How to visualise RFM data using treemaps

Learn how to assign simple labels to your RFM data and visualise them using treemaps to help make the data easier for non-technical people to understand.

How to visualise RFM data using treemaps
A tree. By Johann Siemens, Unsplash.
14 minutes to read

Recent papers on the Recency, Frequency, Monetary or RFM model, such as the one by Inanc Kabasakal in 2020, have started to adopt text-based labels to help people understand the customer segments. The benefit of labels is that non-technical people don’t need to know what RFM is or what the 125 possible scores relate to.

This is quite handy if you work with RFM scores a lot, because they’re not immediately intuitive to some stakeholders. Labels for RFM aren’t new. They’ve been around for years and I’m sure most RFM advocates have applied them in some way.

Kabasakal attributes the new ones to an analytics company called Putler. They proposed 11 segment names</a>which set out to define the sorts of customers that lie within. Inclusion in each segment depends on a combination of the R score range and the F and M score range.

Customer segment R score range F and M score range
Champions 4-5 4-5
Loyal customers 2-5 3-5
Potential loyalist 3-5 1-3
Recent customers 4-5 0-1
Promising 3-4 0-1
Needing attention 2-3 2-3
About to sleep 2-3 0-2
At risk 0-2 4-5
Can't lose them 0-1 4-5
Hibernating 1-2 1-2
Lost 0-2 0-2

They look quite useful, but oddly, if you try to implement them, you’ll encounter some issues, because the bounds overlap and the F and M score is additive. I definitely see the value in the labels, but I’m not entirely convinced this is the optimal solution.

For example, if Champions are those customers where R is 4-5 and F and M are 4-5, that would cover everyone who is 444 to 555. However, Loyal customers are defined as those where R is 2-5 and F and M are 3-5, so that would include everyone who is 233 to 555. As a result, a customer who is 444 or 555 could appear in either group. Similarly, does a 111 go in Lost or Hibernating, as the defines imply both? Perhaps I’m missing something…

Other approaches to naming RFM segments

Other naming conventions have also been proposed. For example, a recent PhD thesis on RFM by Umit Uysal (2019) split customers up as Stars, Loyal, Potential loyal, Hold and improve, and Risky. Unlike the Putler segments, Uysal’s don’t overlap or leave any gaps, so are more practical to implement. I’ve extracted the bounds from the paper to show this.

Customer segment RFM scores
Star 542-555
Loyal 541
Potential loyal 311-353
Hold and improve 211-255
Risky 111-155

You can, of course, set whatever bounds you like and come up with your own labels, but in this project I thought I’d show how you can create them and how you can use them to get an overview of where your customers lie, something which isn’t always that easy with RFM data.

Load your packages

For this project we can do nearly everything in Pandas, but to visualise our data we’ll need Matplotlib and a great treemap visualisation package called Squarify, which you can install by typing pip3 install squarify into your terminal.

import pandas as pd
import matplotlib.pyplot as plt
import squarify

Import your RFM scores

I’m going to assume you’ve already got your RFM scores. If you don’t know how to calculate Recency, Frequency, and Monetary metrics or you haven’t already converted them into the 125 quintile bins from 111 to 555, please check out my previous articles on quantile-based discretization and k-means clustering and calculating raw RFM data which explain how it’s done. Once you’ve done that, load up your data using Pandas.

df = pd.read_csv('rfm_scores.csv')
customer_id recency_date recency frequency monetary r m f rfm rfm_score
68909 320191 2019-05-17 10:39:03 535 1 31.29 2 2 2 222 6
104010 379219 2020-03-09 09:38:58 238 1 6.85 3 1 3 331 7
82870 341707 2020-06-10 14:24:34 145 2 167.62 4 5 5 455 14
26638 222614 2018-03-06 10:07:10 972 1 4.99 1 1 1 111 3
46884 251148 2018-09-05 13:28:23 789 1 15.00 1 1 1 111 3

Label the RFM segments

To label the segments we can create a simple function which uses if elif statements to assign the appropriate label to the segment depending on where it sits within the bounds I extracted from the Uysal paper.

def label_rfm_segments(rfm):

    if (rfm >= 111) & (rfm <= 155):
        return 'Risky'

    elif (rfm >= 211) & (rfm <= 255):
        return 'Hold and improve'    

    elif (rfm >= 311) & (rfm <= 353):
        return 'Potential loyal'       

    elif ((rfm >= 354) & (rfm <= 454)) or ((rfm >= 511) & (rfm <= 535)) or (rfm == 541):
        return 'Loyal'      

    elif (rfm == 455) or (rfm >= 542) & (rfm <= 555):
        return 'Star'      

        return 'Other'

