International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
Enhancing Customer Segmentation in Virtual Shopping through RFM Analysis Nilam N. Parmar1, Dr. Sweta S. Panchal2 1Research Scholar, Computer Science and Engineering, Dr. Subhash University, Gujarat, India 2Associate Professor, Electronics and Communication, Dr. Subhash University, Gujarat, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Customer segmentation in virtual shopping is
location, purchase behavior, and income behavior. These attributes are primarily categorized based on historical purchasing behavior, which can lead to specific outcomes, such as increased sales and higher profits for the company. [3].
enhanced through RFM analysis, focusing on the recency, frequency, and monetary value of purchases. RFM analysis allows for a deeper understanding of customer behaviors and preferences, enabling businesses to tailor their marketing strategies effectively. By categorizing customers based on their past purchasing patterns, businesses can optimize their approaches for different customer segments. Analyzing the recency, frequency, and monetary value of purchases provides valuable insights that can guide targeted marketing efforts. This study emphasizes the importance of utilizing RFM analysis in virtual shopping environments to improve customer segmentation and enhance overall marketing strategies. By leveraging RFM analysis, businesses can better understand their customers' needs and preferences, leading to more personalized and effective marketing campaigns.
Clustering involves dividing or grouping customers based on their interactions with the company, whether direct or indirect. Customer data can include metrics such as time spent on social media platforms, transaction data, or time spent on specific posts. This paper focuses on the transaction data of customers from a UK online retail ecommerce platform. Although the dataset contains numerous attributes, selecting the most relevant ones is crucial for optimal results. To address this challenge, many data scientists prefer using the K-means algorithm, an unsupervised learning method, in conjunction with the RFM model. RFM stands for recency, frequency, and monetary value of a customer. The collected data can then serve as a foundation for customer segmentation [4].
Key Words: Customer segmentation, Virtual shopping, Recency, Frequency and Monetary Value, RFM Analysis
1. INTRODUCTION
The aim of this paper is to identify the type of customer (super customers, intermediate customers, base customers) and determine their value so that companies can discern which customer classes generate substantial revenue and which do not. This information will help companies develop new market strategies to improve their revenue growth [4].
Marketers are aware that customers have diverse needs and desires. To effectively identify and understand different customer groups, companies have employed various segmentation criteria and techniques, enabling them to offer tailored products and services that meet these varying needs. Segmentation also allows companies to create profitable segments and respond strategically to these segments based on their competitive strengths. However, many marketers struggle with accurately identifying the right customer segments for organizing marketing campaigns [1].
2. LITERATURE SURVEY
To formulate a marketing strategy, segmentation is employed to cluster customers based on their loyalty criteria. Segmenting the customer base is one of the initial steps in developing a business model. This process involves dividing a customer base into uniform subgroups, each considered a distinct marketing audience. Customer segmentation helps quantify customer value, enabling businesses to identify high-revenue clients and those who generate less revenue [2].
Recency, frequency, and monetary (RFM) analysis is an effective method for market segmentation and behavioral analysis. The main advantage of the RFM model is its ability to provide a detailed behavioral analysis of customers, grouping them into homogeneous clusters. Additionally, it helps develop a marketing plan tailored to each specific market segment. RFM analysis enhances market segmentation by examining when customers made purchases (recency), how often they made purchases (frequency), and how much money they spent (monetary). Customers who have bought most recently, most frequently, and have spent the most money are more likely to respond to future promotions [5].
In data segmentation, customers are grouped into sets of individuals with distinct similarities. Relevant attributes for customer segmentation include gender, age, lifestyle,
The strength of the RFM model lies in its use of several observable and objective variables, all of which are derived from each customer's past order history. These variables are
© 2024, IRJET
|
Impact Factor value: 8.226
|
ISO 9001:2008 Certified Journal
|
Page 2164