There are 1 repository under rfm topic.
基于RFM和决策树模型构建专家推荐系统。融合了RFM模型和决策树模型,结合专业运营人员的业务经营,发掘潜在用户,进行推荐营销召回。
spark tutorial for big data mining。包括app流量运营分析、als推荐、smote样本采样、RFM客户价值分群、AHP层次分析客户价值得分、手机定位数据商圈挖掘、马尔可夫智能邮件预测、时序预测、关联规则、推荐电影好友等。
RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promotions and also for future personalization services.
By means of this project I am trying to create a value-based customer segmentation model using RFM(Recency, Frequency, Monetary) analysis in python using pandas, numpy and matplotlib
Crafting & testing a dynamic Recency-Frequency-Monetary model as published in Towards Data Science on Medium.com
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.
Within the scope of the project, I determined the marketing strategies by segmenting the customers of the online shoe store FLO.
Creating a graph using e-commerce data and make a RFM analysis
FLO, which is an online shoe store, wants to divide its customers into segments and determine marketing strategies according to these segments. For this, the behavior of customers will be defined and groups will be formed according to the clutches in these behaviors.
R-Analysis: Identifying high value customers and low value of customers using RFM modelling
Customer segmentation by using the RFM method and K-Means clustering
RFM analysis and customer segmentation with the data of an e-commerce site
Este projeto desenvolvido em Python realiza análise de cohort e RFM para segmentação de clientes de e-commerce.
To identify best and valuable customers for the company, to analyse the customer needs and wants & develop marketing strategies to retain them and invest in the right customer category to increase company profits. Implement Customer LifetTime Value (CLTV) in order to distinguish customer based on their potential lifetime profits, thus invest in long term customer relationship strategy for the customer segments.
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Simple example code to show how to do customer segmentation. Enjoy it!
📊🎯✨ Harness the power of the RFM (Recency, Frequency, Monetary) method to cluster customers based on their purchase behavior! Gain valuable insights into distinct customer segments, enabling you to optimize marketing strategies and drive business growth. 📈💡🚀
RFM ANALYSIS IN PYTHON OF ONLINE RETAIL DATASET.
RFM analysis on Adventure works database
In this section, I will perform RFM analysis and CLTV prediction on the dataset belonging to the FLO store.
A dataset in csv format contains all purchase transaction data, we calculated a score based on how recently the customer purchased, how often they make purchases and how much they spend in dollars on average on each purchase.
The objective of this project is to develop a model that predicts customer behavior whether it'll reponse/accept the marketing campaign or not and RFM Segmentation
What kind of products that cluster and customer segment need of based on time series to get an insight.
This my RFM Analysis project. RFM analysis is a business concept check my WORD Document to know more about it and why it is important.
Segmenting store customers based on RFM (Recency, Frequency & Monetary)
CLTV_customer-lifetime-value-analysis
It is highly related to the Customer Segmentation problem, so with RFM Analysis itself as well
In this section, I will perform RFM analysis and CLTV prediction on the online_retail_ii dataset.