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RFM - Introduction

Aravind Hebbali

2024-02-26

Source: ../../../../R/Packages/statistics/rfm/vignettes/rfm-introduction.Rmd
rfm-introduction.Rmd

Introduction

This article provides a brief introduction to RFM analysis and customer segmentation. If you are looking for a detailed guide, check out our free online course or YouTube tutorial or blog post.

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.

Data

To calculate the RFM score for each customer we need data for a particular time frame and should include the following:

  • a unique customer id
  • date of transaction/order
  • transaction/order amount

Data can be at customer level or transaction level i.e. each row in the data may represent a single transaction of a customer or summary of all transactions of a customer. rfm package includes two sample data sets:

  • rfm_data_orders
head(rfm_data_orders)
##        customer_id order_date revenue first_name last_name
## 1      Brion Stark 2004-12-20      32      Brion     Stark
## 2   Ethyl Botsford 2005-05-02      36      Ethyl  Botsford
## 3   Hosteen Jacobi 2004-03-06     116    Hosteen    Jacobi
## 4        Edw Frami 2006-03-15      99        Edw     Frami
## 5      Josef Lemke 2006-08-14      76      Josef     Lemke
## 6 Julisa Halvorson 2005-05-28      56     Julisa Halvorson
##                         email
## 1      brion_stark@rfmail.com
## 2   ethyl_botsford@rfmail.com
## 3   hosteen_jacobi@rfmail.com
## 4        edw_frami@rfmail.com
## 5      josef_lemke@rfmail.com
## 6 julisa_halvorson@rfmail.com
  • rfm_data_customer
head(rfm_data_customer)
##   customer_id revenue most_recent_visit number_of_orders recency_days
## 1       22086     777        2006-05-14                9          232
## 2        2290    1555        2006-09-08               16          115
## 3       26377     336        2006-11-19                5           43
## 4       24650    1189        2006-10-29               12           64
## 5       12883    1229        2006-12-09               12           23
## 6        2119     929        2006-10-21               11           72
##   first_name last_name                  email
## 1  Maddalena      Erie          merie0@go.com
## 2    Bradley    Sesser      bsesser1@time.com
## 3    Gwenora     Asser      gasser2@issuu.com
## 4   Hendrick      Josh          hjosh3@ed.gov
## 5   Cathleen   Musterd    cmusterd4@hc360.com
## 6     Norrie     Brear nbrear5@techcrunch.com

You can take a look at them to understand the difference between customer and transaction level data. Remember, the data sets are different and the final results will not match.

RFM Score

So how is the RFM score computed for each customer? The below steps explain the process:

  • A recency score is assigned to each customer based on date of most recent purchase. The score is generated by binning the recency values into a number of categories (default is 5). For example, if you use four categories, the customers with the most recent purchase dates receive a recency ranking of 4, and those with purchase dates in the distant past receive a recency ranking of 1.

  • A frequency ranking is assigned in a similar way. Customers with high purchase frequency are assigned a higher score (4 or 5) and those with lowest frequency are assigned a score 1.

  • Monetary score is assigned on the basis of the total revenue generated by the customer in the period under consideration for the analysis. Customers with highest revenue/order amount are assigned a higher score while those with lowest revenue are assigned a score of 1.

  • A fourth score, RFM score is generated which is simply the three individual scores concatenated into a single value.

The customers with the highest RFM scores are most likely to respond to an offer. Now that we have understood how the RFM score is computed, it is time to put it into practice. Use rfm_table_order() to generate the score for each customer from the sample data set rfm_data_orders.

