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Transaction Data

RFM Table

RFM Score

Segments

Introduction

RFM (recency, frequency, monetary) analysis is a behavior based technique used to segment customers by examining their transaction history such as

• how recently a customer has purchased (recency)
• how often they purchase (frequency)
• how much the customer spends (monetary)

It is based on the marketing axiom that 80% of your business comes from 20% of your customers. RFM helps to identify customers who are more likely to respond to promotions by segmenting them into various categories.

Applications

RFM Table

RFM Table

Metrics

Recency

Frequency

Monetary Value

Monetary Score

Frequency Score

Recency Score

RFM Score

RFM Score = Recency Score * 100 + Frequency Score * 10 + Monetary Score

Segments

Segments

Segmented Customer Data

Segments

Segment Size

## # A tibble: 12 x 2
##    Segment            Count
##    <chr>              <int>
##  1 At Risk              157
##  2 Potential Loyalist   132
##  3 Others               128
##  4 Champions            116
##  5 Need Attention       100
##  6 Hibernating           97
##  7 About To Sleep        92
##  8 Lost                  75
##  9 Loyal Customers       43
## 10 Promising             21
## 11 Can't Lose Them       17
## 12 New Customers         17

Median Recency

Median Frequency

Median Monetary Value

Your Turn..

If you look at the distribution of segments, around 13% of the customers are in the Others segment For segmentation to be effective and optimal, the Others segment should be eliminated or should have few customers only. Redefine the segments and try to reduce the number of customers in the Others segment.

We have defined 11 segments. Try to combine some of the exisiting segments and bring down the total segments to around 6 or 8.

The RFM score we generated uses score between 1 and 5. Try to create segments by using a score between 1 and 3 i.e. the lowest RFM should be 111 and the highest should be 333 instead of 555.

Reverse the scores i.e. so far we have assigned a score of 5 to customers who visited recently, frequently and had higher transaction amount and 1 to customers who visited way back, rarely and have low transaction amount. Reverse this score pattern and create the segments.