Type:	Package
Title:	Recency, Frequency and Monetary Value Analysis
Version:	0.3.0
Description:	Tools for RFM (recency, frequency and monetary value) 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. References: i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
License:	MIT + file LICENSE
URL:	https://github.com/rsquaredacademy/rfm, https://rfm.rsquaredacademy.com/
BugReports:	https://github.com/rsquaredacademy/rfm/issues
Depends:	R (≥ 3.2)
Imports:	dplyr, ggplot2, magrittr, RColorBrewer, rlang, scales, stats, treemapify, utils, xplorerr
Suggests:	cli, covr, DT, gganimate, gifski, kableExtra, knitr, png, plotly, rmarkdown, rmdformats, testthat (≥ 3.0.0), vdiffr
VignetteBuilder:	knitr
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2024-02-26 08:12:59 UTC; HP
Author:	Aravind Hebbali [aut, cre]
Maintainer:	Aravind Hebbali <hebbali.aravind@gmail.com>
Repository:	CRAN
Date/Publication:	2024-02-26 09:20:02 UTC

rfm package

Description

Tools for customer segmentation analysis

Author(s)

Maintainer: Aravind Hebbali hebbali.aravind@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/rsquaredacademy/rfm

• https://rfm.rsquaredacademy.com/

• Report bugs at https://github.com/rsquaredacademy/rfm/issues

Bar chart data

Description

Data for generating bar charts.

Usage

rfm_barchart_data(rfm_table)

Arguments

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# bar chart data
rfm_barchart_data(rfm_order)

# using customer data
analysis_date <- as.Date('2007-01-01')
rfm_customer <- rfm_table_customer(rfm_data_customer, customer_id,
number_of_orders, recency_days, revenue, analysis_date)

# bar chart data
rfm_barchart_data(rfm_customer)

RFM report

Description

Generates a segmentation analysis report.

Usage

rfm_create_report(
  rfm_table,
  segments,
  interactive = FALSE,
  title = NULL,
  author = NULL,
  folder_name = NULL,
  file_name = NULL
)

Arguments

Examples

## Not run: 
# 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)

# 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)

rfm_create_report(rfm_result, segments, FALSE,
"Customer Segmentation Report")

## End(Not run)

RFM customer data

Description

A dataset containing customer level data.

Usage

rfm_data_customer

Format

A tibble with 39,999 rows and 5 variables:

customer_id

Customer id.

total_amount

Total amount of all orders.

most_recent_visit

Date of the most recent transaction.

number_of_purchases

Total number of transactions/orders.

purchase_interval

Number of days since last transaction/order.

#'

first_name

First name of the customer.

last_name

Last name of the customer.

email

email id of the customer.

RFM transaction data

Description

A dataset containing transactions of different customers.

Usage

rfm_data_orders

Format

A tibble with 49.6 rows and 3 variables:

order_date

Order date.

customer_id

Customer id

revenue

Transaction amount.

first_name

First name of the customer.

last_name

Last name of the customer.

email

email id of the customer.

Heatmap data

Description

Data for generating heatmap.

Usage

rfm_heatmap_data(rfm_table)

Arguments

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# heat map data
rfm_heatmap_data(rfm_order)

# using customer data
analysis_date <- as.Date('2007-01-01')
rfm_customer <- rfm_table_customer(rfm_data_customer, customer_id,
number_of_orders, recency_days, revenue, analysis_date)

# heat map data
rfm_heatmap_data(rfm_customer)

Launch shiny app

Description

Launches shiny app.

Usage

rfm_launch_app()

Examples

## Not run: 
rfm_launch_app()

## End(Not run)

RFM bar chart

Description

Examine the distribution of monetary scores for the different combinations of frequency and recency scores.

Usage

rfm_plot_bar_chart(
  rfm_table,
  bar_color = NULL,
  xaxis_label = NULL,
  sec_xaxis_label = NULL,
  yaxis_label = NULL,
  sec_yaxis_label = NULL,
  print_plot = TRUE
)

Arguments

Value

Bar chart.

