Create segments based on recency, frequency and monetary scores.
rfm_segment( data, segment_names = NULL, recency_lower = NULL, recency_upper = NULL, frequency_lower = NULL, frequency_upper = NULL, monetary_lower = NULL, monetary_upper = NULL )
data | An object of class |
---|---|
segment_names | Names of the segments. |
recency_lower | Lower boundary for recency score. |
recency_upper | Upper boundary for recency score. |
frequency_lower | Lower boundary for frequency score. |
frequency_upper | Upper boundary for frequency score. |
monetary_lower | Lower boundary for monetary score. |
monetary_upper | Upper boundary for monetary score. |
analysis_date <- lubridate::as_date('2006-12-31') rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date) segment_names <- c("Champions", "Loyal Customers", "Potential Loyalist", "New Customers", "Promising", "Need Attention", "About To Sleep", "At Risk", "Can't Lose Them", "Lost") recency_lower <- c(4, 2, 3, 4, 3, 2, 2, 1, 1, 1) recency_upper <- c(5, 5, 5, 5, 4, 3, 3, 2, 1, 2) frequency_lower <- c(4, 3, 1, 1, 1, 2, 1, 2, 4, 1) frequency_upper <- c(5, 5, 3, 1, 1, 3, 2, 5, 5, 2) monetary_lower <- c(4, 3, 1, 1, 1, 2, 1, 2, 4, 1) monetary_upper <- c(5, 5, 3, 1, 1, 3, 2, 5, 5, 2) rfm_segment(rfm_result, segment_names, recency_lower, recency_upper, frequency_lower, frequency_upper, monetary_lower, monetary_upper)#> # A tibble: 995 x 9 #> customer_id segment rfm_score transaction_cou~ recency_days amount #> <chr> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Abbey O'Re~ Loyal ~ 343 6 205 472 #> 2 Add Senger Potent~ 412 3 140 340 #> 3 Aden Lesch~ Potent~ 323 4 194 405 #> 4 Admiral Se~ Loyal ~ 433 5 132 448 #> 5 Agness O'K~ Champi~ 555 9 90 843 #> 6 Aileen Bar~ Champi~ 555 9 84 763 #> 7 Ailene Her~ Loyal ~ 355 8 281 699 #> 8 Aiyanna Br~ Potent~ 321 4 246 157 #> 9 Ala Schmid~ About ~ 212 3 349 363 #> 10 Alannah Bo~ Lost 121 4 619 196 #> # ... with 985 more rows, and 3 more variables: recency_score <int>, #> # frequency_score <int>, monetary_score <int>