Installation

# Install release version from CRAN
install.packages("rfm")

# Install development version from GitHub
# install.packages("devtools")
devtools::install_github("rsquaredacademy/rfm")

Shiny App

rfm includes a shiny app which can be launched using

rfm_launch_app()

or try the live version here.

Read on to learn more about the features of rfm, or see the rfm website for detailed documentation on using the package.

Introduction

## Warning: package 'knitr' was built under R version 3.5.2
## Warning: package 'DT' was built under R version 3.5.2

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 includes a sample data set rfm_data_orders which includes the above details:

rfm_data_orders
## # A tibble: 4,906 x 3
##    customer_id         order_date revenue
##    <chr>               <date>       <dbl>
##  1 Mr. Brion Stark Sr. 2004-12-20      32
##  2 Ethyl Botsford      2005-05-02      36
##  3 Hosteen Jacobi      2004-03-06     116
##  4 Mr. Edw Frami       2006-03-15      99
##  5 Josef Lemke         2006-08-14      76
##  6 Julisa Halvorson    2005-05-28      56
##  7 Judyth Lueilwitz    2005-03-09     108
##  8 Mr. Mekhi Goyette   2005-09-23     183
##  9 Hansford Moen PhD   2005-09-07      30
## 10 Fount Flatley       2006-04-12      13
## # ... with 4,896 more rows

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.

rfm_table_order() takes 8 inputs:

• data: a data set with
  • unique customer id
  • date of transaction
  • and amount
• customer_id: name of the customer id column
• order_date: name of the transaction date column
• revenue: name of the transaction amount column
• analysis_date: date of analysis
• recency_bins: number of rankings for recency score (default is 5)
• frequency_bins: number of rankings for frequency score (default is 5)
• monetary_bins: number of rankings for monetary score (default is 5)

RFM Table

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

rfm_table_order() will return the following columns as seen in the above table:

• customer_id: unique customer id
• date_most_recent: date of most recent visit
• recency_days: days since the most recent visit
• transaction_count: number of transactions of the customer
• amount: total revenue generated by the customer
• recency_score: recency score of the customer
• frequency_score: frequency score of the customer
• monetary_score: monetary score of the customer
• rfm_score: RFM score of the customer

Segments

Let us classify our customers based on the individual recency, frequency and monetary scores.

Segmented Customer Data

We can use the segmented data to identify

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

Once we have classified a customer into a particular segment, we can take appropriate action to increase his/her lifetime value.

Segment Size

Now that we have defined and segmented our customers, let us examine the distribution of customers across the segments. Ideally, we should have very few or no customer in segments such as At Risk or Needs Attention.

segments %>%
  count(segment) %>%
  arrange(desc(n)) %>%
  rename(Segment = segment, Count = n)
## # A tibble: 10 x 2
##    Segment            Count
##    <chr>              <int>
##  1 Loyal Customers      288
##  2 Lost                 185
##  3 At Risk              180
##  4 Potential Loyalist   147
##  5 About To Sleep        58
##  6 Others                48
##  7 Need Attention        34
##  8 Promising             21
##  9 Can't Lose Them       17
## 10 New Customers         17

We can also examine the median recency, frequency and monetary value across segments to ensure that the logic used for customer classification is sound and practical.

rfm_plot_median_recency(segments)

rfm_plot_median_frequency(segments)

rfm_plot_median_monetary(segments)

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.

rfm_rm_plot(rfm_result)

rfm_fm_plot(rfm_result)

rfm_rf_plot(rfm_result)