rfm: Recency, Frequency and Monetary Value Analysis

Author: Aravind Hebbali
License: MIT

Tools for RFM (recency, frequency and monetary) analysis. Generate RFM score from both transaction and customer level data. Visualize the relationship between recency, frequency and monetary value using heatmap, histograms, bar charts and scatter plots. Includes a shiny app for interactive segmentation.

Installation

You can install rfm from github with:

# install.packages("devtools")
devtools::install_github("rsquaredacademy/rfm")

Consistent Prefix

rfm uses consistent prefix rfm_ for easy tab completion.

Shiny App

rfm includes a shiny app for interactive analysis and can be launched using rfm_launch_app().

Vignettes

• RFM Customer Data
• RFM Transaction Data

Usage

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.

Data

To calculate the RFM score for each customer we need transaction data which should include the following:

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

RFM Table

Use rfm_table_order() to generate the RFM score.

analysis_date <- lubridate::as_date('2006-12-31', tz = 'UTC')
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date)
rfm_result
#> # A tibble: 995 x 9
#>    customer_id        date_most_recent recency_days transaction_count
#>    <chr>              <date>                  <dbl>             <dbl>
#>  1 Abbey O'Reilly DVM 2006-06-09              205                6.00
#>  2 Add Senger         2006-08-13              140                3.00
#>  3 Aden Lesch Sr.     2006-06-20              194                4.00
#>  4 Admiral Senger     2006-08-21              132                5.00
#>  5 Agness O'Keefe     2006-10-02               90.0              9.00
#>  6 Aileen Barton      2006-10-08               84.0              9.00
#>  7 Ailene Hermann     2006-03-25              281                8.00
#>  8 Aiyanna Bruen PhD  2006-04-29              246                4.00
#>  9 Ala Schmidt DDS    2006-01-16              349                3.00
#> 10 Alannah Borer      2005-04-21              619                4.00
#>    amount recency_score frequency_score monetary_score rfm_score
#>     <dbl>         <int>           <int>          <int>     <dbl>
#>  1    472             3               4              3       343
#>  2    340             4               1              2       412
#>  3    405             3               2              3       323
#>  4    448             4               3              3       433
#>  5    843             5               5              5       555
#>  6    763             5               5              5       555
#>  7    699             3               5              5       355
#>  8    157             3               2              1       321
#>  9    363             2               1              2       212
#> 10    196             1               2              1       121
#> # ... with 985 more rows

Heat Map

The heat map shows the average monetary value for different categories of recency and frequency scores. Higher scores of frequency and recency are characterized by higher average monetary value as indicated by the darker areas in the heatmap.

rfm_heatmap(rfm_result)

Bar Chart

Use rfm_bar_chart() to generate the distribution of monetary scores for the different combinations of frequency and recency scores.

rfm_bar_chart(rfm_result)

Histogram

Use rfm_histograms() to examine the relative distribution of

• monetary value (total revenue generated by each customer)
• recency days (days since the most recent visit for each customer)
• frequency (transaction count for each customer)

rfm_histograms(rfm_result)

Customers by Orders

Visualize the distribution of customers across orders.

rfm_order_dist(rfm_result)

Scatter Plots

The best customers are those who:

• bought most recently
• most often
• and spend the most

Now let us examine the relationship between the above.

Recency vs Monetary Value

rfm_rm_plot(rfm_result)

Frequency vs Monetary Value

rfm_fm_plot(rfm_result)

Recency vs Frequency

rfm_rf_plot(rfm_result)

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.