Next, we can use a lambda() function to run our label_rfm_segments() function on our dataframe and pass in the RFM score and return the label and assign it to a new column called rfm_segment_name.

df['rfm_segment_name'] = df.apply(lambda x: label_rfm_segments(x.rfm), axis=1)
customer_id recency_date recency frequency monetary r m f rfm rfm_score rfm_segment_name
0 166958 2019-12-18 06:01:18 320 5 188.92 3 5 5 355 13 Loyal
1 191708 2020-04-11 07:49:24 205 15 683.79 3 5 5 355 13 Loyal
2 199961 2017-04-07 08:18:45 1305 1 16.99 1 2 1 112 4 Risky
3 199962 2017-04-07 08:20:00 1305 1 11.99 1 1 1 111 3 Risky
4 199963 2017-04-07 08:21:34 1305 1 14.49 1 1 1 111 3 Risky

Analyse the data

To examine what we have in each of our named segments we’ll use the agg() function and calculate some summary statistics. First we’ll check that the customers have been assigned the correct segment label by counting the number with each label and looking at the minimum and maximum RFM scores in each cluster.

The splits here will, of course, depend on the binning or clustering method you used to create your scores. Quantile-based discretization will give you more even numbers than K-means clustering, but the latter is often more useful.

    customers=('customer_id', 'count'),
    min_rfm=('rfm', 'min'),
    max_rfm=('rfm', 'max'),
rfm_segment_name customers min_rfm max_rfm
3 Risky 36070 111 155
0 Hold and improve 36057 211 255
2 Potential loyal 29877 321 353
1 Loyal 42888 354 541
4 Star 35566 455 555

The other really useful thing, and the number one thing that sales staff and marketers will ask for, are the details on the underlying data and how it impacts the assignation of the segment label. For example, how much do you need to spend to become a “Star”?

    min_recency=('recency', 'min'),
    max_recency=('recency', 'max'),
    min_frequency=('frequency', 'min'),
    max_frequency=('frequency', 'max'),
    min_monetary=('monetary', 'min'),
    max_monetary=('monetary', 'max'),
rfm_segment_name min_recency max_recency min_frequency max_frequency min_monetary max_monetary
1 Loyal 13 442 1 46 0.0 9719.65
4 Star 13 195 1 392 0.0 1036590.86
2 Potential loyal 196 442 1 5 0.0 5389.95
0 Hold and improve 443 782 1 38 0.0 11234.70
3 Risky 783 1305 1 147 0.0 10247.25

This shows that our “Hold and improve” segment contains customers who haven’t been seen for between 443 and 782 days, so are likely to be lapsed, while those in the “Risky” pot, haven’t been seen for 783 to 1305 days, so are probably long churned. I think this shows the benefit of choosing your own labels over using standardised ones. They don’t work on every data set, especially ones with a long tail of lapsed customers.

Visualising the segments using a treemap

Now we’ve assigned Uysal’s RFM segment labels to the customers in our data set we’ll create a treemap to visualise the data. First, we’ll create a new dataframe called df_treemap that includes the count of the total customers within each RFM segment, then we’ll use Matplotlib and Squarify to create a treemap visualisation.

df_treemap = df.groupby('rfm_segment_name').agg(
    customers=('customer_id', 'count')
rfm_segment_name customers
0 Hold and improve 36057
1 Loyal 42888
2 Potential loyal 29877
3 Risky 36070
4 Star 35566
fig, ax = plt.subplots(1, figsize = (10,10))

              color=['tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray']


The treemap gives us a really clear view of the named RFM segments and the relative volumes of customers present in each one. For this particular data set, where the tail is long and there are likely to be loads of truly lapsed customers in the “Risky” and “Hold and improve” segments, it could be beneficial to use an alternative binning approach so that the lapsed customers are dumped into a “churned” segment and can be excluded from costly marketing activity.

Further reading

  • Kabaskal, İ., 2020. Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study in E-Retailing. International Journal of InformaticsTechnologies, 13(1).

  • Putler Analytics – RFM analysis for successful customer segmentation,, 26.04.2019.

  • Uysal, Ü.C., 2019. RFM-based Customer Analytics in Public Procurement Sector (Doctoral dissertation, Ankara Yıldırım Beyazıt Üniversitesi Sosyal Bilimler Enstitüsü).

Matt Clarke, Saturday, March 06, 2021

Matt Clarke Matt is an Ecommerce and Marketing Director who uses data science to help in his work. Matt has a Master's degree in Internet Retailing (plus two other Master's degrees in different fields) and specialises in the technical side of ecommerce and marketing.