# RFM - Transaction Level Data

#### Aravind Hebbali

#### 2019-01-14

## Introduction

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 transaction data which should include the following:

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

`rfm` includes a sample data set `rfm_data_orders` which includes the above details:

    rfm_data_orders

    ## # A tibble: 4,906 x 3
    ##    customer_id         order_date revenue
    ##    <chr>               <date>       <dbl>
    ##  1 Mr. Brion Stark Sr. 2004-12-20      32
    ##  2 Ethyl Botsford      2005-05-02      36
    ##  3 Hosteen Jacobi      2004-03-06     116
    ##  4 Mr. Edw Frami       2006-03-15      99
    ##  5 Josef Lemke         2006-08-14      76
    ##  6 Julisa Halvorson    2005-05-28      56
    ##  7 Judyth Lueilwitz    2005-03-09     108
    ##  8 Mr. Mekhi Goyette   2005-09-23     183
    ##  9 Hansford Moen PhD   2005-09-07      30
    ## 10 Fount Flatley       2006-04-12      13
    ## # ... with 4,896 more rows

## 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`.

`rfm_table_order()` takes 8 inputs:

- `data`: a data set with
  - unique customer id
  - date of transaction
  - and amount
- `customer_id`: name of the customer id column
- `order_date`: name of the transaction date column
- `revenue`: name of the transaction amount column
- `analysis_date`: date of analysis
- `recency_bins`: number of rankings for recency score (default is 5)
- `frequency_bins`: number of rankings for frequency score (default is 5)
- `monetary_bins`: number of rankings for monetary score (default is 5)

## RFM Table

    analysis_date <- lubridate::as_date("2006-12-31", tz = "UTC")
    rfm_result <- rfm_table_order(rfm_data_orders, customer_id, order_date, revenue, analysis_date)
    rfm_result

customer_id | date_most_recent | recency_days | transaction_count | amount | recency_score | frequency_score | monetary_score | rfm_score
--- | --- | --- | --- | --- | --- | --- | --- | ---
Abbey O’Reilly DVM | 2006-06-09 | 205 | 6 | 472 | 3 | 4 | 3 | 343
Add Senger | 2006-08-13 | 140 | 3 | 340 | 4 | 1 | 2 | 412
Aden Lesch Sr. | 2006-06-20 | 194 | 4 | 405 | 3 | 2 | 3 | 323
Admiral Senger | 2006-08-21 | 132 | 5 | 448 | 4 | 3 | 3 | 433
Agness O’Keefe | 2006-10-02 | 90 | 9 | 843 | 5 | 5 | 5 | 555
Aileen Barton | 2006-10-08 | 84 | 9 | 763 | 5 | 5 | 5 | 555
Ailene Hermann | 2006-03-25 | 281 | 8 | 699 | 3 | 5 | 5 | 355
Aiyanna Bruen PhD | 2006-04-29 | 246 | 4 | 157 | 3 | 2 | 1 | 321
Ala Schmidt DDS | 2006-01-16 | 349 | 3 | 363 | 2 | 1 | 2 | 212
Alannah Borer | 2005-04-21 | 619 | 4 | 196 | 1 | 2 | 1 | 121

`rfm_table_order()` will return the following columns as seen in the above table:

- `customer_id`: unique customer id
- `date_most_recent`: date of most recent visit
- `recency_days`: days since the most recent visit
- `transaction_count`: number of transactions of the customer
- `amount`: total revenue generated by the customer
- `recency_score`: recency score of the customer
- `frequency_score`: frequency score of the customer
- `monetary_score`: monetary score of the customer
- `rfm_score`: RFM score of the customer

## Heat Map

The heat map shows the average monetary value for different categories of recency and frequency scores. Higher scores of frequency and recency are characterized by higher average monetary value as indicated by the darker areas in the heatmap.

    rfm_heatmap(rfm_result)

## Bar Chart

Use `rfm_bar_chart()` to generate the distribution of monetary scores for the different combinations of frequency and recency scores.

    rfm_bar_chart(rfm_result)

## Histogram

Use `rfm_histograms()` to examine the relative distribution of

- monetary value (total revenue generated by each customer)
- recency days (days since the most recent visit for each customer)
- frequency (transaction count for each customer)

    rfm_histograms(rfm_result)

## Customers by Orders

Visualize the distribution of customers across orders.

    rfm_order_dist(rfm_result)

## Scatter Plots

The best customers are those who:

- bought most recently
- most often
- and spend the most

Now let us examine the relationship between the above.

