RFM - Transaction Level Data

Aravind Hebbali

2018-04-09

Introduction

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 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:

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    32.0
##  2 Ethyl Botsford      2005-05-02    36.0
##  3 Hosteen Jacobi      2004-03-06   116  
##  4 Mr. Edw Frami       2006-03-15    99.0
##  5 Josef Lemke         2006-08-14    76.0
##  6 Julisa Halvorson    2005-05-28    56.0
##  7 Judyth Lueilwitz    2005-03-09   108  
##  8 Mr. Mekhi Goyette   2005-09-23   183  
##  9 Hansford Moen PhD   2005-09-07    30.0
## 10 Fount Flatley       2006-04-12    13.0
## # ... with 4,896 more rows

RFM Score

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

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:

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:

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

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:

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.

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.

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.

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

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

Median Frequency

Median Monetary Value

References