RFM (Recency, Frequency, and Monetary) Model provides auto-segmentation and buckets users into categories such as Loyal, Promising, At Risk, etc. based on their behavior. These auto segments can be used in multiple different ways such as user analysis, churn analysis, and campaign effectiveness. RFM can also be used for predictive segmentation, customers who are more likely to respond to promotions, and also for future personalization.
RFM analysis is a widely used marketing model for behavior-based customer segmentation. This was primarily used in the retail industry and made its way into digital marketing. It groups customers based on their transaction history – how recently, how often and how much did they buy.
RFM Analysis can be used to answer questions like -
- Who are your loyal customers?
- Which are the customers who are most likely to churn?
- Which customers are purchasing the most on your platform?
- Which are the customers who can be turned into the best customers with little effort?
- Which customers are most likely to engage with your campaigns?
Let's deep dive and understand how it works -
RFM analysis is a customer behavior segmentation method that uses customers' past interactions such as a visit to the platform or purchase of an item and based on these interactions divides customers into different RFM groups.
The primary factors are R, F, and M for this model, which are explained below-
- R is RECENCY - Time since the last visit to the app/site or time since the last purchase.
- F is FREQUENCY - The total number of times a user has visited the app/site or total number of purchases
- M is MONETARY - Total money spent by a user or total time spent watching content
Users who are showing similar behavior on R, F, M, and RFM scores are grouped into the same RFM buckets or segments. These segments are named with respect to user behavior. The list of segments and the respective description is provided below:
|These users are your most active users. They have the highest recency and frequency scores.
Users who visited most recently, visited most often, and spend the highest
|These users have the highest frequency of use with strong recency scores.
Users who visited recently, visited often, and spent a great amount
|These users have visited your app very recently and have the potential to become loyalists or champions.
A recent user, who spent a good amount
|These users are your most recent users with low frequency scores. Strong candidates to encourage repeat use.
User visited most recently, but not often, has not spent much
|These users have high recency scores with the potential to become high frequency users.
Average recency, frequency, and monetary scores
|These users have above average recency and frequency scores.
User has spent a good amount but long ago (not visited recently)
|About to Sleep
|These users have below average recency and frequency scores. May slip away if not engaged with.
Below average recency, frequency, and monetary values
|These users have above average frequency but low recency scores. Strong candidates to re-engage.
|Can't Lose Them
|These users were active at one point in your app, but haven’t been back recently. Strong candidates to re-engage.
The user has spent a great amount and visited often but long ago (not visited recently)
|These users have the lowest recency and frequency scores. May be lost.
The user's last visit was long back, visits are not often and has not spent much
|User visited most recently, and also often, but has not spent much
|Lowest recency, frequency, and monetary scores.
You may see a minor difference in counts when you compare these numbers with the numbers in your dashboard. However, the numbers are directionally correct.
Updated 3 months ago