final project report interdisciplinary research method course topic research the churn rate of each region in the retail sector by using rfm model

FINAL PROJECT REPORT

INTERDISCIPLINARY RESEARCH METHOD COURSE TOPIC: RESEARCH THE CHURN RATE OF EACH REGION IN THE RETAIL SECTOR BY USING RFM

MODEL

Lecturer:

1 Ho Trung Thanh, Ph.D 2 Nguyen Van Ho, MA

Group 8:

1 Vũ Hồng Thanh Trúc

2 Trần Ngọc Bích Trân 3 Nguyễn Huyền Trang 4 Nguyễn Phương Oanh 5 Phùng Nguyễn Đăng Khoa

Members of Group 8

4 Nguyên Phương Oanh K214110846

; | Phùng Nguyễn Đăng K214111323

Acknowledgements

For the successful completion of the course, our group would like to thank the instructor - Ph.D Ho Trung Thanh, Faculty of Information Systems, University of Economics and Law for sharing valuable knowledge and guiding us carefully There are suggestions made by the lecturer during the process of implementing the thesis so that our group can complete the thesis in the best way

After studying the topic and finishing the course, our group learned and accumulated knowledge and experience from the previous lecturers to improve and develop ourselves Besides, this is also an opportunity to help us realize what we need to improve to prepare for a long journey ahead

Due to our limited knowledge and lack of practical experience, the content of the research paper is difficult to avoid shortcomings We look forward to receiving more advice and istruction from lecturers

Thanks and Best regards!

Commitment

Our group has read and understood the violations of academic honesty Our group hereby declares that the thesis "Research the churn rate of each region in the retail sector by using RFM model " is our group's own research work which has been researched, read and translated by our group, collected and implemented under the guidance of Dr Ho Trung Thanh

Except for the reference results, the citations from other studies have been cited, this study of our group has never been published in any previous work.

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Chapter 5Š Solution G000 12 1112211 110121112011 1111111101111 1k1 H 21151 kh kh cay 29 3.1 Why do customers chuTn? : c 122111212211 111 1115151211101 1 5111551115811 key 29 5.2 EEữective solution to reduce customer chuT1 - sóc: ca c2 32 srrvskesreesres 29 5.3 What are the various channels you can use to regain custormer$? - 31 Conclusion and Euture WOFFES Đ LH ng TH ng HH HH HH 33

List of Tables

Table 2.1.2 Score description according to RFM Table 2.1.2 Score description according to RFM Table 3.1 Field Description

Table 3.5.1 Customer segmentation

List of Figures

Figure 3.1 The proposed model

Figure 3.2 Sales Module

Figure 3.3.2.Descriptive data analysis Figure 3.3.3 Numerical Histogram

Figure 3.3.4 Correlation between Order Quantity and Total Amount Figure 3.4.1 RFM values in Excel

Figure 3.4.2 Calculating of RFM

Figure 3.5.1 The RFM score of each customer Figure 3.5.2 The customer segmentation matrix Figure 4.1.1 The distribution of Recency Figure 4.1.2 The distribution of Frequency Figure 4.1.3 The distribution of customer segments Figure 4.2 Customers churn rate by region

List of Acronyms

GANTT CHART

ABSTRACT

Customer segmentation is necessary because you can't treat them the same, using the same content, the same channels, and the same priorities; therefore, RFM was invented as a tool to analyze customer behavior RFM is part of Marketing Analysis and has been used to analyze customer value In recent years, many modern tools such as K- means clustering, EM clustermg and Fuzzy C-means clustering have been developed, but RFM is still used because of its simplicity

The topic “Research the churn rate of each region in the retail sector by using REM model” was conducted with the aim of finding out the churn rate of customers in the retail sector and based on that, this study suggests which customer groups are potential for the company To answer the research question, we used qualitative and quantitative research methods through 6400 customer samples, applied the scales to suit the context and group of research subjects and used RFM analysis to segment customers based on Scoring; besides that, the tool that our group chose to support this research was Excel

This research has presented a system of solutions to enhance the company's competitiveness in the retail sector, better understanding of customer behavior, customers’ purchasing trends and know the company's potential customer segmentation in different aspects Moreover, the research also proposes some specific recommendations for the company to develop marketing strategy, customer retention strategy and succeed more and more in the future

Keywords: RFM model, customer segmentation, churn rate, CLV, retail sector.

comprehending these trends and utilizing them In such a fierce competitive environment, it is important to keep old customers instead of finding new customers to save costs To achieve this goal, businesses must understand the churn rate of each specific customer segment and have different strategies for them