#### Recency vs Monetary Value

Customers who visited more recently generated more revenue compared to those who visited in the distant past. The customers who visited in the recent past are more likely to return compared to those who visited long time ago as most of those would be lost customers. As such, higher revenue would be associated with most recent visits.

    rfm_rm_plot(rfm_result)

#### Frequency vs Monetary Value

As the frequency of visits increases, the revenue generated also increases. Customers who visit more frquently are your champion customers, loyal customers or potential loyalists and they drive higher revenue.

    rfm_fm_plot(rfm_result)

#### Recency vs Frequency

Customers with low frequency visited in the distant past while those with high frequency have visited in the recent past. Again, the customers who visited in the recent past are more likely to return compared to those who visited long time ago. As such, higher frequency would be associated with the most recent visits.

    rfm_rf_plot(rfm_result)

## Segments

Let us classify our customers based on the individual recency, frequency and monetary scores.

Segment | Description | R | F | M
--- | --- | --- | --- | ---
Champions | Bought recently, buy often and spend the most | 4 - 5 | 4 - 5 | 4 - 5
Loyal Customers | Spend good money. Responsive to promotions | 2 - 5 | 3 - 5 | 3 - 5
Potential Loyalist | Recent customers, spent good amount, bought more than once | 3 - 5 | 1 - 3 | 1 - 3
New Customers | Bought more recently, but not often | 4 - 5 | <= 1 | <= 1
Promising | Recent shoppers, but haven’t spent much | 3 - 4 | <= 1 | <= 1
Need Attention | Above average recency, frequency & monetary values | 2 - 3 | 2 - 3 | 2 - 3
About To Sleep | Below average recency, frequency & monetary values | 2 - 3 | <= 2 | <= 2
At Risk | Spent big money, purchased often but long time ago | <= 2 | 2 - 5 | 2 - 5
Can’t Lose Them | Made big purchases and often, but long time ago | <= 1 | 4 - 5 | 4 - 5
Hibernating | Low spenders, low frequency, purchased long time ago | 1 - 2 | 1 - 2 | 1 - 2
Lost | Lowest recency, frequency & monetary scores | <= 2 | <= 2 | <= 2

## Segmented Customer Data

We can use the segmented data to identify

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

Once we have classified a customer into a particular segment, we can take appropriate action to increase his/her lifetime value.

## Segment Size

Now that we have defined and segmented our customers, let us examine the distribution of customers across the segments. Ideally, we should have very few or no customer in segments such as `At Risk` or `Needs Attention`.

    rfm_segments %>%
      count(segment) %>%
      arrange(desc(n)) %>%
      rename(Segment = segment, Count = n)

    ## # A tibble: 8 x 2
    ##   Segment            Count
    ##   <chr>              <int>
    ## 1 Loyal Customers      278
    ## 2 Potential Loyalist   229
    ## 3 Champions            158
    ## 4 Hibernating          111
    ## 5 At Risk               86
    ## 6 About To Sleep        50
    ## 7 Others                48
    ## 8 Needs Attention       35

## Segments

We can also examine the median recency, frequency and monetary value across segments to ensure that the logic used for customer classification is sound and practical.

### Median Recency

    data <-
        rfm_segments %>%
        group_by(segment) %>%
        select(segment, recency_days) %>%
        summarize(median(recency_days)) %>%
        rename(segment = segment, avg_recency = `median(recency_days)`) %>%
        arrange(avg_recency)

      n_fill <- nrow(data)

      ggplot(data, aes(segment, avg_recency)) +
        geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
        xlab("Segment") + ylab("Median Recency") +
        ggtitle("Median Recency by Segment") +
        coord_flip() +
        theme(
          plot.title = element_text(hjust = 0.5)
        )

### Median Frequency

    data <-
        rfm_segments %>%
        group_by(segment) %>%
        select(segment, transaction_count) %>%
        summarize(median(transaction_count)) %>%
        rename(segment = segment, avg_frequency = `median(transaction_count)`) %>%
        arrange(avg_frequency)

    n_fill <- nrow(data)

    ggplot(data, aes(segment, avg_frequency)) +
        geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
        xlab("Segment") + ylab("Median Frequency") +
        ggtitle("Median Frequency by Segment") +
        coord_flip() +
        theme(
          plot.title = element_text(hjust = 0.5)
        )

### Median Monetary Value

    data <-
        rfm_segments %>%
        group_by(segment) %>%
        select(segment, amount) %>%
        summarize(median(amount)) %>%
        rename(segment = segment, avg_monetary = `median(amount)`) %>%
        arrange(avg_monetary)

    n_fill <- nrow(data)

    ggplot(data, aes(segment, avg_monetary)) +
        geom_bar(stat = "identity", fill = brewer.pal(n = n_fill, name = "Set1")) +
        xlab("Segment") + ylab("Median Monetary Value") +
        ggtitle("Median Monetary Value by Segment") +
        coord_flip() +
        theme(
          plot.title = element_text(hjust = 0.5)
        )

## References

- Data Mining: Concepts and Techniques , Second Edition , Jiawei Han University of Illinois at Urbana-Champaign Micheline Kamber.
- https://joaocorreia.io/blog/rfm-analysis-increase-sales-by-segmenting-your-customers.html
- http://www.sciencedirect.com/science/article/pii/S1877050910003868