RFM is a customer segmentation method used to analyze customer value based on behavior. Grouping customers into segments allows more personalized marketing instead of general email blasts. Targeting a specific segment of customers has some benefits such as:
- Targeted marketing messages for customers with similar attributes
- Lower marketing costs
- Focus on the most profitable segments of customers
- Higher chance of better conversion rates and return-on-ad-spend
The 3 key features of RFM are:
Recency: How much time has elapsed since a customer’s last activity. Activity is usually a purchase, although variations are sometimes used, e.g., the last visit to a website.
Frequency: How often has a customer made transactions or interacted during a certain amount of time. Customers with frequent activities are generally more engaged, and probably more loyal, than customers who rarely do so.
Monetary Value: Reflects how much a customer has spent during a particular period of time. Big spenders should usually be treated differently than customers who spend little.
RFM allows us to sort and arrange our customers from best to worst quickly. Starting from the loyal high-spending customers with top scores on all three metrics, high spending but new customers that scores high on recency and monetary value but low on frequency, high-value customers who haven’t made a purchase in a while, and low-value customers who get low scores on all three. Based on these, we can create more relevant, contextualized marketing campaigns.
Link to the dataset here.