Open AccessData Mining Approach for Customer Segmentation
Author(s) -
Anshumala Jaiswal
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.35140
Subject(s) - cluster analysis , computer science , market segmentation , field (mathematics) , data mining , segmentation , k means clustering , customer value , database marketing , artificial intelligence , machine learning , marketing , business , mathematics , marketing management , market economy , hierarchy , economics , pure mathematics , relationship marketing
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.