a python project RFM analysis, Implementation of RFM in python. Also, covered some basic concepts of pandas such as handling duplicates, groupby, and qcut() for bins based on sample quantiles.
DESCRIPTION
RFM stands for Recency, Frequency and Monetary.
- It is the easiest form of customer database segmentation
- Often used for reactivation campaigns, high valued customer programs, combating churn etc
RFM METRICS RECENCY The freshness of customer activity. e.g. time since last activity. FREQUENCY The frequency of customer transactions. e.g. the total number of recorded transactions. MONETARY The willingness to spend. e.g. the total transaction value RFM Metrics
R: Time since last transaction ● F: Total number of transactions ● M: Total transactions value Transactions can only increase customer value in the segmentation.
TRANSACTION EXAMPLES E-COMMERCE- Orders, visits SOCIAL MEDIA- Sharing, liking, engagement GAMING- In-app purchases, levels played Purchase history Website visits Social engagement
Typical example using amazon.com- Recency is the last time you bought product from amazon. Frequency is the total number you have purchased from the site. Monetary is the total value of all the purchased item.