dat4action / machine-learning-for-marketing

Cohort and RFM (Recency-Frequency-Monetary) Analysis with Unsupervised Machine Learning models

MACHINE LEARNING FOR MARKETING - RFM / Cohort Analysis

(Cohort Analysis and RFM with unsupervised Models)

OBJETIVES

My main objetives are to to develop a Cohort Analysis oriented to detect retention rates an a Cluster Segmentation of Clients according to a RFM model.

PROJECT DESCRIPTION

The cohort Analysis

This analysis basically breaks down users into different groups instead of analyzing them as a whole unit. One example would be putting users who have become customers at approximately the same time into one group or cohort. That is exactly the case i analized in this study in order to detect "Retention rates". That is which percentage of the cohort is still being retained along certain time lapse.

The RFM analysiss

RFM stands for Recency-Frequency-Monetary.

• Recency: How much time has elapsed since a customer’s last activity or transaction with the brand? Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website or use of a mobile app. In most cases, the more recently a customer has interacted or transacted with a brand, the more likely that customer will be responsive to communications from the brand.
• Frequency: How often has a customer transacted or interacted with the brand during a particular period of time? Clearly, customers with frequent activities are more engaged, and probably more loyal, than customers who rarely do so. And one-time-only customers are in a class of their own.
• Monetary: Also referred to as “monetary value,” this factor reflects how much a customer has spent with the brand during a particular period of time. Big spenders should usually be treated differently than customers who spend little. Looking at monetary divided by frequency indicates the average purchase amount – an important secondary factor to consider when segmenting customers.

Segment the customers using this criteria will allow to apply different comercial strategies depending on the cluster the customer belongs to. So its very important strategic information. To determine the cluster im going to perform a k-means algorithm.