Objectives

This study has the objectives to figure out the churn rate of each customer segmentation in the retail sector using RFM model and recommend which groups of customers that deserve the companies’ attention Therefore, there are two main problems that need to be solved in this study:

- Categorize the customer segments using the RFM model - Calculate the churn rate of each customer segment Objects and scopes

Space scope: The space scope is about RFM model and Customer Life Value model

Research method

Qualitative research through synthesizing, selecting and determining the relationship between variables from previous studies to find the proposed model Apply the scales to suit the context and group of research subjects In addition, the research team also applied quantitative research through 18485 customer samples and used RFM analysis to segment customers based on Scoring

Tools and Programing language

In this study, we use Excel - a software which is created by Microsoft to segment customers and draw statistical charts for qualitative research

Structure of project Chapter 1: Literature Overview Chapter 2: Theoretical Background Chapter 3: Proposed Model Chapter 4: Experimental Results Chapter 5: Solution

Chapter 1: Literature Review

Chapter overview

Before going into the research, we will review some of the previous domestic and foreign studies relating to customer segmentation in this chapter

Through these studies, we will identify the weaknesses that still exist in the research papers From there, we determine new research directions and suitable research methods 1.1 Domestic Overview

Regarding the subject area, there are few domestic studies related to customer segmentation and the RFM model than foreign studies However, the research papers also did quite well in a particular area

Ho Bach Nhat and Nguyen Huu Ngam used descriptive statistical methods in their research on the segment of credit customers in Long Xuyen City and Dinh Tien Minh, Le Vu Lan Oanh also used that method in their research on the segment of shopping customers in Ho Chi Minh City These studies offer suggestions and guidance for businesses, however, are restricted to a particular region, and thus they cannot be extrapolated to other locations with different characteristics

The research "Thai d6 va y định mua rau Vietgap của người tiêu dùng TPHCM” and “Các nhân t6 ảnh hưởng đến sự hài lòng của khách hàng cá nhân về dịch vụ Internet Banking" and “ Phân tích hành vi khách hàng hướng đến phân khúc thị trường từ dữ liệu big data: trường hợp của Sacombank” by Phạm Văn Hậu also uses 1n-depth analytical models Moreover, the application is quite simple and ineffective, so there are many limitations such as: could not find out the consumption habits of customers, have not classified the potential customers correctly

Pham Kien Trung, Nguyen Duc Thang, Le Van Chien, and Nguyen Van Thuong have applied the K-means algorithm to cluster target customers Research gives us more information about customers, which helps in effective customer care and helps the company target the right customers for new products and services The Research is still incomplete due to a lack of information on customer behavior, habits, and preferences

1.2 Oversea Overview

Today, the target group of customers is becoming more and more popular with companies in order to optimize benefits and profits for the company both in Vietnam and abroad.One of the widely applied grouping models is RFM, In terms of application, the studies indicate that the grouping of RFM customers can be applied to many different ways depending on the strategy and vision of the business: there are businesses to reach customers effectively, businesses to build marketing strategies, but the limitations of

these studies are generally applied RFM only in some specific cases that cannot be applied to large and diverse data sets: Selin Yilmaz, Cédric Chanez, Peter Cuony, Martin Kumar Patel(2022); Selin Yilmaz, Cédric Chanez, Peter Cuony, Martin Kumar Patel(2019); Tianyuan Zhang, Sérgio Moro, Ricardo F Ramos (2014); ianyuan Zhang, Sérgio Moro, Ricardo F Ramos; P.Anitha, Malini M.Patil (2019); Tingzhong Wang(2022); Jun Wu, Li Shi, Wen-Pin Lin, Sang-Bing Tsai, Yuanyuan Li, Liping Yang, and Guangshu Xu (2020); Ching-HTNMT Cheng, You-Shyang Chen (2009); Daqing Chen, Sai Laing Sain, Kun Gou (2012); Rendra Gustriansyah, Nazori Suhandi, Fery Antony (2021); Maria Teresa Ballestar, Pilar Grau-Carles, Jorge Sainz (2018) The above researches show that the application of RFM model can be applied practically, however, the above articles only apply RFM to a small industry or a small data set, so it is difficult to assess the practical effectiveness of applying it to a huge data set

