Create segments based on recency, frequency and monetary scores.
Usage
rfm_segment(
data,
segment_names = NULL,
recency_lower = NULL,
recency_upper = NULL,
frequency_lower = NULL,
frequency_upper = NULL,
monetary_lower = NULL,
monetary_upper = NULL
)Arguments
- data
An object of class
rfm_table.- 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.
Examples
# analysis date
analysis_date <- as.Date('2006-12-31')
# generate rfm score
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date,
revenue, analysis_date)
# segment names
segment_names <- c("Champions", "Potential Loyalist", "Loyal Customers",
"Promising", "New Customers", "Can't Lose Them",
"At Risk", "Need Attention", "About To Sleep", "Lost")
# segment intervals
recency_lower <- c(5, 3, 2, 3, 4, 1, 1, 1, 2, 1)
recency_upper <- c(5, 5, 4, 4, 5, 2, 2, 3, 3, 1)
frequency_lower <- c(5, 3, 2, 1, 1, 3, 2, 3, 1, 1)
frequency_upper <- c(5, 5, 4, 3, 3, 4, 5, 5, 3, 5)
monetary_lower <- c(5, 2, 2, 3, 1, 4, 4, 3, 1, 1)
monetary_upper <- c(5, 5, 4, 5, 5, 5, 5, 5, 4, 5)
# generate segments
segments <- rfm_segment(rfm_result, segment_names, recency_lower,
recency_upper, frequency_lower, frequency_upper, monetary_lower,
monetary_upper)
segments
#> # A tibble: 995 × 12
#> customer_id segment rfm_score transaction_count recency_days amount
#> <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 Abbey O'Reilly Potential Loy… 343 6 205 472
#> 2 Add Senger New Customers 412 3 140 340
#> 3 Aden Lesch Loyal Custome… 323 4 194 405
#> 4 Aden Murphy Potential Loy… 544 7 98 596
#> 5 Admiral Senger Potential Loy… 433 5 132 448
#> 6 Agness O'Keefe Champions 555 9 90 843
#> 7 Aileen Barton Champions 555 9 84 763
#> 8 Ailene Hermann Potential Loy… 355 8 281 699
#> 9 Aiyanna Bruen About To Sleep 321 4 246 157
#> 10 Akeelah Walsh Potential Loy… 445 7 160 779
#> # ℹ 985 more rows
#> # ℹ 6 more variables: recency_score <int>, frequency_score <int>,
#> # monetary_score <int>, first_name <chr>, last_name <chr>, email <chr>