README (original) (raw)

Tools for customer segmentation using RFM (recency, frequency and monetary) analysis.

# Install rfm from CRAN
install.packages("rfm")

# Or the development version from GitHub
# install.packages("pak")
pak::pak("rsquaredacademy/rfm")

RFM (recency, frequency, monetary) analysis is a behavior based technique used to segment customers by examining their transaction history such as:

It is based on the marketing axiom that 80% of your business comes from 20% of your customers. RFM analysis helps to identify customers who are more likely to respond to promotions by segmenting them into various categories.

# 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)

# rfm score
rfm_result
#> # A tibble: 995 x 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
#> # i 990 more rows
#> # i 6 more variables: frequency_score <int>, monetary_score <int>,
#> #   rfm_score <dbl>, first_name <chr>, last_name <chr>, email <chr>

# 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 x 12
#>   customer_id    segment         rfm_score transaction_count recency_days amount
#>   <chr>          <chr>               <dbl>             <int>        <dbl>  <int>
#> 1 Abbey O'Reilly Potential Loya~       343                 6          205    472
#> 2 Add Senger     New Customers         412                 3          140    340
#> 3 Aden Lesch     Loyal Customers       323                 4          194    405
#> 4 Aden Murphy    Potential Loya~       544                 7           98    596
#> 5 Admiral Senger Potential Loya~       433                 5          132    448
#> # i 990 more rows
#> # i 6 more variables: recency_score <int>, frequency_score <int>,
#> #   monetary_score <int>, first_name <chr>, last_name <chr>, email <chr>

rfm includes a shiny app for interactive RFM analysis. In the latest release, we have added project management features to allow users to save/clone their projects. Below is a quick demo of the shiny app:

If you encounter a bug, please file a minimal reproducible example using reprex on github. For questions and clarifications, use StackOverflow.