scholarly journals Customer Segmentation using RFM Model and K-Means Clustering

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 .

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

SinkrOn ◽  
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.

scholarly journals Single-Valued Neutrosophic Minimum Spanning Tree and Its Clustering Method

2014 ◽  
Vol 23 (3) ◽  
pp. 311-324 ◽  
Author(s):  
Jun Ye
Keyword(s):  
Machine Learning ◽  
Fuzzy Sets ◽  
Spanning Tree ◽  
Clustering Algorithm ◽  
Minimum Spanning Tree ◽  
Distance Measure ◽  
Intuitionistic Fuzzy Sets ◽  
Clustering Method ◽  
Neutrosophic Sets ◽  
Generalized Distance

AbstractClustering plays an important role in data mining, pattern recognition, and machine learning. Then, single-valued neutrosophic sets (SVNSs) are a useful means to describe and handle indeterminate and inconsistent information, which fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-value neutrosophic information, the article proposes a single-valued neutrosophic minimum spanning tree (SVNMST) clustering algorithm. Firstly, we defined a generalized distance measure between SVNSs. Then, we present an SVNMST clustering algorithm for clustering single-value neutrosophic data based on the generalized distance measure of SVNSs. Finally, two illustrative examples are given to demonstrate the application and effectiveness of the developed approach.

scholarly journals Data Mining Approach for Customer Segmentation

2021 ◽  
Vol 9 (VI) ◽  
pp. 1008-1012
Author(s):  
Anshumala Jaiswal
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Machine Learning Algorithms ◽  
Clustering Methods ◽  
Clustering Technique ◽  
Data Mining Approach ◽  
Huge Data ◽  
Or Groups ◽  
Data Points

In Marketing world, rapidly increasing competition makes it difficult to sustain in this field, marketers have to take decisions that satisfy their customers. Growth of an organization is highly depended on right decisions by the organization. For that, they have to collect deep knowledge about their customer's needs. Substantial amount of data of customers is collected daily. To manage such a huge data is not a piece of cake. An idea is to segment customers in different groups and go through each group and find the potential group among pool of customers. If it is done manually, it will require lot of human efforts and also consume lot of time. For reducing the human efforts, machine learning plays an important role. One can find various patterns which is used to analyze customers database using machine learning algorithms. Using clustering technique, customers can be segmented on the basis of some similarities. One of the best procedures for clustering technique is by using K-means algorithm. The k-means clustering algorithm is one of the widely used data clustering methods where the datasets having “n” data points are partitioned into “k” groups or cluster [1].in this paper. K is number of clusters or groups or segments and elbow method is used for determining value of K.

scholarly journals Customer Segmentation Using Machine Learning

2021 ◽  
Author(s):  
Nikhil Patankar ◽  
Soham Dixit ◽  
Akshay Bhamare ◽  
Ashutosh Darpel ◽  
Ritik Raina
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Clustering Algorithm ◽  
Customer Segmentation ◽  
Behavioral Characteristics ◽  
Popular Method ◽  
Marketing Campaign ◽  
Target Individual ◽  
Behavioral Aspects

Nowadays Customer segmentation became very popular method for dividing company’s customers for retaining customers and making profit out of them, in the following study customers of different of organizations are classified on the basis of their behavioral characteristics such as spending and income, by taking behavioral aspects into consideration makes these methods an efficient one as compares to others. For this classification a machine algorithm named as k-means clustering algorithm is used and based on the behavioral characteristic’s customers are classified. Formed clusters help the company to target individual customer and advertise the content to them through marketing campaign and social media sites which they are really interested in.

scholarly journals On a segmentation of Coopextra customers in Thu Duc district

2019 ◽  
Vol 3 (1) ◽  
pp. 28-36
Author(s):  
Le Hong Dien ◽  
Nguyen Phuc Son ◽  
Pham Hoang Uyen ◽  
Le Van Hinh
Keyword(s):  
Machine Learning ◽  
Customer Segmentation ◽  
Clustering Method ◽  
Market Segments ◽  
Target Groups ◽  
Unsupervised Machine Learning ◽  
Customer Group ◽  
Customer Care ◽  
Marketing Methods ◽  
Adjustment Of Parameters

Customer segmentation is the process of grouping customers based on similar characteristics such as behavior, shopping habits…so that businesses can do marketing to each customer group effectively and appropriately. Customer segmentation helps businesses determine different strategies and different marketing approaches to different groups. Customer segmentation helps marketers better understand customers as well as provide goals, strategies and marketing methods for different target groups. This paper aims to examine the customer segmentation using clustering method in statistics and unsupervised machine learning. The algorithms used are K-means and Elbow which are famous algorithms that have been successfully applied in many areas such as marketing, biology, library, insurance, finance... The purpose of clustering is to find meaningful market segments. However, the adoption and adjustment of parameters in the algorithms so as to find significant customer segmentations remain a challenge at present. In this paper, we used data of customers of Thu Duc CoopExtra and found significant customer segmentations which can be useful for more effective marketing and customer care by the supermarket.  

scholarly journals Taking into account Qualitative and Textual Variables in Hierarchical Ascending Clustering (HAC)

2019 ◽  
Vol 8 (3) ◽  
pp. 1555-1561
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Free Text ◽  
Clustering Methods ◽  
Clustering Method ◽  
Textual Data ◽  
Different Types ◽  
Qualitative Variables ◽  
Comparison Of The Results ◽  
Numeric Attributes

In Machine Learning, the clustering methods are the mains unsupervised methods. Their objectives is to partition a set of objects in some homogeneously groups. Clustering methods in general and more particularly Hierarchical Ascending Clustering (HAC) techniques are based on metrics and ultra-metrics. Metrics are used to evaluate the similarities between two objects; and ultra-metrics are used to estimate the similarity of two groups or the similarity of an element and a group. The main characteristic of these metrics and ultra-metrics is the fact that they are only adapted to numerical variables or can be reduced to them. With the advent of Data Mining and Data Science, most of the datasets to be analyzed contain different types of variables. In the same dataset, we can find numeric attributes, qualitative variables and free text fields very often together. Despite this diversity of variables in the same dataset, the existed clustering methods are generally build to use only an unique kind of attribute. In this paper, we propose an approach to take account different types of attributes in the same clustering method. The method proposed is a variant of HAC methods that can take into account both numerical, qualitative and textual data. Our approach is based on a metric call Phi-Similarity we developed in order to estimate the proximity of two objects, each of them is describe by a vector of attributes of different types. The developed method will be implemented with the scientific computing language R and applied to real survey data. A comparison of the results will be made with HAC techniques based on classical metrics with the Ward criterion as aggregation criteria. For classical algorithms, we will limit ourselves to the variables of the database compatible with them. This work of comparison will highlight the gain in precision in terms of classification brought by our method compared to the classic versions of HAC

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
Learning Methods ◽  
Comprehensive Review
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