The criteria for each parameter are unique to each company.

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Client card in Carrot quest in the lead database
For example, this is what a client card looks like in Carrot quest in the lead database
We have a detailed article on how a CDP can help you collect and use customer data.

User grouping and evaluation
Next, you need to create a system for evaluating clients according to each of the three RFM criteria. Most often, a three-point system is used for this.

Recency (Age of purchase):

1 point - purchased recently (active);
2 points - sleeping;
3 points - bought a long time ago (outgoing).
Frequency (Purchase frequency):

1 point - bought frequently;
2 points - rarely purchased;
3 points - bought once.
Monetary (Investments):

1 point - large check;
2 points – average bill;
3 points - low check.

Analysis of results
As a result, for each client you will have a description of three list of afghanistan cell phone number numbers, for example, 121. In total, 27 combinations are possible. These segments will help you understand which clients are the most valuable.

111
bought recently, buy often, big check 121
bought recently, buy rarely, big check 131
bought recently, bought once, big check
112
recently bought, buy often, average bill 122
recently bought, rarely bought, average bill 132
recently bought, bought once, average bill
113
bought recently, buy often, low check 123
bought recently, buy rarely, low check 133
bought recently, bought once, low bill
211
sleeping, buy often, high check 221
sleeping, buy rarely, high check 231
sleeping, bought one time, high check
212
sleeping, buy often, average bill 222
sleeping, rarely buy, average bill 232
sleeping, bought one time, average bill
213
sleeping, buy often, low check 223
sleeping, buy rarely, low check 233
sleeping, bought one time, low check
311
leaving, buy often, high check 321
leaving, buy rarely, high check 331
outgoing, bought one-time, high check
312
leaving, buy often, average bill 322
leaving, buy rarely, average bill 332
outgoing, bought one time, average bill
313
leaving, buy often, low check 323
leaving, buy rarely, low check 333
outgoing, bought one-time, low check
Dividing customers into 27 groups based on the RFM method
Based on the results of the analysis, you can develop personalized marketing strategies for each user segment. Different messages will help retain loyal customers, encourage average ones to make more frequent purchases, and bring back "sleeping" ones.

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How to work with different segments in RFM
Communicating with different segments helps to use data to manage the customer base. Based on the portraits of “ideal” buyers, you can attract potential ones. And you can also manage the needs of current customers, transferring them from one segment to another and gradually turning them into regular ones.