---
title: "RFM - Introduction"
author: "Aravind Hebbali"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{RFM - Introduction} 
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

```{r, echo=FALSE, message=FALSE}
library(rfm)
library(dplyr)
library(rlang)
library(scales)
library(knitr)
library(kableExtra)
library(magrittr)
library(ggplot2)
library(DT)
library(grDevices)
library(RColorBrewer)
options(knitr.table.format = "html")
```

This article provides a brief introduction to RFM analysis and customer segmentation. If you are looking for a detailed guide, check out our free [online course](https://rsquared-academy.thinkific.com/courses/customer-segmentation-using-rfm-analysis) or YouTube [tutorial](https://www.youtube.com/watch?v=275X7yaSsoQ) or blog [post](https://blog.rsquaredacademy.com/customer-segmentation-using-rfm-analysis/).

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

- how recently a customer has purchased (recency)
- how often they purchase (frequency)
- how much the customer spends (monetary)

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

## Data

To calculate the RFM score for each customer we need data for a particular time frame and should include the following:

- a unique customer id 
- date of transaction/order
- transaction/order amount

Data can be at customer level or transaction level i.e. each row in the data may represent a single transaction of a customer or summary of all transactions of a customer. `rfm` package includes two sample data sets:

- `rfm_data_orders`

```{r rfm_data_orders}
head(rfm_data_orders)
```

- `rfm_data_customer`

```{r rfm_data_customer}
head(rfm_data_customer)
```

You can take a look at them to understand the difference between customer and transaction level data. **Remember, the data sets are different and the final results will not match.**

## RFM Score

So how is the RFM score computed for each customer? The below steps explain the process:

- A recency score is assigned to each customer based on date of most recent purchase. The score is generated by binning the recency values into a number of categories (default is 5). For example, if you use four categories, the customers with the most recent purchase dates receive a recency ranking of 4, and those with purchase dates in the distant past receive a recency ranking of 1.

- A frequency ranking is assigned in a similar way. Customers with high purchase frequency are assigned a higher score (4 or 5) and those with lowest frequency are assigned a score 1.

- Monetary score is assigned on the basis of the total revenue generated by the customer in the period under consideration for the analysis. Customers with highest revenue/order amount are assigned a higher score while those with lowest revenue are assigned a score of 1.

- A fourth score, RFM score is generated which is simply the three individual scores concatenated into a single value.

The customers with the highest RFM scores are most likely to respond to an offer. Now that we have understood how the RFM score is computed, it is time to put it into practice. Use `rfm_table_order()` to generate the score for each customer from the sample data set `rfm_data_orders`.

```{r rfm_table_order, eval=FALSE}
analysis_date <- as.Date("2006-12-31")
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date)
rfm_result
```

```{r rfm_table_order2, eval=TRUE, echo=FALSE}
analysis_date <- as.Date("2006-12-31")
rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date)
rfm_result$rfm[1:10, ] %>%
  select(customer_id, recency_days, transaction_count, amount, rfm_score, recency_score, frequency_score, monetary_score, first_name, last_name, email) %>% 
  kable() %>%
  kable_styling()
```

## Segments

Let us segment our customers based on the individual recency, frequency and monetary scores. Keep in mind that creating segments based on RFM score is a very subjective endeavour. Having good business and domain knowledge will allow the user to generate effective segments. There is no one size fits all solution here.

```{r segments, echo=FALSE}
segment <- c("Champions", "Potential Loyalist", "Loyal Customers", 
             "Promising", "New Customers", "Can't Lose Them", 
             "At Risk", "Need Attention", "About To Sleep", "Lost")
description <- c(
  "Bought recently, buy often and spend the most",
  "Recent customers, spent good amount, bought more than once",
  "Spend good money. Responsive to promotions",
  "Recent shoppers, but haven't spent much",
  "Bought more recently, but not often",
  "Made big purchases and often, but long time ago",
  "Spent big money, purchased often but long time ago",
  "Above average recency, frequency & monetary values",
  "Below average recency, frequency & monetary values",
  "Bought a long time ago, average amount spent"
)
recency <-   c("5", "3 - 5", "2 - 4", "3 - 4", "4 - 5", "1 - 2", "1 - 2", "1 - 3", "2 - 3", "1 - 1")
frequency <- c("5", "3 - 5", "2 - 4", "1 - 3", "1 - 3", "3 - 4", "2 - 5", "3 - 5", "1 - 3", "1 - 5")
monetary <-  c("5", "2 - 5", "2 - 4", "3 - 5", "1 - 5", "4 - 5", "4 - 5", "3 - 5", "1 - 4", "1 - 5")
segments <- data.frame(
  Segment = segment, Description = description,
  R = recency, `F` = frequency, M = monetary
)

segments %>%
  kable() %>%
  kable_styling(full_width = FALSE, font_size = 12)
```

## Segmented Customer Data

We can use the segmented data to identify 

- best customers
- loyal customers
- at risk customers
- and lost customers

Once we have segmented a customer, we can take appropriate action to increase his/her lifetime value. 

```{r criteria, echo=FALSE}
segment_names <- c("Champions", "Potential Loyalist", "Loyal Customers", 
                   "Promising", "New Customers", "Can't Lose Them", 
                   "At Risk", "Need Attention", "About To Sleep", "Lost")

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)

segments <- rfm_segment(rfm_result, segment_names, recency_lower, recency_upper,
frequency_lower, frequency_upper, monetary_lower, monetary_upper)

segments %>%
  head(10) %>%
  kable() %>%
  kable_styling()
```

Let us quickly summarize the segments to get an overview of the number of customers, orders and average order value in each of them.

#### Segment Summary

```{r segment_summary}
rfm_segment_summary(segments)
```

`rfm` package offers visualization tools to validate the segments generated from the RFM score. Below are a few of them:

#### Segmentation Plot

```{r segment_plot, fig.align='center', fig.width=7, fig.height=7}
segments %>%
  rfm_segment_summary() %>%
  rfm_plot_segment()
```

#### Segment Summary Plot

```{r segment_sumamry_plot, fig.align='center', fig.width=7, fig.height=7}
segments %>%
  rfm_segment_summary() %>%
  rfm_plot_segment_summary()
```

#### Revenue Distribution

```{r revenue_dist_plot, fig.align='center', fig.width=7, fig.height=7}
segments %>%
  rfm_segment_summary() %>%
  rfm_plot_revenue_dist()
```