RFM Segmentation

Defining RFM Modeling

• Similar to the previous loyalty-monetary segmentation process, an RFM is another multi-dimensional tiered model

• Specifically, recency–frequency–monetary (or RFM) uses three metrics:

  • Recency
  • Frequency
  • Monetary

• Here, recency represents the time that has passed since a customer's previous engagement

  • E.g. days since last purchase
  • E.g. hours since last email open

• And, frequency represents the number of interactions made by a customers

  • E.g. average number of email clicks per month
  • E.g. number of purchases in May
  • E.g. average number of purchases per week

• Lastly, monetary represents the spend made by a customer

  • E.g. average sales per month
  • E.g. sales in March
• Each of these three metrics can be mapped to a scored quantile in a tiered model
• Visually, an RFM model can be thought of as a cube, since there are $3$ dimensions

Illustrating Manual Steps for RFM Modeling

1. Assign quantile scores to each customer

   • These scores represent quantiles (e.g. $1-5$) based on some metric (e.g. recency, frequency, monetary)
2. Optional: Aggregate quantile scores
3. Analytically assign scores to segments
4. Profile segments

Illustrating Clustering Steps for RFM Modeling

1. Assign quantile scores to each customer

   • These scores represent quantiles (e.g. $1-5$) based on some metric (e.g. recency, frequency, monetary)
2. Optional: Aggregate quantile scores
3. Cluster scores to retrieve segments
4. Profile segments

References

• Textbook about Algorithmic Marketing
• Example of Tiered Modeling
• Course about Customer Segmentation
• Course about Segmentation Analysis