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, ...)
| data | A |
|---|---|
| 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. |
rfm_table_customer returns a tibble with the following columns:
Unique id of the customer.
Number of days since the most recent transaction.
Total number of transactions of the customer.
Revenue from the customer.
Recency score of the customer.
Frequency score of the customer.
Monetary score of the customer.
RFM score of the customer.
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 9.00 777 2 #> 2 2290 115 16.0 1555 4 #> 3 26377 43.0 5.00 336 5 #> 4 24650 64.0 12.0 1189 5 #> 5 12883 23.0 12.0 1229 5 #> 6 2119 72.0 11.0 929 5 #> 7 31283 112 17.0 1569 4 #> 8 33815 142 11.0 778 3 #> 9 15972 43.0 9.00 641 5 #> 10 27650 131 10.0 970 3 #> # ... with 39,989 more rows, and 3 more variables: frequency_score <int>, #> # monetary_score <int>, rfm_score <dbl>