RFM Analysis — Customer Segmentation
One of the efficient customer segmentation of an organization is the segmentation of customers into similar behavioral groups based on their RFM (Recency, Frequency and Monetary) values[1].
Customer Segmentation
Customer segmentation is one of the efficient methods for managing various customers with different preferences. It is the process of dividing heterogeneous groups of customers into homogeneous groups based on common characteristics and attributes. Customer segmentation increases not only satisfaction but also the expected profit for a company. Various marketing strategies applied in customer segmentation could enhance the value of customers. By meeting customers’ needs, companies maintain long-term relationships with customers. Moreover, companies can improve revenues by acquiring and retaining valuable customers at low cost [2].
What is RFM Model?
Recency Frequency Monetary (RFM) model is a popular technique for customer segmentation based on the analysis of purchase behaviors. The model analyzes purchase records and represents each transaction by three dimensions. In RFM, all customers are scored individually and ranked according to each dimension. As a result, customer groups with similar purchase patterns are identified. Each group is represented with a 3-digit RFM combination that summarizes a purchase pattern typical within members’ transaction history [3].
What are Recency, Frequency and Monetary Values?
The calculated RFM values are summarized to clarify customer behavior patterns using the following RFM variables:
- Recency (R): The time since the customer’s last purchase
- Frequency (F): The total number of purchases during a specific period. (How often a customer makes a purchase)
- Monetary (M): Monetary value spent during one specific period (How much money a customer spends on purchases) [4]
Creating Segments by Using RFM Scores
The RFM model is the most frequently adopted segmentation technique that comprises three measures (recency, frequency and monetary), which are combined into a three-digit RFM cell code, covering five equal quintiles (20 % group) [5].
Basically customers will be scaled from 1–5 will result in, at the most, 125 different RFM scores (5x5x5), ranging from 111(lowest) to 555(highest). Each RFM cell is different from each other and indicates the the customer habits. Below in the given figure represents the customer segments. For segment creation R and F values are used. Vertical axis represents F and horizontal axis represents R value [6].
Customer Segments:
Champions — Bought recently, spend the most and buy often!
Loyal Customers — Frequently spend good money buying products. Responsive to promotions.
Potential Loyalist — Recent customers, however spent a good amount and bought more than once.
New Customers - Bought most recently, but not often.
Promising - Recent shoppers, but haven’t spent much.
Customers Needing Attention — Above-average recency, frequency and monetary values. They may not have bought very recently though.
About To Sleep — Below average recency, frequency, and monetary values. Will lose them if not reactivated.
At Risk — They spent big money and purchased often. But the last purchase was a long time ago. Need to bring them back!
Can’t Lose Them — Often made the biggest purchases but they haven’t returned for a long time.
Hibernating — The last purchase was long ago. Low spenders with a low number of orders.
Lost — Lowest recency, frequency, and monetary scores [7].
How to do RFM Analysis in Python?
Data Set : https://archive.ics.uci.edu/ml/datasets/Online+Retail+II
Data Set Information:
This Online Retail II data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011. The company mainly sells unique all-occasion gift-ware. Many customers of the company are wholesalers.
- Data Understanding
2. Data Preparation
3. Calculating RFM Metrics
4. Calculating RFM Scores
5. Naming & Analysing RFM Segments
At this stage, segments are obtained. From this point on, the important thing is to be able to make correct comments and applications according to these segments. Segments can be selected one by one or together to look at statistical values such as mean and median.
Finally the obtained results can be converted into excel files and can be shared with others.
Thank you for reading!
REFERENCES:
[1] Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2018). RFM ranking–An effective approach to customer segmentation. Journal of King Saud University-Computer and Information Sciences.
[2] Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning.
[3] KABASAKAL, İ. (2020). Customer segmentation based on recency frequency monetary model: A case study in E-retailing. Bilişim Teknolojileri Dergisi, 13(1), 47–56.
[4] Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model.
[5] Wei, J. T., Lin, S. Y., & Wu, H. H. (2010). A review of the application of RFM model. African Journal of Business Management, 4(19), 4199–4206.
