Automated Market Analysis by RFMx Encoding Based Customer Segmentation using Initial Centroid Selection Optimized K-means Clustering Algorithm

Customer segmentation has been one of hottest topics of marketing efforts. The traditional sources of data used for segmentation are demographics, monetary value of transactions, types of product/service selected. Today, data gathered by location based services can also be used for customer segmentation. In this chapter a real world case study is summarized and the initial segmentation results are presented. As the application, data gathered from beacons sited in 4000 locations and Fuzzy c-means clustering algorithm are used. The steps of the application are as follows: (1) Categorization of the shops, (2) Summarization of the location data, (3) Applying fuzzy clustering technique, (4) Analyzing the results and profiling. Results show that customers' location data can provide a new perspective to customer segmentation.

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An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm

Mathematical Problems in Engineering ◽

10.1155/2020/8884227 ◽

2020 ◽

Vol 2020 ◽

pp. 1-7

Author(s):

Jun Wu ◽

Li Shi ◽

Wen-Pin Lin ◽

Sang-Bing Tsai ◽

Yuanyuan Li ◽

...

Keyword(s):

Clustering Algorithm ◽

Management Strategies ◽

Customer Segmentation ◽

Customer Relationship ◽

Total Consumption ◽

Value Analysis ◽

Online Sales ◽

Real World Problem ◽

High Level ◽

Purchase Behaviors

In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.

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Customer Segmentation Based on a Novel Hierarchical Clustering Algorithm

2009 Chinese Conference on Pattern Recognition ◽

10.1109/ccpr.2009.5343957 ◽

2009 ◽

Author(s):

Suqun Cao ◽

Quanyin Zhu ◽

Zhiwei Hou

Keyword(s):

Hierarchical Clustering ◽

Clustering Algorithm ◽

Customer Segmentation ◽

Hierarchical Clustering Algorithm

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Implementation of Clustering Algorithm Method for Customer Segmentation

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8815 ◽

2020 ◽

Vol 17 (2) ◽

pp. 1388-1395

Author(s):

Nurmalasari ◽

Anna Mukhayaroh ◽

Siti Marlina ◽

Sari Hartini ◽

Sri Muryani ◽

...

Keyword(s):

Clustering Algorithm ◽

Customer Segmentation ◽

Marketing Strategies ◽

Goods And Services ◽

Customer Group ◽

Digital Era ◽

The Everyday ◽

The Right ◽

Online Shops ◽

Potential Customers

The intense competition in the sale of goods and services in the digital era of e-commerce requires to manage customers optimally. Some online shops try to improve their marketing strategies by classifying their customers. This study aims to determine potential customers, namely loyal customers. Potential customers can be determined by customer segmentation. Sampling from several online shops in Indonesia. The model used for segmentation is RFM (Recency, Frequency, and Monetary) and data mining techniques, namely clustering method with the K-Means algorithm. The results of this segmentation research divide the customer into 2 clusters. The best number of clusters is determined based on the Davies Bouldin index. The first cluster is cluster 0 consisting of 261 customers with RFM Score between 111–543. The first cluster includes the Everyday Shopper group. The second cluster, cluster 1 consists of 102 customers with RFM Score 443–555. The second cluster includes the Golden Customer group. With the existence of research on customer segmentation, it is expected to help in grouping customers so that companies can determine the right strategy for each group of customers.

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Data Driven Customer Segmentation for Vietnamese SMEs in Big Data Era

Macro Management & Public Policies ◽

10.30564/mmpp.v3i2.3553 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Pham Thi Tam ◽

Duong Minh Son ◽

Trinh Le Tan ◽

Hoang Ha

Keyword(s):

Clustering Algorithm ◽

Design Thinking ◽

Small And Medium Enterprises ◽

Marketing Research ◽

R Package ◽

Customer Segmentation ◽

Data Set ◽

The United Kingdom ◽

Marketing Activities ◽

Medium Enterprises

Almost Vietnamese big businesses often use outsourcing services to do marketing researches such as analysing and evaluating consumer intention and behaviour, customers’ satisfaction, customers’ loyalty, market share, market segmentation and some similar marketing studies. One of the most favourite marketing research business in Vietnam is ACNielsen and Vietnam big businesses usually plan and adjust marketing activities based on ACNielsen’s report. Belong to the limitation of budget, Vietnamese small and medium enterprises (SMEs) often do marketing researches by themselves. Among the marketing researches activities in SMEs, customer segmentation is conducted by tools such as Excel, Facebook analytics or only by simple design thinking approach to help save costs. However, these tools are no longer suitable for the age of data information explosion today. This article uses case analysing of the United Kingdom online retailer through clustering algorithm on R package. The result proves clustering method’s superiority in customer segmentation compared to the traditional method (SPSS, Excel, Facebook analytics, design thinking) which Vietnamese SMEs are using. More important, this article helps Vietnamese SMEs understand and apply clustering algorithm on R in customer segmenting on their given data set efficiently. On that basis, Vietnamese SMEs can plan marketing programs and drive their actions as contextualizing and/or personalizing their message to their customers suitably

