RFM (Recency, Frequency, and Monetary) model is a customer segmentation technique that segments customers into different groups based on how recently they made a purchase, how often they make purchases, and how much they spend. This information helps you identify the most valuable customers and understand their behavior, which can be used to create targeted marketing campaigns and drive customer loyalty.

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.

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RFM analysis in only available for Shopify stores.

RFM Analysis can 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?

Factors for RFM Analysis

The primary factors are R, F, and M for this model, explained below:

  • R (RECENCY): Time since the last visit to the app/site or time since the last purchase.
  • F (FREQUENCY): The total number of times a user has visited the app/site or the total number of purchases.
  • M (MONETARY): Total money spent by a user or total time spent watching content.

Segment Buckets for RFM Score

Users showing similar behavior on R, F, M, and RFM scores are grouped into the same RFM buckets or segments. These segments are named for user behavior.

User SegmentDescription
ChampionsThese are the most active users. They have the highest recency and frequency scores. Users who visited most recently, most often, and spent the highest.
LoyalistsThese users have the highest frequency of use with strong recency scores. Users who visited recently, often, and spent a great amount.
High PotentialThese users have visited your store recently and have the potential to become loyalists or champions. A recent user, who spent a good amount.
New CustomersThese users are your most recent users with low-frequency scores. Strong candidates to encourage repeat use.
Users who visited most recently, but not often, and have not spent much.
Platonic FriendsThese users have average recency scores with the potential to become high-frequency users.
Average recency, frequency, and monetary scores.
Need NurturingThese users have above-average recency and frequency scores. Users who have spent a good amount but long ago (not visited recently).
About to SleepThese users have below-average recency and frequency scores. May slip away if not engaged with. Below-average recency, frequency, and monetary values.
Can't Lose ThemThese users were active at one point in your app, but havenโ€™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).
HibernatingThese users have the lowest recency and frequency scores. May be lost. The user's last visit was long back, visits are not often, and have not spent much.