The practical application of RFM model also has many problems: firstly, collecting a large amount of customer data, this will be difficult for large companies and there are many business areas and how to choose to get data such as: E Emawati, S S K Baharin and F Kasmin (2021); Tingzhong Wang(2022) For that reason, some studies will introduce improvements in the exploitation of customer data, but the study points to 44 things that need to be considered and classified In addition to the improvements, they will not stop there The algorithms for calculating RFM are also extremely diverse and there is no in-depth study to confirm the effectiveness of those algorithms The choice of clustering algorithm will greatly affect the exact results of RFM ( citing Jun Wu, Li Shi, Wen-Pin Lin, Sang-Bing Tsai, Yuanyuan Li, Liping Yang, and Guangshu Xu (2020); Jun Wu, Li Shi, Wen-Pin Lin, Sang-Bing Tsai, Yuanyuan Li, Liping Yang, and Guangshu Xu (2020)

Some studies such as Willem Verbeke, Bart Dietz & Ernst Verwaal (2010); Chu Fang and Haiming Liu (2021); Wafa Qadadeha & Sherief Abdallahb (2003) have introduced improved methods of customer grouping algorithm but generally not yet highly effective when improved models and algorithms can work effectively in scientific research is still not guaranteed However, we already know that the K-Means algorithm will fit a large data set compared to calculations based on conventional quartiles, but K- Means is very sensitive to extraneous data

1.3 Summary

In summary, domestic and foreign studies have certain limitations for data processing, limited research scope, and lack of some grand information about the research subjects, such as consumer abandonment behavior.

In this study, the original RFM model applied by our research team aims to solve the limitation in data processing to show the customer chum rate by segment in the retail sector From there, suggest new solutions and solutions for the business's marketing strategies.

Chapter 2: Theoretical Background

Chapter overview

Vietnam's retail market is booming, more and more businesses are entering this potential market Therefore, retail companies compete very hotly for many customers RFM model is more and more widely used, almost every company uses this model to identify potential customers and then come up with effective marketing strategies In this chapter, theoretical foundations, definitions, theories, operating principles, scientific hypotheses, as well as tools used in the thesis will be presented We will figure out the definition of RFM model, market segmentation, CLV - customer lifetime value, how customer consumption behavior is segmented according to RFM model, customer clustering according to RFM Scoring, and calculate customer chum rate 2.1 The rationale of RFM:

2.1.1 Consumer behavior segmented by RFM:

RFM-based customer segmentation has been used by direct marketers for over 60 years to target the right group of customers within their customer base, to save on mailing costs and improve sales and profits

An RFM model is a behavior-based model that is used to analyze customer behavior and then make predictions based on customer-related data sets collected and stored at an enterprise First widely introduced by Arthur M Hughes through work in 1996 - originally called RFM analysis, later commonly referred to as RFM modeling RFM stands for Recency, Frequency and Monetary, the meaning of each factor is as follows:

- Recency: the last transaction time of the customer This metric tells us if a customer is actually active near the time of the review The larger this index, the higher the customer's tendency to leave From there, a bad warning is drawn for businesses that should have new products or policies to properly serve the needs and tastes of customers

- Frequency: Frequency of customer purchases (how long or how many products) The more times customers buy, the greater the sales value they bring to the business However, this value is not enough to become the main evaluation basis because it depends on the value of the new order to assess the potential of the

customer

- For example, according to this research method, the research data period is from 7/2017 to 5/2021, the total number of customer purchases will be determined during this time Customers who regularly shop will be the ones most likely to

become loyal customers at a high level in the future This information also helps in determining customer satisfaction with the company's retail business services - Monetary: Total amount purchased by the customer This is the most intuitive factor affecting the sales of the business This index helps businesses know how much money customers have spent to buy the company's products Thereby, the company will have data information to classify customers This is the basis for the company to build appropriate programs and strategic solutions for products or services according to customers' ability to pay or pay

RFM is a marketing technique used to gauge the behavior of a company's customers According to a domestic research paper on the banking sector, it has been proved that the higher the value of R and F, the more customers can measure the behavior of using services at the bank In addition, the higher the M-value, the more likely a customer can see that they can have more than one consumption transaction RFM analysis provides fundamental benefits to a retail company:

- Use objective scales — provide a superior and concise description of the customer - Simple — the company can use it effectively without the need for sophisticated

software or data analysts

- Intuitive — the output of this segmentation method is easy to understand and interpret

Customer clustering process using RFM Scoring method:

A recent hit score is assigned to each customer based on the date of the most recent purchase Scores are generated by pooling recent hit values into several categories (default is 5) For example, if you use four categories, customers with the most recent purchase date will receive a recent visit rating of 4, and customers with a past purchase date will receive a rating of 4 The last visit was 1

