Data for generating 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 data
rfm_heatmap_data(rfm_order)
#> frequency_score recency_score monetary
#> 1 1 1 208.3982
#> 2 1 2 246.9815
#> 3 1 3 246.3000
#> 4 1 4 213.4194
#> 5 1 5 262.1667
#> 6 2 1 380.5000
#> 7 2 2 407.5385
#> 8 2 3 355.0833
#> 9 2 4 375.1667
#> 10 2 5 370.0278
#> 11 3 1 530.2400
#> 12 3 2 516.6486
#> 13 3 3 446.6087
#> 14 3 4 451.8788
#> 15 3 5 467.0000
#> 16 4 1 575.4091
#> 17 4 2 599.4706
#> 18 4 3 603.7857
#> 19 4 4 617.0714
#> 20 4 5 564.5085
#> 21 5 1 746.6667
#> 22 5 2 873.1053
#> 23 5 3 846.9048
#> 24 5 4 866.4324
#> 25 5 5 862.4000
# 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)
#> frequency_score recency_score monetary
#> 1 1 1 536.8644
#> 2 1 2 570.5215
#> 3 1 3 579.9108
#> 4 1 4 584.2790
#> 5 1 5 593.4045
#> 6 2 1 811.7163
#> 7 2 2 813.6919
#> 8 2 3 810.9384
#> 9 2 4 821.7216
#> 10 2 5 812.9264
#> 11 3 1 953.3083
#> 12 3 2 961.4024
#> 13 3 3 949.6547
#> 14 3 4 946.6957
#> 15 3 5 945.6492
#> 16 4 1 1091.6442
#> 17 4 2 1087.0097
#> 18 4 3 1090.6269
#> 19 4 4 1099.8690
#> 20 4 5 1096.4770
#> 21 5 1 1333.6814
#> 22 5 2 1368.3935
#> 23 5 3 1390.5560
#> 24 5 4 1385.8354
#> 25 5 5 1395.0101