R/calculateRFMScores.R

In ninakoenig/RFMPackage: Calculate Recency, Frequency and Monetary Value scores from transaction data.

#' calculateRFMScores
#'
# Description
#' Calculate a weighted RFM score: recency, frequency, and monetary value.
#
# Arguments
#'@param data - A data.table containing the transaction details for every customer. Transaction date has to be of the lubridate form "yyyy-mm-dd" and have name "TransDate".
#'@param r - Weight of recency.
#'@param f - Weight of frequency.
#'@param m - Weight of monetary value.
#'
#'@details
#' \code{data} contains the transactional data. The dataset must contain a
#' column labeled "Customer" that allows unique customer identification
#' and a column labeled "TransDate", indicating the purchase date. The column
#' "PurchAmount" specifies the total spending per purchase.
#
# Return Value
#'@return Returns a data.table containing the recency, frequency and monetary
#'score as well as the weighted final score and the group membership.
#
# Some useful keyboard shortcuts for package authoring:
#
#   Build and Reload Package:  'Cmd + Shift + B'
#   Check Package:             'Cmd + Shift + E'
#   Test Package:              'Cmd + Shift + T'

# Part 5: The RFM function ####
# ------------------------------------------------------------------------------

calculateRFMScores <- function(data, weight_recency=1, weight_frequency=1, weight_monetary=1){
  require(lubridate)
  require(data.table)
  require(Hmisc)

  # Ensure that the weights add up to one
  weight_recency2 <- weight_recency/sum(weight_recency, weight_frequency, weight_monetary)
  weight_frequency2 <- weight_frequency/sum(weight_recency, weight_frequency, weight_monetary)
  weight_monetary2 <- weight_monetary/sum(weight_recency, weight_frequency, weight_monetary)

  # RFM measures
  max.Date <- max(data$TransDate)
  temp <- data[,list(
    recency = as.numeric(max.Date - max(TransDate)),
    frequency = .N,
    monetary = sum(PurchAmount)/.N),
    by="Customer"
    ]

  # RFM scores
  temp <- temp[,list(Customer,
                     recency = as.numeric(cut2(-recency, g=3)),
                     frequency = as.numeric(cut2(frequency, g=3)),
                     monetary = as.numeric(cut2(monetary, g=3)))]

  # Overall RFM score
  temp[,finalscore:=weight_recency2*recency+weight_frequency2*frequency+weight_monetary2*monetary, by=Customer]

  # RFM group
  temp[,group:=round(finalscore)]

  # Return final table
  return(temp)
}