RFM AN
ALYSIS FOR CUSTOMER
SEGMENTATION USING MACHINE LEARNING: A
SURVEY OF A DECADE OF RESEARCH
Sushilkumar Chavhan
Assistant Professor, Department of Information Technology Yeshwantrao Chavan College of
Engineering, Nagpur, Maharashtra, (India).
R. C. Dharmik
Assistant Professor, Department of Information Technology Yeshwantrao Chavan College of
Engineering, Nagpur, Maharashtra, (India).
Sachin Jain
Assistant Professor, Department of Computer Science Oklahoma State University
Stillwater, (United States).
Ketan Kamble
Student, Department of Information Technology YCCE, Nagpur, Maharashtra, (India).
Reception: 07/11/2022 Acceptance: 22/11/2022 Publication: 29/12/2022
Suggested citation:
Chavhan, S., Dharmik, R. C., Jain, S., y Kamble, K. (2022). RFM analysis for customer segmentation using
machine learning: a survey of a decade of research. 3C TIC. Cuadernos de desarrollo aplicados a las TIC, 11(2),
166-173. https://doi.org/10.17993/3ctic.2022.112.166-173
https://doi.org/10.17993/3ctic.2022.112.166-173
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed. 41 Vol. 11 N.º 2 August - December 2022
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ABSTRACT
Customer segmentation is a method of categorizing corporate clients into groups based on shared
characteristics. In this study, we looked at the different customer segmentation methods and execute
RFM analysis by using various clustering algorithms. Based on RFM values (Recent, Frequency, and
Cost) of customers, the successful classification of company customers is divided into groups with
comparable behaviors. Customer retention is thought to be more significant than acquiring new
clients are analyzed on two different databases. Results show the significance of each method.
Comparison is helps for selection of better customer segmentation.
KEYWORDS
Clustering, Classification, RFM, Customer segmentation.
https://doi.org/10.17993/3ctic.2022.112.166-173
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed. 41 Vol. 11 N.º 2 August - December 2022
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1. INTRODUCTION
Customer Segmentation is way of organization of customers with respect to the various features. In recent
years there has been a huge boom in opposition between companies to stay in the field. The income of the
organization may be stepped forward through a patron segmentation model. According to the Pareto
principle (Srivastava, 2016), 20% of the clients make a contribution greater to the sales of the organization
than the relaxation Customer segmentation is the exercise of dividing an organization’s clients into
agencies that mirror similarity amongst clients in every group. The intention of segmenting clients is to
determine how to narrate to clients in every section that allows you to maximize the fee of every patron to
the business. Customer segmentation has the ability to permit entrepreneurs to cope with every patron
withinside the simplest way. Using the massive quantity of statistics to be had on clients (and ability
clients), a patron segmentation evaluation lets in entrepreneurs to identify discrete agencies of clients with
an excessive diploma of accuracy primarily based totally on demographic, behavioral and different
indicators.
Evaluation of RFM (Recency, Frequency, and Monetary) is a famend approach is worn for comparing the
clients primarily based totally on their shopping for behavior. Scoring method was developed to test
Recent, Frequency, and Finance ratings. Finally, ratings of all three variables are strengthened as RFM
ratings from different ranges (Haiying and Yu, 2010) which are compiled to anticipate recants trends for
studying existing and higher sponsor transactions history. Next step is defined as the remaining time the
consumer buys. The latest currency is the type of days the sponsor takes between purchases. The latest
small payment means that the sponsor visits the organization frequently in a timely manner. Similarly,
extra money means that the sponsor is less likely to go to the organization soon. Frequency is
described because the variety of transaction a patron makes in a selected period. The better the fee of
frequency the greater unswerving are the clients of the organization.
Cash is defined as the amount spent by the investor over a period of time in a favorable period. The
improvement in the amount of money spent by the large sales they provide to the organization. Each
sponsor is given 3 different ratings of the latest, frequency, and economic volatility. Score points are
used within a range from five to 1. The core quintile is given a five-point scale, while the others are
given 4, 3, 2 and 1.
In recent years, there has been a significant increase in the number of opposition groups among
companies in care within the arena. Customer retention is more important than purchasing the latest
customers. Customer segregation allows people’s messages to speak more to target audiences.
2. LITERATURE REVIEW
Segmentation is middle of the advertising and marketing approach due to the fact exclusive consumer
organizations mean the want for exclusive advertising and marketing mixes primarily based totally on
consumer conduct and its needs. Many authors give the segamentaion methods to increase the profit
and sustain the company position. (Jiang and Tuzhilin, 2009) proposed K-Classifiers Segmentation
algorithm which recognized that each client segmentation and consumer focused on are important to
enhance the marketing performances. K-Classifiers Works as optimizer who have two tasks. Above
method more resources to the ones clients who supply greater returns to the company. (He and Li,
2016) proposed a 3-dimensional approach to improving consumer health (CLV), customer pride and
customer behavior. The authors conclude about the customers and the requirements for a better
service. A segment used to meet customer expectations and suggest better service. (Sheshasaayee and
Logeshwari, 2017) used RFM Analysis which provides the usage of CRM (Customer Relationship
Management). Authors analyzed the customers by segmenting them which helps to increase company
profits. Further they enhanced the segmentation by using Fuzzy Clustering Method which classified
them into the appropriate scoring strategies based on their needs.