Deprecated Functions

rfm_bar_chart() has been deprecated and will be made defunct. It has been provided for compatibility with older versions only, and will be made defunct at the next release.

Instead use the replacement function rfm_plot_bar_chart().

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# bar chart
rfm_plot_bar_chart(rfm_order)

RFM heatmap

Description

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.

Usage

rfm_plot_heatmap(
  data,
  brewer_n = 5,
  brewer_name = "PuBu",
  xaxis_label = NULL,
  yaxis_label = NULL,
  plot_title = NULL,
  legend_title = NULL,
  interactive = FALSE,
  print_plot = TRUE
)

Arguments

Deprecated Functions

rfm_heatmap() has been deprecated and will be made defunct. It has been provided for compatibility with older versions only, and will be made defunct at the next release.

Instead use the replacement function rfm_plot_heatmap().

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# heat map
# ggplot2
rfm_plot_heatmap(rfm_order)

# plotly
rfm_plot_heatmap(rfm_order, interactive = TRUE)

# using customer data
analysis_date <- as.Date('2007-01-01')
rfm_customer <- rfm_table_customer(rfm_data_customer, customer_id,
number_of_orders, recency_days, revenue, analysis_date)

# heat map
rfm_plot_heatmap(rfm_customer)

RFM histograms

Description

Histograms of recency, frequency and monetary value.

Usage

rfm_plot_histogram(
  rfm_table,
  metric = "recency",
  hist_bins = 9,
  hist_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  interactive = FALSE,
  print_plot = TRUE
)

Arguments

Value

Histograms

Deprecated Functions

rfm_histograms() has been deprecated and will be made defunct. It has been provided for compatibility with older versions only, and will be made defunct at the next release.

Instead use the replacement function rfm_plot_histogram().

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# histogram
# ggplot2
rfm_plot_histogram(rfm_order, metric = "frequency")

# plotly
rfm_plot_histogram(rfm_order, metric = "frequency", interactive = TRUE)

# using customer data
analysis_date <- as.Date('2007-01-01')
rfm_customer <- rfm_table_customer(rfm_data_customer, customer_id,
number_of_orders, recency_days, revenue, analysis_date)

# histogram
rfm_plot_histogram(rfm_customer)

Median plots

Description

Segment wise median recency, frequency & monetary value plot.

Usage

rfm_plot_median_recency(
  rfm_segment_table,
  sort = FALSE,
  ascending = FALSE,
  flip = FALSE,
  bar_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  axis_label_size = 8,
  axis_label_angle = 315,
  bar_labels = TRUE,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

rfm_plot_median_frequency(
  rfm_segment_table,
  sort = FALSE,
  ascending = FALSE,
  flip = FALSE,
  bar_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  axis_label_size = 8,
  axis_label_angle = 315,
  bar_labels = TRUE,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

rfm_plot_median_monetary(
  rfm_segment_table,
  sort = FALSE,
  ascending = FALSE,
  flip = FALSE,
  bar_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  axis_label_size = 8,
  axis_label_angle = 315,
  bar_labels = TRUE,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

Arguments

Examples

# 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)

# 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)

# plots
# visualize median recency
rfm_plot_median_recency(segments)

# plotly
rfm_plot_median_recency(segments, interactive = TRUE)

# sort in ascending order
rfm_plot_median_recency(segments, sort = TRUE, ascending = TRUE)

# default sorting is in descending order
rfm_plot_median_recency(segments, sort = TRUE)

# horizontal bars
rfm_plot_median_recency(segments, flip = TRUE)

# median frequency
rfm_plot_median_frequency(segments)

# median monetary value
rfm_plot_median_monetary(segments)

Customers by orders

Description

Visualize the distribution of customers across orders.

