Recency, frequency, monetary and RFM score.
Usage
rfm_table_order(
data = NULL,
customer_id = NULL,
order_date = NULL,
revenue = NULL,
analysis_date = NULL,
recency_bins = 5,
frequency_bins = 5,
monetary_bins = 5,
...
)Arguments
- data
A
data.frameortibble.- customer_id
Unique id of the customer.
- order_date
Date of the transaction.
- revenue
Revenue from the customer.
- analysis_date
Date of analysis.
- recency_bins
Number of bins for recency or custom threshold.
- frequency_bins
Number of bins for frequency or custom threshold.
- monetary_bins
Number of bins for monetary or custom threshold.
- ...
Other arguments.
Value
rfm_table_order returns a list with the following:
- rfm
RFM table.
- analysis_date
Date of analysis.
- frequency_bins
Number of bins used for frequency score.
- recency_bins
Number of bins used for recency score.
- monetary_bins
Number of bins used for monetary score.
- threshold
thresholds used for generating RFM scores.
Examples
analysis_date <- as.Date("2006-12-31")
rfm_table_order(
rfm_data_orders, customer_id, order_date, revenue,
analysis_date
)
#> # A tibble: 995 × 11
#> customer_id recency_days transaction_count amount recency_score
#> <chr> <dbl> <int> <int> <int>
#> 1 Abbey O'Reilly 205 6 472 3
#> 2 Add Senger 140 3 340 4
#> 3 Aden Lesch 194 4 405 3
#> 4 Aden Murphy 98 7 596 5
#> 5 Admiral Senger 132 5 448 4
#> 6 Agness O'Keefe 90 9 843 5
#> 7 Aileen Barton 84 9 763 5
#> 8 Ailene Hermann 281 8 699 3
#> 9 Aiyanna Bruen 246 4 157 3
#> 10 Akeelah Walsh 160 7 779 4
#> # ℹ 985 more rows
#> # ℹ 6 more variables: frequency_score <int>, monetary_score <int>,
#> # rfm_score <dbl>, first_name <chr>, last_name <chr>, email <chr>
# access rfm table
result <- rfm_table_order(
rfm_data_orders, customer_id, order_date,
revenue, analysis_date
)
result$rfm
#> # A tibble: 995 × 11
#> customer_id recency_days transaction_count amount recency_score
#> <chr> <dbl> <int> <int> <int>
#> 1 Abbey O'Reilly 205 6 472 3
#> 2 Add Senger 140 3 340 4
#> 3 Aden Lesch 194 4 405 3
#> 4 Aden Murphy 98 7 596 5
#> 5 Admiral Senger 132 5 448 4
#> 6 Agness O'Keefe 90 9 843 5
#> 7 Aileen Barton 84 9 763 5
#> 8 Ailene Hermann 281 8 699 3
#> 9 Aiyanna Bruen 246 4 157 3
#> 10 Akeelah Walsh 160 7 779 4
#> # ℹ 985 more rows
#> # ℹ 6 more variables: frequency_score <int>, monetary_score <int>,
#> # rfm_score <dbl>, first_name <chr>, last_name <chr>, email <chr>
# using custom threshold
rfm_table_order(rfm_data_orders, customer_id, order_date, revenue,
analysis_date,
recency_bins = c(115, 181, 297, 482), frequency_bins = c(4, 5, 6, 8),
monetary_bins = c(256, 382, 506, 666)
)
#> # A tibble: 995 × 11
#> customer_id recency_days transaction_count amount recency_score
#> <chr> <dbl> <int> <int> <int>
#> 1 Abbey O'Reilly 205 6 472 3
#> 2 Add Senger 140 3 340 4
#> 3 Aden Lesch 194 4 405 3
#> 4 Aden Murphy 98 7 596 5
#> 5 Admiral Senger 132 5 448 4
#> 6 Agness O'Keefe 90 9 843 5
#> 7 Aileen Barton 84 9 763 5
#> 8 Ailene Hermann 281 8 699 3
#> 9 Aiyanna Bruen 246 4 157 3
#> 10 Akeelah Walsh 160 7 779 4
#> # ℹ 985 more rows
#> # ℹ 6 more variables: frequency_score <int>, monetary_score <int>,
#> # rfm_score <dbl>, first_name <chr>, last_name <chr>, email <chr>