(Shah and Singh, 2012) provides the K-means algorithm and K-medoids algorithms for clustering.
The presented techniques do not always yield the best answer, but they do minimise the cluster error
criterion. They came to the conclusion that when the number of clusters grows, the new method takes
https://doi.org/10.17993/3ctic.2022.112.166-173
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed. 41 Vol. 11 N.º 2 August - December 2022
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less time to run than existing methods. (Sheshasaayee and Logeshwari, 2017) developed hybrid
method which combine RFM and LTV methods. Authors used K-means and Neural Network
algorithms for segmentation with two phase models. They suggested having better optimizer for
customer categorization. Using logistic regression, (Liu, Chu, Chan, and Yu, 2014) proposed
predicting customer attrition. Individual marketing methods can be used to identify customers with
similar churn value and to keep them. Benefit customer segmentation using various methodologies
allows customers to be classified based on their relationships, allowing marketers to focus their
marketing efforts on their strengths and target benefit categories accordingly.
3. TYPES OF CUSTOMER SEGMENTATION
Customer segmentation models come in a range of shapes and sizes, ranging from simple to complex,
and they can be used for a variety of purposes. Demographic, Recency, Frequency Monetary (RFM),
High-Value Customers, Customer Status, Behavioral, and Psychographic models are some of the most
common models.
3.1 DEMOGRAPHIC
It is a method of segmenting customers based on characteristics such as age, gender, ethnicity, income,
education, religion, and career (Lu, Lin, Lu, and Zhang, 2014).
3.2 RFM
It is a direct segmentation strategy whose main goal is to categories clients based on the time since
their previous purchase, the total number of purchases they’ve made (frequency), and the amount
they’ve spent (monetary) (Sheshasaayee and Logeshwari, 2017).
3.3 HVCS (HIGH-VALUE CUSTOMER)
It’s an extended RFM segmentation for any firm, focusing on what traits they have in common so you
can get more of them.
3.4 CUSTOMER STATUS
It is a mechanism which check the status of customer which categories as active and lapsed. The focus
of this method is to how the customer engaged by the company on the time period as a status.
3.5 BEHAVIORAL SEGMENTATION
It is a mechanism which check the status of customer which categories as active and lapsed. The focus
of this method is to how the customer engaged by the company on the time period as a status.
3.6 PSYCHOGRAPHIC SEGMENTATION
It is allows grouping the customers based on attitudes, beliefs, or even personality traits. For this we
require good data analysis method. Analysis done on all above attributes.
3.7 GEOGRAPHIC SEGMENTATION
It is allows grouping the customers based on geographical location i.e region, city, country etc. It is
used when target area is location wise improvement of services and increased the profit.
4. MAJOR CLUSTERING TECHNOLOGIES FOR CUSTOMER
SEGMENTATION
4.1 K –MEANS
https://doi.org/10.17993/3ctic.2022.112.166-173
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed. 41 Vol. 11 N.º 2 August - December 2022
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It is a popular unsupervised method that accepts parameters and k value as number of clusters as
inserting and separating data into clusters with high intra-cluster similarities. K-Means is a method
that repeats itself, adding the number of centroids before each multiplication. Depending on the inches
calculated for each multiplication, data points are allocated among distinct sets. Using min max
normalization, the RFM values are normalized (Lee and Memon, 2016).
4.2 FUZZY C-MEANS
It is a Method (?, ?) that allows a specific piece of data to appear to numerous clusters. It no longer
determines a clusters club records for a given information factor. Rather, the probabilities of a specific
information factor with similarities are determined. The advantage of this method over previously
discussed K-Means is that the final result obtained of a large and comparable database is most suitable
than a set of K-method rules, because in the KMeans method, as cluster formation of based on data
element. The normalization in this approach is done using min max normalization. Cluster RFM value
is based on cluster (Zahrotun, 2017) value.
4.3 REPETITIVE MEDIAN K-MEANS
It’s a novel approach to determining the initial centroids for the K-Means method. The traditional K–
Means algorithm’s range of iterations and computational time is reduced by choosing preliminary
centroids with it’s proposed distribution. RFM values will be combined and sorted into three vectors,
R’, F’, and M ’, respectively. The initial centroids are calculated using the median value of each
vector. The median values are derived k times iteratively from the R’, F’, and M’ values, depending on
the value of k. (number of segments).
5. RFM ANALYSIS USING ON ONLINE SHOPPING DATA
RFM: according to following definitions. The RFM method divides clients into segments. It
categorises clients based on their previous purchase transactions, taking into account criteria like as
Recency (R ): Last purchase date in specified session. Frequency (F):Purchase count in the specified
session. Monetary (M):Value of Purchase in the specified session Based on forms needs, we define a
season with different intervals for this model and we calculate RFM values for each customer.
Working with a set of customer activity data in an online retail store year-round from the University of
California Irwin (UCI) repository was used to evaluate system performance.
The following is a sample customer separation process. STEP 1: Sort the customer by recency.
STEP 2 AND 3: Sort the customer from most to least frequent customer and summarized the F, M
score. STEP 4: Rank customers by combining R,F, and M ranking
Fig1: Sample Data.
https://doi.org/10.17993/3ctic.2022.112.166-173
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed. 41 Vol. 11 N.º 2 August - December 2022
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