Usage

rfm_plot_order_dist(
  rfm_table,
  flip = FALSE,
  bar_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  bar_label_size = 3,
  bar_labels = TRUE,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

Arguments

Value

Bar chart.

Deprecated Functions

rfm_order_dist() has been deprecated and will be made defunct. It has been provided for compatibility with older versions only, and will be made defunct at the next release.

Instead use the replacement function rfm_plot_order_dist().

Examples

# using transaction data
analysis_date <- as.Date('2006-12-31')
rfm_order <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)

# order distribution
rfm_plot_order_dist(rfm_order)

# horizontal bars
rfm_plot_order_dist(rfm_order, flip = TRUE)

# plotly
rfm_plot_order_dist(rfm_order, interactive = TRUE)

# using customer data
analysis_date <- as.Date('2007-01-01')
rfm_customer <- rfm_table_customer(rfm_data_customer, customer_id,
number_of_orders, recency_days, revenue, analysis_date)

# order distribution
rfm_plot_order_dist(rfm_customer)

Revenue distribution

Description

Customer and revenue distribution by segments.

Usage

rfm_plot_revenue_dist(
  x,
  flip = FALSE,
  colors = c("#3b5bdb", "#91a7ff"),
  legend_labels = c("Revenue", "Customers"),
  plot_title = "Revenue & Customer Distribution",
  xaxis_label = NULL,
  yaxis_label = NULL,
  axis_label_size = 8,
  axis_label_angle = 315,
  bar_labels = FALSE,
  bar_label_size = 2,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

Arguments

Examples

# 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)

# 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)

# segment summary
segment_overview <- rfm_segment_summary(segments)

# revenue distribution
# ggplot2
rfm_plot_revenue_dist(segment_overview)

# flip
rfm_plot_revenue_dist(segment_overview, flip = TRUE)

# plotly
rfm_plot_revenue_dist(segment_overview, interactive = TRUE)

RFM Segmentation Plot

Description

Generates tree map to visualize segments.

Usage

rfm_plot_segment(
  table,
  metric = "customers",
  interactive = FALSE,
  print_plot = TRUE
)

Arguments

Examples

# 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)

# 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)

# segment summary
segment_overview <- rfm_segment_summary(segments)

# treemaps
# default metric is customers
rfm_plot_segment(segment_overview)

# treemap of orders
rfm_plot_segment(segment_overview, metric = "orders")

# plotly
rfm_plot_segment(segment_overview, metric = "revenue", interactive = TRUE)

Segment Scatter Plots

Description

Generate scatter plots to examine the relationship between recency, frequency and monetary value.

Usage

rfm_plot_segment_scatter(
  segments,
  x = "monetary",
  y = "recency",
  plot_title = NULL,
  legend_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

Arguments

Value

Scatter plot.

Examples

# 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)

# 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)

# visualize
# ggplot2
rfm_plot_segment_scatter(segments, "monetary", "recency")

# plotly
rfm_plot_segment_scatter(segments, "monetary", "recency", interactive = TRUE)

Visulaize segment summary

Description

Generates plots for customers, orders, revenue and average order value for each segment.

Usage

rfm_plot_segment_summary(
  x,
  metric = NULL,
  sort = FALSE,
  ascending = FALSE,
  flip = FALSE,
  bar_color = NULL,
  plot_title = NULL,
  xaxis_label = NULL,
  yaxis_label = NULL,
  axis_label_size = 8,
  axis_label_angle = 315,
  bar_labels = TRUE,
  interactive = FALSE,
  animate = FALSE,
  print_plot = TRUE
)

Arguments

Examples

# 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)

# 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)

# segment summary
segment_overview <- rfm_segment_summary(segments)

# plot segment summary
# summarize metric for all segments
# ggplot2
rfm_plot_segment_summary(segment_overview)

# plotly
rfm_plot_segment_summary(segment_overview, interactive = TRUE)

# select metric to be visualized
rfm_plot_segment_summary(segment_overview, metric = "orders")

# sort the metric in ascending order
rfm_plot_segment_summary(segment_overview, metric = "orders", sort = TRUE,
  ascending = TRUE)

# default sorting is in descending order
rfm_plot_segment_summary(segment_overview, metric = "orders", sort = TRUE)

# horizontal bars
rfm_plot_segment_summary(segment_overview, metric = "orders", flip = TRUE)

RFM Scatter plot

Description

Examine the relationship between recency, frequency and monetary values.