Frequency ratings are assigned in a similar way Customers with a high frequency of purchases are given a higher score (4 or 5) and those with the lowest frequency of purchases are given a score of I

The monetary score is assigned on the basis of the total revenue generated by the customer in the period under consideration for analysis Customers with the highest

sales/orders are given a higher score while those with the lowest sales are given a score of 1

The fourth point, the generated RFM point, is simply three individual points concatenated into a single value

Details of customer segmentation table according to RFM will be described in the table

5 The best Very high Very high Very high Table 2.1.2 Score description according to RFM

2.2 Market segment definition:

Around the world, there are many definitions of market segmentation:

According to Philip Kotler, “Market segmentation is the breakdown of customers into a homogeneous subset of customers, where any subset can be envisioned as a market target to be achieved with a given mix of customers separate marketing”

According to William J Stanton, “Market segmentation is the process of dividing the total heterogeneous market for a good or service into several segments Each of these segments tends to be homogeneous in all important respects.”

Market segmentation in retail business 1s understood as dividing the market into different segments in which each segment will have a certain product or set of services for a certain group of people These segments are called market segments, 1.c a group of consumers who respond equally to the same set of marketing stimuli, and market segmentation is the process of consumer segmentation grouped on the basis of differences such as needs, personality or search propensity,

The goal of market segmentation in the retail sector is to divide the market into smaller markets with customers with similar needs that are easier to recognize, capture, and respond to more effectively Market segmentation helps companies create products and services that meet the needs of specific customers and focus marketing resources more efficiently Through that, the company will determine which is its target market

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Criteria for market segmentation:

Market segmentation by age: choosing a set of potential customers will make it easier to choose products suitable for that age

Market segmentation by object: it is necessary to determine the audience the company wants to target in order to have orientation in choosing the right product Segmentation by demand: retail companies should research and understand the needs of users to choose suitable business items

Market segmentation by item: Identify potential products (in terms of long-term consumption criteria of users, uniqueness of products and services, etc.) and find out the level of competition competition of other retail businesses to provide orientation on business products

2.3 CLV - customer lifetime value:

CLV - Customer Lifetime Value, this is the term for defining all the strategies that a business has used to attract customers, convert, or retain customer satisfaction and take advantage of it for the purpose of increasing revenue and developing its brand All of these customer lifecycle value strategies aim to engage customers throughout their entire buying journey Or simply defined, it is the journey from point A to point B that the customer will take until they actually make the final purchase

CLV is the value a customer contributes to the company during their lifetime Loyal customers are people who bring long-term and sustainable profits to the business because of their high life-cycle value, it was discovered early by Kotler (1974) with the present value definition of future profit streams projected for a certain period of time the transaction horizon with customers and emphasis on profitable customers

Four stages of a customer life cycle:

Awareness: during this time the target customer has a need for the product, it will be converted into a potential customer

Purchases: After going through phase 1, customers will move on to stage 2 — it will be their first purchase at your business, from a business they already feel the most

II

Purchase Frequency - This is considered as the number of times that a customer will purchase from the company in a given period of time A customer who purchases regularly is likely to return for more

Customer Value - This is just how much money a customer has spent over a certain period of time A customer who spends more means more likely to pay you in the future

In general, there are different methods for calculating CLV for organizations in different sectors In this paper, due to value-based segmentation and customer lifetime visit time, frequency, and currency (RFM), the characteristics selected by this method include last purchase date, number of visits purchase is the customer's buying frequency, the total amount spent

2.4 Churn rate - customer churn rate:

Customer Chum Rate is the percentage of a business's customers that no longer purchase or interact with a business for a given time or at all A high Customer Turn Rate means that a number of customers no longer want to buy goods and services from a business Customer Turn Rate or customer consumption rate is a mathematical calculation of the percentage of customers who are unlikely to make another purchase from a business

Or according to Avery, customer churn rate is a measure of the percentage of people who ended a customer relationship with a business during a particular period Typically, churn rates are calculated on a monthly, quarterly, or yearly basis, depending on the industry and product of the business The annual rate is the default unit for most companies, however companies price products on a monthly basis For example, cell phone service providers, gyms, or software companies often track customer rates on a monthly basis Some companies with rapid customer chum, or significant customer loss, will also evaluate this rate on a monthly basis

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Chapter 3: Proposed Model

Chapter overview 3.1 Proposed model