RFM table (customer data)

Recency, frequency, monetary and RFM score.

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

Arguments

data	A data.frame or tibble.
customer_id	Unique id of the customer.
n_transactions	Number of transactions/orders.
recency_days	Number of days since the last transaction.
total_revenue	Total revenue from the customer.
analysis_date	Date of analysis.
recency_bins	Number of bins for recency.
frequency_bins	Number of bins for frequency.
monetary_bins	Number of bins for monetary.
...	Other arguments.

Value

rfm_table_customer returns a tibble with the following columns:

customer_id

Unique id of the customer.

recency_days

Number of days since the most recent transaction.

transaction_count

Total number of transactions of the customer.

amount

Revenue from 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.

Examples

analysis_date <- lubridate::as_date('2007-01-01', tz = 'UTC')
rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
recency_days, revenue, analysis_date)
#> # A tibble: 39,999 x 8
#>    customer_id recency_days transaction_count amount recency_score
#>          <dbl>        <dbl>             <dbl>  <dbl>         <int>
#>  1       22086          232                 9    777             2
#>  2        2290          115                16   1555             4
#>  3       26377           43                 5    336             5
#>  4       24650           64                12   1189             5
#>  5       12883           23                12   1229             5
#>  6        2119           72                11    929             5
#>  7       31283          112                17   1569             4
#>  8       33815          142                11    778             3
#>  9       15972           43                 9    641             5
#> 10       27650          131                10    970             3
#> # ... with 39,989 more rows, and 3 more variables: frequency_score <int>,
#> #   monetary_score <int>, rfm_score <dbl>

# access rfm table
result <- rfm_table_customer(rfm_data_customer, customer_id, number_of_orders,
recency_days, revenue, analysis_date)
result$rfm
#> # A tibble: 39,999 x 8
#>    customer_id recency_days transaction_count amount recency_score
#>          <dbl>        <dbl>             <dbl>  <dbl>         <int>
#>  1       22086          232                 9    777             2
#>  2        2290          115                16   1555             4
#>  3       26377           43                 5    336             5
#>  4       24650           64                12   1189             5
#>  5       12883           23                12   1229             5
#>  6        2119           72                11    929             5
#>  7       31283          112                17   1569             4
#>  8       33815          142                11    778             3
#>  9       15972           43                 9    641             5
#> 10       27650          131                10    970             3
#> # ... with 39,989 more rows, and 3 more variables: frequency_score <int>,
#> #   monetary_score <int>, rfm_score <dbl>