RFM

RFM allows grouping customers by segments with different value for your company and create different communication strategies for every group.

Segments description

Traditionally, customers can be grouped to these segments:

Champions – Bought recently, buy often and spend the most! Reward them. Can be early adopters for new products. Will promote your brand.

Loyal Customers – Spend good money with us often. Responsive to promotions. Upsell higher value products. Ask for reviews. Engage them.

Potential Loyalist – Recent customers, but spent a good amount and bought more than once. Offer membership / loyalty program, recommend other products.

Recent Customers – Bought most recently, but not often. Provide on-boarding support, give them early success, start building relationship.

Promising – Recent shoppers, but haven’t spent much. Create brand awareness, offer free trials

Customers Needing Attention – Above average recency, frequency and monetary values. May not have bought very recently though. Make limited time offers, Recommend based on past purchases. Reactivate them.

About To Sleep – Below average recency, frequency and monetary values. Will lose them if not reactivated. Share valuable resources, recommend popular products / renewals at discount, reconnect with them.

At Risk – Spent big money and purchased often. But long time ago. Need to bring them back! Send personalized emails to reconnect, offer renewals, provide helpful resources.

Can’t Lose Them – Made biggest purchases, and often. But haven’t returned for a long time. Win them back via renewals or newer products, don’t lose them to competition, talk to them.

Hibernating – Last purchase was long back, low spenders and low number of orders. Offer other relevant products and special discounts. Recreate brand value.

Lost – Lowest recency, frequency and monetary scores. Revive interest with reach out campaign, ignore otherwise.

Calculation

RFM segments are calculated by standard 5⨉5⨉5 matrix.

Platform takes all purchases for N months. By default, N=12. Ask your manager to change this number, if you need it.

Algorithm removes 2% of anomalies (by monetary, frequency and recency). And distribute all customers to their cell in the matrix.

Example

Initial data:

• Time frame – 12 months
• The first purchase was 12 months ago
• The last purchase was today
• Min purchases per customer – 1
• Max purchases per customer – 10
• Min revenue per customer – $100
• Max revenue per customer – $1000

Matric steps as results:

• R – 12/5 = 2.4 month
• F - 2 purchases
• M - $200

To simplify, 3D matrix is flattened to 2D matrix: R ⨉ 𝑓(FM).

As a result, this table is used to group customers by segments:

Moving between segments

RFM segments are recalculated daily. In the process, some customers can transfer from one segment to another. You can create trigger messages for these events.