analysis_date <- as.Date("2006-12-31")
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date)
rfm_result
customer_id recency_days transaction_count amount rfm_score recency_score frequency_score monetary_score first_name last_name email
Abbey O’Reilly 205 6 472 343 3 4 3 Abbey O’Reilly
Add Senger 140 3 340 412 4 1 2 Add Senger
Aden Lesch 194 4 405 323 3 2 3 Aden Lesch
Aden Murphy 98 7 596 544 5 4 4 Aden Murphy
Admiral Senger 132 5 448 433 4 3 3 Admiral Senger
Agness O’Keefe 90 9 843 555 5 5 5 Agness O’Keefe
Aileen Barton 84 9 763 555 5 5 5 Aileen Barton
Ailene Hermann 281 8 699 355 3 5 5 Ailene Hermann
Aiyanna Bruen 246 4 157 321 3 2 1 Aiyanna Bruen
Akeelah Walsh 160 7 779 445 4 4 5 Akeelah Walsh

Segments

Let us segment our customers based on the individual recency, frequency and monetary scores. Keep in mind that creating segments based on RFM score is a very subjective endeavour. Having good business and domain knowledge will allow the user to generate effective segments. There is no one size fits all solution here.

Segment Description R F M
Champions Bought recently, buy often and spend the most 5 5 5
Potential Loyalist Recent customers, spent good amount, bought more than once 3 - 5 3 - 5 2 - 5
Loyal Customers Spend good money. Responsive to promotions 2 - 4 2 - 4 2 - 4
Promising Recent shoppers, but haven’t spent much 3 - 4 1 - 3 3 - 5
New Customers Bought more recently, but not often 4 - 5 1 - 3 1 - 5
Can’t Lose Them Made big purchases and often, but long time ago 1 - 2 3 - 4 4 - 5
At Risk Spent big money, purchased often but long time ago 1 - 2 2 - 5 4 - 5
Need Attention Above average recency, frequency & monetary values 1 - 3 3 - 5 3 - 5
About To Sleep Below average recency, frequency & monetary values 2 - 3 1 - 3 1 - 4
Lost Bought a long time ago, average amount spent 1 - 1 1 - 5 1 - 5

Segmented Customer Data

We can use the segmented data to identify

  • best customers
  • loyal customers
  • at risk customers
  • and lost customers

Once we have segmented a customer, we can take appropriate action to increase his/her lifetime value.

customer_id segment rfm_score transaction_count recency_days amount recency_score frequency_score monetary_score first_name last_name email
Abbey O’Reilly Potential Loyalist 343 6 205 472 3 4 3 Abbey O’Reilly
Add Senger New Customers 412 3 140 340 4 1 2 Add Senger
Aden Lesch Loyal Customers 323 4 194 405 3 2 3 Aden Lesch
Aden Murphy Potential Loyalist 544 7 98 596 5 4 4 Aden Murphy
Admiral Senger Potential Loyalist 433 5 132 448 4 3 3 Admiral Senger
Agness O’Keefe Champions 555 9 90 843 5 5 5 Agness O’Keefe
Aileen Barton Champions 555 9 84 763 5 5 5 Aileen Barton
Ailene Hermann Potential Loyalist 355 8 281 699 3 5 5 Ailene Hermann
Aiyanna Bruen About To Sleep 321 4 246 157 3 2 1 Aiyanna Bruen
Akeelah Walsh Potential Loyalist 445 7 160 779 4 4 5 Akeelah Walsh

Let us quickly summarize the segments to get an overview of the number of customers, orders and average order value in each of them.

Segment Summary 

## # A tibble: 10 × 5
##    segment            customers orders revenue   aov
##    <chr>                  <int>  <int>   <int> <dbl>
##  1 About To Sleep           102    283   23449  82.9
##  2 At Risk                   28    216   22227 103. 
##  3 Can't Lose Them           47    274   32446 118. 
##  4 Champions                 35    316   31646 100. 
##  5 Lost                     148    393   35324  89.9
##  6 Loyal Customers          170    799   76562  95.8
##  7 Need Attention            10     57    4562  80.0
##  8 New Customers            104    329   28837  87.6
##  9 Potential Loyalist       342   2211  204856  92.6
## 10 Promising                  9     28    4249 152.

rfm package offers visualization tools to validate the segments generated from the RFM score. Below are a few of them:

Segmentation Plot

Segment Summary Plot

Revenue Distribution