahmini (Customer Lifetime Value Prediction)
This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.
Customer Lifetime Value calculation and prediction analyis by an existing customer dataset with Python.
Trabajo Final correspondiente a la Especialización en Ciencia de Datos ITBA 2019
In this section, I will perform RFM analysis and CLTV prediction on the online_retail_ii dataset.
The data set named Online Retail II includes online sales transactions of a UK-based retail company between 01/12/2009 - 09/12/2011.
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.
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.
This is a case study in my Data Analyst Path by Miuul.
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.
CRM Analytics with RFM segmentation, CLTV calculation, and ML (un/supervised) to predict future segmentations.
CRM analytics is the process of analyzing customer data to gain insights for improving customer relationships and driving business growth. We use it to make data-driven decisions, optimize customer engagement, personalize marketing, identify opportunities, and enhance customer satisfaction.
CLTV_customer-lifetime-value-analysis
In this section, I will perform RFM analysis and CLTV prediction on the dataset belonging to the FLO store.
The main objective of this project is to forecast the Customer Lifetime Value (CLTV) using user and policy data.
Goal i s here to create RFM customer segments and find CLTV for existing customers
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