easyRFM - An easy way to RFM analysis by R

Koji MAKIYAMA

Overview

About RFM analysis:

• RFM (customer value) - Wikipedia

RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in retail and professional services industries.

RFM stands for

• Recency - How recently did the customer purchase?
• Frequency - How often do they purchase?
• Monetary Value - How much do they spend?

First, ready transaction data like below:

head(data)

##   id payment       date
## 1  1    1710 2014-12-23
## 2  2    6130 2014-12-31
## 3  2    2870 2014-12-19
## 4  2     440 2014-12-27
## 5  3    2080 2014-12-28
## 6  3    8220 2014-12-18

The "id" means customer ID, the "payment" means a payment for purchase and the "date" means a purchase date.

Then you can execute RFM analysis by a simple command:

result <- rfm_auto(data)

The result contains three elements and one function.

result$rfm is which class each customer was assigned.

head(result$rfm)

##   id    Recency Frequency Monetary RecencyClass FrequencyClass
## 1  1 2014-12-23         1     1710            3              1
## 2  2 2014-12-31         3     9440            5              3
## 3  3 2014-12-28         2    10300            4              2
## 4  4 2014-12-28         4    14360            4              4
## 5  5 2014-12-15         4     6820            2              4
## 6  7 2014-12-25         2     5430            4              2
##   MonetaryClass
## 1             1
## 2             4
## 3             4
## 4             5
## 5             3
## 6             2

result$breaks is the breaks for each classes.

result$breaks

## $recency_breaks
## [1] "2014-12-01 JST" "2014-12-14 JST" "2014-12-21 JST" "2014-12-25 JST"
## [5] "2014-12-29 JST" "2015-01-01 JST"
## 
## $recency_breaks_days
## Time differences in days
## [1] 31 18 11  7  3  0
## 
## $frequency_breaks
## [1] 0 1 2 3 4 8
## 
## $monetary_breaks
## [1]   120  3600  6100  9100 14000 38000

result$classes is the ranges for each classes.

result$classes

## $recency_class
## [1] "2014-12-01 00:00:00 to 2014-12-14" "2014-12-14 00:00:01 to 2014-12-21"
## [3] "2014-12-21 00:00:01 to 2014-12-25" "2014-12-25 00:00:01 to 2014-12-29"
## [5] "2014-12-29 00:00:01 to 2015-01-01"
## 
## $recency_class_days
## [1] "31 to 18" "17 to 11" "10 to 7"  "6 to 3"   "2 to 0"  
## 
## $frequency_class
## [1] "1"      "2"      "3"      "4"      "5 to 8"
## 
## $monetary_class
## [1] "120 to 3600"    "3601 to 6100"   "6101 to 9100"   "9101 to 14000" 
## [5] "14001 to 38000"

result$get_table() is function which creates tables with slicing.

result$get_table("RF", M_slice=4:5)

##           Frequency
## Recency     1  2  3  4 5 to 8
##   31 to 18  7 13  4  4      2
##   17 to 11  4 24 32  6      6
##   10 to 7   0 15 24 21      7
##   6 to 3    3 16 37 24     18
##   2 to 0    1 12 26 17     24

If you don't indicate slice, it uses all.

result$get_table("RF")

##           Frequency
## Recency      1   2   3   4 5 to 8
##   31 to 18 120  43   8   4      2
##   17 to 11  65  72  38   8      6
##   10 to 7   43  56  42  22      7
##   6 to 3    31  56  48  30     18
##   2 to 0    18  47  41  18     25

How to install

install.packages("devtools") # if you have not installed "devtools" package
devtools::install_github("hoxo-m/easyRFM")

Try it with sample data

easyRFM package provide rfm_generate_data() function to generate sample data for rfm_auto():

data <- rfm_generate_data()
head(data)

##   id payment       date
## 1  1    9790 2014-12-10
## 2  1    1080 2014-12-23
## 3  2    1150 2014-12-05
## 4  2    6050 2014-12-23
## 5  2    2380 2014-12-24
## 6  2    4310 2014-12-21

Try rfm_auto() and look over the result:

result <- rfm_auto(data)

How to input to rfm_auto()

If your data have different column names from default: "id", "payment" and "date", for example:

head(data)

##   customer_id payment purchase_date
## 1           1    1710    2014-12-23
## 2           2    6130    2014-12-31
## 3           2    2870    2014-12-19
## 4           2     440    2014-12-27
## 5           3    2080    2014-12-28
## 6           3    8220    2014-12-18

You can indicate the column names:

result <- rfm_auto(data, id="customer_id", payment="payment", date="purchase_date")

If your data have different date format from default: "yyyy-mm-dd", for example:

head(data)

##   id payment       date
## 1  1    1710 2014/12/23
## 2  2    6130 2014/12/31
## 3  2    2870 2014/12/19
## 4  2     440 2014/12/27
## 5  3    2080 2014/12/28
## 6  3    8220 2014/12/18

You can indicate date format:

result <- rfm_auto(data, date_format = "%Y/%m/%d")

For more information for date_format, see Date-time Conversion Functions to and from Character.

You can use datetime object(POSIXlt or POSIXct) instead of date, for example:

head(data)

##   id payment                date
## 1  1    1710 2014/12/23 00:18:23
## 2  2    6130 2014/12/31 17:26:00
## 3  2    2870 2014/12/19 05:28:46
## 4  2     440 2014/12/27 16:58:33
## 5  3    2080 2014/12/28 10:54:42
## 6  3    8220 2014/12/18 02:28:57

result <- rfm_auto(data, date_format = "%Y/%m/%d %H:%M:%S")

Application

data <- rfm_generate_data(10000, begin="2014-10-01", end="2015-01-01", seed=123)
result <- rfm_auto(data, breaks=list(r=6, f=5, m=5))

result$get_table("RF", M_slice=4:5)

##           Frequency
## Recency      1   2   3   4 5 to 9
##   92 to 58  55 104  67  19      3
##   57 to 38  37 174 158  69     23
##   37 to 25  15 152 220 136     66
##   24 to 15  17 153 240 141    117
##   14 to 7    5 134 241 196    135
##   6 to 0    12 117 273 205    150

leaved_customers <- result$get_sliced_rfm(R_slice=1:2, F_slice=2:5, M_slice=4:5)
leaving_customers <- result$get_sliced_rfm(R_slice=3:4, F_slice=4:5, M_slice=4:5)
good_customers <- result$get_sliced_rfm(R_slice=5:6, F_slice=4:5, M_slice=4:5)