Recency, Frequency and Monetary Value Analysis.
rfm
Overview
Tools for customer segmentation using RFM (recency, frequency and monetary) analysis.
Installation
# Install rfm from CRAN
install.packages("rfm")
# Or the development version from GitHub
# install.packages("pak")
pak::pak("rsquaredacademy/rfm")
Usage
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 analysis helps to identify customers who are more likely to respond to promotions by segmenting them into various categories.
To calculate the RFM score we need the following info for each customer:
- a unique customer id
- date of transaction/order
- transaction/order amount
# analysis date
analysis_date <- as.Date('2006-12-31')
# generate rfm score
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)
# rfm score
rfm_result
#> # A tibble: 995 x 11
#> customer_id recency_days transaction_count amount recency_score
#> <chr> <dbl> <int> <int> <int>
#> 1 Abbey O'Reilly 205 6 472 3
#> 2 Add Senger 140 3 340 4
#> 3 Aden Lesch 194 4 405 3
#> 4 Aden Murphy 98 7 596 5
#> 5 Admiral Senger 132 5 448 4
#> # i 990 more rows
#> # i 6 more variables: frequency_score <int>, monetary_score <int>,
#> # rfm_score <dbl>, first_name <chr>, last_name <chr>, email <chr>
# segment names
segment_names <- c("Champions", "Potential Loyalist", "Loyal Customers",
"Promising", "New Customers", "Can't Lose Them",
"At Risk", "Need Attention", "About To Sleep", "Lost")
# segment intervals
recency_lower <- c(5, 3, 2, 3, 4, 1, 1, 1, 2, 1)
recency_upper <- c(5, 5, 4, 4, 5, 2, 2, 3, 3, 1)
frequency_lower <- c(5, 3, 2, 1, 1, 3, 2, 3, 1, 1)
frequency_upper <- c(5, 5, 4, 3, 3, 4, 5, 5, 3, 5)
monetary_lower <- c(5, 2, 2, 3, 1, 4, 4, 3, 1, 1)
monetary_upper <- c(5, 5, 4, 5, 5, 5, 5, 5, 4, 5)
# generate segments
segments <- rfm_segment(rfm_result, segment_names, recency_lower,
recency_upper, frequency_lower, frequency_upper, monetary_lower,
monetary_upper)
segments
#> # A tibble: 995 x 12
#> customer_id segment rfm_score transaction_count recency_days amount
#> <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 Abbey O'Reilly Potential Loya~ 343 6 205 472
#> 2 Add Senger New Customers 412 3 140 340
#> 3 Aden Lesch Loyal Customers 323 4 194 405
#> 4 Aden Murphy Potential Loya~ 544 7 98 596
#> 5 Admiral Senger Potential Loya~ 433 5 132 448
#> # i 990 more rows
#> # i 6 more variables: recency_score <int>, frequency_score <int>,
#> # monetary_score <int>, first_name <chr>, last_name <chr>, email <chr>
Plotting Engines
rfm supports the following plotting engines:
Shiny App
rfm includes a shiny app for interactive RFM analysis. In the latest release, we have added project management features to allow users to save/clone their projects. Below is a quick demo of the shiny app:
Resources
Getting Help
If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.