Usage

rfm_rm_plot(
  segments,
  xaxis_label = NULL,
  yaxis_label = NULL,
  plot_title = NULL,
  print_plot = TRUE
)

rfm_fm_plot(
  segments,
  xaxis_label = NULL,
  yaxis_label = NULL,
  plot_title = NULL,
  print_plot = TRUE
)

rfm_rf_plot(
  segments,
  xaxis_label = NULL,
  yaxis_label = NULL,
  plot_title = NULL,
  print_plot = TRUE
)

Arguments

Value

Scatter plot.

Deprecated Functions

rfm_rm_plot(), rfm_fm_plot() and rfm_rf_plot() have been deprecated and will be made defunct. These functions have been provided for compatibility with older versions only, and will be made defunct at the next release. Instead use the replacement function rfm_plot_segment_scatter().

Segmentation

Description

Create segments based on recency, frequency and monetary scores.

Usage

rfm_segment(
  data,
  segment_names = NULL,
  recency_lower = NULL,
  recency_upper = NULL,
  frequency_lower = NULL,
  frequency_upper = NULL,
  monetary_lower = NULL,
  monetary_upper = NULL
)

Arguments

Examples

# 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)

# 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

Segment summary

Description

An overview of customer segments.

Usage

rfm_segment_summary(segments)

Arguments

Examples

# 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)

# 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)

# segment summary
rfm_segment_summary(segments)

RFM table (customer data)

Description

Recency, frequency, monetary and RFM score.

Usage

rfm_table_customer(
  data = NULL,
  customer_id = NULL,
  n_transactions = NULL,
  recency = NULL,
  total_revenue = NULL,
  analysis_date = NULL,
  recency_bins = 5,
  frequency_bins = 5,
  monetary_bins = 5,
  ...
)

Arguments

Value

rfm_table_order returns a list with the following:

Examples

analysis_date <- as.Date('2007-01-01')

# data includes days since last visit
rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
recency_days, revenue, analysis_date)

# data includes last visit date
rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
most_recent_visit, revenue, analysis_date)

# access rfm table
result <- rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
recency_days, revenue, analysis_date)
result$rfm

# using custom threshold
rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
recency_days, revenue, analysis_date, recency_bins = c(115, 181, 297, 482),
frequency_bins = c(4, 5, 6, 8), monetary_bins = c(256, 382, 506, 666))

RFM table (transaction data)

Description

Recency, frequency, monetary and RFM score.

Usage

rfm_table_order(
  data = NULL,
  customer_id = NULL,
  order_date = NULL,
  revenue = NULL,
  analysis_date = NULL,
  recency_bins = 5,
  frequency_bins = 5,
  monetary_bins = 5,
  ...
)

Arguments

Value

rfm_table_order returns a list with the following:

Examples

analysis_date <- as.Date("2006-12-31")
rfm_table_order(
  rfm_data_orders, customer_id, order_date, revenue,
  analysis_date
)

# access rfm table
result <- rfm_table_order(
  rfm_data_orders, customer_id, order_date,
  revenue, analysis_date
)
result$rfm

# using custom threshold
rfm_table_order(rfm_data_orders, customer_id, order_date, revenue,
  analysis_date,
  recency_bins = c(115, 181, 297, 482), frequency_bins = c(4, 5, 6, 8),
  monetary_bins = c(256, 382, 506, 666)
)