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Clustering Algorithm For Determining Marketing Targets Based Customer Purchase Patterns And Behaviors

SinkrOn ◽

10.33395/sinkron.v6i1.11191 ◽

2021 ◽

Vol 6 (1) ◽

pp. 137-143

Author(s):

Amir Mahmud Husein ◽

Februari Kurnia Waruwu ◽

Yacobus M.T. Batu Bara ◽

Meleyaki Donpril ◽

Mawaddah Harahap

Keyword(s):

Clustering Algorithm ◽

Customer Segmentation ◽

Monetary Model ◽

Business World ◽

Homogeneous Groups ◽

Rfm Model ◽

Proposed Model ◽

Marketing Analysis ◽

Purchase Patterns ◽

Potential Customers

Customer segmentation is one of the most important applications in the business world, specifically for marketing analysis, but since the Corona Virus (Covid-19) spread in Indonesia it has had a significant impact on the level of digital shopping activities because people prefer to buy their needs online, so It is very important to predict customer behavior in marketing strategy. In this study, the K-Means Clustering technique is proposed on the RFM (Recency, Frequency, Monetary) model for segmenting potential customers. The proposed model starts from the data cleaning stage, exploratory analysis to understand the data and finally applies K-Means Clustering to the RFM Model which produces three clusters based on the Elbow model. In cluster 0 there are 2,436 customers, in cluster1 1,880 and finally in cluster2 there are 18 customers. RFM analysis can segment customers into homogeneous groups quickly with a minimum set of variables. Good analysis can increase the effectiveness and efficiency of marketing plans, thereby increasing profitability with minimum costs.

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A STUDY OF COMPARING CONCEPTUAL AND PERFORMANCE OF K-MEANS AND FUZZY C MEANS ALGORITHMS (CLUSTERING METHOD OF DATA MINING) OF CONSUMER SEGMENTATION

Jurnal Riset Informatika ◽

10.34288/jri.v2i2.116 ◽

2020 ◽

Vol 2 (2) ◽

pp. 49-54

Author(s):

Yunita Sinambela ◽

Sukrina Herman ◽

Ahsani Takwim ◽

Septian Rheno Widianto

Keyword(s):

Data Mining ◽

Clustering Algorithm ◽

Life Time ◽

Customer Segmentation ◽

Customer Data ◽

Fuzzy C Means ◽

Consumer Segmentation ◽

Fuzzy C Means Clustering ◽

Role Based ◽

And Performance

Consumers an important asset in a company that should be maintained properly especially potential customers. Tight competition requires companies to focus on the needs of the customer wants. Consumer segmentation is one of the processes carried out in the marketing strategy. To support the grouping process results consumers or consumer segmentation data mining is the support of a very important role. Based on mapping studies on data mining in support of consumer segmentation obtained two algorithms are often used for consumer segmentation include a K-Means Clustering and Fuzzy C-Means clustering. The attributes used for mining in customer segmentation processes are customer data, products, demographics, consumer behavior, transactions, RFMDC, RFM (Recency, Frequency Monetary) and LTV (Life Time Value). And it is important to combine the clustering algorithm to algorithm Classification, Association, and CPV to get the potential value of each cluster.

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Customer Segmentation using RFM Model and K-Means Clustering

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst2183118 ◽

2021 ◽

pp. 591-597

Author(s):

Rahul Shirole ◽

Laxmiputra Salokhe ◽

Saraswati Jadhav

Keyword(s):

Machine Learning ◽

Data Science ◽

Clustering Algorithm ◽

Customer Segmentation ◽

Clustering Method ◽

Good Relationship ◽

Rfm Model ◽

Input Dataset ◽

Segmentation Approach ◽

Business Analyst

Today as the competition among marketing companies, retail stores, banks to attract newer customers and maintain the old ones is in its peak, every company is trying to have the customer segmentation approach in order to have upper hand in competition. So Our project is based on such customer clustering method where we have collected, analyzed, processed and visualized the customer’s data and build a data science model which will help in forming clusters or segments of customers using the k-means clustering algorithm and RFM model (Recency Frequency Monetary) for already existing customers. The input dataset we used is UK’s E-commerce dataset from UCI repository for Machine Learning which is based on customer’s purchasing behavioral. At the very simple the customer clusters would be like super customer, intermediate customers, customers on the verge of churning out based on RFM score .Along with this we also have created a web model where an e-commerce startup or e-commerce business analyst can analyze their own customers based on model we created .So using this it will be easy to target customers accordingly and achieve business strength by maintaining good relationship with the customers .

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