Customer Analytics in R
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Updated
Jul 16, 2023 - R
Customer Analytics in R
Build a RFM (Recency Frequency Monetary) model for Retail Customers
Customer segmentation using rfm analysis.
Transformación de las factuaras de ventas a atributos valiosos para Clusterizar
An analysis and approach to customer segmentation
This is a data analysis project from Dicoding to pass the Learning Data Analysis with Python class. This project aims to analyse and create a simple dashboard based on data from Capital Bikeshare.
A project using SQL that centers around implementing the RFM analysis model to extract valuable insights from a sales dataset.
This project aims to conduct an analysis of costumers behavior and perception of the brand, by implementing different marketing analytics techniques and methods: RFM (recency, frequency, monetary) model, churn classification, MBA (market basket analysis) and sentiment analysis.
Perform customer segmentation using RFM analysis. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value).
This is a repository with various analytic projects.
Customer segmentation is dividing the customers into segments based on RFM scores. In this project I've used RFM model in R to generate RFM score.
RFM Analysis for Automobile Company's Customers
This data project analyses a UK-based retailer's customers based on their Recency, Frequency and Monetary values and accordingly assists with customer re-segmentation work.
Customer Segmentation Analysis with RFM, using Python and Power BI.
Segment customers based on their transaction performance similarities using business metrics (RFM & cohort analysis) and KMeans model.
This project showcases how to perform Recency, Frequency, and Monetary (RFM) analysis using the powerful Polars DataFrame library in Python.
A journey through understanding customer segmentation using python with the general goal of encouraging data driven decision making
Offered insights on which customers are truly at risk to churn and provided strategic target incentives to extend the customer’s lifetime value.
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